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Data interchange standards in healthcare: semantic interoperability in preoperative assessment

Ahmadian, L.

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Download date:01 Oct 2021 Data Interchange Standards in Healthcare

DataData InterchangeInterchange SStandardstandards iinn HHealthcareealthcare SemanticSemantic IInteroperabilitynteroperability inin PreoperativePreoperative AssessmentAssessment

Without standards and common terms, widespread information sharing cannot occur. Leila Ahmadian

Leila Ahmadian

Data Interchange Standards in Healthcare: Semantic Interoperability in Preoperative Assessment

Leila Ahmadian

© Leila Ahmadian, Amsterdam, The Netherlands Data Interchange Standards in Healthcare: Semantic Interoperability in Preoperative Assessment PhD thesis: University of Amsterdam ISBN: 978-90-6464-447-4

Layout: L.Ahmadian Cover design: Reza Khajouei Print: Ponsen &Looijen Printing partly supported by: Bazis stichting

All rights reserved. No part of this thesis may be reproduced, stored in a retrieval system or transmitted in any form or by any means without permission of the author.

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Data Interchange Standards in Healthcare: Semantic Interoperability in Preoperative Assessment

ACADEMISCH PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel

op woensdag 26 januari 2011, te 14:00 uur

door

Leila Ahmadian

geboren te Isfahan, Iran

Promotiecommissie:

Promotor: Prof. dr. ir. A. Hasman

Co-promotores: Dr. N.F. de Keizer Dr. ir. R. Cornet

Overige Leden: Prof. dr. J. van der Lei Prof. dr. J.H.M. Schonk Prof. dr. P.J.M. Bakker Prof. dr. M. W. Hollmann Dr. W.A. van Klei

Faculteit der Geneeskunde

Contents

Chapter 1 Introduction 9

Chapter 2 Data collection variation in preoperative 21 Assessment

Chapter 3 Development of a national core dataset for 37 preoperative assessment

Chapter 4 Facilitating preoperative assessment guidelines 51 representation using SNOMED CT

Chapter 5 The Role of standardized data and terminological 71 systems in computerized clinical decision support systems

Chapter 6 Construction of an interface terminology on 97 SNOMED CT: generic approach and its application in intensive care

Chapter 7 General discussion 117

Summary 129

Dutch summary 135

Acknowledgement 141

Curriculum Vitae 145

ChapterChapter 1

IntroductionIntroduction

Introduction

1. Introduction

Although patient information systems have the potential to improve the quality of care [1, 2] they have not been marketed on a large scale yet and they slowly become part of practices of physicians [3-5]. Capital requirements, technical issues, problems concerning data interchange standards and high maintenance costs are the primary barriers for implementation [4, 5]. Studies showed that adoption of patient information systems in some fields like anesthesia, comprising perioperative care, is much less than in others [4, 6-9]. A major difference between an anesthesia information management system (AIMS) and most other patient information system products is that extensive communication with diverse monitors, acquiring real-time patient data, is required. The AIMS may receive information from patient information subsystems or modules, from patient-connected devices, and from the anesthesiologist. Thus, it is important that an AIMS is integrated with the patient information system and interacts with other devices and their systems to ensure seamless and paperless flow of information and optimal patient care [6, 9]. This interaction requires both a standardized data model (structure) and standardized terminology (content). One barrier at the moment is that AIMS configuration often includes the development of a customized set of terms and phrases to be used in the department where it is installed. This lack of standardization of terms in AIMS inhibits the sharing of data even with AIMS from the same vendor at different institutions [9]. Moreover, the use of simple taxonomies is insufficient to allow secondary uses such as complex data mining and knowledge discovery. The data that are collected as part of the anesthesia record, when combined with information from other sources, can be an important resource for delivering the best care to the patient. However, as these data are used outside of their source, their meaning must remain unambiguous. Increasingly, data sharing is likely to transcend national borders, so that the chosen standards must have both an international and a national perspective [6]. The Health Level 7 (HL7) Reference Information Model (RIM) and the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) are used by the international community as standard information model and standard terminology. The main focus of this thesis is on standardization of the content of AIMS by using terminological systems, especially on using SNOMED CT as a standard reference terminological system in the preoperative assessment part of AIMS. 1.1. Preoperative assessment

Preoperative assessment is a check-up before having surgery to see if there is anything that puts patients at high risk for complications around the time of the surgery [10]. The more co-morbid conditions a patient has, the higher the risk for perioperative complications [10]. A detailed evaluation of a patient's health condition during preoperative assessment makes physicians and nurses aware of any potential problem that could occur in the peroperative or postoperative phase of surgery [11]. The purpose of this evaluation is to estimate and reduce the mortality and morbidity associated with surgery, to determine required anesthesia and equipment during operation based on the patient’s condition, to increase

11 Chapter 1 quality of care, to inform the patient about anesthesia, and to obtain informed consent [12, 13]. A proper preoperative assessment also contributes to the reduction of postoperative incidents and late surgery cancellation and a better determination of postoperative care [14, 15]. A study published in 2000 showed that inadequate preoperative assessment was the cause of around 40% of the deaths attributed to anesthesia in Australia [16]. It was also reported that the mortality rate in patients who had an inadequate preoperative assessment was 6 times higher than in patients who had been assessed properly [16]. All patients scheduled for surgery should be considered for preoperative assessment. Depending on the patient’s condition different healthcare providers may be involved in the assessment. For very low-risk procedures, such as dental extractions or cataract surgery, the assessment may only involve the oral surgeon or ophthalmologist confirming the absence of significant risk factors [10]. In some healthcare settings the preoperative assessment is done by a nurse consulting with an anesthesiologist when needed. For more complex procedures, evaluation by a physician experienced in preoperative assessment or an anesthesiologist may be judicious. Depending on the patient’s condition it is sometimes necessary to consult other specialists to obtain information [13]. For carrying out the preoperative assessment in the past every patient was normally admitted to the hospital a day before surgery. The assessment was done on the day before surgery resulting in inappropriate patient optimization for the surgery as healthcare providers might not have enough time to manage some patient conditions before surgery. This resulted in many surgery cancellations [17]. Nowadays, an increasing number of patients are evaluated in preoperative assessment clinics [18]. The assessment is often done some days or weeks before surgery by an anesthesiologist, not necessarily the one that will provide anesthesia, or collaboratively by other healthcare providers [19]. This provides required time for healthcare providers to optimize patient conditions before surgery. However, this results in decreased personal contact between the actual anesthesia care provider and the patient. Consequently, this situation and such a multidisciplinary setting require an increasing reliance on information that is obtained during the preoperative assessment: one should have access to detailed and ‘objective’ information right before surgery without the need to perform parts or all of a preoperative assessment again. To share the collected data during preoperative assessment among different healthcare providers and different settings standardization of these data is required. 1.2. Standardization

A high-priority challenge for medical informatics is interoperability of patient information systems. Interoperability with regard to a specific task is said to exist between two applications when one application can accept data (including data in the form of a service request) from the other and perform the task in an appropriate and satisfactory manner (as judged by the user of the receiving system) without the need for extra operator intervention. Interoperability involves many methodological and practical aspects and requires the creation, acceptance, and implementation of clinical data standards to ensure that data contained in a patient information system is available and meaningful to other information systems. It depends upon two important concepts: syntax and semantics [20, 21]. Syntax

12 Introduction refers to the structure of communication (among which the information model) and semantics is about the meaning of the communicated data. Information models allow transactions to flow consistently between systems or organizations because they contain instructions (or specifications) of format, data elements, and structure. Semantic interoperability requires unique codes for clinical concepts such as diseases, problem lists, allergies, medications, and diagnoses although the corresponding textual descriptions may vary. Terminological systems are examples of semantic standards. Terminological systems are systems that contain standardized terms pertaining to a certain domain. They are designed for collection, description, processing and presentation of terms, i.e. lexical items belonging to specialized domains. They play an important part in knowledge management and they are the keys to clear and consistent language usage within a domain. For many decades various terminological systems were developed for different domains using different structures ranging from strict hierarchies to semantic nets [22]. They provide an invaluable source of (structured) knowledge, and developed from single- purpose systems to systems serving a range of purposes, varying from recording patient information to providing decision support, and supporting epidemiological research [23]. To address this broadening range of purposes, medical terminological systems have grown in size and complexity [24]. The first terminological systems were developed as pre-defined systematic list and used to group together the medical terms reported by healthcare providers for statistical purposes [24, 25]. Contemporary terminological systems provide a broad range of concepts and a set of formal rules to manipulate them. The rules allow users to create new concepts based on already defined concepts. This functionality makes the range of concepts broader and therefore better facilitates data entry. New terminological systems are conceived for computer use and their typical complexity and evolution rate preclude presentation on paper [25]. One of the terminological systems which enable healthcare providers to record data at the appropriate level of granularity is SNOMED CT. This terminology provides the core general terminology for the patient information system and contains more than 311,000 active concepts with unique meanings and formal logic-based definitions organized into hierarchies. Its primary hierarchies include Clinical finding, Procedure, Observable entity, Body structure, etc [26]. SNOMED CT contains the semantic relationship IS_A within a single hierarchy and attribute relationships which connect concepts in different hierarchies e.g. Body structure with Clinical finding and Procedure [27]. Coding using SNOMED CT not only offers the facility to provide greater detail about a patient than was possible in previous coding schemes, it also allows for additional context information to be coded and associated with the patient. Fully interoperable systems require a mutually agreed-upon set of data elements as well as standard terminologies and standard information models. Variance among the data sets that is not recognized can affect the information flow as well as the workflow. Agreed-upon datasets indicate what type of data should be collected in information systems. Together, the datasets and terminological systems provide re-usable content for systems that can be shared by standard information models [28]

13 Chapter 1 1.3. Data collection in preoperative assessment

Preoperative assessment considers information from multiple sources that may include the patient’s medical records, interview, physical examination, and findings from medical tests and evaluations [13]. The main parts of the assessment are history taking and physical examination, focusing on risk factors for cardiac, pulmonary and infectious complications, and a determination of a patient's functional capacity [29]. The American Society of Anesthesiologists (ASA) believes that the assessment firstly of anesthetic risks associated with the patient’s medical conditions, therapies, alternative treatments, surgical and other procedures, and secondly of options for anesthetic techniques are essential components of basic anesthetic practice [13]. Further evaluation or laboratory testing may be required, depending on the patient's underlying medical condition and the planned procedure [30, 31]. With the collected data during the preoperative assessment, healthcare providers can develop a plan of treatment, identify and minimize risk factors, and involve support systems if necessary [11]. As the preoperative assessment is a multidisciplinary domain, involving at least nurses, anesthesiologists and surgeons, data should be collected in a standardized way to increase effective communication. Communication problems were reported as the main reason for incidents leading to planned processes not being followed [16]. An essential means to prevent miscommunication and reassessment is having an agreed-upon dataset. A defined dataset gives healthcare providers a clear statement about what should be collected. Moreover, it facilitates the flow of information between healthcare providers and patients and prevents information to be neglected by both sides. Implementation of a standardized dataset in AIMS will support healthcare providers in data collection and will facilitate implementation of clinical guidelines. 1.4. Clinical guidelines

Clinical guidelines are systematically developed statements designed to help healthcare providers and patients in making decisions about appropriate healthcare for specific circumstances [32]. They provide a common standard of care both within a healthcare organization and among different organizations. In general, clinical guidelines aim to describe appropriate care based on the best available scientific evidence and broad consensus, to reduce inappropriate variation in practice, improve patient outcomes and reduce costs of patient care [33, 34]. To achieve these goals, clinical guidelines should be provided at the point of care. Therefore, the paper-based guideline has to be translated into a computer interpretable format, a process called guideline formalization [35]. The guideline concepts should be represented by standard terminologies to support sharing, reusability and general interoperability. 1.5. Decision support systems

Simply collecting and retrieving data is only one reason to implement an AIMS. An ideal system should appropriately annotate the patient data and use specific combinations of information to provide clinical decision support [6]. Clinical decision support systems (CDSSs) are computer systems designed to support clinicians’ decision making about

14 Introduction individual patients at the point of care when these decisions are being made[36]. Wyatt and Spiegelhalter proposed a more specific definition of CDSS: they stated that a CDSS is an active knowledge system that uses two or more items of patient data to generate case- specific advice [37]. A CDSS brings existing knowledge and relevant patient data to the point of care. It has the potential to make clinical guidelines available to physicians. Generally, CDSSs have the potential to aid clinicians in collecting relevant data, making clinical decisions, managing medical actions more effectively, and thereby making fewer practice errors, achieving a higher standard of care and reduced costs [6, 36]. To achieve these goals CDSSs should have the possibility to get the required data from the patient information system. CDSSs should be seamlessly integrated in the clinical workflow and the patient information system [38, 39]. Integration of CDSSs with the patient information system requires standard information models and standard terminologies. Terminological systems facilitate the binding between the concepts used in the patient information system and the concepts defined in decision rules. If the system does not have access to a terminological system it is not possible to make use of terminological reasoning with the rules of a CDSS [40]. Thus, the rule “if a patient is obese, then anesthesia type and dose should be adjusted” has to be present in the system as many times as there are types of obesity, each rule differing in the mentioned type of obesity: “central obesity”, “Mauriac’s syndrome”, “buffalo obesity”, “drug-induced obesity”, etc. Moreover, if physicians use different synonyms to refer to one type of obesity, e.g. using terms “android fat distribution”, “truncal obesity”, or “fat body with thin limbs” to refer to the concept “central obesity”, new rules should be defined in the system as many times as the number of synonyms. In a terminological system all types of this condition are subsumed by the concept “obesity” and all synonyms of a concept are related to a single concept as descriptions thereof. Therefore, if an AIMS uses a terminological system, this system will be able to infer that a patient with condition “Mauriac’s syndrome” is obese and the CDSS will give the physician the required advice regarding anesthesia type and anesthetic dose during preoperative assessment. In this situation only one rule will be required to represent this recommendation, improving the readability of the knowledge base and easing its maintenance [40]. 1.6. Interface terminology on SNOMED CT

Reference terminologies are systematic collections of concepts covering most areas of clinical information. They represent and aggregate the information that makes up a given medical domain’s conceptual knowledge and store this information in the form of terms, concept identifiers, and semantic relationships [41-43]. A reference terminology can provide a foundation upon which healthcare organizations, information system vendors, the insurance industry, government and others can aggregate and analyze data for the improvement of healthcare [44]. It is impractical to use a comprehensive reference terminology such as SNOMED CT including its large number of concepts for direct documentation of patient information. Specific applications tend to focus on a restricted set of SNOMED CT, such as terms related only to ophthalmology. These so-called interface

15 Chapter 1 terminologies can be used to present relevant parts of the terminology, depending on the clinical context and local requirements. Interface terminologies are designed to facilitate the interaction between healthcare providers and structured medical information [45]. Interface terminologies generally consist of a rich set of flexible and colloquial phrases that can be used for clinical documentation [46, 47]. To create an interface terminology developers often select a subset of a reference terminology and tailor it to a specific setting. For example, for documentation of the preoperative assessment, a drop down list to select the past history of clinical findings in AIMS can be tailored to those histories that should be considered during the preoperative assessment. This will shield users from the complexity and large amount of concepts provided in a reference terminology and adapt easier to the user requirements. On the other hand, reference terminologies do not have the necessary coverage to be the exclusive content provider for the structured medical information. To overcome this problem, sometimes developers of interface terminologies have to add some new concepts to the pre-existing concepts to make the interface terminology complete. However, despite the advantages of interface terminologies there is no formal method for creating interface terminologies. 1.7. Research questions of this thesis

Healthcare providers should have an agreed-upon dataset for collecting the patient data in preoperative assessment. This dataset should be presented in a standardized format to facilitate data shareability and interaction of AIMS with existing patient information systems. This interaction is essential because many of the potential benefits, e.g. providing guideline-based decision support system regarding patient allergies, depend on computer systems communicating with each other [48]. This communication requires standardized data and will be facilitated by using standardized terminological systems. The preoperative dataset should contain concepts required to facilitate implementation of guideline-based decision support systems. The concepts defined in the dataset should be presented into the system by using an interface terminology providing preoperative assessment concepts. In this thesis the following research questions were addressed:  Which data should be collected during preoperative assessment?  Can SNOMED CT be used to represent concepts required to implement preoperative assessment guidelines?  Do CDSSs described in the literature use standardized data, and which terminological systems were used by CDSSs for coding data?  How can an interface terminology on SNOMED CT be constructed?

1.8. Outline of this thesis

After this introductory chapter, different studies addressing the above-mentioned research questions are described. To get insight into the collected data during the preoperative assessment, to help us to design standardized content for the preoperative assessment part of AIMS, in chapter two

16 Introduction of this thesis we search the literature to see which data items are commonly collected during the preoperative assessment. As a first step toward data standardization, in this project we developed a core dataset for preoperative assessment, based on the insight obtained in the second chapter. The development process of this dataset is presented in chapter three. The designed subset is intended to be implemented in AIMSs to support healthcare providers in data collection and for other purposes. As we aim to use SNOMED CT as a comprehensive standard terminology for presentation of data in AIMSs, to implement the preoperative assessment guidelines in AIMS, in chapter four we investigate to what extent SNOMED CT covers concepts used in these guidelines. We also investigate why some of the guideline concepts can not be represented by SNOMED CT. In chapter five, we search the literature to investigate whether existing CDSSs used standardized data to invoke the system or generate advice. We also investigate which terminological systems have been used in CDSSs to code data. Moreover, in this chapter, we present a survey among authors of the studies on CDSSs to determine the obstacles of CDSS implementation. The aforementioned steps in the chapters two, three and four are fundamental steps in designing a SNOMED CT subset for preoperative assessment based on the developed dataset. In chapter six we develop a generic approach for constructing an interface terminology on SNOMED CT. To illustrate the developed approach we applied it to the domain of intensive care. This domain was chosen because firstly in the ICU domain recent and running projects on the application of SNOMED CT provide us with a good opportunity to test our developed approach [49-51]. Secondly, a majority of the ICU population consists of postoperative patients and for most of them ICU admission is planned based on preoperative assessment. In this way, more insight is gained in the full perioperative process. Finally, in chapter seven the results of the various studies are synthesized and discussed and future research is suggested.

17 Chapter 1

References

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18 Introduction

[17] van Klei WA, Moons KG, Rutten CL, Schuurhuis A, Knape JT, Kalkman CJ, et al. The effect of outpatient preoperative evaluation of hospital inpatients on cancellation of surgery and length of hospital stay. Anesth Analg 2002 Mar;94(3):644-9. [18] van Klei WA, Grobbee DE, Rutten CL, Hennis PJ, Knape JT, Kalkman CJ, et al. Role of history and physical examination in preoperative evaluation. Eur J Anaesthesiol 2003 Aug;20(8):612-8. [19] van Klei WA, Hennis PJ, Moen J, Kalkman CJ, Moons KG. The accuracy of trained nurses in pre-operative health assessment: results of the OPEN study. Anaesthesia 2004 Oct;59(10):971-8. [20] Kim C. Clinical data standards in health care: five case studies. California healthcare foundation; 2005. [21] Mead CN. Data interchange standards in healthcare IT--computable semantic interoperability: now possible but still difficult, do we really need a better mousetrap? J Healthc Inf Manag 2006;20(1):71-8. [22] de Keizer NF, Abu-Hanna A. Understanding terminological systems. II: Experience with conceptual and formal representation of structure. Methods Inf Med 2000 Mar;39(1):22-9. [23] Cornet R, Abu-Hanna A. Auditing description-logic-based medical terminological systems by detecting equivalent concept definitions. Int J Med Inform 2008 May;77(5):336-45. [24] Cornet R, de Keizer NF, Abu-Hanna A. A framework for characterizing terminological systems. Methods Inf Med 2006;45(3):253-66. [25] Rossi MA, Consorti F, Galeazzi E. Standards to support development of terminological systems for healthcare telematics. Methods Inf Med 1998 Nov;37(4-5):551-63. [26] The International Health Terminology Standards Development Organisation. SNOMED CT Clinical Terms User Guide, July 2009. 2009. [27] The International Health Terminology Standards Development Organisation. SNOMED Clinical Terms® Technical Reference Guide, July 2008. 2008 Jun 1. [28] Carter J, Evans J, Tuttle M, Weida T, White T, Harvell J, et al. Making the "minimum data set" compliant with health information technology standards. U.S. Department of Health and Human Services, Assistant Secretary for Planning and Evaluation Office of Disability, Aging and Long-Term Care Policy; 2006. [29] King MS. Preoperative evaluation. Am Fam Physician 2000 Jul 15;62(2):387-96. [30] Michota FA, Frost SD. The preoperative evaluation: use the history and physical rather than routine testing. Cleve Clin J Med 2004 Jan;71(1):63-70. [31] Michota FA, Jr. The preoperative evaluation and use of laboratory testing. Cleve Clin J Med 2006 Mar;73 Suppl 1:S4-S7. [32] Field MJ, Lohr KN. Clinical Practice Guidelines: Directions for a New Program. Institute of Medicine, Washington, DC: National Academy Press; 1990. [33] Grimshaw JM, Russell IT. Effect of clinical guidelines on medical practice: a systematic review of rigorous evaluations. Lancet 1993 Nov 27;342(8883):1317-22. [34] Woolf SH, Grol R, Hutchinson A, Eccles M, Grimshaw J. Clinical guidelines: potential benefits, limitations, and harms of clinical guidelines. BMJ 1999 Feb 20;318(7182):527-30. [35] de Clercq PA, Blom JA, Korsten HH, Hasman A. Approaches for creating computer- interpretable guidelines that facilitate decision support. Artif Intell Med 2004 May;31(1):1-27. [36] Denekamp Y. Clinical decision support systems for addressing information needs of physicians. Isr Med Assoc J 2007 Nov;9(11):771-6.

19 Chapter 1

[37] Wyatt J, Spiegelhalter D. Field trials of medical decision-aids: potential problems and solutions. Proc Annu Symp Comput Appl Med Care 1991;3-7. [38] Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010 Jan;17(1):25-33. [39] Wright A, Sittig DF. A framework and model for evaluating clinical decision support architectures. J Biomed Inform 2008 Dec;41(6):982-90. [40] Nies J, Steichen O, Jaulent MC. Archetypes as interface between patient data and a decision support system. AMIA Annu Symp Proc 2007;1060. [41] Campbell KE, Oliver DE, Spackman KA, Shortliffe EH. Representing thoughts, words, and things in the UMLS. J Am Med Inform Assoc 1998 Sep;5(5):421-31. [42] Cimino JJ. Desiderata for controlled medical vocabularies in the twenty-first century. Methods Inf Med 1998 Nov;37(4-5):394-403. [43] Evans DA, Cimino JJ, Hersh WR, Huff SM, Bell DS. Toward a medical-concept representation language. The Canon Group. J Am Med Inform Assoc 1994 May;1(3):207-17. [44] Spackman KA, Campbell KE, Cote RA. SNOMED RT: a reference terminology for health care. Proc AMIA Annu Fall Symp 1997;640-4. [45] Rosenbloom ST, Brown SH, Froehling D, Bauer BA, Wahner-Roedler DL, Gregg WM, et al. Using SNOMED CT to represent two interface terminologies. J Am Med Inform Assoc 2009 Jan;16(1):81-8. [46] Campbell JR. Semantic features of an enterprise interface terminology for SNOMED RT. Stud Health Technol Inform 2001;84(Pt 1):82-5. [47] Poon AD, Fagan LM, Shortliffe EH. The PEN-Ivory project: exploring user-interface design for the selection of items from large controlled vocabularies of medicine. J Am Med Inform Assoc 1996 Mar;3(2):168-83. [48] Balust J, Macario A. Can anesthesia information management systems improve quality in the surgical suite? Curr Opin Anaesthesiol 2009 Apr;22(2):215-22. [49] Patrick J, Wang Y, Budd P, Rector A, Brandt S, Rogers BJ, et al. Developing SNOMED CT Subsets from Clinical Notes for Intensive Care Service. 7th Annual HINZ Conference and Exhibitions 2008. Rotorua, New Zealand. 2010. [50] Ryan A, Patrick J, Herkes R. Introduction of enhancement technologies into the intensive care service, Royal Prince Alfred Hospital, Sydney. HIM J 2008;37(1):40-5. [51] Tvede I, Bredegaard K, Andersen JS. Quality improvements based on detailed and precise terminology. Stud Health Technol Inform 2010;155:71-7.

20 ChapterChapter 2

DataData ccollectionollection vvariationariation iinn ppreoperativereoperative assessmentassessment

Ahmadian L, Cornet R, van Klei WA, de Keizer NF Accepted for publication in CIN: Computers, Informatics, Nursing Chapter 2

Abstract

Introduction: This study is a systematic literature review to identify data collected in the preoperative assessment. Methods: The PubMed and CINAHL databases were searched for articles published from 1997 to 2007. From the included articles data items that were described as part of the preoperative assessment were extracted. Identified data items were categorized into 13 categories originating from SNOMED CT. Results: Forty-one relevant articles were found. Preoperative assessment was equally performed in outpatient clinics and in-hospitals. The assessment was performed between the day of surgery and 30 days before surgery by anesthesiologists (51%) and/or nurses (39%) and/or other professionals (34%). The included articles described 541 data items. The two largest categories of data were “past history of clinical finding”, and “physical examination procedure” with 212 and 75 data items. Only 6 data items “age”, “diabetes”, “ECG”, “cardiovascular diseases”, “hypertension”, and “cigarette smoking and other use of tobacco” were stated in 50% or more of the articles. Conclusion: This study revealed a high diversity of data being collected during the preoperative assessment. Because of the diversity of patients one undisputed preoperative assessment dataset is hard to define. However, to solve the problem of data exchangeability, professionals should at least use a common core dataset.

22 Diversity in preoperative assessment data

2.1. Introduction

The purposes of preoperative assessment are to estimate and to reduce, based on the patient’s condition, the morbidity and mortality risks associated with surgery, and to determine the required anesthesia and equipment for the anesthesia and operation [1]. Accurate identification of the patient’s problems before surgery will support patient’s optimization for anesthesia and surgery, and determine indications for the requirement of postoperative intensive or critical care [2-4]. However, there is no agreement about the detailed information which should be collected during preoperative assessment to identify patients’ problems [5, 6]. To reduce hospitalization, the emphasis these days is on ambulatory surgery and same- day admission, which makes it impossible to perform the required preoperative assessment on the day before surgery [7]. Therefore, the assessment is often done some days or weeks before surgery by an anesthesiologist, not necessarily the one that will provide anesthesia, or collaboratively by other professionals such as specialized nurses [8], and sometimes partially based on a patient self-administered screening questionnaire. This results in decreased personal contact between the actual anesthesia care provider and the patient. Consequently, the decreased contact requires increased reliance on information that is obtained by non direct care providers during the preoperative assessment. This multidisciplinary preoperative assessment setting requires appropriate, accurate and timely data to enhance communication between healthcare providers. Although, it is impossible to have a fixed preoperative assessment dataset for all patients undergoing surgery due to diversity in patient conditions, it is important to have an agreed-upon core dataset for all surgical patients. Variation in data collection impedes the use of patient data by different care providers at different locations and therefore often results in reassessment. Data in the preoperative assessment should be collected in a structured and standardized way to facilitate effective communication and reuse of these data for secondary purposes such as clinical research. Standardization of data increases cost-effectiveness in patient referral, because reassessment is prevented. We performed a systematic literature review to identify data collected in the preoperative assessment. This review functions as a first step towards determining which data items should be collected in the preoperative assessment and provides a basis for designing a core dataset for preoperative assessment. By this review, we will determine the most common used data items in the preoperative assessment. The result of this review will be discussed in an expert committee for defining a core dataset for the preoperative assessment. Because the preoperative assessment differs for different groups of patients, this core dataset will be extended accordingly. This study is part of a larger effort to define an international standardized perioperative dataset, and carried out in collaboration with the International Organization for Terminology in Anesthesia (IOTA) with members from the Canadian, British and American anesthesiology-related societies [9]. IOTA’s mission is to create a standardized terminology for the global anesthesia community. IOTA was created

23 Chapter 2 by the Data Dictionary Task Force (DDTF) of the Anesthesia Patient Safety Foundation (APSF) in the USA [9]. 2.2. Methods

In order to find all relevant articles describing preoperative-assessment data collection, the PubMed and CINAHL databases were searched for papers published between January 1997 and June 2007. Only English language articles which fulfilled the identified search terms related to pre-operative, assessment, history taking, physical examination, diagnostic test and anesthesia were included. The keywords of a review article [6] about the role of history taking and physical examination in the preoperative evaluation were the basis for the search strategy. The keywords were extended; for instance, not only “pre operative”, but also “pre anesthesia”, and “pre surgery” were used as keywords, in order to increase the possibility of retrieving more articles and to obtain three relevant articles selected beforehand [10-12]. Finally, four sets of relevant key words and Medical Subject Headings (MeSH) terms were used. First, the search was carried out in both databases using sets 1, 2 and 3. Set 1 included terms related to preoperative care, set 2 contained assessment-related terms, and set 3 referred to possible ways of or sources for data collection in the preoperative assessment. To reduce the large number of hits, the titles of the first 300 retrieved articles were reviewed by two reviewers to determine the common terms used in the titles of the relevant articles. Set 4 containing these common terms was added to the search strategy to increase the specificity of the retrieved articles. The search terms were combined using “OR” within the sets and “AND” among them (figure 2.1). The retrieved articles from the PubMed and CINAHL databases were compared to remove duplicates. To determine the recall of the search strategy we compared our search results with the references of the above mentioned review article [6]. Two reviewers (LA and NdK) independently judged all titles and abstracts, and interobserver reliability was assessed by Cohen's kappa. Disagreements were discussed with a third reviewer (RC) and the final decision reflected consensus of all three reviewers. In the absence of an abstract or when inclusion of an article could not be decided upon on the basis of the abstract, full texts of the articles were reviewed. Articles were selected based on the following inclusion and exclusion criteria. All articles describing routinely collected preoperative assessment data were considered. Reviews as well as original studies were included when they focused on collecting data applicable to all kind of preoperative assessment patients. With “reviews” we refer to conceptual papers and opinion papers in which authors present their views and opinions, as opposed to “original papers”. To prevent duplication, we only collected data items from these “reviews” if the authors did not refer to other studies already included in our study. Articles were included if they described the preoperative assessment data in general or for one or more general categories of diseases, e.g. the preoperative assessment for cardiovascular or pulmonary patients undergoing a surgical procedure. As in this study we were looking for common data items in the preoperative assessment, articles about the preoperative assessment of any specific surgery such as thyroidectomy or thoracotomy

24 Diversity in preoperative assessment data were excluded as they might describe data which are only applicable to those specific surgeries. Articles describing data for a specific patient population such as obese patients, children, or diabetes patients were excluded for the same reason. Moreover, articles that describe data collected for patients with a specific condition, e.g. patients using a herbal medication which interacts with anesthesia agents, and articles about patients at risk for specific complications such as post-operative bleeding, were excluded. Evaluation studies on the necessity of specific tests, or on specific parts of the physical examination, and impact of different treatments or methods on risk factors, mortality, and morbidity were excluded. Finally, editorials and letters were excluded.

Figure 2.1: Distinct phases in the process of collecting relevant publications and their results.

Next to the data items collected in the preoperative assessment, some additional data about the time between the preoperative assessment and the operation; about the disciplines involved in the preoperative process; and about the location, where data were collected, were extracted from all included articles.

25 Chapter 2

To extract the data items, the full text of the included articles, i.e. text, tables, forms, and appendices were reviewed. All preoperative-assessment data items mentioned by the authors were included. For the extraction of the data items, we focused on each data item separately and counted each data item independently. We did not aggregate the data items (e.g. aggregate “myocardial infarction” as a “cardiovascular disease”). For instance, if authors mentioned the term “angina pectoris” it counts as “angina pectoris” not as “cardiovascular disease”; if authors referred to a generic term “cardiovascular disease” we just counted it as “cardiovascular disease. To facilitate data presentation, the extracted data items collected in the preoperative assessment were initially classified into seven categories based on the existing literature [12, 13]. As the number of the data items within each category was large, the categories were extended to 13 categories according to the consensus of authors to have better presentation of the data items. The category “demographic history detail” was separated from the category “administrative information”, the category “family history” was separated from “past history of clinical finding”, the category “diagnostic procedure” from “laboratory test”, the category “functional finding” from “patient status observation”, and the category “preoperative evaluation, anesthesia” from “physical examination procedure”. In this way five other categories were added to the initial categories. The categories were determined and extended so that all extracted data items could be placed in one category without any ambiguity. In categorizing data, we did not pay attention to the classification of data items in the reviewed articles but followed our consensus categorization. For example, some articles considered “alcohol drinking” as a “past history” data item, but we categorized it as “behavior finding”. Standard names for the categories originated from preferred terms of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT)1. For example, terms such as “past medical history”, “history of problems”, “history of past illness”, and “past medical problems” are all synonymous terms for the category “past history of clinical finding”, the name of which was chosen based on SNOMED CT. Each data item was collected, and categorized into one of the 13 categories without considering any extra information, such as qualifiers or values related to it and also without considering the way how these data were collected (question, multiple choice and so on). For example, we chose the term “cigarette smoking and other use of tobacco” for the different ways which this data item was presented without considering the number of pack years. Likewise the data item “myocardial infarction” was selected regardless of the time of occurrence (e.g. within 3 months before surgery). Although this extra information about the data items is important in the preoperative assessment, it is considered a further specification of the data items, which is disregarded in this research. These further specifications will be considered in the core dataset which we are going to design. Each reported item, from a general term such as “cardiovascular disease” to a specific term such as “aortic stenosis”, was collected.

1 http://www.ihtsdo.org/snomed-ct/

26 Diversity in preoperative assessment data 2.3. Results

The search strategy using 1, 2, and 3 sets of the key words resulted in 5212 articles in PubMed and 2469 in CINAHL. The search results, after adding the fourth set of keywords, are presented in figure 2.1. A total of 41 articles met our inclusion criteria. The interobserver reliability calculated by Cohen’s kappa for the articles selection was 0.87. The recall of the search strategy was checked against references of another review article [6]. Eight references of this article were relevant and 6 (75%) of them were retrieved by our search strategy. The detailed characteristics of the included articles are presented in table 2.1. Forty nine percent of the articles described preoperative assessment data collection for all patients undergoing a surgical procedure, 15 % of the studies focused on patients undergoing non- cardiac surgeries, 10% described data collection for surgeries carried out in outpatient clinics and the rest explained preoperative assessment data for specific groups of patients. For example, one article described preoperative assessment data for all types of general surgeries excluding cardiac, thoracic, neuro- and vascular surgeries and emergency operations.

Table 2.1: Characteristics of the included articles

Details of the included articles No. (%)

Publication year 1997-2000 13 (32) 2001-2004 16 (39) 2005-2007 12 (29)

Study design Experimental study 7 (17) Randomized control trial 1 (2) Non-randomized trial 6 (15) Observational study 9 (22) Cross sectional study 4 (10) Case control study 3 (7) Cohort study 2 (5) Descriptive study 25 (61)

Country of study United States 24 (59) United Kingdom 5 (12) France 2 (5) Brazil 2 (5) Other countries [Canada, Hong Kong, 8 (19) [each country one study] India, Italy, Kuwait, The Netherlands, New

Zeeland and Thailand]

27 Chapter 2

Preoperative assessment was performed between 30 days before surgery and the day of surgery. Anesthesiologists (mentioned in 21 (51%) articles), nurses (mentioned in 16 (39%) articles), and other professionals such as surgeons and consultants (mentioned in 14 (34%) articles), were involved in the preoperative assessment. In half of the cases more than one discipline was involved in the preoperative assessment. For example, of 21 articles mentioning the involvement of anesthesiologists in the preoperative assessment, 8 articles reported the cooperation between anesthesiologists and nurses, and 6 reported cooperation with another discipline. The cooperation between nurses and other disciplines was stated in 2 articles. In 5 articles, disciplines involved in the preoperative assessment were not mentioned. Eighteen articles (43%) mentioned that the preoperative assessment was done in an outpatient clinic, 6 articles (15%) reported that the assessment was performed in hospitals and in 17 articles (42%) the location was not mentioned. In total 541 distinct data items were extracted and 13 categories were used to classify the items: demographic history detail; past history of clinical finding; functional finding; behavior finding; family history; patient status observation; review of medication; physical examination procedure; laboratory test; diagnostic procedure; preoperative evaluation, anesthesia; surgical procedure; and administrative information. The complete list of the data items can be obtained from the authors. Table 2.2 shows per category the number of articles mentioning the category or at least one data item belonging to that category, and the number of the data items included in the category. The right column in this table shows as an example the 3 data items within a category that were most frequently reported by the authors. About 40% of the data items (n=212) were related to the category “past history of clinical finding”. Categories “physical examination procedure” and “review of medication” with 75 and 72 data items respectively were the second and third largest categories. None of the data items were reported in all of the articles. Four hundred and ninety four data items were mentioned in at most 25% of the articles, while only 6 data items were stated in more than 50% of the articles. Those six data items were “age” (n=29), “diabetes” (n=28) “ECG” (n=26) “cardiovascular diseases” (n=23), “hypertension or high blood pressure” (n=22), and “cigarette smoking and other use of tobacco” (n=22). The categories “demographic history detail”, “past history of clinical finding”, “behavior finding” and “diagnostic procedure” were the only categories including data items reported in more than 50% of the articles. Most of the data items (73%) were reported in 1 to 4 articles and more than one third of the data items were mentioned in only one article. As depicted in figure 2.2, the number of times that data items were mentioned varied. In all 41 articles, 197 data items were mentioned once, 92 data items were mentioned in 2 different articles and 1 data item was mentioned in 29 articles.

28 Diversity in preoperative assessment data

Table 2.2: Frequency of collected data items as explicitly reported by authors of the included studies per category

Category Number of articles Nr. of Three most frequently mentioned data items (references) data (number of articles mentioning the data item) items in category Demographic 33[2, 5, 8, 10, 13-41] 12 Age (29) history detail Sex (16) Weight/BMI (12) Past history of 40[2, 5, 8, 10, 12-47] 212 Diabetes (28) clinical finding Cardiovascular diseases (23) Hypertension/ high blood pressure(22)

Functional 22[5, 8, 10, 12, 13, 13 Exercise tolerance (12) finding 15-19, 24, 29, 31-33, Functional capacity (11) 35-39, 45, 47] Prosthesis (7) Behavior 25[10, 12-20, 24, 26, 4 Cigarette smoking and other use of tobacco (22) finding 28, 29, 31-37, 39-41, Alcohol drinking (21) 47] Illicit drugs (10) Family history 18[2, 10, 12, 15-18, 8 Anesthesia- related problems (10) 24, 29, 31-33, 35, 36, Malignant hyperthermia (6) 40, 41, 45] Surgical-related complications (2) Patient status 25[5, 8, 10, 12-16, 24 Pregnancy (11) observation 19, 21-23, 26, 28, 29, Comorbidities (9) 31-35, 37, 39-41, 47] Current illness( cold, cough,…) (4) Review of 33[2, 5, 8, 10, 12-19, 72 Anticoagulant medications (18) medication 21, 22, 24-26, 28-41, Aspirin-containing medications (15) 46, 47] Herbal medications(14) Physical 35[2, 5, 8, 12-24, 26, 75 Cardiovascular examinations (16) examination 27, 29-39, 41-44, 46, Respiratory examinations (14) procedure 47] Blood pressure (13) Laboratory 34[2, 8, 10, 12-27, 51 Glucose (18) test 30-38, 41-44, 46, 48] CBC (17) PT, PTT(16) Diagnostic 30[2, 8, 12-27, 31-33, 8 ECG (26) procedure 35, 37, 38, 41-44, 46, Chest x ray (18) 48] Echocardiography and pulmonary function tests (6) Surgical 30[2, 5, 8, 10, 12, 13, 11 Planned operation (17) procedure 15, 16, 18-22, 24-26, Diagnoses (10) 28-39, 45, 47] Risk of surgery/ anesthesia (10) Preoperative 34[2, 5, 8, 10, 12-24, 31 Anesthesia-related problems or complications (15) evaluation, 26-29, 31-37, 39-41, Airway examinations(13) anesthesia 45-47] Obesity (13) Administrative 20[5, 13, 15, 16, 19, 20 Informed consent (12) information 20, 24, 31-33, 35-37, Family or other support after surgery and during 39, 41, 46] hospitalization (10) Information about how to contact with patient’s family after surgery (5) Total 41 541

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250

200

150

100 are mentioned N times

50

0 1 3 5 7 9 11131517192123252729 Number of data items that Number of articles in which a data item is mentioned (N)

Figure 2.2: Occurrence of data items in the 41 included articles

2.4. Discussion

The large number of different data items found in this study supports the assumption that there is no agreement about which data items should be collected during the preoperative assessment. Large variation in data collection in the preoperative assessment impedes data exchangeability and emphasizes the need for standardization of pre-operative data collection. Standardization in a multidisciplinary setting like the preoperative assessment improves better communication and increases quality of care [49, 50]. Based on the data collected in the preoperative assessment, for some patients, physicians need time to optimize the patient’s condition preferably without disrupting surgical schedules [51]. Our study showed that this time period varies from the day of surgery to 30 days before surgery. This finding can not be generalized as the report rate on this data item was very low (44%) and some of the articles described this data ambiguously (“date separate from surgery” or “before admitting to hospital”), making it impossible to correctly calculate the variability. Moreover, the reported time period might be influenced by some socio economical aspects such as the number of available anesthesiologists and the number of patients on the waiting list. The ASA practice advisory has proposed that for all elective surgeries the anesthesiologist has to be informed properly about patient’s conditions before the day of surgery [52].

30 Diversity in preoperative assessment data

Although in this study we focused on data collection applicable to all preoperative assessment patients, and we excluded data collection for specific patient categories, our result revealed a large diversity of data items. Whereas each healthcare setting focuses on a limited set of data in the preoperative assessment, we retrieved 541 distinct data items in 13 categories. Although data collected during the preoperative assessment can be diverse depending on patient conditions, our inclusion criteria for selecting articles limited the patient diversity. Therefore, we expected to be able to identify a common set of data consisting of items mentioned in most (e.g. 90% or 95%) of the included articles. However, our review showed a large diversity which is illustrated by the fact that none of the data items was described in more than 75% of articles and only 6 data items were considered in more than 50% of the articles. The categories “past history of clinical finding”, “physical examination procedure”, and “review of medication” contained the largest number of data items. This can be explained by the fact that collecting data about these three categories reveals important pre-existing patient conditions without requiring costly tests. The frequency of the reported data items in categories such as “laboratory test” and “preoperative evaluation, anesthesia” was a little higher than the frequency of the reported data items in the category “past history of clinical finding”. The ASA practice advisory has stated that pre-anesthesia physical examination at least should include airway, pulmonary and cardiovascular examination [52]. These were among the frequent data items in our study. This practice advisory also mentioned that the preoperative assessment should include review of medical records and patient interview to get information about current diagnoses, treatments and medical conditions but it does not explicitly describe which data items should be collected [52]. Frequently mentioned data items included in this literature review which can be obtained from medical record review or patient interviews were “age”, “diabetes”, “cardiovascular diseases”, “hypertension or high blood pressure”, “cigarette smoking and other use of tobacco”, “alcohol drinking”, and “pulmonary diseases”. There are some limitations in this study. First, some studies may have been missed, because of existing limitations in MeSH terms (e.g. lack of preoperative assessment as a MeSH term) and defined keywords indexing in PubMed and CINAHL; and because our search was restricted to those articles that somehow refer to the preoperative assessment in the title. We used the keywords of a previous review [6] as the basis of our search strategy and expanded them to 4 sets of keywords. The recall of our search strategy was 75%, estimated by determining which relevant references of the review article[6] were retrieved. Given the extensive search strategy and the recall of 75% it is likely that most of the relevant articles have been found. Furthermore, inclusion of more articles will most likely merely strengthen the conclusion that there is a large diversity in the preoperative assessment data collection. Second, because we only addressed studies describing data applicable for the general preoperative patient population, we may have missed some studies which were about specific patient population but described the general preoperative assessment data. Third, authors of the included articles may not have mentioned all data items, but may have reported only those that are of interest to them or to the context of that article. Some data items are routinely collected in all cases and are important parts of the

31 Chapter 2 preoperative assessment such as allergies, history of surgeries, vital signs, and NPO status. However, they were not frequently reported in all included articles probably because they were not relevant in the context of that paper and therefore they are not presented in table 2.2 as frequent data items. Authors are likely to consider some data items irrelevant for reporting e.g. data from the categories “demographic history detail” and “administrative information”. Therefore the included articles may not report all preoperative-assessment- related data and specifically figures on these categories have to be interpreted with caution. The diversity of data collected by this review indicates that almost each health care setting collects a different dataset. Designing a standard dataset seems necessary to overcome the variety of data collected in different settings and will result in more effective communication in multidisciplinary settings such as the preoperative assessment setting. The majority of cancelled surgeries resulted from inadequate communication between disciplines involved in the preoperative assessment [53]. The study of Kluger showed that deficiencies in data in medical records were the most common cause of failures in communication [53]. Although it is difficult to introduce a comprehensive dataset for the preoperative assessment, because of its wide domain and diversity of patients undergoing on operation, there is a need for it. Such a dataset would give involved professionals a similar understanding of patients’ conditions and facilitate patient referrals across health care settings, thus increasing the quality of care. Another issue that can be solved by such a standard dataset is potential miscommunication between patients and physicians. Patients commonly forget to mention important information (e.g. using Warfarin) to caregivers or caregivers forget to ask for it [5, 53]. A structured preoperative interview supports the transfer of all necessary information, from patient to anesthesiologist and vice versa. Using a standardized dataset will not only provide a structure for health care professionals in providing and obtaining all necessary data but it will also improve the recording of data which may lead to an improved patient outcome [5, 53]. Based on their clinical needs, institutions could extend the core dataset with more specific data items, e.g. preoperative pulse oximetry for patients with pulmonary disorders [54]. Our study was carried out to get insight into contemporary individual data items that are collected in the preoperative assessment as a basis to design a core (inter)national preoperative dataset. As identifying essential data items in the preoperative assessment only through frequency of citation is not enough, in parallel with this study an expert committee was established to design a draft of the dataset based on expert consensus. The defined data items in this draft were compared with the frequently mentioned data items (data items mentioned at least in more than 25% of the included articles) extracted in this review study. The development process of designing this preoperative assessment dataset can be found in [55]. The designed dataset can be obtained from the authors. The next step will be the design of an anesthesia information management system (AIMS) to collect and present data in a standardized and structured way, so that the exchangeability of data across different settings is facilitated.

32 Diversity in preoperative assessment data

We recommend using standard terminological systems such as SNOMED CT, and Logical Observation Identifiers Names and Codes (LOINC)2 to standardize the semantics of the data items and their values within the AIMSs. SNOMED CT is a reference terminology designed for documenting patient data and contains concepts, descriptions, and relationships between the concepts. It offers flexibility in expressing clinical concepts, and enables documentation of very detailed clinical data and, when required, aggregation on a more general level [56, 57]. LOINC covers clinical observations and laboratory tests which are part of the preoperative assessment. Moreover, to standardize the architecture of the AIMSs to facilitate data flow among different systems, we recommend using information models such as Health Level 7 Reference Information Model (HL7 RIM) [58]. The systems should also be designed in such a way to help health care providers to find, perceive and interpret data in the system easily [59]. This study demonstrated the diversity in collected pre-operative assessment data and revealed the most frequently used data elements and the core categories of the preoperative assessment data. This diversity would result in lack of effective communication, impeding continuity of care and create an obstacle to reuse the data for other applications. The observed diversity demonstrates the necessity of defining a standard preoperative dataset, in order to enable adequate data availability in the multidisciplinary preoperative assessment setting.

2 http://loinc.org/

33 Chapter 2

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[25] Capdenat Saint-Martin E, Michel P, Raymond JM, Iskandar H, Chevalier C, Petitpierre MN, et al. Description of local adaptation of national guidelines and of active feedback for rationalising preoperative screening in patients at low risk from anaesthetics in a French university hospital. Qual Health Care 1998 Mar;7(1):5-11. [26] Starsnic MA, Guarnieri DM, Norris MC. Efficacy and financial benefit of an anesthesiologist-directed university preadmission evaluation center. J Clin Anesth 1997 Jun;9(4):299-305. [27] Hunt M. Nurses can enhance the pre-operative assessment process. Nurs N Z 2006 Nov;12(10):20-1. [28] Charuluxananan S, Suraseranivongse S, Punjasawadwong Y, Somboonviboon W, Nipitsukarn T, Sothikarnmanee T, et al. The Thai Anesthesia Incidents Study (THAI Study) of anesthetic outcomes: I. Description of methods and populations. J Med Assoc Thai 2005 Nov;88 Suppl 7:S1-13. [29] de Oliveira Filho GR, Schonhorst L. The development and application of an instrument for assessing resident competence during preanesthesia consultation. Anesth Analg 2004 Jul;99(1):62-9. [30] Alsumait BM, Alhumood SA, Ivanova T, Mores M, Edeia M. A prospective evaluation of preoperative screening laboratory tests in general surgery patients. Med Princ Pract 2002 Jan;11(1):42-5. [31] Clark E. Preoperative assessment. Primary care work-up to identify surgical risks. Geriatrics 2001 Jul;56(7):36-40. [32] Latham LB. Preanesthetic evaluation. Dent Clin North Am 1999 Apr;43(2):217-29. [33] Dunn D. Preoperative assessment criteria and patient teaching for ambulatory surgery patients. J Perianesth Nurs 1998 Oct;13(5):274-91. [34] Essin DJ, Dishakjian R, deCiutiis VL, Essin CD, Steen SN. Development and assessment of a computer- based preanesthetic patient evaluation system for obstetrical anesthesia. J Clin Monit Comput 1998 Feb;14(2):95-100. [35] Pinkowish MD. The preoperative evaluation for noncardiac surgery: less is more. Patient Care 2000 Mar 30;34(6):110. [36] Saufl NM. Preparing the older adult for surgery and anesthesia. Journal of PeriAnesthesia Nursing 2004 Dec;19(6):372-8. [37] Patton CM. Preoperative nursing assessment of the adult patient. Seminars in Perioperative Nursing 1999;8(1):42-7. [38] Ebell MH. Preoperative evaluation for noncardiac surgery. American Family Physician 2004 Apr 15;69(8):1977-80. [39] Williams GD. Preoperative assessment and health history interview. Nursing Clinics of North America 1997 Jun;32(2):395-416. [40] Thomson PJ, Fletcher IR, Downey C. Nurses versus clinicians -- who's best at pre-operative assessment? Ambulatory Surgery 2004 Dec;11(1-2):33-6. [41] Forister JG, Blessing JD. Considerations in preoperative evaluation. Physician Assistant 2002 Nov;26(11):36-46. [42] Ajimura FY, Maia AS, Hachiya A, Watanabe AS, Nunes MP, Martins MA, et al. Preoperative laboratory evaluation of patients aged over 40 years undergoing elective non-cardiac surgery. Sao Paulo Med J 2005 Mar 2;123(2):50-3. [43] Mantha S, Roizen MF, Madduri J, Rajender Y, Shanti NK, Gayatri K. Usefulness of routine preoperative testing: a prospective single-observer study. J Clin Anesth 2005 Feb;17(1):51-7. [44] Kinley H, Czoski-Murray C, George S, McCabe C, Primrose J, Reilly C, et al. Effectiveness of appropriately trained nurses in preoperative assessment: randomised controlled equivalence/non- inferiority trial. BMJ 2002 Dec 7;325(7376):1323. [45] Hilditch WG, Asbury AJ, Jack E, McGrane S. Validation of a pre-anaesthetic screening questionnaire. Anaesthesia 2003 Sep;58(9):874-7. [46] Bramhall J. The role of nurses in preoperative assessment. Nursing Times 2002 Oct 1;98(40):34-5. [47] Yamada SD, Sweitzer B, McHale MT. Preoperative assessment for gynecologic surgery. Contemporary OB/GYN 2005 Feb;50(2):50-7.

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[48] Finegan BA, Rashiq S, McAlister FA, O'Connor P. Selective ordering of preoperative investigations by anesthesiologists reduces the number and cost of tests. Can J Anaesth 2005 Jun;52(6):575-80. [49] Klein J. Multimodal multidisciplinary standardization of perioperative care: still a long way to go. Curr Opin Anaesthesiol 2008 Apr;21(2):187-90. [50] Lewis A. Health informatics: information and communication. Adv Psychiatr Treat 2002 May 1;8(3):165- 71. [51] Roizen FM. Preoperative Evaluation. In: Ronald DM, editor. Miller's Anesthesia. 6th edn. Philadelphia, Pennsylvania: Churchill Livingstone; 2005. p. 980-91. [52] Practice advisory for preanesthesia evaluation: a report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation. Anesthesiology 2002 Feb;96(2):485-96. [53] Kluger MT, Tham EJ, Coleman NA, Runciman WB, Bullock MF. Inadequate pre-operative evaluation and preparation: a review of 197 reports from the Australian incident monitoring study. Anaesthesia 2000 Dec;55(12):1173-8. [54] Marienau ME, Buck CF. Preoperative evaluation of the pulmonary patient undergoing nonpulmonary surgery. J Perianesth Nurs 1998 Dec;13(6):340-8. [55] Ahmadian L, Cornet R, Kalkman C, de Keizer NF. Development of a national core dataset for preoperative assessment. Methods Inf Med 2009;48(2):155-61. [56] Bowman S. Coordinating SNOMED-CT and ICD-10. J AHIMA 2005 Jul;76(7):60-1. [57] Elevitch FR. SNOMED CT: electronic health record enhances anesthesia patient safety. AANA J 2005 Oct;73(5):361-6. [58] Schadow G, Russler DC, Mead CN, McDonald CJ. Integrating medical information and knowledge in the HL7 RIM. Proc AMIA Symp 2000;764-8. [59] Khajouei R, Jaspers MW. The Impact of CPOE Medication Systems' Design Aspects on Usability, Workflow and Medication Orders. Methods Inf Med 2009 Jul 6;48(4).

36 ChapterChapter 3

DevelopmentDevelopment ooff a nnationalational ccoreore ddatasetataset fforor preoperativepreoperative assessmentassessment

Ahmadian L, Cornet R, Kalkman C, de Keizer NF Methods of Information in Medicine. 2009;48(2):155-61 Chapter 3 Abstract

Objective: To define a core dataset for preoperative assessment to leverage uniform data collection in this domain. This uniformity is a prerequisite for data exchange between care providers and semantic interoperability between health record systems. Methods: To design this core dataset a combination of literature review and expert consensus meetings were used. In the first meeting a working definition for “core dataset” was specified. Subgroups were formed to address major headings of the core dataset. In the following 8 meetings data items for each subheading were discussed. The items in the resulting draft of the dataset were compared to those retrieved from an earlier literature study. In the last 2 expert meetings modifications of the dataset were performed based on the result of this literature review study. Results: Based on expert consensus a draft dataset including 82 data items was designed. Seventy six percent of data items in the draft dataset were covered by the literature study. Nine data items were modified in the draft and fourteen data items were added to the dataset based on input from the literature review. The final dataset of 93 data items covers patient history, physical examination, supplementary examination and consultation, and final judgment. Conclusions: This preoperative-assessment dataset was defined based on expert consensus and literature review. Both methods proved to be valuable and complementary. This dataset opens the door for creating standardized approaches in data collection in the preoperative assessment field which will facilitate interoperability between different electronic health records and different users.

38 Development of a national core dataset

3.1. Introduction

The preoperative risk assessment is an important part of the anesthetic care of patients and contributes to determining the required anesthetic policy and the resources needed during and after surgery [1-3]. It includes an interview with the patient to take history of previous conditions and procedures; physical examination of the patient; a review of medication; and ordering and reviewing of preoperative tests [4, 5]. The preoperative assessment can uncover hidden conditions that may cause problems both during and after surgery, thereby helping health care professionals to reduce perioperative mortality and morbidity rates [6, 7], to shorten the length of stay in the hospital, and to determine whether the patient needs any optimizations before surgery to be as fit as possible for the anesthesia and surgery [8]. It is therefore crucial that preoperative-assessment records contain all information required to fulfill these functions. However, it is still unclear which preoperative-assessment data exactly should be collected. Traditionally, preoperative assessment took place in the hospital the day before surgery which often led to situations in which there was not enough time available to adequately optimize the patient before surgery. Consequently, health care cost increased and quality of care decreased [4, 9]. Performing the assessment some days in advance provides the opportunity to reduce surgical delays and to minimize late surgery cancellations resulting in more cost-effective health care [4]. To this end, preoperative-evaluation clinics were introduced, which led to involvement of more people from different disciplines such as nurses, anesthesiologists, and surgeons in the preoperative process. Exchange of preoperative information among the healthcare personnel involved in preoperative assessment is therefore critical, especially when the patient’s anesthesia will be performed by another anesthesiologist than the one who performed the preoperative assessment. In 2007, the Dutch Health Care Inspectorate reported that a multidisciplinary and standardized approach and teambuilding in the preoperative process are needed. It was also identified that a standard in preoperative-assessment data components is lacking [10, 11]. The lack of such a standard results in preventable errors and double work as care providers re-do the preoperative assessment of their colleagues. All over the world, health-care settings individually choose what data should be collected for the preoperative assessment varying from small to extensive datasets [10]. Although one might think that the more data is collected during preoperative assessment the better the patient will be prepared for surgery, there is no evidence for this. Contrarily, studies showed that collecting more, unnecessary, information and doing unnecessary tests lead to paying attention to issues that were unimportant for the preoperative assessment [7, 12]. Firstly, this can cause harm to the patients due to borderline or false-positive results, secondly it increases healthcare costs [4, 7, 13]. Variation in data collection impedes the use of patient data for direct care and deters data reuse for many other applications [14]. Hence, there is clearly a need to move towards a unified and unequivocal dataset across hospitals. To realize this, the Netherlands Society of Anesthesiologists (NVA) established a committee to design a national preoperative-

39 Chapter 3 assessment dataset. The rationale is to give centers a clear statement about what data to collect. The aim is to provide a unique dataset to be used across preoperative settings in the Netherlands to facilitate better communication and as much as possible prevent reassessment in the case of patient referral within and between hospitals. This article describes the development process and the data items defined in the preoperative assessment dataset. The definition of this preoperative dataset is part of a larger project to design an international, standardized perioperative dataset, led by the International Organization for Terminology in Anesthesia (IOTA) [15]. This organization was created by the Data Dictionary Task Force (DDTF) of the Anesthesia Patient Safety Foundation (APSF) in the USA with the mission to create a standardized terminology for the global anesthesia community. 3.2. Methods

To design this dataset a combination of literature review and expert consensus was used. As depicted in figure 3.1 the development of the (inter)national core dataset for preoperative assessment proceeded in the following (concurrent) stages. 3.2.1. Literature review study To investigate data collection of preoperative assessment in the international literature a systematic PubMed search has been performed. Key words and MeSH terms related to preoperative care, assessment and possible ways of data collection in the preoperative period were used (as more extensively described in [10]). All articles describing the routinely collected preoperative-assessment data were considered and all data items that were part of the preoperative assessment were extracted from the relevant articles. Finally, 32 articles were included and 540 distinct data items were extracted. Data items covered the following categories: demographic history detail; past history of clinical finding; functional finding; behavior finding; family history; patient status observation; review of medication; procedure; physical examination procedure; laboratory test; diagnostic procedure; preoperative evaluation, anesthesia; and administrative information. From the extracted data items, only fifty seven data items (10.5%) were mentioned in 25% or more of the included articles [10]. 3.2.2. Consensus of experts In 2007, the Netherlands Society of Anesthesiologists (NVA) established a collaborative, multidisciplinary consensus-based committee to develop a national core dataset for preoperative assessment. An ad-hoc committee of 12 anesthesiologists and 2 medical informaticians was installed. At the first meeting attendees agreed on the aim and the scope of the core dataset. The working definition of “core dataset” that represents the purpose is: “the NVA-accredited smallest set of data and their definitions, necessary for general preoperative assessment of the risks for (adult) patients in relation with the surgical procedures and the anesthesia”. This core dataset aims to record information applicable for all preoperative-assessment patients. The committee agreed to define the core preoperative-

40 Development of a national core dataset assessment dataset, allowing organizations to add data items which may be needed for more complex and specific cases. It was decided that the data items in the core dataset could differ from the questions asked to patient to collect the data. E.g. whereas patient may be asked about “chest pain”, the corresponding data item is “angina pectoris”. To define the general data categories included in the content of the dataset the ASA practice advisory was used [5]. The American Society of Anesthesiologists (ASA) published this practice advisory as a reference framework for anesthesiologists to carry out the preoperative assessment. It does not contain individual data items. At the second meeting, the major headings of the core dataset were determined, and committee subgroups were formed to address the major headings. These headings were Airway; Allergy; Neurology, cognition and psychiatry; Coagulation; Cardiopulmonary; Endocrine; and General. The initial data items for each major heading were determined by its subgroup. Data items were sent to the other members of the committee to get their opinions about the defined data items. In five consensus meetings of the committee, these initial data items and feedback of each member were discussed. Data items were extended or restricted until agreement was reached. Data items were included if the relevance in the context of the aim and scope of the core dataset was clear and when limited variability of the data elements existed. To include or exclude a data item experts discussed whether a data item is important in the context of anesthesia and surgical risk assessment or whether missing the data item during the assessment would result in problems for the patient. The data items related to general information about the patient including demographic details, behavioral findings such as alcohol drinking; and those which provide information on the operation to be done were included. Additionally, data items that are required for determining validated and frequently used risk scores in the assessment were added. The next step consisted of merging the data elements proposed by the various subgroups, and eliminating the duplications. Based on the outcome of this consultative process the draft of national core dataset for preoperative assessment was created. 3.2.3. Comparing the results of the literature study with the draft of the dataset A comparison was made between the results of the literature review study and the data items determined in the expert-based draft of the dataset. Data items of these two collections were compared both lexically and conceptually by a medical informatician (LA). For the comparison, data items were used that were reported in more than 25% of the included articles. The comparison resulted in four categories which were handed to the experts in a consensus meeting; 1) data items that were lexically and conceptually the same, 2) data items that were conceptually related, (e.g. a data item suggested to be included in the core dataset is the Wilson score, whereas in the literature review study we found data items “range of motion of neck, and head”, “jaw movement”; and “buck teeth” which are different components of the Wilson score [16]); 3) data items that were not reported in the articles of the literature review study; and 4) data items that were not mentioned in the draft dataset.

41 Chapter 3

The results of the comparison of the literature review and the expert-based draft of the dataset were discussed in two consensus meetings and the required modifications were performed.

Figure 3.1: The development process of core dataset for preoperative assessment

42 Development of a national core dataset 3.3. Results 3.3.1. Development process Based on consensus, the experts in the designing committee defined 82 data items in the draft of the dataset. As the objective was to design a core dataset applicable for all surgical cases, the most important data items were added to the dataset, and residual categories were used to cover all possible relevant patient conditions in the dataset, such as “other liver diseases”. The comparison between the results of the literature review study and the data items determined in the consensus meetings showed that in total 76 percent of the data items were lexically and conceptually the same. In general the literature included more detailed and specific data items. For example, in the expert-based dataset two specific liver diseases, “hepatitis-non-A” and “liver cirrhosis” were defined whereas the literature review showed 7 data items such as hepatic failure, cirrhosis, and jaundice. Experts in the designing committee tried to use scores and scales instead of separated data items to describe patient conditions; e.g. Lee risk score, Ramsay score and Wilson score. Table 3.1 shows conceptually related data items. The last column of table 3.1 shows the finally chosen or modified data items as defined by experts after discussing the differences between the literature review and the draft dataset. Data items “hepatitis-non-A”, “cerebral tumor”, “myelum tumor”, “patient in waiting list for renal transplantation” and “inspiratory stridor” defined in the expert-based draft were considered to be too specific and were modified according to what was found in the literature review. Six data items in the draft of the dataset were not mentioned in the literature review study (see table 3.2). After discussion, these data items remained in the dataset without any changes. Ten data items were reported in the included articles in the review study while the experts in the committee did not include them in the draft of the core dataset (see table 3.2). These data items were discussed and 8 of these ten data items were added to the final version of the dataset: “diagnosis”, “alcohol drinking”, “use of illicit drugs”, “anemia”, “arthritis”, “difficulty in communication”, “specification of procedure”, and “auscultation of lung”. To capture “chest x-ray”, the data item “supplementary laboratory tests” defined in the draft of the dataset was modified to “supplementary examination” covering laboratory tests, examinations and imaging. The data item “gastrointestinal diseases” was replaced by “Pyrosis/ regurgitation”. 3.3.2. Final version of the dataset After comparing the data items in the draft dataset with those retrieved from the literature, six data items were deleted, seventeen new data items (fourteen data items based on literature and three new data items) were added, and nine data items were modified. Once required modifications were performed, in total ninety three data items were included in the core dataset. These data items were categorized into four categories: patient history, physical examination, supplementary examination and consultation, and final judgment (table 3.3). The highest number of data items was related to the category “patient history”. Table 3.3 is a summary of the final dataset on a high level of aggregation. As well as

43 Chapter 3 specific data items some residual categories were defined if necessary. The final dataset also includes for example the allergic substances and type of allergic reaction, and the date of some diseases such as angina pectoris, cerebrovascular accident and transient ischemic attack. The complete description of the core dataset can be acquired from the authors.

Table 3.1: Data items that were conceptually related

Categories Data items in the Data items in the literature Final data items in the expert-based dataset review core dataset Patient history Hepatitis non A Hepatitis Hepatitis Cerebral tumor Malignancy Malignancy Myelum tumor Malignancy Malignancy History of heart valve Artificial heart valve History of Artificial heart operation valve implantation Percutaneous Revascularization History of Percutaneous transluminal coronary coronary intervention angioplasty (PTCA) Claudication Leg weakness Data item was deleted Patient in waiting list Renal transplant Data item was deleted for renal transplantation Large vessel disease Vascular heart disease, Three data items were Cerebral vascular disease, added: cerebrovascular peripheral vascular disease diseases, great blood vessel disease (aorta) and peripheral vascular diseases Glucose intolerance Hypoglycemia Glucose intolerance Neuromuscular disease Musculoskeletal disease, and Neuromuscular disease neurological disease remained and data item musculoskeletal disease was added Ramsey score Alert, oriented, cooperative, Richmond Agitation awake Sedation Scale (RASS score) Glasgow coma scale Alert, oriented, cooperative, Glasgow coma scale awake Physical Wilson score Range of motion of neck, Wilson score remained in examination head, and shoulders; jaw the dataset and further movement; and buck teeth details regarding different parts of this score were added Inspiratory stridor Airway obstruction Airway obstruction

44 Development of a national core dataset

Table 3.2: Data items that were reported either only by experts or only in the included article in the literature review

Categories Data items mentioned only by Data items reported only in the literature experts review

Patient history Address and telephone Purpose of the operation/ diagnosis Transplantation Alcohol drinking implantable cardioverter Use of illicit drugs defibrillator (ICD) Anemia (rheumatoid) arthritis Difficulty in communication Specification of procedure Gastrointestinal diseases Physical examination Oxygen Auscultation of lung

Supplementary Chest X ray examination and consultation

Final judgment Lee risk score

3.4. Discussion

The preoperative core dataset was designed to guide the documentation of preoperative assessment through the use of consistent data items across various health care settings. In summary, this dataset includes ninety three data items. As information concerning patient history is an essential component of the preoperative assessment and influential for further examinations and tests [2, 5] the majority of data items (72 out of 93) in the core dataset was in this category. The comparison between the expert-based dataset and the result of the literature review helped the committee to modify the dataset by adding disregarded data items and removing unnecessary items. Nearly all data items which were reported in more than 25% of the articles included in the literature review and which were not included in the expert-based draft of the dataset were added to the final version of the dataset. Moreover, comparing the conceptually related data items in these two collections led the experts to define more general data items which are useful for a core dataset. These accomplishments showed the necessity of performing the literature review next to the expert consensus. As the literature was very diverse and in order to avoid committee members to blindly trust the literature, the result of the literature review were provided to the experts only to check their decisions on what data items should be included in the final version of the core dataset. The results of the literature review could have been provided beforehand, but this would have made it impossible to compare the results from the literature with the expert- based dataset.

45 Chapter 3

Table 3.3: Data items in the core dataset

Categories Data items Patient History Date of birth Gender

Citizen number Patient name Patient number Address Telephone

E-mail address Objection/ limitation to receiving blood products Difficulty in communication Alcohol drinking

Use of illicit drugs Malignancy (active) Allergy Medication Side effects

Preoperative screening date Procedure date Referring specialist Referring specialty

Diagnoses Procedure Specification of procedure(location, laterality, nature ) Blood loss risk Past operation

Family history of anesthesia complication Post operative nausea and vomiting Exercise tolerance Angina pectoris

Dyspnea Atrial fibrillation History of myocardial infarction History of coronary Artery bypass graft History of artificial heart valve implantation

History of heart transplantation History of other heart diseases History of percutaneous coronary intervention History of congestive heart diseases

History of atrial fibrillation

History of valvular heart diseases Congenital heart diseases Pacemaker/ implantable cardiac defibrillator Echo result if performed

Diagnosed hypertension Cerebrovascular diseases Peripheral vascular disorders Great blood vessel diseases (Aorta)

Smoking

Asthma Chronic obstructive pulmonary disease (COPD) Obstructive sleep apnea

46 Development of a national core dataset

Categories Data items Patient History Pulmonary function test (PFT) result if performed Renal failure

Hepatitis Liver cirrhosis Bleeding tendency Coagulation disorders Anemia

Diabetes Secondary diabetic complication/ polyneuropathy Glucose intolerance Hyperthyroidism

Musculoskeletal diseases Rheumatoid diseases Pyrosis/ regurgitation Lumbar injury Morbus Parkinson Cerebral aneurysm Epilepsy Neuromuscular diseases Psychiatric disorders Richmond Agitation Sedation Scale (RASS) Glasgow Coma Scale Physical examination Length Weight Intubation difficulty Dental status Wilson score Mallampati grade Craniofacial abnormality Upper pulmonary obstructive disease Heart rate Blood pressure Heart sound Oxygen Lung Auscultation Supplementary laboratory tests and Supplementary examination consultants Consultation Final Judgment American Society of Anesthesiologists physical status class (ASA class) Lee risk score Informed consent Anesthesia technique Indication for endocarditis prophylaxis

This dataset has been designed as a framework to preoperative assessment health care setting and it does not cover the complete assessment of every surgical case. The current core dataset includes some residual categories such as “other liver diseases” or “other neurological disorders” which have to be made more explicit when applicable. This flexibility enables using the dataset for all centers with any complexity level of surgical cases. To accomplish a whole preoperative assessment more data items may be required.

47 Chapter 3

Any extension to this dataset is allowed as long as the core dataset can be shared by all healthcare settings. The aim was to find a balance between the practicalities of data collection, and the usefulness of data to manage patient’s risks. This dataset includes data items that are important for the risk assessment of the patient and that health care providers would like to know before performing anesthesia or surgery. To use this dataset in the real preoperative-assessment process in a way which is understandable for the patient these data items will be accompanied by a list of questions to address to the patient. E.g. to determine whether a patient has a “angina pectoris” a data item mentioned in the dataset, the patient may be asked whether (s)he has pain in the chest or uses medications such as Nitroglycerin. Among studies in other domains regarding designing a core dataset [17-21] none benefited from a systematic literature review. Simmons and his colleagues [19] designed a national dataset for monitoring diabetes patients and reviewed only 3 published datasets and made a draft of the core dataset and distributed it to 147 specialists. Based on the specialists’ views they decided whether a data item should be included in the dataset. However, their responses rate was only 18%. Moreover, our consensus meetings were real face to face meetings which supported extensive discussion on all data items and resulted in overall agreement. It is doubtful whether this could be reached as easy by using teleconference and emails as used in [21]. This national datasets would improve clearness and uniformity of written communication among clinicians and provide information that is both essential and desirable for patient management. Moreover, the implementation of this dataset in the healthcare settings would prevent costly reassessment. This paper describes some of the numerous activities for standardizing the perioperative dataset. The next step consists of creating a proper data dictionary for the designed dataset to improve common understanding of data items and to standardize definitions and ensure consistency of use [22]. To this end, the elements defined in the consensus meetings will be presented as data items and their values. For each data item, a working definition will be provided, and allowed values will be specified. To integrate the preoperative-assessment dataset with IOTA’s per- and postoperative datasets IOTA’s methodology will be use to uniformly describe all data items [15]. This methodology implies concept modeling according to SNOMED CT1 terminology using Protégé2. SNOMED CT is used to support the electronic exchange of preoperative data with other specialties and across information systems to provide continuity [23], which may result in better and safer patient care. To fulfill this capability, SNOMED CT concepts will be used in an HL7 Reference Information Model (RIM) architecture which facilitates the implementation of an interoperable dataset. The designed dataset and associated data dictionary will be reviewed and updated regularly.

1 http://www.ihtsdo.org/snomed-ct/ , last accessed June 30 2008 2 http://protege.stanford.edu/overview/protege-owl.html , last accessed June 30 2008

48 Development of a national core dataset 3.5. Conclusions

The combination of literature review and expert consensus provided a good foundation for designing the core dataset. This approach may be useful for designing datasets in other domains. The large diversity in the preoperative assessment data collection found by the literature review shows that expert panels are needed to determine the appropriate data items. On the other hand, only using the experts’ consensus would not be sufficient, as they may simply overlook some data items. The literature helped our experts to carry out useful modifications in the dataset. This core dataset will enable healthcare settings to evolve towards standardization of the preoperative assessment and interoperability.

49 Chapter 3

References

[1] Greer AE, Irwin MG. Implementation and evaluation of guidelines for preoperative testing in a tertiary hospital. Anaesth Intensive Care 2002 Jun;30(3):326-30. [2] van Klei WA, Grobbee DE, Rutten CL, Hennis PJ, Knape JT, Kalkman CJ, et al. Role of history and physical examination in preoperative evaluation. Eur J Anaesthesiol 2003 Aug;20(8):612-8. [3] Ferrando A, Ivaldi C, Buttiglieri A, Pagano E, Bonetto C, Arione R, et al. Guidelines for preoperative assessment: impact on clinical practice and costs. Int J Qual Health Care 2005 Aug;17(4):323-9. [4] Lew E, Pavlin DJ, Amundsen L. Outpatient preanaesthesia evaluation clinics. Singapore Med J 2004 Nov;45(11):509-16. [5] Practice advisory for preanesthesia evaluation: a report by the American Society of Anesthesiologists Task Force on Preanesthesia Evaluation. Anesthesiology 2002 Feb;96(2):485-96. [6] Kaafarani HM, Itani KM, Thornby J, Berger DH. Thirty-day and one-year predictors of death in noncardiac major surgical procedures. Am J Surg 2004 Nov;188(5):495-9. [7] Roizen MF. More preoperative assessment by physicians and less by laboratory tests. N Engl J Med 2000 Jan 20;342(3):204-5. [8] Halaszynski TM, Juda R, Silverman DG. Optimizing postoperative outcomes with efficient preoperative assessment and management. Crit Care Med 2004 Apr;32(4 Suppl):S76-S86. [9] Koppada B, Pena M, Joshi A. Cancellation in elective orthopaedic surgery. Health Trends 1991;23(3):114-5. [10] Ahmadian L, Cornet R, van Klei WA, de Keizer NF. Diversity in preoperative-Assessment Data Collection, a Literature Review. Stud Health Technol Inform 2008;136:127-32. [11] Michota FA, Frost SD. The preoperative evaluation: use the history and physical rather than routine testing. Cleve Clin J Med 2004 Jan;71(1):63-70. [12] Garcia-Miguel FJ, Serrano-Aguilar PG, Lopez-Bastida J. Preoperative assessment. Lancet 2003 Nov 22;362(9397):1749-57. [13] Forister JG, Blessing JD. Considerations in preoperative evaluation. Physician Assistant 2002 Nov;26(11):36-46. [14] Pollock DA, Adams DL, Bernardo LM, Bradley V, Brandt MD, Davis TE, et al. Data elements for emergency department systems, release 1.0 (DEEDS): a summary report. DEEDS Writing Committee. J Emerg Nurs 1998 Feb;24(1):35-44. [15] Monk TG, Sanderson L. The Development of an Anesthesia Lexicon. Seminars in Anesthesia, Perioperative Medicine and Pain, 2004 Jan 6;23(2):93-8. [16] Pearce A. Evaluation of the airway and preparation for difficulty. Best Pract Res Clin Anaesthesiol 2005 Dec;19(4):559-79. [17] Innes G, Murray M, Grafstein E. A consensus-based process to define standard national data elements for a Canadian emergency department information system. CJEM 2001 Oct;3(4):277-84. [18] Ireland D, O'Brien D. In the beginning...the origin and evolution of ASPAN's Perianesthesia Data Elements. J Perianesth Nurs 2007 Dec;22(6):423-7. [19] Simmons D, Coppell K, Drury PL. The development of a national agreed minimum diabetes dataset for New Zealand. J Qual Clin Pract 2000 Mar;20(1):44-50. [20] Thomas K, Emberton M, Mundy AR. Towards a minimum dataset in urology. BJU Int 2000 Nov;86(7):765-72. [21] Campbell HS, Ossip-Klein D, Bailey L, Saul J. Minimal dataset for quitlines: a best practice. Tob Control 2007 Dec;16 Suppl 1:i16-i20. [22] Guidelines for developing a data dictionary. J AHIMA 2006 Feb;77(2):64A-D. [23] Westra BL, Bauman R, Delaney CW, Lundberg CB, Petersen C. Validation of concept mapping between PNDS and SNOMED CT. AORN J 2008 Jun;87(6):1217-29.

50 ChapterChapter 4

FacilitatingFacilitating ppreoperativereoperative assessmentassessment guidelinesguidelines representationrepresentation uusingsing SSNOMEDNOMED CCTT

Ahmadian L, Cornet R, de Keizer NF Journal of Biomedical Informatics. 2010; 43(6): 883-890 Chapter 4

Abstract

Objective: To investigate whether SNOMED CT covers the terms used in preoperative assessment guidelines, and if necessary, how the measured content coverage can be improved. Methods: Preoperative assessment guidelines were retrieved from the websites of (inter)national anesthesia-related societies. The recommendations in the guidelines were rewritten to “IF condition THEN action” statements to facilitate data extraction. Terms were extracted from the IF-THEN statements and mapped to SNOMED CT. Content coverage was measured by using three scores: no match, partial match and complete match. Non-covered concepts were evaluated against the SNOMED CT editorial documentation. Results: From 6 guidelines, 133 terms were extracted, of which 71% (n = 94) completely matched with SNOMED CT concepts. Disregarding the vague concepts in the included guidelines SNOMED CT’s content coverage was 89%. Of the 39 non-completely covered concepts, 69% violated at least one of SNOMED CT’s editorial principles or rules. These concepts were categorized based on four categories: non-reproducibility, classification- derived phrases, numeric ranges, and procedures categorized by complexity. Conclusion: Guidelines include vague terms that cannot be well supported by terminological systems thereby hampering guideline-based decision support systems. This vagueness reduces the content coverage of SNOMED CT in representing concepts used in the preoperative assessment guidelines. Formalization of the guidelines using SNOMED CT is feasible but to optimize this, first the vagueness of some guideline concepts should be resolved and a few currently missing but relevant concepts should be added to SNOMED CT.

52 Facilitating guidelines representation using SNOMED CT

4.1. Introduction

Clinical guidelines are effective tools to reduce practice variation and improve the quality of care [1, 2] by summarizing and describing best practices for specific patient conditions. However, guidelines are frequently developed and implemented on paper. This reduces their availability at the point of care [3]. Paper-based guidelines require physicians to interrupt their workflow to locate, read, and process the guidelines. Therefore, although many guidelines have been developed to assist physicians in different clinical situations, adherence to guidelines, even those that are broadly accepted, is often low [4, 5]. The excessive and growing number of guidelines makes it hard for physicians to remember, find, and appropriately apply guidelines. To help physicians in this regard, guidelines should be provided at the point of care. Providing guidelines at the point of care can be facilitated by integrating them into a decision support system (DSS) or clinical information system. Integrated guidelines help physicians to deliver evidence-based care to patients. By providing patient-specific reminders and advice, a DSS can influence the physicians’ behavior [6-9] to increase adherence to guidelines [6, 7, 10-12] thereby improving patient care significantly [7]. A success factor for DSS implementation is the integration into workflow and information infrastructure [7]. To enable this, guidelines should be represented in a format that enables automated inference based on patient data stored in the clinical information systems. Furthermore, guideline-based DSSs must be able to interact correctly with any clinical information system to enable broad adoption. This can be realized by using a standard information model and standard terminology in both the automated guideline and the clinical information system. In the Netherlands, anesthesiologists intend to adopt SNOMED CT1 as a standard terminology in the domain of preoperative assessment [13]. SNOMED CT will be used to record preoperative assessment patient data in a standardized way in order to be able to reuse the data for multiple applications including a guideline-based DSS. Therefore, in this study we will investigate whether SNOMED CT can be used for this purpose, by answering the following questions: (1) to what extent does SNOMED CT cover the terms used in commonly used preoperative assessment guidelines? (2) If necessary, how can the measured content coverage be improved? 4.2. Background 4.2.1. Guideline composition and guideline representation In general, a guideline consists of the description of the task that has to be performed, eligibility criteria that may evoke the guideline or parts of it, and abort criteria that may cancel following (parts of) the guideline. Furthermore, guidelines have validation attributes

1 http://www.ihtsdo.org/

53 Chapter 4 such as strength of evidence, which indicate whether a guideline or a task in a guideline is supported by the literature or by expert opinions. Whereas most guidelines are formulated as unstructured text or as a simple flowchart, there is a growing need to create interoperable guidelines [14]. To this end, guidelines should be formalized. Efforts have been made toward formalizing guidelines and creating interoperable clinical guidelines and knowledge-based DSSs during recent years [15-20]. These formalizations are generally based on a logical statement that is activated by some relevant event, such as entering or storing patient data. The logical statement will be activated if the patient data is recognized as satisfying the eligibility criteria of a guideline or of a task in the guideline. 4.2.2. The use of terminologies in guideline representation An important step in creating interoperable guidelines is the binding of terminology used in the clinical information system to terminology used in guideline representation. In some guideline representations, such as old versions of the Arden syntax, institution- specific terms must be mapped to the specific terms used in the representation in order to activate a logical statement in the guideline [18]. This kind of guideline representation cannot support different synonyms used by different care providers for a clinical concept, e.g., K+, or serum potassium. Therefore, as the naming of patient data varies among institutions, patient data elements defined in the formalized guideline will need to be changed when the guideline is shared [18]. In the Arden syntax this problem has become known as the “curly braces problem”, because the data-acquisition statements of Arden syntax contain non-standardized data names and expressions in curly braces [21]. To overcome problems caused by use of different terminology, the GLIF (guideline interchange format) formalization used a list of acceptable synonyms for each data element [16]. In EON, which is a component-based architecture for building guideline-based decision support systems, and Asbru, which is a machine-readable language used for guideline representation, specific domain ontologies are defined [15, 20]. Patient data should be first mapped to these domain ontologies designed for guideline representation. Recent researches have focused on increased interoperability using standard terminologies in guideline representation and clinical information systems [22, 23]. Achour et al. [22] used the Unified Medical Language System (UMLS) to create a domain ontology and thereby facilitate interoperability and reusability of the guideline representation expressed by the Arden syntax. GLIF3 adopted a version of HL7 v3 RIM (Health Level 7 version 3 Reference Information Model) as its data model, and used controlled terminologies such as ICD-9 and SNOMED [24]. Guidelines elements model (GEM) is an XML-based guideline document model intended to facilitate translation of natural language guideline documents into a format that can be processed by computers. GEM promotes this translation by describing concepts pertinent to guideline representation, attributes of those concepts, and relationships among the concepts. GEM also has an element called “definition” which stores important guideline terminology as well as the meaning of the terms [25].

54 Facilitating guidelines representation using SNOMED CT

Another application of standard terminologies in the context of guideline formalization was in the SAGE (Standards-based Shareable Active Guideline Environment) project in which a framework for encoding and disseminating guidelines has been developed [26]. A set of reference terminologies including SNOMED CT, LOINC2 , National Drug File- Reference Terminology3, and RxNorm4 was used to support semantic interoperability [23]. SAGE obtains patient data from the local clinical information systems to activate the logical statements in the guideline. Therefore, standards-based coded content in a SAGE guideline must be mapped to corresponding codes used in the clinical information systems. Bernstein and Andersen in their work describe how the guideline system and the electronic health records can be integrated by the use of archetypes and SNOMED CT [27]. To eliminate the process of context-specific mapping of data between the guideline and the patient data in the anesthesia information management systems, in our project we will use SNOMED CT as a standard terminology for recording patient data as well as within guideline representation. 4.3. Methods and materials 4.3.1. SNOMED CT SNOMED CT is a comprehensive clinical healthcare terminology that can be used as the foundation for electronic medical records and other applications. It is constantly updated and its revisions are released twice a year. In this study, the July 2008 release was used. It contains more than 315,000 active concepts with unique meanings, about 807,000 descriptions, including synonyms of defined concepts, and approximately 1,236,000 relationships between the concepts. Concepts in this terminology are defined based on description logic and organized into hierarchies with multiple levels of granularity. This representation enables documentation of very detailed clinical data and, when required, aggregation on a more general level. SNOMED CT is a concept-based system that supports post-coordination. Post-coordination is the ability to express new concepts by combining pre-coordinated (pre-defined) ones. This provides the possibility of creating new concepts by qualifying pre-coordinated concepts [28]. SNOMED CT, with these possibilities, may have good coverage of terms used in the guidelines, because of the varying level (from very high to very low) of aggregation of terms used in the guidelines. 4.3.2. Selection of preoperative guidelines To perform this study, we retrieved (inter)national guidelines related to preoperative assessment. As our goal was not to find a complete set of preoperative assessment guidelines, but to retrieve widely accepted guidelines, extensive searches through the

2 http://loinc.org/ 3 http://www.nlm.nih.gov/research/umls/sourcereleasedocs/2008AB/NDFRT/ 4 http://www.nlm.nih.gov/research/umls/rxnorm/

55 Chapter 4 websites of the (inter)national anesthesia-related societies were performed. Table 4.1 shows a complete list of the websites explored. Guidelines were included if they completely or partially dealt with the preoperative assessment.

Table 4.1: Websites of anesthesia-related societies on which guidelines were searched for Society Website ASA: American Society of Anesthesiologists http://www.asahq.org/ IARS: International Anesthesia Research Society http://www.iars.org/ ESA: European Society of Anaesthesiology http://www.euroanesthesia.org/ APSF: Anesthesia Patient Safety Foundation http://www.apsf.org/ EACTA: European Association of Cardiothoracic Anaesthesiologists http://www.eacta.org/ ESCTAIC: European Society for Computing and Technology in http://www.esctaic.org/ Anaesthesia and Intensive Care SCATA: society for computing and technology in anaesthesia http://scata.org.uk WFSA: World Federation of Societies of Anaesthesiologists http://www.anaesthesiologists.org

4.3.3. Data extraction The retrieved preoperative assessment guidelines consisted of narrative text. To evaluate the content coverage of SNOMED CT regarding concepts used in these guidelines, terms had to be extracted from these narrative texts. First, from the guideline texts we determined those parts that explicitly represent recommendations. Some of the selected guidelines contained not only pre-operative assessment recommendations, but also per- and post-operative process recommendations. Only parts of the guidelines related to pre-operative assessment were included in this study. Then, to facilitate data extraction, each guideline recommendation was rewritten as an “IF condition THEN action” statement [29]. Each recommendation was analyzed to determine which part referred to the condition (e.g., eligibility criteria and abort criteria such as age or physiology of the patient), and which part referred to an action (e.g., performing a test, planning a procedure). Examples are described in Table 4.2. Conditions and actions were written as noun phrases by a medical informatician (LA) and ambiguities were discussed with other medical informaticians (RC, NdK) and an anesthesiologist. When analyzing the noun phrases, their semantics need to be fully understood. This means that the representations of the guideline terms not only contain the concepts relevant for SNOMED CT, but all semantic dimensions (qualities) identifiable in the guideline text as well as the logical structure of the resulting constructs [30]. 4.3.4. Data categorization To determine what kind of terms were contained in the included guidelines, extracted terms were categorized based on Virtual Medical Record (VMR) classes, the approach that was used in the SAGE project [31]. VMR is a set of classes that define a generic information model for the purpose of authoring guidelines [32]. These classes are consistent with the

56 Facilitating guidelines representation using SNOMED CT structures of the European pre-standard ENV13606, which is an architecture for the electronic healthcare record [32]. VMR classes can also be regarded specializations of HL7 RIM Act classes such as Clinical Observation, Healthcare Goal, and Patient Encounter [33]. Consistency with existing standards can open up the possibility of a standardized methodology for embedding guideline-based decision support systems in electronic medical records. VMR includes attributes that are suitable for modeling guidelines, whereas the HL7 RIM is a fundamental model, which is not directly usable as a representation of patient data relevant to the task of guideline-based decision support. VMR consists of thirteen classes: Agent, Alert, Allergy, Appointment, Encounter, Goal, Medication Order, Observation, VMR Order, Problem, Procedure, Referral, and Composite Clinical Model5 [31].

Table 4.2: Examples of terms extraction from the included guidelines Free-text recommendation, If (condition) Then (action) extracted from guideline text Patients indicated for bariatric surgery - Bariatric surgery - Routine preoperative assessment should undergo routine preoperative - Pulmonary function assessment assessment. …In addition to the - Bone density assessment routine pre-operative assessment, the - Indirect calorimetry assessment patient undergoes further assessment (depending on the procedure…) for pulmonary function, bone density, indirect calorimetry. These guideline focus on the - Surgery with significant - Assessment of the presence of perioperative management of patients blood loss expectation congenital blood disorder undergoing surgery or other invasive - Other invasive procedure - Assessment of the presence of procedures in which significant blood with significant blood loss acquire blood disorder loss is expected ….Preoperative expectation - Assessment of the use of vitamins evaluation should include checking for the presence of congenital or acquired blood disorders, the use of vitamins or …

4.3.5. Mapping extracted terms to SNOMED CT concepts Concept-based mapping was used to map all extracted terms to corresponding SNOMED CT concepts. The extracted terms were first mapped to pre-coordinated SNOMED CT concepts (concepts pre-defined in SNOMED CT). If an extracted term did not have a corresponding representation among SNOMED CT’s pre-coordinated concepts, post- coordination was used. Post-coordination is the ability to express new concepts by combining pre-defined ones. To post-coordinate a concept, first we tried to compose it using SNOMED CT qualifiers. If this was not possible a concept was post-coordinated based on the SNOMED CT concept model, described in Appendix J of the “Technical Reference Guide of SNOMED CT” [34]. The final mapping was given one of three scores: no match, partial match or complete match. A match was considered complete when the semantics of the term extracted from the guideline were equivalent to those of the

5 http://sage.wherever.org/, last accessed 24 March 2009.

57 Chapter 4 corresponding SNOMED CT concept [28]. For instance, the extracted term “pure central apnea” was considered a complete match with the SNOMED CT concept “central sleep apnea syndrome.” A match was considered partial when one concept subsumed the other [28]. For instance, “length of upper incisors” extracted from a guideline was partially mapped to the superordinate “dental measure.” A non-match was scored when a semantically equivalent SNOMED CT concept was not available and could not be post- coordinated, e.g., “impairment of upper airway protective reflexes.” Guidelines often use terms for aggregated concepts, e.g., “cardiovascular disease,” to denote all concepts of a given type. Therefore, we also analyzed which guideline terms needed to be mapped to a concept in SNOMED CT including its subordinates. For example, the term “cardiovascular disease” extracted from a guideline refers to the concept “disorder of cardiovascular system” and all concepts subsumed by this concept, which were more than 5200 concepts. 4.3.5.1. Partial and non-matches To answer the second research question about how the content coverage of SNOMED CT can be improved, we further investigated all partial and non-matches. This investigation was done based on the editorial documentation of SNOMED CT regarding the content inclusion principles and process [35]. This document describes what should and what should not be included in SNOMED CT. It contains three basic principles for creating and sustaining semantic interoperability of clinical information: Understandability, Reproducibility and Usefulness (URU). These principles are defined as follows:  Understandability: The meaning must be able to be communicated and understood by an average health care provider without reference to inaccessible, hidden or private meanings.  Reproducibility: It is not enough for one individual to say they think they understand a meaning. It must be shown that multiple people understand and use the meaning in the same way.  Usefulness: The meaning must have some demonstrable use or applicability to health or health care. Next to the basic principles, the SNOMED CT documentation contains specific rules for submission of new content. These rules are related to content that should not be included in SNOMED CT, e.g., classification derived phrases, numeric ranges, and procedures categorized by complexity. To investigate whether there is a justification why SNOMED CT does not completely capture concepts used in the preoperative assessment guidelines, partially matching and non-matching concepts were evaluated against the URU principles and the rules for submission of new content. Concepts that did not follow the principles and rules were categorized based on the violated principle or rules. Investigation and categorization of concepts were done by two experts (LA, NdK) in SNOMED CT and in case of disagreement solved by consensus discussions together with a third SNOMED CT expert

58 Facilitating guidelines representation using SNOMED CT

(RC), based on input provided by the chief terminologist of the International Health Terminology Standards Development Organization (IHTSDO), which maintains SNOMED CT. 4.4. Results 4.4.1. Selected guidelines The explored websites contained six guidelines [36-41] related to preoperative assessment, all of which were included in this study. Five of the included guidelines were developed by the American Society of Anesthesiologists and one, concerning obesity, was developed by the Bariatric Scientific Collaborative Group: a joint group of the major European Scientific Societies, which are active in the field of obesity management. The publication dates of the included guidelines were between 1999 and 2007. At the time of the study, the latest versions of the guidelines were used. Five of the guidelines have been published in Anesthesiology Journal and one in the International Journal of Obesity. 4.4.2. Data extraction and categorization Recommendations were extracted from the included guidelines. These recommendations were subsequently transformed into 24 IF-THEN statements. Each IF-THEN statement included one or more terms to describe conditions and/or actions. In total, 133 terms were identified in the IF-THEN statements. Forty-one terms (31%) were extracted from the IF part of the statements (condition part) describing eligibility and abort criteria; 92 terms (69%) were extracted from the THEN part of the statements (action part) describing the tasks that should be done. Extracted terms were categorized into six VMR classes: Encounter, Observation, Procedure, Problem, VMR order, and Referral. Figure 4.1 shows the distribution of the terms based on the VMR classes.

N=1| 1% N=7| 5%

N=17| 13% N=40|30% Encounter

Observation

Procedure

Problem N=30| 23% VMR order

Referral N=38| 29%

Figure 4.1: Distribution of extracted terms based on VMR classes

59 Chapter 4 4.4.3. Mapping results SNOMED CT had better coverage for actions than for conditions (figure 2). Twenty-seven percent of the conditions had no matching SNOMED CT concepts against 10% of the terms describing actions.

80 74

70 63

60

50 Complet e mat ch 40 Partial match

27 No Mat ch 30

20 16 10 10 10 Percentage of coverage coverage of Percentage 0 If (condit ion) Then (act ion)

Figure 4.2: Content coverage of SNOMED CT based on concepts extracted from IF part or THEN part of the logical statements (n = 133)

Seventy one percent (n = 94) of the extracted concepts were completely matched with SNOMED CT concepts (Figure 4.3). About half of the non-complete matches (n = 19) were represented partially and mapped to the closest concepts. The other half (n = 20) did not have an appropriate corresponding representation in SNOMED CT. In total, 22.3% of all complete matches and 30% of all partial matches were achieved through post-coordination. The class “observation” had the lowest percentage of complete matches and the highest percentage of non-matching concepts. Some terms in the guidelines that express aggregations needed to be mapped to a concept with its subordinates. In this study 63% (n = 46) of complete pre-coordinated concepts (n = 73) needed to be mapped to a concept with its subordinates. Table 4.3 shows some examples of these concepts. Furthermore, guideline concepts are often complex and may not always be appropriately mapped to a SNOMED CT concept. This occurs when an extracted concept refers to a set (more than one) of concepts that does not completely cover one entire branch (a concept with its subordinates) of SNOMED CT but just a part of a branch. For instance, the extracted concept “physical examination” can be mapped to the SNOMED CT concept “physical examination procedure” while the concept “physical examination under local anesthesia,” a subordinate of the aforementioned SNOMED CT concept, is not a concept of interest of the guidelines’ authors when stating the aggregated term “physical examination.”

60 Facilitating guidelines representation using SNOMED CT

110 100 0 14.3 90 80

70 10 22.5 11.8 60 15.7 100 50 15.8 85.7 40 63.3 30 55 58.8 54.9 39.5 Percentage of coverage Percentage 20 0 29 2.5 2.6 4.5 10 13.2 13.3 13.3 17.6 15 12.5 7.5 11.8 9.7 0 0 000 00 No match No match No match No match No match No match No match No Partial match Partial match Partial match Partial match Partial match Partial match Partial match Partial Complete match Complete match Complete match Complete match Complete match Complete match Complete match Encounter Observation Procedure Problem VMR order Referral Total N=40N=38 N=30 N=17 N=7 N=1 N=133 Precoordination Post-coordination

Figure 4.3: Content coverage of SNOMED CT based on VMR classes

Table 4.3: Examples of extracted concepts that need to be mapped to a concept including its subordinates Extracted terms from the SNOMED CT concepts Examples of SNOMED CT guidelines subordinates General anesthesia 50697003 | general anaesthesia | - Inhalation general anesthesia - Total intravenous anaesthesia Physical examination 5880005 | physical examination - Taking patient vital signs procedure | - Cardiovascular physical examination Metabolic disorders 75934005 | metabolic disease | - Amyloidosis - Disorder of acid-base balance Obstetric procedure 386637004 | obstetric procedure | - Removal of ectopic fetus - Artificial rupture of membranes|

4.4.3.1. Partial and non-matches The result of the in-depth investigation of partially and non-matching concepts, based on the SNOMED CT editorial documentation [35], shows that 69% (n = 27) of these concepts should not be represented in SNOMED CT because they violated at least one of the basic principles or rules for submission of new content. Disregarding these concepts the coverage of SNOMED CT was 89% (94/106) (see table 4.4). Most of these concepts were non- reproducible. Table 4.5 categorizes the reasons for not representing an extracted guideline term by a SNOMED CT concept. The categories consist of the violated basic principle (in all cases lack of reproducibility) and rules. Concepts violating a rule for submission of new content may also violate the basic principle. For instance, classification-derived phrases can be rejected because they fail the basic tests of understandability, reproducibility, and usefulness. These kinds of concepts such as “other cardiovascular problems” are vague, as

61 Chapter 4 they depend on what has been specified in the context. Most of the concepts categorized under classification-derived phrases were concepts including the word “other.”

Table 4.4: Coverage of SNOMED CT for concepts extracted from guidelines SNOMED CT coverage Concepts from the guidelines Non-vague concepts Vague concepts Complete matches 94 0 Partial and non-matches 12 27 Total 106 27

One third (n = 12) of the partially and non-matching concepts did not violate any basic principle or rule (table 4.6). Partial matches mostly consist of mappings to a generic concept, which is a superordinate of the extracted concept. For instance, the extracted concept “Length of upper incisors” has been mapped to the generic concept “dental measure.”

Table 4.5: Concepts that could not be represented by SNOMED CT Violated principle or rules Concepts Non reproducibility Factors related to difficult airways Risk factors of pulmonary aspiration Diseases that may affect gastric emptying or fluid volume Minimizing regurgitation opportunity Minimizing pulmonary aspiration opportunity Directed physical examination Organ ischemia risk factors Coagulopathy risk factors Medication causing an allergic reaction Increased perioperative risk from obstructive sleep apnea Frequent arousals during sleep Classification-derived phrases Procedure without anesthesia Other gastrointestinal motility disorders Other invasive procedures Surgeries with occurrence or expectation of significant blood loss Abnormalities of the upper or lower airway not associated with sleep apnea Daytime hypersomnolence from other causes Obesity in the absence of sleep apnea Other cardiovascular problems Other congenital medical conditions Other acquired medical conditions Numeric ranges <28 days < 35 kg < 1 year Procedures categorized by Major noncardiac surgery complexity Extensive airway examination

62 Facilitating guidelines representation using SNOMED CT

Table 4.6: Partially matching and non-matching concepts that should be represented by SNOMED CT Partial matches Extracted concepts Closest matches Length of upper incisors 251291004 | dental measure | Relation of maxillary and mandibular incisors 25272006 | dental occlusion | during normal jaw closure Visibility of uvula 424242006 | on examination - soft palate, fauces and uvula visible | Length of neck 364412008 | measure of neck | Expected severity of postoperative pain 405161002 | pain level | Acquired blood disorders 414022008 | finding of cellular component of blood | No matches Relation of maxillary and mandibular incisors during voluntary protrusion of mandible Compliance of mandibular space Impairment of upper airway protective reflexes Non surgical procedure Invasiveness of the procedure Shape of palate

4.5. Discussion

Our study shows that to facilitate preoperative assessment guideline representation using SNOMED CT not only SNOMED CT needs to be extended, but also deficiencies in the guidelines should be solved. The guidelines that we included in this study are common in the preoperative assessment practice. SNOMED CT covered 71% of the total concepts used in the included guidelines and 89% of the non-vague concepts of the preoperative assessment guidelines. SNOMED CT has higher coverage for the terms related to actions (the extracted terms from THEN part of the logical statements) than for the terms related to conditions (the extracted terms from IF part of the logical statements). A logical statement in a guideline will be activated if patient data is recognized as satisfying the condition that is defined in the guideline representation. Therefore, a condition in the guideline representation should be defined in a concept-based way, mapped to several synonyms that physicians may use for stating patient data related to that condition in the clinical information system. Providing conditions in the guideline representation with the help of terminological systems containing synonyms eases proper guideline activation. If the guideline-based DSS is only designed for clinical advice, it is less important that the action part of a guideline can be mapped to patient data; the advice can even be presented as free text. However, in case the system is used for accountability to evaluate the delivered care, actions as well as conditions need to be defined in the guideline formalization in a concept-based way. Partial and non-matching conditions were mostly related to the aggregated terms, which authors used for referring to a set of conditions, e.g., “surgeries with occurrence of significant blood loss.” Therefore, to facilitate guideline representation presenting guidelines with more specific and detailed data items is required.

63 Chapter 4

Evaluation of partially and non-matching concepts, which were 29% of the extracted concepts, based on editorial documentation of SNOMED CT [35] revealed that 69% of these concepts should not be included in SNOMED CT because they correspond to underspecified concepts in the preoperative assessment guidelines. These concepts did not follow the URU principles required for semantic interoperability and/or the SNOMED CT rules regarding submission of new content. Some terms used in the preoperative assessment guidelines such as “extensive airway examination” are vague and non-reproducible, and require clarification before guidelines can be formalized. In our study 81% of the underspecified concepts were related to the IF part of the statements referring to inclusion and exclusion criteria of a guideline or a recommendation in a guideline. Some of the guidelines refer to other external resources for more information on inclusion and exclusion criteria and some of them request the knowledge of the experts in the field. To create an interoperable guideline to be implemented in a DSS all these concepts should be elicited and automatically extracted from the electronic medical records. A terminology can be used to assess whether specific inclusion or exclusion criteria are met. Disregarding the vague concepts, the evaluation shows that SNOMED CT could adequately represent 89% of the total number of extracted terms. This result is in line with the coverage achieved in other studies [3, 42-44], i.e., between 86% and 94%, in representing guidelines’ concepts by using SNOMED CT. Sonnenberg and Hagery [45] did a study about the number of clinical concepts required to implement two selected guidelines and the degree to which they are currently captured by the electronic medical records. They found that only a minority of terms (24%) referred to simple concepts, collected in the medical records in the form required for application of the guideline. They found that guidelines lack explicit definitions of many important terms necessary for automating guidelines. This finding is described in more studies [46, 47]. Similar to the study of Peleg et al. [46], we also often encountered that a guideline term was too general to appear as a patient data item in electronic medical records. Guidelines often employ general or aggregated terms that require knowledge not contained in the guideline document [45]. For example, one of our guidelines used the general term “metabolic disorders,” without mentioning different types of this class of diseases, whereas the clinical information system stores e.g., “amyloidosis.” Providing this type of knowledge and determining if actual patient data belongs to the concept mentioned in the guideline requires a terminology service that can return all valid subordinates for the concept used in the guideline [23]. SNOMED CT fully supports this functionality by its hierarchical structure. However, in some cases mapping of the aggregated concepts to SNOMED CT seemed to be possible, but the subordinates under the mapped SNOMED concept had a different meaning compared to the guideline. This makes representation of the guidelines hard. For example, the subordinates of the SNOMED CT concept “nose and throat examination” includes the concept “rhinolaryngologic examination under general anesthesia” that is not part of the preoperative airway examinations, which is mentioned in the guideline. Creating a SNOMED CT subset with excluding such subordinates of a concept may often allow for a precise definition of the set of concepts of interest [23].

64 Facilitating guidelines representation using SNOMED CT

Evaluating partially and non-matching concepts using the editorial documentation of SNOMED CT [35] helped us to achieve a better understanding of the coverage of SNOMED CT in the evaluated preoperative assessment domain. To our knowledge, this is the first study that took a closer look at non-covered concepts to find out the possible reasons. This helped us to clearly distinguish deficiencies attributable to the guidelines or to SNOMED CT. Thus, it made clear where to put our efforts to further improve the content coverage of SNOMED CT for the evaluated domain. Moreover, highlighted deficiencies of guidelines may lead to revisions that make those guidelines more useful [48]. Other studies can apply our approach to explicitly distinguish the deficiencies associated to the terminological systems and deficiencies associated to the guideline. The results of our study confirm the results of other studies [16, 49, 50] that point out vague terms in the paper-based guidelines. This vagueness reveals the necessity of defining more specific guidelines as a first prerequisite to achieve the goal of guideline formalization [49]. The use of unstructured text hampers the unambiguous clarification and identification of all terms used in the guidelines. Given that most guidelines contain recommendations that are formulated as unstructured text, the results of our study regarding the guidelines in the field of the preoperative assessment are probably generalizable to guidelines developed in other fields. To overcome this problem, we recommend developing the guideline and guideline implementation by a DSS concurrently and collaboratively. Studies have demonstrated that such an approach provides both benefits to the quality of the guideline as well as to the DSS [51]. Using one terminological system to present the guideline concepts in our study can be a limitation. As shown in other study [3], possibly better results could have been achieved when different standard terminologies covering different domains were combined, e.g., UMLS6. This limitation should be addressed by future work. We decided not to use UMLS to represent the preoperative assessment guideline concepts as we intend to make use of the compositional (i.e., ability to use post-coordination) and formal functionalities (i.e., formal definition of concepts) of SNOMED CT, which are not included in UMLS. These functionalities are needed for guideline representation to present generic terms. The problem of non-covered concepts will be solved partly by combining other terminological systems. Some of these concepts (those presented in table 4.6) such as “Visibility of uvula” should be presented in SNOMED CT because they are non-vague and they follow the SNOMED CT editorial principles or rules. We will submit the list of non- covered concepts to the International Health Terminology Standards Development Organization (IHTSDO) for possible inclusion in the next international SNOMED CT release. More sophisticated post-coordination mapping can be developed in order to provide more mappings. For instance, to present the concepts like “length of upper incisor” and “length of neck”, it should be possible to combine the anatomical sites with different measurements to create a post-coordinated concept. An additional recommended solution is for guideline developers to identify how such information is documented in electronic medical records as part of the guideline development process. In the medical records it is

6 http://www.nlm.nih.gov/research/umls/

65 Chapter 4 more likely to specifically write “removal of ectopic fetus” instead of general term “obstetric procedure.” The overall goal of this study was to facilitate interoperable guidelines and decision support systems in routine preoperative assessment practice. It is important to consider that the problem of guideline sharing is more than just a terminology problem; it is a conceptual problem of the interface between the electronic medical records and guideline-based CDSS [52]. A standard terminology would solve only a part of the problem. Guidelines dependent on the structure of local electronic medical records and the structures of the medical records in different settings are often too different. Therefore, a common information model such as HL7 RIM might help to create a uniform model [21]. Differences in the selection and naming of patient data are a fundamental problem for sharing guidelines that act upon patient data in clinical information systems. Lack of structured and standardized methods for data recording may result in lack of semantic interoperability between terms used for data recording and terms used in guidelines. This has to be solved before guideline formalization [16]. In our case, we first tried to solve this problem by using SNOMED CT subsets as domain ontology for data recording in the preoperative assessment setting [13]. Then, based on the result of this study, we will extend these subsets to cover the required concepts for guideline representation. Based on mapped concepts; new concepts, attributes, and relations will be added to the defined domain ontology. 4.6. Conclusion Encoding guidelines using SNOMED CT highlights the importance of consistent and explicit definitions of concepts when creating a guideline. Many guidelines are significantly difficult to formalize as they include vague terms. This negatively affected the content coverage of SNOMED CT in representing concepts used in preoperative assessment guidelines. To optimize and implement SNOMED CT, guideline vagueness should be resolved first and a few currently missing concepts should be added to this terminology.

66 Facilitating guidelines representation using SNOMED CT

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[18] Hripcsak G. Writing Arden Syntax Medical Logic Modules. Comput Biol Med 1994 Sep;24(5):331-63. [19] Fox J, Johns N, Rahmanzadeh A. Disseminating medical knowledge: the PROforma approach. Artif Intell Med 1998 Sep;14(1-2):157-81. [20] Musen MA, Tu SW, Das AK, Shahar Y. EON: a component-based approach to automation of protocol-directed therapy. J Am Med Inform Assoc 1996 Nov;3(6):367-88. [21] Jenders RA, Sujansky W, Broverman CA, Chadwick M. Towards improved knowledge sharing: assessment of the HL7 Reference Information Model to support medical logic module queries. Proc AMIA Annu Fall Symp 1997;308-12. [22] Achour SL, Dojat M, Rieux C, Bierling P, Lepage E. A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development. J Am Med Inform Assoc 2001 Jul;8(4):351-60. [23] McClure R, Campbell JR, Nyman MA, Glasgow J. Guidelines and Standard Terminology: Making Standard Terminology Work In Clinical Guidelines. Denver, USA 2006. [24] Boxwala AA, Peleg M, Tu S, Ogunyemi O, Zeng QT, Wang D, et al. GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J Biomed Inform 2004 Jun;37(3):147-61. [25] Shiffman RN, Karras BT, Agrawal A, Chen R, Marenco L, Nath S. GEM: a proposal for a more comprehensive guideline document model using XML. J Am Med Inform Assoc 2000 Sep;7(5):488-98. [26] Beard N, Campbell JR, Huff SM, Leon M, Mansfield JG, Mays E, et al. Standards-based Sharable Active Guideline Environment (SAGE): A Project to Develop a Universal Framework for Encoding and Disseminating Electronic Clinical Practice Guidelines. Proc AMIA Symp. 2002 p. 973. [27] Bernstein K, Andersen U. Managing care pathways combining SNOMED CT, archetypes and an electronic guideline system. Stud Health Technol Inform 2008;136:353-8. [28] de Keizer NF, Bakhshi-Raiez F, de JE, Cornet R. Post-coordination in practice: evaluating compositional terminological system-based registration of ICU reasons for admission. Int J Med Inform 2008 Dec;77(12):828-35. [29] Shiffman RN, Brandt CA, Liaw Y, Corb GJ. A design model for computer-based guideline implementation based on information management services. J Am Med Inform Assoc 1999 Mar;6(2):99-103. [30] Straub RH, Duelli M, Semfinder AG. With Semantic Analysis from Noun Phrases to SNOMED CT and Classification Codes, Semantic Mining Conference on SNOMED CT. 2006. [31] Parker CG, Rocha RA, Campbell JR, Tu SW, Huff SM. Detailed clinical models for sharable, executable guidelines. Stud Health Technol Inform 2004;107(Pt 1):145-8. [32] Johnson PD, Tu SW, Musen MA, Purves I. A virtual medical record for guideline-based decision support. Proc AMIA Symp 2001;294-8. [33] Smith B, Ceusters W. HL7 RIM: an incoherent standard. Stud Health Technol Inform 2006;124:133-8. [34] The International Health Terminology Standards Development Organisation. SNOMED Clinical Terms® Technical Reference Guide, July 2008. 2008 Jun 1. [35] The International Health Terminology Standards Development Organisation. SNOMED Clinical Terms Editorial Guidelines, Content Inclusion Principles and Process. 2008 Jan 5.

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[36] American Society of Anesthesiologists Task Force on Preoperative Fasting. Practice guidelines for preoperative fasting and the use of pharmacologic agents to reduce the risk of pulmonary aspiration: application to healthy patients undergoing elective procedures. Anesthesiology 1999 Mar;90(3):896-905. [37] American Society of Anesthesiologists Task Force on Management of the Difficult Airway. Practice guidelines for management of the difficult airway. Anesthesiology 2003 May;98(5):1269-77. [38] American Society of Anesthesiologists Task Force on Acute Pain Management. Practice guidelines for acute pain management in the perioperative setting. Anesthesiology 2004 Jun;100(6):1573-81. [39] American Society of Anesthesiologists Task Force on Perioperative Blood Transfusion and Adjuvant Therapies. Practice guidelines for perioperative blood transfusion and adjuvant therapies. Anesthesiology 2006 Jul;105(1):198-208. [40] Fried M, Hainer V, Basdevant A, Buchwald H, Deitel M, Finer N, et al. Inter-disciplinary European guidelines on surgery of severe obesity. Int J Obes (Lond) 2007 Apr;31(4):569-77. [41] Gross JB, Bachenberg KL, Benumof JL, Caplan RA, Connis RT, Cote CJ, et al. Practice guidelines for the perioperative management of patients with obstructive sleep apnea: a report by the American Society of Anesthesiologists Task Force on Perioperative Management of patients with obstructive sleep apnea. Anesthesiology 2006 May;104(5):1081-93. [42] Hrabak KM, Campbell JR, Tu SW, McClure R, Weida RT. Creating interoperable guidelines: requirements of vocabulary standards in immunization decision support. Stud Health Technol Inform 2007;129(Pt 2):930-4. [43] James R, Campbell MD, Karen M, Harabak RN. Controlled Vocabulary Requirements for Guideline Interoperability: A Study in Clinical Standards, MEDINFO. 2004 p. 1539. [44] Kim HY, Cho IS, Lee JH, Kim JH, Sim DH, Kim Y. Matching between the concepts of knowledge representation for a hypertension guideline and SNOMED CT. AMIA Annu Symp Proc 2008;1005. [45] Sonnenberg FA, Hagerty CG. Computer-interpretable clinical practice guidelines. Where are we and where are we going ? Yearb Med Inform 2006;145-58. [46] Peleg M, Keren S, Denekamp Y. Mapping computerized clinical guidelines to electronic medical records: knowledge-data ontological mapper (KDOM). J Biomed Inform 2008 Feb;41(1):180-201. [47] Tierney WM, Overhage JM, Takesue BY, Harris LE, Murray MD, Vargo DL, et al. Computerizing guidelines to improve care and patient outcomes: the example of heart failure. J Am Med Inform Assoc 1995 Sep;2(5):316-22. [48] Tjahjono D, Kahn CE, Jr. Promoting the online use of radiology appropriateness criteria. Radiographics 1999 Nov;19(6):1673-81. [49] Codish S, Shiffman RN. A model of ambiguity and vagueness in clinical practice guideline recommendations. AMIA Annu Symp Proc 2005;146-50. [50] Patel VL, Arocha JF, Diermeier M, Greenes RA, Shortliffe EH. Methods of cognitive analysis to support the design and evaluation of biomedical systems: the case of clinical practice guidelines. J Biomed Inform 2001 Feb;34(1):52-66. [51] Goud R, Hasman A, Strijbis AM, Peek N. A parallel guideline development and formalization strategy to improve the quality of clinical practice guidelines. Int J Med Inform 2009 Aug;78(8):513-20. [52] Schadow G, Russler DC, McDonald CJ. Conceptual alignment of electronic health record data with guideline and workflow knowledge. Int J Med Inform 2001 Dec;64(2-3):259-74.

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70 ChapterChapter 5

TheThe RoleRole ooff sstandardizedtandardized ddataata aandnd terminologicalterminological systemssystems inin computerizedcomputerized clinicalclinical decisiondecision supportsupport systemssystems

Ahmadian L, van Engen-Verheul M, Bakhshi-Raiez F, Peek N, Cornet R, de Keizer NF Accepted for publication in International Journal of Medical Informatics Chapter 5 Abstract

Introduction: Clinical decision support systems (CDSSs) should be seamlessly integrated with existing clinical information systems to enable automatic provision of advice at the time and place where decisions are made. It has been suggested that a lack of agreed data standards frequently hampers this integration. We performed a literature review to investigate whether CDSSs used standardized data and which terminological systems have been used to code data. We also investigated whether a lack of standardized data was considered an impediment for CDSS implementation. Methods: The relevant articles were identified based on a former literature review on CDSS and on CDSS studies identified in AMIA’s ‘Year in Review’. Authors of these articles were contacted to check and complete the extracted data. A questionnaire among the authors of included studies was used to determine the obstacles in CDSS implementation. Results: We identified 77 articles published between 1995 and 2008. Twenty-two percent of the included articles used only numerical data in CDSS. Fifty one percent of the studies that used coded data applied an international terminology where ICD (International Classification of Diseases) (68%) and LOINC (Logical Observation Identifiers Names and Codes) (12) were the most frequently used ones. More than half of the authors experienced barriers in CDSS implementation. In most cases these barriers were related to the lack of electronically available standardized data required to invoke or activate the CDSS. Conclusion: Many CDSSs applied different terminological systems to code data. This diversity hampers the possibility of sharing and reasoning with data within different systems. The results of the survey confirm the hypothesis that data standardization is a critical success factor for CDSS development.

72 Data standardization in CDSSs

5.1. Introduction

It has been demonstrated that clinical guidelines provided by real-time clinical decision support systems (CDSSs) significantly improve patient care [1] and reduce practice variability [2, 3]. The success of CDSSs requires that they are seamlessly integrated with clinical workflow and with existing patient information systems [4, 5] to enable the automatic provision of advice at the time and place where decisions are made. However, integrating CDSSs with other information systems has been shown difficult [6]. It has been suggested that this is due to lack of agreed standards for semantic interoperability [7-11]. Semantic interoperability is the ability of computer systems to exchange information and have that information properly interpreted by the receiving system in the way as intended by the transmitting system [12, 13]. Achieving semantic interoperability requires not only the use of communication standards such as HL7 with its underlying models and specifications, but also needs common concepts and their interpretation, including concept grammar and terminological systems [14]. A terminological system relates concepts of a particular domain among themselves and provides their terms and possibly their definitions and codes [15]. Terminological systems facilitate the integration of CDSSs with the patient information system by binding the patient data in the patient information system with the concepts in the decision rules of a CDSS. They smooth the progress of CDSS development by enabling terminological reasoning. For example, without a terminological system the CDSS rule “If a patient already suffered from a renal disease, then a urine analysis test should be done before surgery” would have to be repeated for each type of renal disease i.e. polycystic kidney disease, pyelonephritis, renal acidosis, etc. When a terminological system is used all these subtypes would be recognized as types of renal disease and only one rule will be sufficient to represent this preoperative assessment recommendation. In this way the readability of the knowledge base and its maintenance are simplified [16]. Although in theory the benefits of terminological systems for facilitating CDSS implementation are clear, there is a lack of knowledge on the actual role of terminological systems in CDSSs in clinical practice. In this study we analyzed the literature regarding CDSSs and performed a survey to answer the following questions: 1) Do CDSSs use standardized (numerical and/or coded) data? 2) Do authors of CDSS studies consider a lack of standardized data an impediment in CDSSs implementation? and 3) If coded data were used, e.g. for diagnoses or procedures, which terminological systems have been used to represent this data type? 5.2. Methods 5.2.1. Materials Our starting point was the set of included articles from the systematic review of Garg et al. on effects of computerized clinical decision support systems on practitioner performance and patient outcomes. This systematic review was based on literature retrieved from MEDLINE, EMBASE, Evidence-Based Reviews databases (Cochrane Database of

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Systematic Reviews, ACP Journal Club, Database of Abstracts of Reviews of Effects, and Cochrane Central Register of Controlled Trials), and INSPEC bibliographic databases [17]. It covers 88 randomized controlled trials (RCTs) and 12 non-randomized trials from 1974 till September 2004. In our study, we included all RCTs from 1995 referenced by Garg et al. One option to extend this set of articles with articles published after 2004 was to use Garg’s search strategy for the more recent time period. Due to time and resource limitations we decided to use CDSS studies identified by the American Medical Informatics Association (AMIA)’s “Year in Review” from October 2004 till October 2008. During each “Year in Review” session of the Annual AMIA Fall Symposium the previous year’s publications of RCTs in the medical informatics field are discussed. They identify RCTs examining more than 100 patients or providers by extensive literature review and a poll of American College of Medical Informatics (ACMI) fellows. The strategy used can be found on [18]. As AMIA’s ‘Year in Review’ was restricted to RCTs we decided to also restrict ourselves to RCTs from Garg’s review. Based on full-text review, only studies evaluating a CDSS that provided a computerized advice based on patient-specific data items were included. 5.2.2. Data extraction To systematically capture the information that was relevant for answering the research questions, an extraction form based on reviews of the literature [1, 9, 17] and expert consensus was designed. The form has 3 parts: General information about the study, CDSS characteristics, and knowledge representation. The first part includes data items regarding publication year; study design; clinical setting, and arena; and findings on the effect of the CDSS on patient or practitioner performance outcomes. For each study we collected up to three primary outcomes mentioned in the article. The second part consists of data items regarding activation of the CDSS, the systems integration within its surrounding information infrastructure and the system’s style of communication. The last part consists of items such as the data types used in decision rules i.e. numerical data, coded data or free text that invoked the system or generated the advice, and the use of terminological systems for coding the data (see appendix A for data extraction form). The extraction form was examined for coverage, clarity, and content validity in several consensus meetings. Four randomly selected articles were reviewed by all six authors of this study, and extracted data were discussed to refine the extraction form and solve ambiguities in the form. To have the same interpretation of the identified data items during the data extraction the definition of each data item was described (see appendix B). In addition, the data extraction form was circulated for external review. Two authors of recently published articles on CDSSs [19, 20] checked whether the data items were sufficiently clear. For each included study the extraction form was completed by two independent reviewers. Disagreements were resolved through discussion between the two reviewers. If reviewers could not reach an agreement, disagreements were discussed with other authors. The filled-in extraction form was sent to the corresponding author of the included studies to

74 Data standardization in CDSSs check the data extracted from their article and to complete any missing data. A document including the definitions of the concepts that we used in our data extraction form was accompanied (see appendix B). In addition we asked the authors five questions: four of these questions were about data types used in the system and the application of terminological systems; the fifth question was whether authors had ever decided not to start or to abandon developing a CDSS because of problems regarding required data or other types (e.g. financial or organizational) of problems (see appendix A, section II). Authors were sent one email message and, if necessary, up to two reminders. When primary authors did not respond or could not be reached we contacted the second author or the last author. To test differences between the use of standardized data versus non standardized data regarding features of CDSSs and practitioner performance or patient outcomes we used chi square statistics. We interpreted P≤0.05 as statistically significant. 5.3. Results 5.3.1. Study selection Garg’s review and AMIA’s “Year in review” resulted in 112 potentially relevant articles. Of these, 77 articles [6, 21-96] were included (figure 5.1). Most of the excluded studies (n=31) described a system that did not provide computerized advice based on patient- specific data items. Authors of 48 (62%) studies [6, 21, 22, 24, 25, 29, 32-34, 36-38, 41, 42, 44, 48, 52-54, 57, 60, 61, 63, 64, 66, 67, 69, 72, 75-79, 81-85, 87-96] confirmed the extracted data and provided answers to the additional questions.

Articles identified by Articles identified by AMIA’s Year in Review Garg’s review between between 2004 and 2008 1970 and 2004 (n=100) (n=59)

Excluded (n=47): - No randomized controlled trial (n=12) - Studies carried out before 1995 (n= 35)

RCT articles of Garg review >1995 (n= 53)

Articles retrieved in full text for further screening (n= 112 ) Excluded (n= 35 ): - Computerized advice not based on patient- specific data (n= 31) - No comparision to situation without CDSS (n=4)

Articles included in review (n= 77)

Figure 5.1: Selection process of studies on clinical decision support systems

75 Chapter 5 5.3.2. Description of studies Part one of Table 5.1 describes the characteristics of the included studies. Fifty one percent of the studies have been performed in a multicenter setting, 33% of them were managed by a single health maintenance organization. Most studies described systems which were developed for Disease management (35%) and Treatment (23%), followed by Drug dosing and prescribing (10%), Prevention (12%), Patient education (5%), Screening (5%), Diagnosis (4%), Risk assessment (4%), and Clinical documentation (1%). 5.3.3. Description of clinical decision support systems and Users Part 2 of Table 5.1 shows features about the way the CDSSs were implemented and integrated into the workflow. None of the 77 included studies reported a negative effect of CDSS on patient outcome or practitioner outcome. In 82% (n=45) of 55 integrated CDSSs (second row of part 2 in table 5.1), systems prompted the user automatically and did not need to be initiated manually to get advice. In 44% (n=24 out of 55) of the integrated CDSSs additional input from users was required to get the advice. CDSSs which used a consulting style of communication (systems that give users advice about what they should do) required additional data entry in 74% (n=28 out of 38) of the cases; while critiquing systems (systems that provide feedback on the actions that users perform or intend) required additional data entry in 50% (n=6 out of 12) of the cases, and reminder systems (the systems that remind users of something that they have not done) in 32% (n=8 out of 25) of the cases. System developers mostly used the consulting model (49%) as communication style of CDSSs. Systems which needed to be initiated manually to get advice required additional data entry in 81% (n=22 out of 27) of the cases.

Table 5.1: Characteristics of the included studies and features of decision support systems (n=77). See appendix B for the definitions of the characteristics presented in the table

Characteristics of the included studies Number of studies (%) Publication year 1995-1999 23 (30) 2000-2004 23 (30) 2005-2008 31 (40) Country of study United states 53 (69) United kingdom 9 (11) Canada 3 (4) Norway 3 (4) Italy 2 (3) The Netherlands 2 (3) France 1 (1) Lithuania 1 (1) Multiple countries 3 (4) Study setting a Single center 37 (48) Multiple center, single HMO b 13 (17) Multiple center 26 (34)

76 Data standardization in CDSSs

Characteristics of the included studies Number of studies (%)

Clinical setting a, c Primary care 34 (44) Secondary or tertiary outpatient care 19 (25) Secondary or tertiary inpatient care 22 (29) Clinical arena addressed by CDSS Family medicine or general practice 20 (26) Internal medicine 18 (23) Cardiology 8 (10) Supporting specialties 6 (8) Hospital wide 6 (8) Hematology 3 (4) Home Care or Nursing care 3 (4) Intensive care medicine 3 (4) Psychiatry 2 (3) Other specialties 8 (10) System features Number of studies (%)

System activation d System automatically prompts the user 49 (64) System should be initiated manually 27 (35) System integration Integrated (linked system) 55 (71) Independent (stand-alone system) 22 (29) Style of communication d, e Consulting model (system gives advice about what 38 (49) user should do) Critiquing model (system criticizes user about his/her 12 (16) action ) Reminder system (system reminds user of something 25 (32) that (s)he has not done) System requires data entry System requires user input to give the advice 43 (56) System does not require user input to give the advice 34 (44) Users of the system f Physicians 66 (86) Nurses 21 (27) Paramedics 4 (5) Patients 5 (6) a One study evaluated a web-based clinical decision support systems which was used by patient at home. b Health maintenance organization. c One study was carried out in both primary care and secondary or tertiary outpatient care. d There were one missing data item regarding system activation, and one regarding style of communication. e One system applied two modes of communication consulting and reminder. f one system could have different users.

Authors of 48 studies who responded to our questionnaire reported the following ways of invocation of their CDSS; the CDSS automatically selected the relevant cases in 44% (n=21), cases were selected automatically by another computer application in 15% (n=7), the system was invoked manually by the end-user in 25% (n=12), and the system was invoked by another person (e.g. a research assistant) in 12% (n=6). In 4% (n=2) of the studies the CDSS invocation was changed during the study from automatic invocation to manual invocation.

77 Chapter 5 5.3.4. Data types used in clinical decision support systems Table 5.2 indicates different data types that were used in CDSSs. Of the 77 included studies, 17 (22% of the) studies used only numerical data items, 11 (14%) of the studies used only coded data items, 31 (40% of the) studies used combination of numerical and coded data items, and the other 9 (12%) studies used free text with or without numerical and/or coded data items to invoke the CDSS or generate an advice. In 9 studies the used data types were not described and authors of these studies did not provide the required information. Authors who responded to our questionnaire reported that the numerical data items were mostly used for demographic and health data (n=20) (e.g. age, weight and BMI), in which the data item age (n=18) was the most frequent one, followed by laboratory test results (n=16) (e.g. hemoglobin), and physiological parameters (n=10) (e.g. vital signs). Other numerical data items were medication parameters (n=6) (e.g. medication dosage), results of diagnostic tests (n=6) (e.g. ejection fraction), disease risk factors (n=3) (e.g. cardiac risk score) and other numerical data items (n=5) (such as number of visits and days in the hospital). Studies that used free text (n=9), extracted patient diagnosis, medications or other clinical data from the free text records. Extraction of the data from free text was done, for example, by using a natural language processing method or by personal reviewing of the patient records. For instance, a pharmacist reviewed the patient prescriptions and determined if a prescription should be discontinued based on existing guidelines. More information about coded data can be found in the section 5.3.6. . The percentage of positive practitioner performance outcomes was higher among the systems that did not use free text (79% versus 50%, p-value= 0.038). The percentage of patient outcome seems to be higher among the systems that used standardized data but the difference was not statistically significant (45% versus 33%, p-value=0.51) Table 5.3 presents the frequency of using standardized data (numerical and/or coded) and free text data based on different system features. Standardized data were used more often in systems that automatically prompted the user (p-value=0.038).

Table 5.2: Outcome of clinical decision support systems based on data types used into the system

Data type Number of Number of positive outcomes/ total number of outcomes (% ) studies (%) Practitioner performance Patient outcome outcome Numerical data 57 (74) 44/57 (77) 27/60 (45) Coded data 49 (63) 40/56 (71) 45/57 (79) 17/41 (41) 27/60 (45) Free text 9 (12) 5/10 (50) 3/9 (33) The categories in this table are non-exclusive as one study could use different data types. The presented result is based on all 77 included studies. In 9 studies the used data types were not described. For each study up to three outcomes were considered.

78 Data standardization in CDSSs

Table 5.3: Frequency of using standardized data and free text based on system features. See appendix B for the definition of the concepts presented in the table

System features Data type a P-value Standardized data Free text (numerical and/or coded) System activation a System automatically 40 3 0.038 prompts the user System should be initiated 18 6 manually System integration Integrated (linked system) 42 7 0.681 Independent (stand-alone 17 2 system) System requires data System requires user input 31 5 0.866 entry to give the advice System does not require 28 4 user input to give the advice a Data was missing regarding data types (n=9), and system activation (n=1)

5.3.5. Obstacles in clinical decision support systems implementation We asked authors whether they have ever decided not to start or to discontinue developing a CDSS. In 58% of cases, the authors had experienced problems with developing a CDSS (Figure 5.2). Ninety-two percent of these problems were related to data (standardization) required to develop the CDSS. Eight percent of the experienced problems were non-data related problems including financial or organizational problems.

No implementation cancelled 7 11% Non-data related problems † 6, 9% Data not electronically 28 available 42% Data recorded as free text 12 18% Different data structure in Information System and CDSS Other data-related problems ‡ 10 3 15% 5%

Figure 5.2: Authors’ responses regarding obstacles in clinical decision support systems implementation * * Authors could choose more than one answer. † Non data related problems including financial or organizational problems ‡ Authors mentioned low data quality and incomplete data as other data related problems.

79 Chapter 5 5.3.6. Terminological systems used in clinical decision support systems Studies most frequently used an international terminological system (n=25) compared to national (n=15) or local terminological systems (n=23), where a terminological system is considered international when it is in wide use in multiple countries. Authors who responded to our questionnaire used terminological systems for representing 93 coded data items. Figure 5.3 presents the terminological systems that were used to code these data items. One study could involve several coded data items. International terminological systems were used mostly for representing diagnoses (68%), whereas national terminological systems for representing medications (50%). The international terminological systems that were used were ICD (International Classification of Diseases) n=23 (68%), LOINC (Logical Observation Identifiers Names and Codes) n=4 (12%), and other terminological systems n=7 (20%). Nearly all studies that used international terminological systems were carried out in the USA (n=24), except one study that was performed in The Netherlands. National terminological systems were applied in the USA (n=12), United Kingdom (n=2) and in The Netherlands (n=1). The national terminological systems included NDC (National Drug Code), CPT (Current Procedural Terminology), Read codes, FDA drug list (Food and Drug Administration), and NDF (National Drug File). Other countries used local terminological systems to represent the coded data (e.g. a predefined list of medications). Recent studies used international terminological systems more frequently: 72% (n=18 out of 25) of the studies that utilized international terminological systems were carried out after 2003. In general, terminological systems were more frequently utilized in integrated CDSSs. Eighty eight percent (n=22 out of 25) of the studies that used international terminological systems applied these in integrated systems. Moreover, 93% (n=14 out of 15) of the studies that used national terminological systems and 70% (n=16 out of 23) of studies that used local terminological systems applied these in integrated systems. While studies that used local terminologies required additional input from user in 57% (n=13 out of 23) of the cases, those studies that applied international terminological systems and national terminological systems required additional input in only 32% (n=8 out of 25) and 40% (n=6 out of 15) of the cases respectively. 5.4. Discussion

This literature review showed that 22 percent of the studies used only numerical data items in a CDSS, 14% of the studies used only coded data, and 40% of the studies combined numerical data with coded data to invoke the CDSS or generate an advice. The lack of standardized data is mentioned by a majority of responders of our questionnaire as a major obstacle in CDSS development and implementation. The most frequently used terminological system was one of the ICD family, but still 42% of the studies used a local terminological system to standardize data.

80 Data standardization in CDSSs

45 Vaccination 40 2 Allergy 35 4 1 1 4 Behavior findings and medical 30 3 history 2 3 Procedure 25 1 7 2 Diagnostic test 20 1 1 1 8 2 Demographic and health data 15 1 1 23 10 Clinical findings 7 10 5 Medication 5 3 0 Diagnosis Local terminology National terminology International terminology

Figure 5.3: Terminological systems used in clinical decision support systems * * The presented results in this figure are based on the studies that used coded data and their authors responded to our questionnaire. The international terminological systems were ICD: The International Classification of Diseases (n=23), LOINC: Logical Observation Identifiers Names and Codes (n=4) , DRG: Diagnosis-Related Group (n=3), ATC codes: Anatomical Therapeutic Chemical Classification System (n=1), GPI: Generic Product Identifier (n=1), ICPC: International Classification of Primary Care (1), and DSM IV Diagnostic and Statistical Manual of Mental Disorders (n=1).

The specificity of the CDSS advice varied considerably, which can be explained by the number of data items that were used by the CDSS to trigger relevant recommendations. Some systems simply checked a numerical data item, e.g. patient’s age, to discern appropriate interventions, whereas others used multiple factors (e.g., diagnoses, laboratory results, and medications) in generating recommendations. Numerical data are an easy way of standardization as numerical values are unambiguous, and interpretable by both human and computer. Consequently, such values are easy to use for reasoning in CDSSs. They require a standardized measurement method and unit to be exchanged in a standardized format among different systems. The CDSSs studied in this review used different terminological systems to present coded data to be used for decision making. The diversity of these terminological systems is an obstacle for the CDSS shareability. This diversity even existed within country borders. The most frequently used terminological system, ICD, groups together similar diseases and procedures and organizes related entities for easy retrieval [97]. Currently there is widespread enthusiasm for introducing CDSSs in healthcare. However, uptake has been slow, and multiple challenges have arisen at every phase of

81 Chapter 5 development and implementation. The majority of these challenges, as indicated by the authors of the included studies in this review, were related to semantic interoperability. If developers of CDSSs could pass the first challenge “availability of required data”, they may face other data related challenges such as different style of data documentation (free text) or different information models, which are used for presentation of data in existing patient information systems. To our knowledge this study is the first literature review focusing on the role of data standardization and terminological systems in CDSS implementation. Other literature reviews [1, 4, 9, 11] on CDSS features did not investigate these features of the CDSS as a factor affecting the system performance. Real improvement in the success of CDSSs will not come with only solving technical issues, but also with the more accurate capture of data items required for decision support, obtained through the maintenance of large standardized medical databases [98-101]. Wright and Sittig [102] developed a four-phased framework for evaluating architectures for CDSS that consist of: Feature determination, Existence and use, Utility, and Coverage. They pointed among other features of CDSS the following success features: “Avoids vocabulary issues”, “Shareability”, and “content integrated into workflows”. An important step in creating interoperable CDSSs is the binding of terminology used in patient information system to terminology used in the decision rules. In some knowledge representation languages like the older version of the Arden syntax a term used in a patient information system had to be mapped to the specific terms used in the decision rules to activate a logical statement [103]. As this kind of language can not support using different but synonymous terms, any encoding of clinical knowledge in the decision rules must be adapted to the local institution in order to use the local vocabulary. In the Arden syntax this problem has become known as the “curly braces problem”, because Arden syntax contains non-standardized names and expressions in curly braces. This problem affects the shareability of the defined decision rules. To overcome this problem some knowledge representation languages defined domain ontologies and used them in their decision rules [104, 105]. Recent knowledge representations such as GLIF (Guideline Interchange Format) and SAGE (Standards-Based Active Guideline Environment) deal with vocabulary issues by specifying a clinical information model which includes vocabulary standards. Using standard terminological systems in guideline formalization and in patient information systems will facilitate the interoperability and reusability of the formalized guidelines and thereby ease implementation of the guideline into a CDSS [106, 107]. In the domain of preoperative assessment we developed a core dataset and we intend to create SNOMED CT subsets for items in this dataset for documentation of patient information in anesthesia information management system (AIMS) [108]. We also formalized the preoperative assessment guidelines by using SNOMED CT to create guideline-based DSS in AIMS and to facilitate binding the concepts used in the guidelines with concepts captured in AIMS [109]. This will eliminate the process of context-specific mapping of data between the CDSSs and the patient data in AIMS. Moreover, sharing CDSS rules with other systems using the same terminology will be facilitated.

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The results of our study show that international terminological systems were used mostly in integrated systems, providing the possibility of sharing decision support content. It has been described that systems that are provided as an integrated component of health information systems are significantly more likely to succeed than stand-alone systems [1]. Stand-alone systems avoid vocabulary issues entirely since they do not interface with other patient information systems and they can simply be copied from one computer to another. However, this kind of system is not recommended as they request more time and effort from the users as this kind of systems does not have the desirable feature “content integrated into workflows”. Integrated systems reduce the need for additional data entry by the healthcare provider, enable the display of the most up-to-date data and patient information, and maximize healthcare provider exposure to the recommendations. However, 44% of the integrated systems that are evaluated in the included studies of this literature review still required additional data entry by the healthcare provider. Arduous data entry was suggested as a reason for poor system acceptance in other studies [110, 111], as physicians are not willing and do not have time to interact with a system that requires them to do more work. Our study covers the situation of CDSSs over the last 15 years concerning the use of data standardization and terminological systems. It is perceived that some specific features of CDSS improve patient outcome and practitioner performance. In this study we also found that the practitioner performance was significantly improved in studies that did avoid using free text compared to those systems that used free text (Table 5.2). However, due to the limited amount of studies, underreporting of data standardization, and heterogeneity of systems and sites included we are not able to provide strong evidence on this subject. Future reports of CDSS evaluations should provide as much detail as possible when describing the systems including the use of terminological systems and information models in a structural way. The trend towards using international terminological systems may be consolidated with the world-wide uptake of SNOMED CT, a terminological system that provides formal representation which can facilitate defining decision rules. SNOMED CT is considered to be a reference terminological system which is designed to document the information during the course of patient care and due to its formal representation of concepts and their characteristics. As such, it is one of the most promising terminological systems to bind CDSS to electronic patient records [97, 112]. However, no mention was made of the application of this terminological system in CDSSs described by any of the included articles. This result is in line with findings of a literature review on SNOMED CT [113]. As the implementation of SNOMED CT, is expected to increase rapidly in many setting in coming years we recommend specific evaluation studies in these settings. 5.4.1. Limitations Some limitations of this study need to be mentioned. First, we did not run a new search strategy as we relied on articles identified by Garg’s search strategy [17] and updated it by studies identified by AMIA’s “Year in Review”. Garg’s search strategy was a comprehensive search that was run in several databases and Masys et al applied a broad search string and a poll of experts in the field to indentify the relevant studies for the “Year

83 Chapter 5 in Review”. Second, as we restricted our inclusion to RCTs, some relevant studies might be missed. Moreover, some CDSSs may not be evaluated or their evaluation results were not reported as a scientific study. For instance, we know that Kaiser Permanente Health Connect is an information management system including CDSS which uses SNOMED CT [114] but we did not find any RCT on CDSS using SNOMED CT. Nevertheless, we believe that our results are not influenced by these choices, as one can not say the included systems were developed in a fundamentally different manner than those that were not included. This is very unlikely given the diversity of systems and settings (academic versus non-academic, commercial versus non-commercial) that were included in our review. On the other hand, in the RCTs investigators generally evaluate systems that have the potential of being used in practice and applied at a larger scale. A third limitation is that many studies did not clearly report on data items that are used for CDSS invocation or advice generation, and on any terminological systems used for presenting coded data. To overcome this limitation, we contacted the authors of the included studies. Our response rate was 62% which is comparable to Garg’s study [17]. Some bias might be introduced in the question regarding abandoning the development of a CDSS, because of the suggestive formulation of this question and its answer categories. However, we started the answer categories with two answers describing the absence of any problem and any non-data related problem. Therefore, we expect that the overall conclusion that a majority of authors observed some obstacles in CDSS implementation due to a lack of data standardization is still valid. 5. 5. Conclusion

Still a lot of work needs to be done to come to fully integrated and interoperable CDSSs. This can be explained by the fact that CDSSs applied different terminological systems to code data items. This diversity hampers the possibility of sharing and reasoning with data within different systems. Using local terminological systems, which were the case in presentation of about half of the coded data, will negatively affect the shareability of the data and decision rules. A survey among authors of articles included in this study revealed that the lack of standardized data is a major obstacle for CDSS implementation. To adequately use a CDSS, quality, availability and standardization of data are essential.

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[97] Bowman S. Coordinating SNOMED-CT and ICD-10. J AHIMA 2005 Jul;76(7):60-1. [98] Reisman Y. Computer-based clinical decision aids. A review of methods and assessment of systems. Med Inform (Lond) 1996 Jul;21(3):179-97. [99] Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004 Mar;11(2):104-12. [100] Coiera E, Westbrook J, Wyatt J. The safety and quality of decision support systems. Yearb Med Inform 2006;20-5. [101] Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003 Jun 23;163(12):1409- 16. [102] Wright A, Sittig DF. A framework and model for evaluating clinical decision support architectures. J Biomed Inform 2008 Dec;41(6):982-90. [103] Hripcsak G. Writing Arden Syntax Medical Logic Modules. Comput Biol Med 1994 Sep;24(5):331-63. [104] Musen MA, Tu SW, Das AK, Shahar Y. EON: a component-based approach to automation of protocol- directed therapy. J Am Med Inform Assoc 1996 Nov;3(6):367-88. [105] Shahar Y, Miksch S, Johnson P. The Asgaard project: a task-specific framework for the application and critiquing of time-oriented clinical guidelines. Artif Intell Med 1998 Sep;14(1-2):29-51. [106] Achour SL, Dojat M, Rieux C, Bierling P, Lepage E. A UMLS-based knowledge acquisition tool for rule- based clinical decision support system development. J Am Med Inform Assoc 2001 Jul;8(4):351-60. [107] Boxwala AA, Peleg M, Tu S, Ogunyemi O, Zeng QT, Wang D, et al. GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines. J Biomed Inform 2004 Jun;37(3):147-61. [108] Ahmadian L, Cornet R, Kalkman C, de Keizer NF. Development of a national core dataset for preoperative assessment. Methods Inf Med 2009;48(2):155-61. [109] Ahmadian L, Cornet R, de Keizer NF. Facilitating pre-operative assessment guidelines representation using SNOMED CT. J Biomed Inform 2010 Aug 3. [110] Margolis CZ, Warshawsky SS, Goldman L, Dagan O, Wirtschafter D, Pliskin JS. Computerized algorithms and pediatricians' management of common problems in a community clinic. Acad Med 1992 Apr;67(4):282-4. [111] Nilasena DS, Lincoln MJ. A computer-generated reminder system improves physician compliance with diabetes preventive care guidelines. Proc Annu Symp Comput Appl Med Care 1995;640-5. [112] Rosenbloom ST, Miller RA, Johnson KB, Elkin PL, Brown SH. Interface terminologies: facilitating direct entry of clinical data into electronic health record systems. J Am Med Inform Assoc 2006 May;13(3):277- 88. [113] Cornet R, de Keizer NF. Forty years of SNOMED: a literature review. BMC Med Inform Decis Mak 2008;8 Suppl 1:S2. [114] Dolin RH, Mattison JE, Cohn S, Campbell KE, Wiesenthal AM, Hochhalter B, et al. Kaiser Permanente's Convergent Medical Terminology. Stud Health Technol Inform 2004;107(Pt 1):346-50.

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Appendix A: Data extraction form and questions Section I: Data extraction form

Name reviewer: Study number: First author: Inclusion  Yes  No, Reason: No DSS  Other: General information about study Country: Year of publication:  Single center Study Setting  Multicenter, single HMO  Multicenter Outcome measure 1:  Practitioner performance  + effect ……………….. Primary ………..……...  Patient outcomes  0 effect Outcome Measures Outcome measure 2:  Practitioner performance  + effect ……………….. and  Patient outcomes  0 effect findings ……………….. Outcome measure 3:  Practitioner performance  + effect ……………….. ………………..  Patient outcomes  0 effect  Counseling (psychotherapy)  Diagnosis  Patient education  Evaluation  Disease management Clinical task  Prevention (Single answer)  Rehabilitation  Risk assessment  Screening  Treatment  Drug dosing and prescribing (CPOE only)  Clinical documentation Clinical domain:  Primary care Clinical setting  Secondary/tertiary out patient care

 Secondary/tertiary inpatient care System characteristics  Physicians  Nurses Users of the system  Paramedic  Patients  System automatically prompts the user System activation  System should be initiated manually  Yes Requires data entry  No  Independent (Stand-alone system) System integration  Integrated or linked system  Consulting model Style of communication  Critiquing model  Reminder systems

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Knowledge representation  Yes Numeric  No  Unknown  Yes Data type Free text  (Multiple answers possible) No  Unknown  Yes Coded items  No  Unknown  Local terminological system, Name: Terminological system that is used for  National terminological system, Name: representing coded data  International terminological system, Name:  Not applicable

Comments regarding extracted data:

Section II: Questions Below, we distinguish the procedure to invoke the Decision Support System (DSS) from the algorithm to generate the actual advice. Please answer the following questions: 1- How was your DSS invoked during the study? [Please pick one answer] o The DSS automatically selected the relevant cases. o Cases were selected automatically but by a separate computer application, after which the DSS was started. o The system was invoked manually by the end-user. o The system was invoked manually by another person (e.g. a research assistant). o In another way [please explain]:

2- What kind of data was used in the procedure to invoke the DSS? [Multiple answers possible] o Numerical data (e.g., patient age, INR, cholesterol, blood pressure). Please specify which data items: o Coded data (e.g. patient with diagnosis=‘C21234’), either based on (inter)national coding systems or a local pre-defined list of items (it refers also to a simple list e.g. defined for “gender” including male, female, …). o Non-coded free text data. Please specify which data items:

3- What kind of data was used by your DSS to generate the advice, once it had been invoked? [Multiple answers possible] o No other than those mentioned in Question 2. o Numerical data (e.g. age, INR, cholesterol, blood pressure). Please specify which data items: o Coded data (e.g. patient with diagnosis=‘C21234’) either based on (inter)national coding systems or local, pre-defined list of coded (it refers also to a simple list e.g. defined for “gender” including male, female, …). o Non-coded, free text data. Please specify which data items:

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Questions 4 only has to be answered if your DSS used coded data. 4- Which types of clinical data were used to invoke the system and to generate the advice? Please provide for each used data type whether any coding system or terminological system is used to standardize the data (and which ones). Coding system or terminological system Data item International Please indicate (E.g., gender, terminological whether this data Local coding National system diagnosis, anti- type was used to coagulation list, or pre- terminological E.g. ICD9CM, invoke the system or defined list of system UMLS SNOMED medication, to generate the advice procedure, ..) data (Provide name) CT, RxNorm, DSM IV (Provide name) E.g. List of anti- anticoagulation Invoke system coagulants was medications defined

5- Have you ever decided not to start, or to abandon, developing a decision support system because of problems with data needed to invoke the system or data needed to generate advice by the system? [Multiple answers possible] o No, this never happened o No, but it did happen because of non-data-related problems e.g. financial problems, personnel related problems etc. o Yes, because the required data was not (electronically) available. o Yes, because the required data was electronically only recorded as free text. o Yes, because the required data had a different structure than what was needed in the decision algorithm. o Yes, because of other data-related reasons, namely:

Comments regarding defined data in DSS:

Appendix B: Definitions of concepts used in the data extraction form.

Study setting

Single centre: Study was performed in a single centre. Multicentre, single HMO: Study was performed in multiple centers, belonging to one Health Maintenance Organization (HMO). Multicenter: Study was performed in multiple centers, not belonging to one HMO.

Clinical task

Counseling (psychotherapy): DSS was used for psychological therapy directed at mental health problems. Diagnosis: DSS was used for identification of a medical condition or disease.

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Patient education: DSS was used for impart information to patients to alter their health behaviors or improve their health status. Evaluation: DSS was used for assessment of patients’ health condition. Disease management: DSS was used to coordinate health care interventions and communications for populations with conditions in which patient self-care efforts are significant. It is the process of reducing healthcare costs and/or improving quality of life for individuals by preventing or minimizing the effects of a disease, usually a chronic condition, through integrative care. Prevention: DSS was used for primary prevention of disease such as immunization. Secondary prevention of disease should be classified as ‘Disease management’, not prevention. Rehabilitation: DSS was used during the treatment to develop, maintain and restore maximum physical and psychosocial function throughout life after a medical event. Risk assessment: DSS was used to assess the risk to develop a disease or health outcome. Screening: DSS was used to detect a disease in individuals without signs or symptoms of that disease. This can be in people who belong to a certain group (for example, all children of a certain age), or in a smaller group of people based on the presence of risk factors (for example, because a family member has been diagnosed with a hereditary disease). Treatment: DSS was used to give advice regarding a type of therapy (for example medication) used to remedy a health problem. Drug dosing and prescribing (CPOE only): Determination of the right drug and dose using a CPOE system (Computer Physician Order Entry). Trials concerning drug dosing and prescribing not through CPOE should be classified as ‘Treatment’. Clinical documentation: DSS was designed to notify the users about the completeness of patient information.

Clinical domain

The medical specialty which was involved in the study. When an intervention involved multiple specialties, for example with a preventive intervention, the category ‘hospital wide’ should be chosen. When no medical specialties were involved but other hospital staff, for example the laboratory, the category ‘supporting specialties’ should be chosen.

Clinical setting

Primary care: Health services that play a central role in the local community. It refers to the work of health care professionals who act as a first point of consultation for all patients, for example a general practitioner or family doctor. Secondary/ tertiary outpatient care: Service provided by medical specialists and specialized consultative care for not hospitalized patients. Secondary/ tertiary inpatient care: Service provided by medical specialists and specialized consultative care for hospitalized patients.

Users of the system

Users of the system are those who receive the system’s advice.

DSS activation

System prompted the user automatically: If users of the system do not need to take any action for getting the advice of the system, for example when a reminder automatically shows up. System should be initiated manually: If it is required to take any action for getting the advice of the system, for example starting up the program and entering data.

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Requires data entry to use the DSS

When any data, for example patient characteristics need to be entered into the system by the end users to get the advice this box should be ticked.

DSS integration

Independent (stand alone system): A system that is operational without being linked to other systems. Integrated or linked system: A system that is operational and linked with other systems, for example with a medication order system or the electronic health record.

Style of communication

Consulting model: The system gives users an advice about what they should do. Critiquing model: The system criticizes the things that users do, or intend to do. Reminder system: If the system reminds users of something that they have not done, then this system is a reminder system.

Data type

Type of data required to invoke the DSS, or generate an advice including numerical, coded and free text data.

Terminological system that is used for representing coded data

Local terminological system: terminological system, developed in the institution(s) in which the study is performed. National terminological system: terminological system, developed and maintained within one country. International terminological system: terminological system, developed and maintained by an international organization.

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96 ChapterChapter 6

ConstructionConstruction ooff aann iinterfacenterface tterminologyerminology oonn SNOMEDSNOMED CCT:T: GenericGeneric aapproachpproach aandnd iitsts applicationapplication inin intensiveintensive ccareare

Bakhshi-Raiez F, Ahmadian L, Cornet R, de Jonge E, de Keizer NF Methods of Information in Medicine. 2010;49(4):349-59 Chapter 6 Abstract

Objective: To provide a generic approach for developing a domain-specific interface ter- minology on SNOMED CT and to apply this approach to the domain of intensive care. Methods: The process of developing an interface terminology on SNOMED CT can be re- garded as six sequential phases: domain analysis, mapping from the domain concepts to SNOMED CT concepts, creating the SNOMED CT subset guided by the mapping, extending the subset with non-covered concepts, constraining the subset by removing irrelevant content, and deploying the subset in a terminology server. Results: The APACHE IV classification, a standard in the intensive care with 445 diagnostic categories, served as the starting point for designing the interface terminology. The majority (89.2%) of the diagnostic categories from APACHE IV could be mapped to SNOMED CT concepts and for the remaining concepts a partial match was identified. The resulting initial set of mapped concepts consisted of 404 SNOMED CT concepts. This set could be extended to 83,125 concepts if all taxonomic children of these concepts were included. Also including all concepts that are referred to in the definition of other concepts lead to a subset of 233,782 concepts. An evaluation of the interface terminology should reveal what level of detail in the subset is suitable for the intensive care domain and whether parts need further constraining. In the final phase, the interface terminology is implemented in the intensive care in a locally developed terminology server to collect the reasons for intensive care admission. Conclusions: We provide a structure for the process of identifying a domain-specific inter- face terminology on SNOMED CT. We use this approach to design an interface terminology on SNOMED CT for the intensive care domain. This work is of value for other researchers who intend to build a domain-specific interface terminology on SNOMED CT.

98 Construction of an interface terminology

6.1. Introduction

Systematic recording of clinical data is central to the use, exchange, analysis, and inter- pretation of this data. To this end, numerous terminological systems have been developed. A terminological system interrelates concepts of a particular domain and provides concepts with related terms and possibly definitions and codes [1]. Terminological systems can be distinguished as aggregate terminologies, reference terminologies, and interface terminologies, each used for different purposes and each serving different requirements regarding their intended use and domain [2– 4]. Aggregate terminologies, such as ICD-9 CM, are used to categorize disease encounters into disjoint classes [3, 5, 6]. The lack of structure and formal semantic definitions for classes in most aggregate terminologies however results in shortcomings when aiming for data re-use [7]. Reference terminologies, such as SNOMED CT, are meant to address these re-use issues by providing precise meaning by formal concept definitions (i.e. semantic definitions), required for consistent and computer-readable coding and storage of clinical data [8, 9]. Finally, interface terminologies are used for actual data entry into electronic medical records, facilitating display and collection of clinical data in a simple way while simultaneously linking user’s own descriptions to structured data elements in a reference terminology or aggregate terminology [8]. SNOMED CT is regarded the most comprehensive terminological system for coding clinical information [10, 11]. Its wide coverage and semantic structure aim at making it applicable as a reference terminology while the mappings to existing aggregate terminologies such as ICD-9 CM enable aggregate terminology features. SNOMED CT also includes several terms for each concept that can be used in an interface terminology for data entry. However, its use as an interface terminology in a clinical setting is the subject of discussion in many studies (e.g. [8, 12, 13]). In case all of SNOMED CT is provided to users for systematic data collection, its comprehensiveness forms an impediment, as it con- tains large amounts of concepts that are irrelevant for most clinical domains. Furthermore, the extensiveness of SNOMED CT, covering all kinds of medical domains, does not guarantee that SNOMED CT completely covers the detailed information necessary for data collection in a specific clinical setting [8, 12–14]. Accordingly, instead of providing all of SNOMED CT to the users, an interface terminology on SNOMED CT is proposed to provide easier adoption to the user requirements, i.e. the non-relevant content of SNOMED CT is excluded and the relevant concepts and terms which are not present in SNOMED CT are added to the interface terminology [15]. Despite its advantages for structured data entry, no formal methods to develop a domain-specific interface terminology on SNOMED CT have been described. The present medical informatics literature has dealt with different parts of this process, such as mapping from domain concepts to SNOMED CT concepts [16], terminology modularization techniques [17]. However, thus far no papers concern the methodology underlying the process of designing an interface terminology on SNOMED CT as a whole. To fill this gap, the objective of this paper is to provide a generic approach

99 Chapter 6 for developing a domain-specific interface terminology on SNOMED CT. An application to the domain of intensive care is used to illustrate this process. The paper is organized as follows. Section 6.2 provides an overview of the sub- processes of developing a domain-specific interface terminology on SNOMED CT. Section 6.3 presents an application of this process to the domain of intensive care. Section 6.4 discusses the merits and limitations of the study. Section 6.5 concludes the paper. 6.2. Methods

6.2.1 SNOMED CT

SNOMED CT is the world’s largest concept-based terminological system. The January 2008 release, which was used in this study, contains 284,777 active concepts associated with 737,695 active terms and interrelated by 860,865 active relationships, which can be hierarchical (i.e. Is-A relationships) or non-hierarchical (i.e. any of about 60 attribute relationship types such as “finding site” or “causative agent”). SNOMED CT is a compositional terminology, i.e. it supports post-coordination, the use of composite expressions of concepts to define and refine (new) concepts [18–21]. 6.2.2 Interface Terminology Development Process

We propose that the process of developing an interface terminology on SNOMED CT involves six sequential phases: domain analysis, mapping from domain concepts to SNOMED CT concepts, creating the SNOMED CT subset based on the mapping results, extending the subset with non-covered concepts (i.e. user-defined concepts, relationships, and terms which are not included in SNOMED CT), constraining the subset by eliminating irrelevant content, and deploying the subset in a terminology server. In the following sections each of these phases is briefly described. An overview of the process and the output of each phase is provided in Figure 6.1. 6.2.2.1. Domain Analysis It is important that the interface terminology provides the level of granularity needed by the users. Therefore, to develop a domain-specific interface terminology on SNOMED CT, the relevant parts of SNOMED CT need to be extracted. Domain analysis is defined as the knowledge acquisition process by which the relevant information for a particular interface terminology is identified [22]. To this end, different resources, such as historical data in medical files, medical domain expert knowledge, and/or existing standards or domain ontologies can be used [15, 16, 23]. Using historical data is intricate and time-consuming; medical files, especially paper-based files, are often unstructured and there is a large variation in terminology used among care providers. Semi-automatic or fully automatic processing tools can facilitate the process of terminology acquisition from historical data [16, 24]. However, these tools are usually domain and language-specific, and, if not available, costly to build. Gathering the knowledge from medical domain experts is also not straightforward since these experts cannot be brought together frequently for knowledge

100 Construction of an interface terminology elicitation sessions and they can overlook important concepts [22]. Existing standards or terminologies within the domain of interest can provide an ample starting point. These are usually based on expert agreement and provide a good overview of the knowledge needed within a certain domain.

Figure 6.1: Interface terminology development

6.2.2.2. Mapping from Domain Concepts to SNOMED CT Concepts Mapping is defined as creating a link between the content of two systems, in this case the domain concepts derived in the domain analysis and the target-terminological system such as SNOMED CT, through semantic correspondence [6, 25]. Mapping can be done with different methods comprising (semi-)automated or manual procedures [25–27]. Manual mapping of large amounts of source concepts (e.g. when creating a mapping between a

101 Chapter 6 system like ICD-10-CM and SNOMED CT) is time-consuming and automated tools might be used to assist this task [26, 28]. Automated mapping tools are usually domain- and language-specific, and, if not available, costly to build. The level of confidence in automated mapping can vary from application to application [29, 30]. In all cases, manual review is required to validate the output of the automated mapping and to map the portions that failed the automated mapping [27, 29, 30]. In case of manual mapping, more than one reviewer is needed and agreement statistics need to be calculated to determine inter-rater reliability. 6.2.2.3. SNOMED CT Subset Creation A SNOMED CT subset is a group of concepts, descriptions and/or relationships relevant for use in a given domain. To create the subset, in a database for instance, terminology modularization or segmentation techniques such as hierarchy traversal can be used [16, 17, 31]. In extraction by traversal, the SNOMED CT hierarchy is considered as a rooted directed acyclic graph and the subset is extracted by starting at a target concept and following its links to other concepts to include them in the subset [17]. Hereby, the structure of the SNOMED CT hierarchy remains intact in the subset. In downwards traversal, the algorithm only goes down the Is-A hierarchy from the target concepts, including all their subordinates. In upwards traversal, the algorithm only goes up the Is-A hierarchy from the target concepts, including all their super-ordinates. Figure 6.2 gives an illustration of these hierarchy traversal algorithms [17]. In downwards traversal or upwards traversal from at- tributes, also the attributes of the target concepts are traversed.

Legend

Target concepts

Is-A relationships

Attribute relationships

Upwards traversal

Downwards traversal

Figure 6.2: Examples of hierarchy traversal algorithms: in downwards traversal the algorithm goes down the Is-A hierarchy from the target concepts, including all their subordinates, and in upwards traversal the algorithm goes up the Is-A hierarchy from the target concepts, including all their super-ordinates.

The hierarchy traversal mechanism that is used for subsetting depends on the purpose for which the interface terminology will be used. In case the interface terminology is going to be used to record detailed information in medical files, the subordinates (downwards

102 Construction of an interface terminology traversal) and the related attributes (downwards traversal from attributes) of the target concepts should be included. 6.2.2.4. Extending the Subset with Local Concepts, Relationships, and Terms The advantages of creating an interface terminology on SNOMED CT are among others the possibilities to include concepts, relationships, and terms which are not present in SNOMED CT required by the user [15]. Such extensions can be placed into one of the three categories [32]: 1) Adding a new interface term, i.e. a term that does not exist in SNOMED CT, but that describes an already existing SNOMED CT concept. 2) Adding a new leaf concept as a subordinate of an existing leaf concept, i.e. generation of an entirely new concept in the SNOMED CT hierarchy, along with its preferred and interface terms and relationships (Figure 6.3). 3) Adding a new node concept as a superordinate of existing concept(s) or leaf concept(s), i.e., the concept is absent, and the missing concept is a parent of one or more existing SNOMED CT concepts. In order to add the concept to the hierarchy, it should be “grafted” into some branches of the SNOMED CT tree (Figure 6.3).

Legend Original Subset Extended Subset

Is-A relationships

Attribute relationships New leaf concept New node concept New Is-A relationship New Attribute relationships

Figure 6.3. Examples of subset extension: a new leaf concept and a new node concept are added to the hierarchy together with their relationships.

6.2.2.5. Constraining the Subset Subsetting SNOMED CT and subsequently extending the subset result in a (smaller) set of interrelated SNOMED CT and local concepts, descriptions and relationships. However, this subset may still contain a large amount of concepts and relationships that are irrelevant and may require further constraining. This can be done by systematically filtering irrelevant content from the subset. The included non-human concepts in SNOMED CT, for instance, might be irrelevant for clinical domains and can be excluded from the subset. When one develops a subset of diagnoses one might restrict to concepts coming from the subtype hierarchy disease (disorder). Another possibility is limiting the graph depth, by terminating the downwards traversal when some criterion is reached, relating either to the depth of the graph or to the size of some other property of the target concept [17, 31]. It is also possible to clean the subset manually by removing the irrelevant and anomalous parts after an evaluation with the end users.

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6.2.2.6. Deploying the Subset in a Terminology Server Interface terminologies are designed to support interactions between humans and structured medical information systems. To achieve this goal, human interfaces to computer applications, i.e. terminology servers, are needed to provide the content of an interface terminology to its users [33]. Together with a client for knowledge browsing, terminology servers can be used at the point of care to enter patient observations, findings, and events into the computerized patient records. 6.3. Case Study: An Interface Terminology on SNOMED CT for Intensive Care

In intensive care, diagnostic information is often recorded in different systems in free text or using a specific classification system resulting in registration insufficiency. For instance, calculation of case-mix-adjusted mortality risks in the Acute Physiology and Chronic Health Evaluation (APACHE) IV prognostic model requires a variety of patient information, such as physiological parameters, co-morbidities and reason for ICU admission. The reason for ICU admission is captured using the APACHE IV classification [34]. However, the diagnostic categories in this APACHE IV classification lack the detail and structure needed for an unambiguous description of health problems. Therefore, the same information about the reason for intensive care admission is often separately registered in medical files and discharge letters, but with more detail and in free text. Hence, there is a need for a standard terminological system to support multiple use of single recorded diagnostic information. To facilitate this goal, the use of an interface terminology on SNOMED CT is proposed. For our case study this interface terminology should enable recording of detailed and structured reasons for intensive care admission in daily care practice, from which the corresponding APACHE IV category can be determined automatically. But it should also be useful for other purposes, e.g. aggregation of patient groups in research, automated discharge letters and triggering decision rules. 6.3.1. Domain Analysis

Using historical data for terminology acquisition was difficult as diagnostic information in intensive care is mostly recorded in free text. Automatic processing of this data was not feasible as there were no tools available to translate the Dutch intensive care free text entries into English terms or into terms from another language for which a SNOMED CT translation exists. A few standards are available for the domain of intensive care. For instance, the reasons for intensive care admission required for the calculation of the APACHE II and APACHE IV prognostic models are collected using APACHE II reasons for admission classification including 54 diagnoses and the APACHE IV reasons for intensive care admission classification including 445 diagnoses, respectively. In general, the prognostic models serve the purpose of calculating case-mix-adjusted mortality rates to measure the quality of care

104 Construction of an interface terminology

[35, 36]. However, the corresponding classifications are also used for aggregation of patient groups for retrospective research. Within this case study we will use the APACHE IV classification to start identifying the intensive-care-specific SNOMED CT subset, as the APACHE IV classification is the most extended and widely used classification within the domain of intensive care [34]. In spite of its extensiveness and wide use, the use of the APACHE IV classification as an interface terminology is limited, due to the fact that no synonyms are used to describe the diagnostic categories, and because of its strict mono-hierarchical structure. In the APACHE IV classification, each diagnostic category is first classified as non-operative or postoperative, representing acute conditions leading to ICU admission and procedures after which patients are monitored on the ICU, respectively. Next, categories are distinguished by body system (e.g. cardiovascular, neurologic, and respiratory) or a transplant- or trauma-related category, and then by diagnosis (e.g. “coronary artery bypass graft”, “bone marrow transplant”, and “face injury”). A residual “other” category is used for unlisted diagnoses within each body system, transplant, and trauma category (e.g. “non-operative gastrointestinal disorder – other”). 6.3.2 Mapping from APACHE IV to SNOMED CT

Each of the 445 APACHE IV diagnostic categories was mapped to one or more SNOMED CT concepts. Given the relatively small amount of categories to be mapped, the mapping was performed manually. Composite APACHE IV diagnostic categories (i.e. containing more than one diagnosis such as “chest trauma with spinal trauma”) were first split into atomic diagnoses (i.e. “chest trauma” plus “spinal trauma”). These atomic diagnoses were then each mapped to a SNOMED CT concept. Finally, the composite categories were represented as the conjunction of the mapped SNOMED CT concepts. Some atomic diagnoses are used in more than one composite diagnostic category. The atomic category “chest trauma”, for instance, is part of 16 APACHE IV diagnostic categories in the trauma group. Each of such atomic diagnoses needed to be mapped only once to a SNOMED CT concept. The concepts in SNOMED CT were navigated using the CliniClue1 browser (version 2006.2.30), a look-up engine for SNOMED CT concepts. Mapping consisted of three consecutive activities: 1. Interpreting and analyzing the meaning of the APACHE IV diagnostic categories. To resolve ambiguities in the diagnostic categories, the researchers consulted an intensivist. This was done for 10% (n = 45) of the diagnostic categories. For instance, from the structure of the APACHE IV classification it was not clear what the exact meaning of the diagnostic category “non operative heart transplant” was. It could be mapped to the SNOMED CT concept “planned operative procedure for heart transplantation” which is a preoperative concept or to “cardiac transplant disorder” which is a concept meant to de- scribe possible complications of previous heart transplantation. Intensivist consultation

1 The Clinical Information Consultancy Ltd. http://clininfo.cliniclue.com/home

105 Chapter 6 revealed that both are possible and therefore, both matches are included in the current mapping. The ambiguous terms encountered in all classes of the APACHE IV classification with a majority (12 categories) in the transplant class. 2. Matching one atomic APACHE IV diagnostic category to one or more active SNOMED CT concept(s). The diagnostic categories were first mapped to pre-coordinated concepts. The following three rules were applied to search for the right APACHE IV diagnostic categories in the SNOMED CT hierarchy: I) selection of the correct SNOMED CT category, e.g. non-operative APACHE IV diagnostic categories were searched for in the “disorder” subtype hierarchy of SNOMED CT and post-operative APACHE IV diagnostic categories were searched for in the “procedure” subtype hierarchy of SNOMED CT, II) selection of the appropriate search term, i.e. start with the APACHE IV term and in case of unsatisfactory results, continue the search with the corresponding synonyms and abbreviations, and III) verify the final mapping by examining the relationships of the SNOMED CT concept to ensure a correct concept-based mapping. In case no pre- coordinated match was available, a post-coordinated match was searched for. Post- coordination was based on the concept model of SNOMED CT and was not restricted to the functionalities of the CliniClue interface. To this end, post-coordination instructions in the SNOMED CT guides were followed [19, 20, 37]. In case an exact match could not be found in SNOMED CT, the APACHE IV diagnostic category was mapped to an appropriate superordinate concept. 3. Assessing each matched category-concept pair on how well they matched by marking each category-concept pair as “complete match” (i.e. an APACHE IV category matches to a semantically equivalent SNOMED CT concept), “non-match” (i.e. no semantically equivalent SNOMED CT concept is available) or “partial match” (i.e. matches to superordinate concepts). The mapping process was performed independently by two medical informatics re- searchers (FBR and LA), both practiced in coding medical data and with knowledge of the APACHE IV classification. Furthermore, the researchers were educated in SNOMED CT with extensive knowledge of the SNOMED CT guidelines beforehand. Before the mapping, there was a general agreement between the two researchers on the mapping activities, including how the quality of the matches would be defined and how post-coordinated matches would be represented. The final match was based on consensus between the two researchers. In case of disagreement, a third researcher (RC), a SNOMED CT expert, was involved. The interrater reliability, i.e. the percentage of the APACHE IV diagnostic categories that were similarly mapped to one or more SNOMED CT concepts by the two researchers, was 90%. For the remaining diagnostic categories, in half of the cases consensus was reached and half of the diagnostic categories were also searched for by the third researcher to achieve consensus. At the end, the diagnostic categories in the APACHE IV classification were mapped to 404 atomic SNOMED CT concepts in the disorder and procedure subtype hierarchies. Additionally, 67 concepts were needed to post-coordinate concepts. Table 6.1 provides

106 Construction of an interface terminology some examples of the different mapping types. Table 6.2 provides the results of the mapping between the APACHE IV classification and SNOMED CT.

Table 6.1: Examples of the mapping types

APACHE IV SNOMED CT concept(s) diagnosis category Complete match Pre- Colon cancer or rectal cancer Malignant tumor of colon (disorder) coordinated Malignant tumor of rectum (disorder) Complete match Post- Repair of ventricular aneurysm Is-a: Repair of aneurysm coordinated Procedure site – Direct: cardiac ventricular structure| Partial match to Other gastrointestinal cancer Malignant neoplasm of gastrointestinal tract a superordinate (disorder) concept

SNOMED CT provided complete matches for 89.2% of the diagnostic categories. There were no non-matches. For 10.8% of the diagnostic categories a partial match was found. Partial matches were all related to the diagnostic categories including the word “Other” (e.g. “Non-operative cardiovascular disorder – Other”). The use of post-coordination sup- ported the good matching scores: 39.1% of all complete matches and 31.4% of all partial matches were realized through post-coordination.

Table 6.2: Results of the mapping from APACHE IV classification to SNOMED CT APACHE IV Number of Number of Number of Number of classes diagnostic Complete match (%) partial match (%) “Other categories categories” Pre Post Pre Post in partial matches Cardiovascular 101 63 (62.0) 25(25.0) 10 (10.0) 3 (3.0) 13 Gastrointestinal 57 32 (56.0) 18 (32.0) 7 (12.0) 0 (0.0) 7 Genitourinary 35 18 (51.5) 13 (37.4) 0 (0.0) 4 (11.1) 4 Hematology 18 13 (72.0) 2 (11.0) 3 (17.0) 0 (0.0) 3 Metabolic 18 15 (83.0) 1 (6.0) 0 (0.0) 2 (11.0) 2 Musculoskeletal 20 12 (60.0) 5 (25.0) 3 (15.0) 0 (0.0) 3 Neurological 54 37 (69.0) 11 (20.0) 4 (7.0) 2 (4.0) 6 Respiratory 46 31 (67.0) 9 (20.0) 4 (9.0) 2 (4.0) 6 Transplant 24 16 (67.0) 6 (25.0) 2 (8.0) 0 (0.0) 2 Trauma 72 5 (7.0) 65 (90.0) 0 (0.0) 2 (0.03) 2 Total 445 242 (54.4) 155 (34.8) 33 (7.4) 15 (3.4) 48

107 Chapter 6 6.3.3. Subset Creation in SNOMED CT

As mentioned the interface terminology will be used to record clinical data for daily care processes, automatic generation of discharge letters, and for calculation of APACHE IV mortality risks. Therefore, we need to isolate the concepts related to the APACHE IV diagnostic categories and their subordinates and attributes by downwards traversal. To this end, SNOMED CT’s core data structure was imported into a Micro-soft Access Database to isolate the subset using SQL queries. The results of the mapping procedure (i.e. the mapped SNOMED CT concepts) were used as target concepts. From each target concept, downwards traversal and downwards traversal from the attributes was performed (Figure 6.2) [17]. All identified links and nodes were added to the subset during iterative steps. Table 6.3 shows the number of disorder and procedure concepts and the number of attributes added to these core concepts in the subset during each iterative downwards traversal step. If the graph traversal depth Table 6.3: Number of disorder and would be limited to, for example, two steps procedure concepts added to the subset for the 404 source concepts resulted in each iterative downwards traversal step identification of 22,606 disorder and procedure concepts, but when the graph Downwards Number of disorder and transversal step procedure concepts traversal depth would not be limited this 0 404 would result in 83,125 disorder and pro- 1 5570 2 22606 cedure concepts in the subset to be used in 3 44206 the interface terminology. At the end, the 4 62212 5 73818 Microsoft Access database included three 6 79705 SNOMED CT tables, i.e. concepts table, 7 82031 8 82806 descriptions table and relationships table 9 83064 containing the SNOMED CT subset. 10 83122 11 83125

6.3.4 Extending the Subset with Local Concepts, Relationships, and Descriptions

In this phase of the development, the isolated SNOMED CT subset was extended with local concepts, relationships, and descriptions to represent the APACHE IV diagnostic categories which were not covered (by pre-coordinated concepts) in SNOMED CT. The extensions were based on the categories described in Section 6.2.2.4. For all concepts representing a diagnostic category from the APACHE IV classification, the APACHE IV term with the re- lated APACHE IV code was added as the preferred term. For diagnostic categories that were mapped to a post-coordinated concept, the post-coordinated match was added as a pre-coordinated concept using the SNOMED CT guides [19, 20, 37]. Also for the partial matches, a new pre-coordinated concept was added to the SNOMED CT subset. This step was necessary to enable the aggregation of the detailed clinical information to the appropri- ate APACHE IV categories required for calculation of APACHE IV mortality risks. Finally, the hierarchical relations between the APACHE IV categories were added to the

108 Construction of an interface terminology subset to represent the APACHE IV classification tree in the subset. For instance, we added the class “APACHE IV Medical disorder” as a subordinate of the SNOMED CT concept “Clinical finding” and then added “Is-A” relations from all APACHE IV classes (i.e. system disorders, transplant, and trauma categories) to this class. This step was necessary to enable creation of aggregated patient data based on APACHE IV categories. The type and the amount of extensions to the extracted subset are presented in Table 6.4. Figure 6.4 provides some examples of the different extension types in the SNOMED CT hierarchy. The extensions were all added to the extracted SNOMED CT core data tables. The subset was extended with 325 concepts, 1243 relationships for these concepts, and 597 descriptions.

Table 6.4: Examples of extension types, i.e. new content to the SNOMED CT subset Extension Number Examples types APACHE IV Relationship Linked Concept(s) in the diagnostic category with subset the code New 599 Repair of abdominal aortic Preferred 405525004 | repair of description aneurysm (228) term to aneurysm of abdominal aorta | New leaf 168 Repair of ventricular Is-a 75087007 | repair of concept aneurysm (227) aneurysm | Procedure 21814001 | cardiac site - Direct ventricular structure | New leaf 113 Abdomen/face Is-a 128069005 | injury of concept with trauma (192) Is-a abdomen | multiple 125593007 | injury of face | parents New node 47 Carbon monoxide, arsenic Is-a 75478009| Poisoning | concept or cyanide poisoning (146)

Leaf concepts 17383000| Toxic effect of Is-a Carbon monoxide, arsenic or to the newly carbon monoxide | cyanide poisoning (146) added node 64189001| Arsine Is-a Carbon monoxide, arsenic or concept poisoning | cyanide poisoning (146) 66207005| Toxic effect of Is-a Carbon monoxide, arsenic or cyanide | cyanide poisoning (146)

6.3.5. Constraining the Subset

It is hard to decide to which level the depth of the graph should be restricted. Use of SNOMED CT in daily care practice requires high granularity but if the depth of the graph is not restricted and only the non-human concepts in SNOMED CT were excluded from the subset this would result in 233,782 concepts, 660,008 descriptions and 665,394 relationships in the database. This can hardly be named a SNOMED CT subset. The use of the SNOMED CT subset in real practice should reveal to which level of depth the SNOMED CT graph should be restricted and whether the SNOMED CT subset needs further constraining. Therefore, this phase of the interface terminology development will be repeated after initial evaluation in an intensive care setting.

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SNOMED CT Concept

APACHE IV diagnostic groups

Finding by site Disease

APACHE IV medical disorder (0)

…………………. …………………… …………………… ………………………. Gastro Intestinal Disorder (61)

Disorder of large Neoplasm of intra- Neoplasm of intestinal tract Malignant neoplasm Malignant tumor of Gastrointestinal cancer (54) intestine abdominal organs of abdomen digestive organ

Neoplasm of large Malignant tumor of intestine intestine

Malignant tumor of large intestine

…………………… Colon or rectal cancer (52) ……………………

Malignant tumor of colon Malignant tumor of rectum

Leaf concept to the newly added node concept SNOMED CT concept New description New node concept New IS-A relationship

Figure 6.4: Subset-extension examples in SNOMED CT

6.3.6. Terminology Server

The interface terminology was deployed using the local terminology server DICE (Diagnoses for Intensive Care Evaluation). DICE consists of a SOAP-based Java ter- minology service together with a client for knowledge browsing and post-coordination [38]. The DICE client can be used to add controlled compositional descriptions to clinical records. The implementation of DICE offers physicians three ways to search for the appropriate reason for admission: a) a short list containing the most frequently occurring reasons for admission in an ICU, b) the APACHE IV classification entry, and c) entry of (a part of) its preferred or synonymous term. The system returns all terms matching the given free-text query. The user can then select anyone of the returned terms, after which, a list of subordinate concepts is shown, if these exist. Once a concept is selected, DICE enables post-coordination to provide concepts with more detailed information. For example, Sepsis can be further qualified by finding site, causative agent and the underlying diagnosis, but such further specification is not mandatory. In case a concept can not be found in the interface terminology it is also possible to enter the diagnostic information in free text. Finally, users can also provide comments on each entry.

110 Construction of an interface terminology 6.4. Discussion

The use of all of SNOMED CT as an interface terminology in a specific clinical setting is the subject of discussion in many studies [8, 12, 13]. It has been argued that using SNOMED CT for a particular specialist application requires the creation of an interface terminology based on a domain-specific subset [15]. Nevertheless, very few studies focused on the process of designing such an interface terminology on SNOMED as a whole. In this study we contribute to this issue by enumerating and combining the different phases in the processes of identifying an interface terminology on SNOMED CT. As far as we know, this study is the only work that provides an extensive overview of the total process and can be used as a reference by other researchers who also intend to develop a domain-specific interface terminology on SNOMED CT. Due to the lack of a Dutch SNOMED CT translation using historical Dutch data as a starting point for the subset is infeasible. Furthermore as in this case study one of the important use cases is to calculate mortality risks we used the APACHE IV classification as a starting point to identify the intensive-care-specific SNOMED CT subset. The APACHE IV classification is the most extensive and widely used classification for the intensive care. Use of the interface terminology by its end users should reveal whether the SNOMED CT subset based on the APACHE IV classification is sufficient for collection of diagnostic information for multiple uses, including daily care processes. It should also shed light on the optimal size of the interface terminology, i.e., whether a full extension is needed and useful, or whether a limited extension better enables the collection of diagnostic infor- mation. Eventually, other resources might be required to optimize our interface terminology content [22]. The free text entries and comments provided by the users in DICE, for instance, might provide a common ground for this purpose. To identify the equivalent concepts for the APACHE IV categories in SNOMED CT, a mapping was realized. Previous studies on mapping aggregate terminologies to SNOMED CT have shown that the mapping can be influenced by the structure and the content characteristics of both systems [4, 6, 25]. In the APACHE IV classification, for instance, some categories are provided with classification rules, e.g. “Respiratory Arrest” is provided with the additional rule “without cardiac arrest”. While these kinds of rules are used in aggregate terminologies to make clear what should and what should not belong to a class, they are not included in terminological systems such as SNOMED CT, which are used to document clinical data [21]. Consequently, these rules are also not accounted for in the mapping. Furthermore, the diagnostic categories including the word “other” (e.g. “other cardiovascular disorder” or “other respiratory disorder”) are residual categories for con- ditions that cannot be allocated to more specific categories. These categories formed the largest part of the “partial match” group. Again, while these kinds of categories are used in aggregate terminologies to classify data, they are not included in formally defined reference terminological systems such as SNOMED CT [21]. These diagnostic categories were all matched to a superordinate concept in SNOMED CT and were included as a pre- coordinated concept in the extended subset.

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An intensivist was consulted to interpret ambiguous APACHE IV categories. As this interpretation may be subjective, a duplicate interpretation by two independent domain experts is advised, with a conflict resolution process similar to that used for the mapping. Post-coordination has been proposed and demonstrated as an approach to improve the content coverage (12). Also in this study, the use of post-coordination resulted in better matching scores as 34.9 % of all complete matches and 31.4 % of all partial matches were realized through post-coordination. The use of post-coordination is thus an important factor to bear in mind when considering SNOMED CT for data collection. The results of the mapping procedure were used to create the relevant SNOMED CT subset. One of the advantages of the sub-setting is to isolate the users from the complexity of SNOMED CT and to extend the subset with user-required content which was not included in SNOMED CT [12]. Depending on the restriction of the depth of the graph traversal the numbers of disorder and procedure concepts range from 5570 (i.e., 2% of all active SNOMED CT concepts) to 83,125 (29% of all active SNOMED CT concepts). In addition, when fully expanding the subset, more than 150,000 concepts can be added as attribute values to further specify the disorder and procedure concepts, resulting in a subset of 233,782 concepts (82% of all active SNOMED CT concepts). A previous study showed that a subset of about 2700 concepts from SNOMED CT was sufficient to cover 96% of clinical notes for patients admitted to the intensive care over a period of five years [16]. Even under the condition that the study was performed in another setting with a different basic assumption, it indicates that it is likely that restricting the level of the graph is necessary. However, the domain of intensive care is rather complex and broad, involving a diversity of clinical problems. A simple appendicitis, for instance, may lead to severe sepsis with a wide range of possible underlying micro biological agents. So, for the interface ter- minology to facilitate sharing and aggregating this kind of data for different purposes, it should enable a detailed registration of widely diverging clinical problems and their at- tributes. An evaluation of the interface terminology by its end users should reveal whether shrinking is necessary and, if so, what parts could be reduced. The advantage of creating an interface terminology is among others the possibility to include concepts, relationships, and terms which are not present in the reference terminology, but which are required by the user. Therefore, as diagnostic categories such as “head trauma with chest trauma” occur frequently in the ICU and are part of the APACHE IV classification, these should be included as pre-coordinated concepts in an intensive-care- specific interface terminology. Yet, because SNOMED CT does not support the creation of collections (i.e., defining A “with” B), it is hard to represent these composite diagnostic categories correctly [39]. Instead, in SNOMED CT, collections are usually defined using the logical “And”. For instance, the concept “195878008|Pneumonia and influenza” is modeled as a taxonomic child of “6142004 |Influenza” AND of “75570004| Viral pneumonia”, referring to situations in which a patient has both influenza and viral pneumonia. Although not strictly logically correct, in line with the SNOMED CT modeling, within our study we also represented the composite diagnostic categories such as “head trauma with chest trauma” as a taxonomic child of two or more other concepts. As

112 Construction of an interface terminology argued before, further research is needed to gain more insight into the consequences this representation will have in practice, mainly for the purposes of querying patient data [39]. A major issue in using the (extended) subset in an interface terminology in practice is its maintenance. A new version of SNOMED CT is published every six months and may introduce (large) changes in the terminology content [40–42]. Also the SNOMED CT rules regarding for instance post-coordination might change [42]. These changes can affect the local extensions and consequently the consistency of the captured data. Therefore, in order to retain its utility, the interface terminology needs to be updated on a regular basis and an update mechanism, as for instance described in the SNOMED CT Reference Set Specification, should be in place to organize this process [40, 43, 44]. The real test of our interface terminology will come when it is used in practice in a computer-based patient record. Although a lot of studies have focused on the content coverage of medical terminologies, only a few examined how clinicians interact with the terminological system during data entry [45, 46]. In the next phase of our project, we will evaluate the interface terminology in an intensive care setting in an electronic health record. The evaluation will not only focus on the terminology content, but also on the user interface of the terminology client [3]. Especially the effect that the size and comprehensiveness of the content have, and ways in which users can be adequately supported to handle these need further study. 6.5. Conclusion

To our knowledge, we are the first to report on the process of developing a domain-specific interface terminology on SNOMED CT. The application of the proposed approach to the domain of intensive care resulted in an interface terminology which can be used to facilitate sharing and aggregation of data for different purposes. Future work should reveal whether the method is also applicable for other domains.

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118 General discussion

The main objective of this thesis is the identification and development of a core dataset and the application of standardized data and terminological systems in patient information systems. We focused on the preoperative assessment domain and on the application of SNOMED CT for representing concepts used in the preoperative assessment guidelines. Four research questions were formulated that were addressed in chapters two to six.  Which data should be collected during preoperative assessment?  Can SNOMED CT be used to represent concepts required to implement preoperative assessment guidelines?  Do clinical decision support systems (CDSSs) described in the literature use standardized data, and which terminological systems were used by CDSSs for coding data?  How can an interface terminology on SNOMED CT be constructed? This chapter summarizes and discusses the work presented in the previous chapters. It puts forward the principle findings and addresses the strengths and weakness of our work in relation to other studies. Finally, it discusses implications of the study and directions for future research. 7.1. Principal findings 7.1.1. Development of a core dataset in preoperative assessment The first research question was answered in the chapters two and three. In chapter two we carried out a systematic review to identify data collected during preoperative assessment. The results of this study revealed that a large diversity exists in the data items that are collected for evaluation of patients undergoing a procedure. The diversity of data indicates that almost each health care setting collects a different dataset for preoperative assessment. This diversity is an obstacle e.g., in case of patient referral to other health care settings, and hence limits the utility of the data [1, 2]. To overcome this problem a standard dataset is required. A standard dataset that is feasible, relevant and accessible to its end users would improve the quality of clinical care and would provide the possibility of carrying out multicentre research [1]. The results of the second chapter provided us with insight about which data items are collected during preoperative assessment. However, we could not design a standard dataset based on these results because the observed diversity was too high. Therefore, we combined the obtained results of chapter two with the consensus of experts to develop a core dataset for preoperative assessment. The experts developed a draft preoperative assessment dataset and subsequently this dataset was compared with the results of the literature review. The combination of literature review and expert consensus appeared to be a good approach for designing this dataset. The development process and the resulting dataset were presented in chapter three. Comparing the draft dataset with the results of the literature review helped experts to add missing data items to the dataset and, if required, modify other data items. In total, 6 data items were deleted from the draft dataset, 17 data items were added and 9 data items were modified. Finally, 93 data items were identified and divided into four categories: patient history, physical examination, supplementary examination and

119 Chapter 7 consultation, and final judgment. Each identified data item can get different values, e.g. different types of cardiovascular diseases such as congestive heart failure or heart valve stenosis can be considered as values for the data item “cardiovascular diseases”. 7.1.2. Representation of concepts in preoperative assessment guidelines by SNOMED CT The second research question was answered in chapter four. The data from the dataset are intended to be used for multiple purposes, including clinical decision support. As this requires a standardized representation, in chapter four we investigated to what extent SNOMED CT covers the concepts used in preoperative assessment guidelines. In this study six (inter)national guidelines were reviewed. All terms used in one of the six guidelines were mapped to SNOMED CT concepts. The International Health Terminology Standards Development Organization (IHTSDO) developed an editorial guideline that describes what should and what should not be included in SNOMED CT [3]. To investigate why not all preoperative guideline concepts were covered by SNOMED CT concepts we evaluated the non-covered concepts against this editorial documentation. Part of the non-covered concepts was vague and therefore not included in SNOMED CT. It was concluded that it is feasible to use SNOMED CT to represent the preoperative assessment guideline concepts after solving the problem of vague terms and after a small set of currently missing but relevant concepts is added to SNOMED CT. To present the recommendations of these guidelines at the point of care the guidelines have to be implemented in CDSSs. 7.1.3. Data standardization in clinical decision support systems Data standardization is essential to implement guideline-based decision support systems that can be integrated with different information systems. Therefore, in chapter five we answered the third research question whether existing CDSSs used standardized data, and which terminological systems were used by CDSSs for coding data. It was found that many CDSSs were using numerical data, which is a relatively easy way of standardization, and if they used coded data items they applied different terminological systems to code these data. This diversity hampers the possibility of sharing and reasoning with data within different systems. Moreover, about half of the coded data were coded using a local terminological system which will negatively affect these possibilities. A survey among authors of articles included in this study revealed that the lack of standardized data is a major obstacle for CDSS implementation. This study showed that there is ample room for developing interoperable CDSSs. 7.1.4. Creating an interface terminology on SNOMED CT To capture standardized data in patient information systems the use of a standard terminological system is required. It is impractical to implement a large comprehensive terminological system like SNOMED CT into the user interface of systems, because of the large number of included concepts and its complexity. Instead, an interface terminology could shield users from this complexity by tailoring it to a specific domain. The development process of an interface terminology on SNOMED CT was described in chapter six. In this study we defined a generic approach for developing an interface

120 General discussion terminology that included six sequential phases: domain analysis, mapping the domain concepts to SNOMED CT concepts, creating the SNOMED_CT subset guided by the mapping, extending the subset with non-covered concepts, limiting the subset by removing irrelevant content, and deploying the subset in a terminology server. To illustrate the developed approach we applied it to develop an interface terminology for reason for admission to the intensive care unit (ICU). 7.2. Strengths and weaknesses of the study and related research Interoperability of patient information systems requires at least a standard set of data with standard semantics. A standard dataset would ease the flow of data among different but interconnected systems, and thereby maximize useful information interchange [4]. The strength of our research is that we investigated the means to standardize the data in the preoperative assessment, focusing both on data collection during the assessment and on the application of the standardized data in guideline-based decision support systems. The methodologies that we applied to develop a dataset, formalize guidelines by using SNOMED CT and develop an interface terminology can be applied to other domains than preoperative assessment or intensive care. In this project we also showed to what extent current CDSSs can benefit from data standardization and the application of terminological systems. 7.2.1. Development process of the dataset Other studies where a core dataset was developed [1, 5-8] either used expert consensus or reviewed a limited number of sources (e.g. reviewing 3 existing datasets) while we applied an extensive search strategy and combined it with expert consensus to arrive at a core dataset for preoperative assessment. Our approach showed that relying only on a literature review is not adequate. We retrieved a large number of data items of which only a few percent was reported in a majority of the studies. Also, as the articles on preoperative assessment may not report all data items that are collected during preoperative assessment, due to their irrelevance in the context of the study, we might not have obtained all data items that are clinically important. To overcome this limitation we could have reviewed the data items that are currently collected in different hospitals in the Netherlands. However, as this project is part of an international project on standardization of perioperative data, which is carried out by the International Organization for Terminology in Anaesthesia (IOTA), we preferred a literature review to obtain an overview of internationally collected data. Moreover, collecting datasets from different hospitals is a labor-intensive task and to determine a core dataset is complex as some data items may only be used in one hospital while others may be used in multiple hospitals. A data item that is collected routinely in multiple hospitals may still be considered clinically irrelevant by experts. Due to lack of clear criteria it is hard to decide which data items are to be included in the core dataset. Thus instead of collecting data from hospitals, we decided to involve a large number of experts in consensus meetings to propose a core data set and to compare this set with the results of the literature review in order to arrive at a valid and concrete dataset.

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To store information about the meaning, relationships to other data, usage, and format of each data item a “data dictionary” is required. Although a data dictionary was created afterwards, creating it concurrently with the dataset would improve data validity and reliability within, across, and outside the development setting [9]. Developers of the dataset should define what they want to know about each concept and how they want to present the concepts into the system. Moreover, we could have used SNOMED CT already during the development of the core dataset. This would have supported the experts to clearly define and name the concepts. For instance, the expert group defined the data item “pacemaker”. One may think that this data item refers to the device, but the experts wanted to represent the SNOMED CT concept “patient with cardiac pacemaker”. 7.2.2. Representation of guidelines using SNOMED CT Data collected during the preoperative assessment should also be available for use in automated guideline-based decision support systems. It is important that all the data needed by the decision support system are available in a standardized format in the information system. We evaluated the content coverage of SNOMED CT regarding preoperative assessment guidelines terms. Compared to other studies on content coverage of terminological systems [10-13] our study was the first to evaluate the reasons why a concept could not be represented by SNOMED CT. This approach will help guideline developers with the selection of the terms that they want to use in the guidelines. Applying this approach will also help others to clearly distinguish between deficiencies attributable to concepts in the evaluated domain or to gaps in SNOMED CT. This will inform them where to put their efforts to further improve the content coverage of SNOMED CT for the evaluated domain. In this study we evaluated a small number of guidelines. Further evaluation with inclusion of more guidelines, such as national and local guidelines, is required to show whether, after solving the guideline deficiencies, SNOMED CT can cover all guideline concepts in the preoperative assessment domain. The main limitation of our project is that we first developed the preoperative assessment core dataset based on the literature and expert consensus and only as a separate and subsequent project extracted and evaluated the terms used in preoperative assessment guidelines to be included in the SNOMED CT subsets. Ideally experts should also have consulted the preoperative assessment guidelines when determining the content of the core dataset. Preliminary analysis shows that over 90% of the terms extracted from the guidelines were already included in the core dataset. Although this does not affect the final result, the remainder should be added to the dataset, and consideration of the guidelines could have made the process of development of the dataset shorter. To extend the preoperative SNOMED CT subsets with the required guideline concepts we applied a simple concept mapping to SNOMED CT. We did not consider how these concepts would be presented in an AIMS. Considering concepts in the context of the information model will provide insight in the way these concepts should be presented in the interface terminology. For instance, how should the term “metabolic disorders” extracted from a guideline be presented in the user interface? For example, the information model can have a slot “metabolic disorders” with a subset of SNOMED CT concepts presenting the

122 General discussion different types of this disease as slot filler. Using the information provided in this slot healthcare providers can choose e.g., from a drop down menu the type of disease that a patient has. 7.2.3. Data standardization in CDSSs In chapter five we investigated obstacles, especially data standardization problems, for implementing CDSSs. To date little is known about the use and the importance of standardized data and terminological systems in CDSSs [14-17]. Our study is a first step in filling this gap. The results of our study provide a valuable foundation for the CDSS developers with respect to data standardization and the application of terminological systems. In our study we could not find information regarding information models underlying CDSSs. The reason might be that we included randomized controlled trials that evaluated the application of CDSSs in practice rather than studies reporting on the development process of CDSSs. These latter studies might explain whether and how they made use of standard information models. However, information regarding the development process of systems is not often part of scientific publications. In the studies included in this review there was no study conducted in the domain of preoperative assessment. This might be due to the low number of patient information systems used in perioperative care and the lack of data standardization in this domain [18-21]. Therefore, there is room for improvement concerning availability and exchangeability of data in the preoperative assessment domain. Our project regarding standardization of data in this domain by application of a shared dataset and standard terminological systems is a step in this direction. 7.2.4. Creating an interface terminology To create an interface terminology for documenting reasons for ICU admission we used the Acute Physiology and Chronic Health Evaluation IV (APACHE IV) classification as a starting point for selection of the concepts from the reference terminology SNOMED CT. The reason for admission is registered based on the APACHE IV classification and is an important data item in the Dutch National Intensive Care Evaluation (NICE) dataset. The APACHE IV classification is an aggregated terminology that lacks the detail and structure needed for an unambiguous description of health problems. The developed interface terminology, described in chapter six, allows users to register detailed diagnosis information. At the same time information to be used for other purposes can be retrieved at an aggregated level. Limitations of the approach for developing an interface terminology are that it is still relatively labor-intensive, and that adequate tools to support the development process are lacking. Furthermore, the method can still lead to overly large interface terminologies when applied to broad domains such as reasons for admission. Application of the method in other, more focused, areas, and in the context of preoperative assessment will be needed to further demonstrate the applicability of the method.

123 Chapter 7 7.3. Implications of the study Of the 3.7 million patients undergoing surgical procedures between 1991 and 2005 in the Netherlands 67,879 died postoperatively [22]. The postoperative mortality not only depends on the surgical procedure itself but also on the presence of co-morbidities or other conditions of the patient that can be identified during preoperative assessment. Patients who had an inadequate preoperative assessment had a six-fold increase in mortality compared to those who had been thoroughly assessed [23]. Communication problems are an important factor contributing to anesthesia incidents [24]. Patient records are not always available to the anesthetist at the time of surgery and often relevant information is difficult to access because of incompatibility of software or the use of paper-based records. Communication problems are the reasons that planned processes are not followed. A study carried out in the Netherlands showed that 6.3% of the planned procedures was cancelled [25]. The Anesthesia Patient Safety Foundation reported that there is a lack of standards in data collection for preoperative assessment. They are of the opinion that the needed data should be defined precisely and that the right amount of data, depending on the user and the circumstances, should be collected [21]. Lack of credible data may result in incorrect information about the intra- and postoperative mortality and morbidity. Automated and standardized perioperative data collection is needed to learn about the true details of critical incidents and disasters [21]. This type of information can be used to improve patient care. Creating a standardized dataset for preoperative assessment not only facilitates both intra- and inter-hospital exchange of data but also provides all required data, which healthcare providers need during and after surgical procedures. It will improve the communication between patient and anesthetist and help physicians not to forget to ask the needed information from the patient. Developing a core dataset that also covers the required concepts implemented by guideline-based decision support systems will facilitate providing evidence-based advices at the point of care. Using an interface terminology based on a standard reference terminological system reduces the variability of data capture and encoding and facilitates interoperability. The research presented in this thesis provides valuable insight in the development of a standardized dataset, and the use of standardized data and the application of terminological systems in AIMS and CDSS. 7.4. Future research There is still a lot to be done to create standardized data in AIMS for preoperative assessment. The core dataset should be extended to also cover all concepts used in all relevant guidelines. Furthermore, SNOMED CT subsets including all values of the identified data items in the core dataset should be created. The generic approach regarding creation of a SNOMED CT subset described in chapter six should be applied for the creation of SNOMED CT subsets for preoperative assessment. The application of a generic approach for designing the SNOMED CT subsets in the preoperative assessment domain will prove the merits of this approach and shed light on possible points for improvements and extensions of the approach. As healthcare providers do have limited time for documenting patient information an efficient graphical user

124 General discussion interface should be developed. An approach should be developed and applied regarding the creation of an efficient structured data entry interface based on existing standard information models such as the Health Level 7 (HL7) Reference Information Model (RIM). IOTA intends to define an international standardized perioperative dataset to be implemented in AIMSs. The work of IOTA is now creating the anesthesia subset of concepts for SNOMED CT. The dataset defined in this thesis should be integrated in IOTA’s dataset. The existing way of data collection and the one presented in this thesis should be compared to gain insight in the effectiveness and efficiency of the designed dataset. It should be evaluated to what extent the application of this standardized dataset will improve adherence to preoperative assessment guidelines and increase patient care quality. The literature review on CDSSs in this thesis showed that the application of standardized data in CDSSs might improve the physician’s performance. However, more research is needed to be able to gain more insight in whether and how the use of standardized data and terminological systems increases the effectiveness of a CDSS in preoperative assessment. It should be evaluated to what extent the application of a shared dataset and standard terminological systems can play a role in improving the quality of care.

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References

[1] Innes G, Murray M, Grafstein E. A consensus-based process to define standard national data elements for a Canadian emergency department information system. CJEM 2001 Oct;3(4):277-84. [2] Pollock DA, Adams DL, Bernardo LM, Bradley V, Brandt MD, Davis TE, et al. Data elements for emergency department systems, release 1.0 (DEEDS): a summary report. DEEDS Writing Committee. J Emerg Nurs 1998 Feb;24(1):35-44. [3] The International Health Terminology Standards Development Organisation. SNOMED Clinical Terms Editorial Guidelines, Content Inclusion Principles and Process. 2008. [4] The National Institute of Standards and Technology. Electronic Data Interchange (EDI). Federal Information Processing Standards Publication, April 1996, 161-2; 1996. [5] Campbell HS, Ossip-Klein D, Bailey L, Saul J. Minimal dataset for quitlines: a best practice. Tob Control 2007 Dec;16 Suppl 1:i16-i20. [6] Ireland D, O'Brien D. In the beginning...the origin and evolution of ASPAN's Perianesthesia Data Elements. J Perianesth Nurs 2007 Dec;22(6):423-7. [7] Simmons D, Coppell K, Drury PL. The development of a national agreed minimum diabetes dataset for New Zealand. J Qual Clin Pract 2000 Mar;20(1):44-50. [8] Thomas K, Emberton M, Mundy AR. Towards a minimum dataset in urology. BJU Int 2000 Nov;86(7):765-72. [9] Guidelines for developing a data dictionary. J AHIMA 2006 Feb;77(2):64A-D. [10] Brown SH, Elkin PL, Bauer BA, Wahner-Roedler D, Husser CS, Temesgen Z, et al. SNOMED CT: utility for a general medical evaluation template. AMIA Annu Symp Proc 2006;101-5. [11] Park HA, Lundberg CB, Coenen A, Konicek DJ. Evaluation of the content coverage of SNOMED-CT to represent ICNP Version 1 catalogues. Stud Health Technol Inform 2009;146:303-7. [12] Sampalli T, Shepherd M, Duffy J, Fox R. An evaluation of SNOMED CT(R) in the domain of complex chronic conditions. Int J Integr Care 2010;10:e038. [13] Elkin PL, Brown SH, Husser CS, Bauer BA, Wahner-Roedler D, Rosenbloom ST, et al. Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists. Mayo Clin Proc 2006 Jun;81(6):741-8. [14] Holbrook A, Xu S, Banting J. What factors determine the success of clinical decision support systems? AMIA Annu Symp Proc 2003;862. [15] Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 2005 Apr 2;330(7494):765. [16] Mollon B, Chong J, Jr., Holbrook AM, Sung M, Thabane L, Foster G. Features predicting the success of computerized decision support for prescribing: a systematic review of randomized controlled trials. BMC Med Inform Decis Mak 2009;9:11. [17] Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010 Jan;17(1):25- 33. [18] Balust J, Egger Halbeis CB, Macario A. Prevalence of anaesthesia information management systems in university-affiliated hospitals in Europe. Eur J Anaesthesiol 2010 Feb;27(2):202-8. [19] Egger Halbeis CB, Epstein RH, Macario A, Pearl RG, Grunwald Z. Adoption of anesthesia information management systems by academic departments in the United States. Anesth Analg 2008 Oct;107(4):1323- 9. [20] Epstein RH, Vigoda MM, Feinstein DM. Anesthesia information management systems: a survey of current implementation policies and practices. Anesth Analg 2007 Aug;105(2):405-11. [21] Gravenstein JS. APSF Retreats, Returns With Report on Perianesthetic Data Management. ASA newsletter 2001;65(2).

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[22] Noordzij PG, Poldermans D, Schouten O, Bax JJ, Schreiner FA, Boersma E. Postoperative mortality in The Netherlands: a population-based analysis of surgery-specific risk in adults. Anesthesiology 2010 May;112(5):1105-15. [23] Runciman WB, Webb RK. Anaesthetic Mortality in Australia 1991-1993. Australian Society of Anaesthetists Newsletter 1994;94:15-7. [24] Kluger MT, Tham EJ, Coleman NA, Runciman WB, Bullock MF. Inadequate pre-operative evaluation and preparation: a review of 197 reports from the Australian incident monitoring study. Anaesthesia 2000 Dec;55(12):1173-8. [25] van Klei WA, Moons KG, Rutten CL, Schuurhuis A, Knape JT, Kalkman CJ, et al. The effect of outpatient preoperative evaluation of hospital inpatients on cancellation of surgery and length of hospital stay. Anesth Analg 2002 Mar;94(3):644-9.

127 Chapter 7

128 SummarySummary Summary

130 Summary

Patient information systems improve the availability of information for healthcare professionals. However, the availability of information is not a key motivator for healthcare professionals to adopt a patient information system. Their motivation lies in saving time and improving medical decision making and that is where semantic interoperability of healthcare information comes into play. Semantic interoperability means that the meaning of information is unambiguously exchanged among patient information systems. It addresses issues of how to best facilitate the coding, transmission and use of meaning seamlessly across health services, providers and patients. The first step towards semantic interoperability is developing an agreed-upon dataset to improve communication among healthcare providers and between healthcare providers and patients. However, one of the biggest impediments to communication is that there may be multiple ways of describing a single concept. To overcome this, a standard terminological system is needed which meaningfully describes concepts. For the preoperative assessment, in which the risks for a patient regarding a planned procedure and anesthesia technique are determined and minimized, semantic interoperability is important as different healthcare providers and different patient information sources such as previous patient healthcare documents and laboratory results are involved. An anesthesia information management system (AIMS), capturing preoperative assessment data, should interact correctly with other systems and the flow of information between different systems with the AIMS should be correct. Re-use of patient data within different patient information systems heavily relies on a shared set of data employed in the systems and represented in a standardized way. Developing a standardized preoperative assessment dataset contributes to reducing miscommunication and reassessment in case of patient referrals. Therefore, in this project as a first step toward data standardization we developed a core dataset for the preoperative assessment. First to get insight into the collected data for preoperative assessment a systematic review was performed in the PubMed and CINAHL databases, the results of which are described in chapter two. From 41 included articles data items that were described as being part of the preoperative assessment were extracted. Five hundred forty one distinct data items were extracted. From the extracted data items only 6 data items were stated in 50% or more of the articles. The large diversity of data items collected during preoperative assessment indicates that there is no agreement about which data items should be collected during the preoperative assessment. To overcome the variety of data collected in different settings, defining a standardized core dataset seems necessary. The results of this systematic review showed that identifying essential data items in the preoperative assessment only through frequency of citation is not enough due to the observed variation in data collection. Chapter three describes the development process of and the resulting preoperative assessment core dataset. First we established an expert committee to develop the first draft of a preoperative assessment dataset based on expert consensus. The major headings of the core dataset were determined, and committee subgroups formed to address the major headings. The subgroups determined data items for each major heading and shared them

131 Summary with other members of the committee. Data items were discussed in five consensus meetings until agreement was reached. Based on the outcome of this consultative process a draft version of a national core dataset for preoperative assessment was created. Then the results of the systematic review (data items that were reported in more than 25 % of the articles) were compared with the data items defined by experts. Eighty two data items were defined in the draft version of the core dataset by experts, of which 76% were covered by the systematic review. After the comparison 6 data items were deleted from the draft dataset, 17 new data items were added and 9 data items were modified. The final version of the core dataset included 93 data items. The comparison between the expert-based dataset and the result of the systematic review helped the committee to modify the dataset by adding disregarded data items and removing unnecessary items. Both methods proved to be valuable and complementary in designing the dataset. This dataset is intended to be implemented in anesthesia information management systems (AIMS), preferably by using SNOMED CT as the standard terminology system. In chapter four we investigated whether SNOMED CT covers the terms used in preoperative assessment guidelines. We searched the websites of the (inter)national anesthesia-related societies to retrieve preoperative assessment guidelines. To facilitate data extraction from the retrieved guidelines, each guideline statement was rewritten as an “IF condition THEN action” statement and then the guidelines’ terms were extracted from these IF-THEN statements. The extracted terms were mapped to SNOMED CT concepts. Post- coordination was used when a pre-coordinated concept could not be found. The final mapping was given one of three scores: no match, partial match or complete match. To investigate why SNOMED CT does not completely cover the concepts used in the preoperative assessment guidelines, partially matching and non-matching concepts were evaluated against the principles and rules of SNOMED CT described in the editorial documentation of SNOMED CT regarding the content inclusion principles and process. The six retrieved guidelines were transferred into 24 IF-THEN statements. From 133 extracted terms from these statements 70% were completely mapped and 15% were partially mapped to SNOMED CT concepts. About 20% of all complete matches and 30% of all partial matches were achieved through post-coordination. In total, 68% of the partial and non-matches should not be represented by SNOMED CT because they violated at least one of the principles or rules of the SNOMED CT documentation. These concepts corresponded with underspecified concepts in the preoperative assessment guidelines. The results of this study showed that to facilitate the presentation of preoperative assessment guideline terms in AIMS by using SNOMED CT not only SNOMED CT needs to be extended, but also deficiencies in the guidelines’ use of terminology should be solved. Chapter five describes a literature review and a survey on the use of standardized data and terminological systems in Clinical Decision Support Systems (CDSSs). The set of included articles of a former systematic review on CDSS was our starting point. This systematic review covers articles published from 1974 till September 2004. Articles published after 1995 were included in our study (n = 46). To consider articles published after September 2004 we included 31 identified articles on CDSSs from AMIA’s “Year in Review”. Each year during a “Year in review” session, AMIA identifies that year’s

132 Summary

Randomized Controlled Trials (RCTs) in the field of medical informatics. A data extraction form was designed and the required data items were extracted from the identified articles. Authors of the articles were contacted to check and complete the extracted data items from their articles and to answer a questionnaire. In total, 77 articles were included in the study. Twenty two percent of the included studies used only numerical data items in the CDSS. Studies that used coded data items in CDSSs applied local terminological systems to code the data items in about half of the cases. The most frequently used international terminological systems were those of the family of International Classification of Diseases (ICD) and Logical Observation Identifiers Names and Codes (LOINC). Authors of the included studies reported that in 58% of the cases they experienced problems with CDSS implementation. In 92% of these cases the problems were related to availability of standardized data required to develop a CDSS. The findings of this study showed that developers of CDSSs used different terminological systems to code the data items, hampering the shareability of information. Moreover, the survey revealed that data standardization is a critical success factor for CDSS development. In chapter six we devised a generic approach for developing a domain-specific interface terminology on SNOMED_CT. The approach includes six sequential phases: domain analysis, mapping from the domain concepts to SNOMED CT concepts, creating the SNOMED CT subset guided by the mapping, extending the subset with non-covered concepts, constraining the subset by removing irrelevant content, and deploying the subset in a terminology server. We applied the proposed approach to the domain of intensive care. For the domain analysis phase of the approach we used the Acute Physiology and Chronic Health Evaluation (APACHE) IV classification, an existing standard in the intensive care domain as the starting point. Each of the 445 APACHE IV diagnostic categories was mapped to SNOMED CT concepts. In total, 89.2% of the diagnostic categories were completely mapped to SNOMED CT and the rest were partially mapped to SNOMED CT. The results of the mapping were used as target concepts to isolate the concepts related to the APACHE IV diagnostic categories and their subordinates and attributes by downward traversal from SNOMED CT. In the extension phase, for all concepts representing a diagnostic category from the APACHE IV classification, the APACHE IV term with the related APACHE IV code was added as the preferred term to the subset. Post-coordinated concepts were added as concepts and for partial matches, new pre-coordinated concepts were also added to the SNOMED CT subset. At the end, the hierarchical relations between the APACHE IV categories were added to the subset. Depending on the restriction of the depth of the graph traversal the number of disorder and procedure concepts ranges from 2% to 29% of all active SNOMED CT concepts. The interface terminology was deployed using a local terminology server. The proposed approach provides a framework for developers of interface terminologies based on a reference terminology. The application of the proposed approach to the domain of intensive care resulted in an interface terminology for registration of reasons for admission of patients hospitalized in the intensive care unit which can be used to facilitate sharing and aggregation of data for different purposes. The research in this thesis provides fundamental steps towards semantic interoperability. It appears important to investigate both the application of a core dataset

133 Summary and use of terminological systems to achieve an interoperable system. Semantic interoperable systems are a promising way to improve healthcare outcomes and costs.

134 SamenvattingSamenvatting Samenvatting

136 Samenvatting

Zorginformatiesystemen verbeteren de beschikbaarheid van informatie voor zorgprofessionals. Deze beschikbaarheid van informatie is echter op zich niet de primaire reden om zorginformatiesystemen te gebruiken. De voornaamste motivatie om een zorginformatiesysteem te gebruiken is het besparen van tijd en het verbeteren van klinische besluitvorming. Hiervoor is semantische interoperabiliteit vereist. Semantische interoperabiliteit is het niet-ambigue uitwisselen van informatie tussen systemen, met behoud van betekenis. Om dit te realiseren moet aandacht worden besteed aan het coderen en uitwisselen van gegevens en context, zodat zorgorganisaties, zorgverleners en patiënten naadloos kunnen samenwerken. De eerste stap op weg naar semantische interoperabiliteit is het ontwikkelen van een breed geaccepteerde dataset om communicatie tussen zorgverleners onderling en tussen zorgverleners en patiënten te verbeteren. Eén van de grootste struikelblokken voor communicatie is echter de mogelijkheid om concepten op verschillende manieren te omschrijven. Om dit probleem op te lossen is een gestandaardiseerd terminologiestelsel vereist waarin concepten op betekenisvolle wijze worden gedefinieerd. Semantische interoperabiliteit is van belang in de preoperatieve assessment, waarin de risico’s voor de patiënt ten aanzien van een geplande chirurgische ingreep en anesthesie worden bepaald en geminimaliseerd. Hierbij zijn namelijk verschillende zorgverleners betrokken die diverse gegevensbronnen gebruiken, zoals verslagen van eerdere verrichtingen, en laboratorium bepalingen. Anesthesie Informatie Management Systemen (AIMS), waarin ook de gegevens van de preoperatieve assessment worden vastgelegd, moeten op correcte wijze informatie uitwisselen met andere systemen en de informatiestromen tussen de systemen moeten optimaal zijn. Hergebruik van patiëntgegevens in verschillende informatiesystemen is sterk afhankelijk van het gebruik van een geaccordeerde dataset die in de systemen wordt gebruikt en waarin data op gestandaardiseerde wijze wordt gerepresenteerd. Het ontwikkelen van een gestandaardiseerde dataset voor de preoperatieve assessment draagt bij aan het verminderen van miscommunicatie en het terugdringen van dubbele assessments wanneer een patiënt wordt doorverwezen. Daarom is in het project beschreven in dit proefschrift, als eerste stap op weg naar standaardisatie van gegevens, een kern dataset voor de preoperatieve assessment ontwikkeld. Om inzicht te krijgen in de data die wordt verzameld tijdens de preoperatieve assessment is allereerst een systematische literatuur review uitgevoerd op de PubMed en CINAHL databases. Zoals beschreven in hoofdstuk 2 zijn uit de 41 geïncludeerde artikelen in totaal 541 data items geëxtraheerd die betrekking hebben op de preoperatieve assessment. Van deze items werden er slechts 6 in 50% of meer van de artikelen genoemd. De grote diversiteit in data items die tijdens de preoperatieve assessment worden verzameld geeft aan dat er geen overeenstemming bestaat over welke items tijdens de preoperatieve assessment verzameld moeten worden. Om deze diversiteit te reduceren is het wenselijk een gestandaardiseerde kern-dataset te definiëren. De resultaten van deze systematische review maken duidelijk dat, gezien de grote diversiteit, het identificeren van de belangrijkste data items in de preoperatieve assessment niet enkel op basis van hun frequentie van voorkomen in de literatuur kan worden gedaan.

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Hoofdstuk 3 beschrijft het proces van het ontwikkelen van een kern-dataset voor de preoperatieve assessment alsmede het resultaat van dat proces. Allereerst werd een expert commissie ingesteld om de eerste versie van de preoperatieve assessment dataset te ontwikkelen op basis van expert consensus. De hoofdcategorieën van de kern-dataset werden vastgesteld, op basis waarvan werkgroepen deze hoofdcategorieën verder uitwerkten. De werkgroepen bepaalden voor elke hoofdcategorie de initiële data items en wisselden deze uit met de overige commissieleden. De data items werden in consensusbijeenkomsten besproken totdat na 5 bijeenkomsten overeenstemming was bereikt. Het resultaat hiervan werd uitgewerkt tot de eerste versie van de landelijke kern- dataset voor preoperatieve assessment. Hierna werden de resultaten van de systematische review (data items die in meer dan 25% van de artikelen werden genoemd) vergeleken met de kern-dataset. De experts hadden 82 data items gedefinieerd in de kern-dataset, waarvan 76% ook door de systematische review werd gedekt. Deze vergelijking leidde tot het verwijderen van 6 data items uit de dataset, het toevoegen van 17 items aan de dataset en het aanpassen van 9 data items. De definitieve versie van de kern-dataset bevat 93 data items. De vergelijking tussen de expert-gebaseerde dataset en de resultaten van de systematische review bood de commissie de mogelijkheid om de dataset aan te passen, door data items toe te voegen die over het hoofd waren gezien, en data items te schrappen die bij nader inzien overbodig waren. Beide methoden bleken nuttig en complementair om tot de dataset te komen. Er wordt naar gestreefd om de dataset in AIMS te implementeren, bij voorkeur gebruik makend van SNOMED CT als onderliggend terminologiestelsel. In hoofdstuk 4 hebben we onderzocht of SNOMED CT de termen bestrijkt die worden gebruikt in de richtlijnen voor preoperatieve assessment. Om deze richtlijnen te verzamelen doorzochten we de websites van (inter)nationale anesthesie-gerelateerde organisaties. Om de data elementen uit de gevonden richtlijnen te kunnen extraheren werd elke regel uit de richtlijnen herschreven als een “ALS conditie DAN actie” regel, waarna de termen uit deze regels werden geëxtraheerd. Deze termen werden gemapt op concepten uit SNOMED CT. Wanneer geen geprecoördineerd concept werd gevonden in SNOMED CT werd gebruik gemaakt van postcoördinatie. De definitieve mapping werd op een 3-punts schaal gescoord als: geen overeenkomst, gedeeltelijke overeenkomst, of volledige overeenkomst. Vervolgens is onderzocht of er een verklaring is wanneer er geen volledig overeenstemmend SNOMED CT concept kan worden gevonden voor een term uit een preoperatieve assessment richtlijn. Hiertoe werden de termen zonder overeenkomst of met een gedeeltelijke overeenkomst geëvalueerd op basis van de principes en regels die in de SNOMED CT redactionele documentatie zijn opgesteld voor het includeren van inhoud. De 6 gevonden richtlijnen werden omgezet in 24 ALS-DAN regels. Van de 133 termen die uit deze regels werden geëxtraheerd kon 70% volledig worden gemapt en kon 15% gedeeltelijk worden gemapt op SNOMED CT concepten. Circa 20% van alle volledige mappings en 30% van de gedeeltelijke mappings werd met behulp van postcoördinatie gerealiseerd. Van de termen die niet of gedeeltelijk overeenkwamen met een SNOMED CT concept voldeed 68% niet aan de principes en regels in de redactionele documentatie van SNOMED CT. Dit betrof slecht gespecificeerde concepten die werden gebruikt in de richtlijnen voor preoperatieve assessment. De resultaten van deze studie tonen aan dat voor het presenteren

138 Samenvatting van richtlijnen in de preoperatieve assessment het niet alleen nodig is om SNOMED CT uit te breiden, maar ook om onvolkomenheden in de richtlijnen te corrigeren. Hoofdstuk 5 beschrijft een literatuur review van een onderzoek naar het gebruik van gestandaardiseerde gegevens en terminologiestelsels in klinische beslissingsondersteunende systemen (KBOSn). Het uitgangspunt voor de review was de verzameling van artikelen die waren opgenomen in een eerder systematische review over KBOSn. Deze review bevat artikelen die zijn gepubliceerd tussen 1974 en september 2004. In onze studie gebruikten we de artikelen die na 1995 waren gepubliceerd (n=46). Om ook artikelen te betrekken die na september 2004 waren gepubliceerd hebben we 31 artikelen over KBOSn geïdentificeerd in AMIA’s “Year in Review”. Elk jaar worden in de “Year in Review” sessie op het AMIA congres de Randomized Controlled Trials (RCTs) op het gebied van medische informatica behandeld. Een data-extractie formulier werd ontwikkeld waarmee de benodigde data items uit de geïncludeerde artikelen werden gefilterd. Met de auteurs van de artikelen werd contact opgenomen om de data items die uit hun artikel(en) waren gefilterd te controleren en completeren en hen werd een vragenlijst voorgelegd. In totaal werden 77 artikelen in de studie opgenomen. Hiervan beschreef 22% het gebruik van enkel numerieke data items in een KBOS. Ongeveer de helft van de studies die in het KBOS gecodeerde data items gebruikten, paste lokaal ontwikkelde terminologiestelsels toe. De meest gebruikte internationale terminologiestelsels waren de verschillende versies van de “International Classification of Diseases” (ICD) en “Logical Observation Identifiers Names and Codes” (LOINC). De auteurs van de studies gaven aan dat ze in 58% van de gevallen problemen hadden ondervonden bij implementatie van een KBOS. In 92% van deze gevallen betrof het problemen met de beschikbaarheid van gestandaardiseerde gegevens, die nodig zijn om een KBOS te ontwikkelen. De bevindingen in deze studie laten zien dat ontwikkelaars van KBOSn diverse terminologiestelsels gebruikten om data items te coderen, wat het delen van informatie bemoeilijkt. Bovendien maakte het onderzoek duidelijk dat standaardisatie van gegevens een kritische succesfactor is voor het ontwikkelen van KBOSn. In hoofdstuk 6 hebben we een generieke methode opgezet voor het ontwikkelen van een domeinspecifieke interface-terminologie op SNOMED CT. Deze methode omvat zes opeenvolgende fases: domein analyse; mapping van de domeinconcepten naar concepten uit SNOMED CT; opstellen van een SNOMED CT subset op basis van de mapping; uitbreiden van de subset met ontbrekende concepten; beperken van de subset door irrelevante inhoud te filteren; beschikbaar stellen van de subset in een terminologieserver. We hebben de voorgestelde methode toegepast in het intensive care domein. Voor de domeinanalyse-fase hebben we als uitgangspunt de “Acute Physiology and Chronic Health Evaluation” (APACHE) IV classificatie genomen, een standaard in het intensive care domein. Elk van de 445 diagnostische categorieën uit de APACHE IV werd gemapt op SNOMED CT concepten. In totaal kon 89.2% van deze categorieën volledig worden gemapt, de overige categorieën konden gedeeltelijk worden gemapt naar SNOMED CT concepten. De resultaten van deze mapping werden gebruikt om een subset te creëren van de aan APACHE IV gerelateerde concepten, de daaraan ondergeschikte concepten en hun kenmerken, alsook de kenmerkwaarden en de daaraan ondergeschikte concepten. In de uitbreidingsfase werd aan elk concept dat een APACHE IV categorie representeerde een

139 Samenvatting voorkeursterm toegevoegd. Gepostcoördineerde concepten werden toegevoegd en voor de gedeeltelijk mappende concepten werden nieuwe geprecoördineerde concepten aan de SNOMED CT subset toegevoegd. Vervolgens werden ook de hiërarchische relaties tussen de APACHE IV categorieën aan de subset toegevoegd. Afhankelijk van het aantal lagen van ondergeschikte concepten die in de subset werden opgenomen varieerde het aantal concepten betreffende ziekten en verrichtingen tussen 2% en 9% van alle actieve SNOMED CT concepten. De interface-terminologie werd beschikbaar gesteld door middel van een lokale terminologie-server. De ontwikkelde methode biedt ontwikkelaars van interface- terminologieën een raamwerk gebaseerd op een referentie-terminologie. De toepassing van de voorgestelde methode in het intensive care domein resulteerde in een interface- terminologie voor het registreren van opnameredenen voor patiënten die op de intensive care worden opgenomen. De geregistreerde gegevens kunnen worden gebruikt om gegevens te delen en te aggregeren voor diverse doeleinden. Het onderzoek in dit proefschrift biedt een aantal basisstappen op weg naar semantische interoperabiliteit. Zowel het toepassen van een kern-dataset als het gebruik van een terminologiestelsel moet worden bewerkstelligd om een interoperabel systeem te realiseren. Semantisch interoperabele systemen bieden een veelbelovende mogelijkheid om zorguitkomsten en kosten te verbeteren.

140 AcknowledgmentAcknowledgment

Acknowledgment

142 Acknowledgment

Since I came to the Netherlands, I have worked with a great number of people. The research project described in this thesis would not have been possible without their support. It is a pleasure to convey my gratitude to them all in my humble acknowledgment.

First of all, I would like to express my deepest sense of gratitude to my supervisors Prof. Dr. Arie Hasman, Dr. Nicolette F. de Keizer and Dr. Ronald Cornet for their patient guidance, encouragement and excellent advice throughout this research. Your involvement with science has triggered and nourished my academic maturity that I will benefit from, for a long time to come. Dear Arie, I gratefully thank you for your constructive comments and your valuable assistance on this thesis. I will never forget the days that we sat together, ate together and listened to stories about different cultures. Dear Nicolette, you always read what I wrote within a couple of days and offered thoughtful opinions. Your ability in reading and reviewing articles was amazing. You were always supportive and generously helped me. It was great for me to have such an opportunity to benefit from your excellent knowledge and invaluable comments. Dear Ronald, your knowledge exceptionally inspires and enriches my growth as a scientist. By asking questions you encouraged me to think about every issue raised during each study. You teach me how to think and how to solve the research problems. Thank you for the time you spent for answering my questions. Dear Nicolette and Ronald I will never forget the beautiful events that you made for us when you invited us to your homes. I am grateful to all of you and hope to keep up our collaboration in the future.

I appreciate the members of doctorate committee Prof. dr. J. van der Lei, Prof. dr. J.H.M. Schonk, Prof. dr. P.J.M. Bakker, Prof. dr. M. W. Hollmann, Dr. W.A. van Klei for their effort in reading and judgment of my thesis.

I would like to express my gratitude to my scholarship sponsor from the Iranian Ministry of Health and Medical Education for financing my project. I am thankful to Dr. Abdollahi, Dr. Naffisi, Dr. Saeid Tehrani, Dr. Okhovati, Dr. Haghdoost, Dr. Mosavi Ayatollahi and Mr. Talebian for their valuable assistance.

I was lucky to get the opportunity to work with Niels Peek. I learned many things from him and I am indebted to him for his fruitful collaboration and valuable advice in my research.

I am grateful to members of the working group “minimal dataset for preoperative assessment” of the Netherlands’ society of anesthesiologists for defining the core preoperative assessment dataset: Jilles Bijker, Cor Kalkman, Peter Houweling, Teus Kappen, Wilton van Klei, Aart Leyssius, Lex Pfaff, Peter Rosseel, Peter Schutte, Mark Simon, Loes Snel, and Johannes Zwijssen. I am especially thankful to Wilton van Klei and

143 Acknowledgment

Peter Houweling for offering thoughtful opinions. Wilton I acknowledge you for your precious time to read my paper and your critical comments about it.

My sincere thanks to Ameen Abu-Hanna for his help and guidance. I greatly benefited from his advice and his valuable experience.

I would also like to thank all my colleagues in department of medical informatics. I benefited from their assistance and their comments during the research meetings. They helped me to learn the Dutch language by encouraging and speaking to me. I appreciate the help and assistance of Dr. Saied Eslami and Dr. Ferishta Bakhshi-Raiez. I would like to thank my paranymphs Sabine van der Veer and Airin Simon for their help and support.

Finally, I wish to express my love and gratitude to my beloved husband and my little angel Ava. Without the efforts of my husband, this book would likely not have been completed. His support was far beyond duty of a husband. I am greatly indebted to him. He took every responsibility for our life here and in the last months of my PhD he donated most of his time to take care of our beautiful daughter while I was busy. I wouldn’t have been able to obtain this degree without his help. The birth of our daughter was the most beautiful event in our life. I will never forget the moment when my husband I together put the tiny finger of our daughter on the mouse pad of the laptop to approve submission of our papers. She brought fortune in our life and all papers that she submitted for us were accepted. She is the origin of my happiness and pleasure.

I pay my respect and love to my parents and all other family members for their love. It has been a desire for my mother to see my doctorate ceremony. I am glad that I could achieve this degree to make her happy. Thanks to my sisters and my brother for their support and lovely welcome when we went to Iran and thanks for preparing with love all things that we wanted to bring to The Netherlands. I would also like to thank my mother and my brothers in-law and their wives for their support and all great events that they made for us when we were in Iran.

144 CurriculumCurriculum vitaevitae Curriculum Vitae

Curriculum vitae Leila Ahmadian was born in Isfahan, Iran. In 1993 she graduated from Asiyeh high school in Isfahan. From 1993 until 1995 she studied her associate degree in the field of medical records in the same city. After graduation she worked in Isfahan University of Medical Sciences for one and half year. Then in 1997 she moved to Shiraz, another city in Iran, to continue her education. After graduation with a bachelor degree she worked for one year in the cardiovascular research center of Isfahan University of Medical Sciences. After that she started her master degree in medical records education in Iran University of Medical Sciences. She defended her master thesis entitled “A study on neurologists' and coders' perceptions on ICD-10 and ICD-NA Classification” in 2001. From 2001 until 2003 she worked as an administrator of the medical records department of Mobarakeh hospital. Then she was employed as a faculty member in Kerman University of Medical Sciences in 2003. She married to her colleague Reza Khajouei in July 2006.

After her marriage she moved with her husband to the Netherlands and started her PhD in august 2006 at the department of Medical Informatics of the Academic Medical Center and University of Amsterdam. Her research project was on data standardization in preoperative assessment by application of terminological systems and a standard dataset. She gave birth to her daughter Ava four months before her PhD thesis defense in Amsterdam.