An Executable Knowledge Base for Clinical Practice Guideline Rules

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Procedia Technology 16 ( 2014 ) 1446 – 1455

CENTERIS 2014 - Conference on ENTERprise Information Systems / ProjMAN 2014 - International Conference on Project MANagement / HCIST 2014 - International Conference on Health and Social Care Information Systems and Technologies An Executable Knowledge Base for Clinical Practice Guideline Rules

Alexandra Pomares Quimbayaa, Oscar Muñozb, Darío Londoñob, Ricardo Bohórquezb, Olga Milena Garcíab, Rafael A. Gonzáleza, María Patricia Amorteguia, Stephanne Rodriguezc, Alvaro Bustamantec

aPontificia Universidad Javeriana – Enginering Faculty, Bogotá, Colombia bOscar Muñoz, Pontificia Universidad Javeriana & Hospital Universitario San Ignacio – Medicine Faculty, Bogotá, Colombia cHospital Universitario San Ignacio, Bogotá, Colombia

Abstract

This paper presents EXEMED, a knowledge base created for the definition of executable rules that can be applied over Electronic Health Records (EHR) in order to measure the EHR compliance with respect to a specific clinical guideline. EXEMED is based on description logic and provides the concepts and roles necessary to express the sets of patients relevant for the definition of rules based on a clinical guideline. Its principle is to provide a mechanism to define the characteristics of patients that are pertinent for a specific clinical guideline through nine concepts, in such a way that they can be obtained from the whole universe of patients. The usefulness of EXEMED was validated applying it to a study case with three different clinical guidelines: Gastrointestinal Bleeding, Chronic Kidney Disease and Hipokalemia.

©© 2014 2014 The The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committees of CENTERIS/ProjMAN/HCIST 2014 Peer-review under responsibility of the Organizing Committee of CENTERIS 2014.

Keywords: Clinical Practice Guideline, Executable rules, Medical guideline rules.

1. Introduction

In order to assure high quality in the provision of health services, the use of clinical practice guidelines is very important. These are defined as a set of "systematically developed statements to assist practitioner and patient decisions about appropriate health care for clinical circumstances" [1]. Clinical practice guidelines are primarily used to increase the quality of patient care, to promote the efficient use of resources [2], and to generate systematic

2212-0173 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the Organizing Committee of CENTERIS 2014. doi: 10.1016/j.protcy.2014.10.164 Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455 1447 recommendations for doctors. The definition of clinical practice guidelines can be done either by health regulatory agencies or by hospitals, [3] and [4], also this definition can be based on NICE and SIGN [5] descriptions. This means that the adaptation of clinical practice guidelines may be modified according to circumstances or to different environmental conditions as shown in [6]. The compliance evaluation of a clinical practice guideline is performed by evaluating a set of indicators related to hospital follow-up, interventions and behaviors, training, background, and diagnostic criteria, among others. These indicators allow health professionals to compare the quality of health care services with the parameters given in the guidelines, in order to take appropriate actions towards a better service to users. The definition of clinical practice guidelines generally includes information related to: objectives, disease indicators, general considerations, interpretations, recommendations, methodology, implementations and development guide, as is shown in [7]. These guides have many variables and relevant information for the proper tracing of the process. However, current guideline documents are very long and do not allow the extraction of this information in a concise and easy way, making it difficult to assess adherence to these guidelines by medical and administrative staff. As a consequence, there has been some research aimed at structuring these guidelines. For example, there are proposals of frameworks that are used to achieve interoperability of content between the Health Level Seven (HL7) and Semantic Web technologies in order to execute clinical guidelines [8]. Another project proposes a methodology and a software tool to mark-up clinical guidelines using a tree structure and a language called OCML [9]. Similarly, languages like PROforma are specifically designed to capture clinical practice guidelines [10] and certain conditions are explained to select a specific language [11]. Furthermore, some methods for the computerization of clinical practice guidelines are used in [12]. Although these proposals are very relevant for the definition of a clinical guideline, they have not been aimed at representating rules that can be executed and validated in an automatic way out of electronic health record databases, in which medical information is stored. The purpose of this paper is to present EXEMED, a strategy based on a knowledge base created for structuring the rules related to clinical guidelines; EXEMED can be used to obtain the input required for the analysis of indicators and for training purposes given its capacity to describe the main rules established in a clinical practice guideline. This paper is organized as follows: Section II presents some previous research. Section III, describes the proposed structure of EXEMED. Section IV, shows the application of the proposal considering three particular diseases taken as a case study. Finally, Section V describes conclusions and future work.

2. Related Work

Clinical practice guidelines research has been primarily oriented to: the methodology for defining a guideline, the extraction of narrative text found in a guideline, and indicator assessment of adherence to the guideline (extracted from electronic medical records). This section analyzes the main research initiatives related to the definition of languages or models to express rules and indicators for evaluating the adherence to clinical guidelines. PROforma, Arden and Gem are compared based on their ability to express rules about clinical guidelines. Basically, PROforma is defined as a process-modeling language that supports the definition of clinical guidelines and protocols as long as a well-defined set of tasks and logical constructs are already in place [10]. PROforma uses schemas with decision tasks. Arden, a standard from HL7, is an organized structure of MLM - Medical Logic Module - that can be triggered. The syntax has categories and slots, within each category there is a set of slots [13], these slots are used for MLM knowledge base maintenance and change control. Arden is useful in many areas, but specifically for clinical guidelines it does not have a standard vocabulary to compare the rule with electronic health records. GEM uses XML schema to describe a comprehensive set of attributes, pertinent concepts, and it can be depicted as a direct graph with indicators from a guideline [14]. Something relevant from GEM is that rules can be categorized as conditional or imperative statements. GEM applies a set of constructs derived from a decision table mode, which offers considerable capabilities for representing and manipulating guideline logic. In PROforma, Arden and Gem the terminology is different, but they support a basic set of guideline tasks, including: decisions, actions and entry criteria [15]. They have similar components in their syntaxes, as can be seen in Table 1.

1448 Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455

Table 1. Syntax Comparison between: PROforma, Arden and GEM PROforma [16], [17] Arden [18], [19] Gem [20], [21] Unambiguously transitions between states Call ‘…..’ IF... THEN of a task, four conditions has to be used: If …. clause can be replaced by Start (x), Discarded (x), Cycle (x) and Conclude … IF... THEN...BECAUSE. Completed (x) Allows PROforma specification, defined For example: Call causes the MLM named For example: in a child with minor closed the value of any expression. The classes "hyperkalemia" to be executed and this head trauma, that are used to instantiate these objects concludes "true" if the potassium is greater IF there was no loss of consciousness, are arranged in an inheritance hierarchy. than 5 THEN skull radiographs are not The value of the precondition property of call `hyperkalemia` a task must be a truth-valued expression. recommended if potassium > 5.0 then BECAUSE the substantial rate of false conclude true end if positive radiographs and the low prevalence of intracranial injury among this specific subset of patients lead to a low predictive value of serious injury.

As shown in Table 1, PROforma has a different condition statement due to the complexity of languages and the ability to be used by a programming tool. In addition to the previous comparison, it is also considered important to analyze what kind of rules these languages can describe. Table 2, compares the ability of each one of the proposals to describe precisely a rule considering it requires at least the elements included in column one.

Table 2. Indicators used by PROforma, Arden and GEM General Indicators PROforma Arden GEM Phase x Diagnosis x x x Time x x x Risk Factor x x Antecedents x x x Severity Level Location x x Medical interventions x x x Adverse event x

Where: x Phase: It is defined as the possible stages where the patient can be. Such as: Triage, Hospitalization, and Outpatient. x Diagnosis: Defined as ICD10 Code [26]. x Time: Defined as the periodicity of an event: years, months, weeks, days, hours, minutes, seconds. x Risk Factor: It includes variables such as: age, sex, ethnicity and weight. x Antecedents: It is the background related to the pathological, pharmacological, surgical, allergy, toxic, obstetric and gynecology aspects. x Severity Level: It defines the level into which the patient is with respect to a specific diagnosis: Stable, Crisis, risk grading. x Location: It describes where the patient is located. Examples are General Hospitalization or Intensive Care Unit x Behaviors and interventions: It includes prescribed medicines, prescribed exams, delivered medicine, delivered exams, procedures. x Adverse event: Unexpected events detected after a medicine prescription or procedure. Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455 1449

As can be seen in Table 2, none of the proposals considers all the concepts required for defining the executable rules of clinical guideline. Even though Arden is the most comprehensive proposal, it is more focused on sharing any kind of medical knowledge and due to that its direct execution for the analysis of clinical practice guidelines is not straightforward. The aim of this paper is to propose an executable knowledge base created to enhance the definition of rules from a clinical practice guideline in order to evaluate them in electronic health records.

3. EXEMED: An Executable Knowledge Base for Clinical Practice Guideline Rules

This section presents EXEMED, a knowledge base created for the definition of executable rules that can be applied over Electronic Health Records (EHR) in order to measure the EHR compliance with respect to a specific clinical practice guideline. EXEMED defines the concepts and roles that can be used to define rules derived from a specific clinical guideline. It is called executable because these rules can be evaluated over the medical repository where the EHRs are stored. The evaluation of a rule enables us to identify whether or not the EHR satisfies it. EXEMED knowledge base was built using OWL DL (Description Logic). DL is a family of formal knowledge representation languages [25]. Using DL it is possible to model concepts and roles that define the terminological part (called TBOX) and individuals and their relationships that define the assertional part (called ABOX). EXEMED allows defining the type of patients that are relevant for a medical guideline using concepts, which correspond to classes and are interpreted as sets of objects in DL, and roles, which correspond to relations and are interpreted as binary relations on objects. Figure 1 describes the kind of partitions that can be expressed using DL concepts and roles. In this figure the set A represents all the patients with a specific disease; for example type 1 Diabetes. The set B represents patients that are located in a specific place in the medical institution; for example, in the intensive care unit. Finally, set C represents all the patients with a medical intervention; for example glycated hemoglobin. The intention of these sets definition is to identify the set D that includes the patients that satisfy the characteristics of the three sets, because they are the ones who satisfy the rule of the guideline that declares that all the patients with diabetes in the intensive care unit should have glycated hemoglobin measured. EXEMED provides the necessary concepts and roles to express the sets of patients relevant for the definition of rules based on a clinical guideline. A rule is interpreted as the intersection of two or more sets. The next sections describe the main concepts included in EXEMED to describe these sets and its application.

Fig. 1. Patient Sets

3.1 EXEMED Concepts

EXEMED proposes the definition of a clinical guideline rule using a group of concepts. The concepts were identified through the analysis and evaluation of the rules and indicators derived from a sample of clinical guidelines and from sessions with medical doctors with four different medical specializations. Each concept can be used to 1450 Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455

define the characteristics of a set of patients. A set may be related to one concept or to several concepts. The concepts are explained in the following list.

1. Medical Intervention is the act, fact or method of interfering with the outcome or course of a medical condition. These concepts are specialized in Laboratory Test, Medical Education, Medicine Supply and Procedure. 2. Location is the place where a medical act, fact or method occurs. The values of location may vary, but typically they include intensive care unit, patient room, pediatric unit and surgical area, among others. 3. Risk Factor represents all the elements that can increase the likelihood of developing a disease. There are specializations of this concept like biological factor, race, unhealthy behavior, absence of protective behavior and age [22]. 4. Severity Level describes the categories that measure the relative impact of a disease on a patient. The level may vary according to the diagnosis, but in general they can be specialized in mild, moderate and severe level. 5. Adverse Event is an injury related to medical management. Medical management includes all aspects of care, including diagnosis and treatment, failure to diagnose or treat, and the systems and equipment used to deliver care. The concept can be specialized in different concepts like pain, fatigue, convulsion, altered mood, nausea and flushing, among others [23][24]. 6. Diagnosis is the identification of the possible patient disease or disorder. It was included because several clinical guidelines include rules associated with a secondary diagnosis; for example diabetes guidelines include rules related to hypertensive patients. 7. Phase during the attention of the patient. For example admission, hospitalization, hospital discharge, etc. 8. Antecedent is a relevant patient fact that occurs before and may be linked to subsequent events. Besides these concepts we define the concept Time that allows describing frequencies related to another concept.

3.2 EXEMED in OWL

In order to validate the soundness of EXEMED, the concepts were defined using an ontology editor called Protégé [27]. This editor allows us to evaluate the proposal and its capacity to be executed. Fig.2.a. illustrates the main concepts represented by classes. As it can be seen, there is an additional class called Patient (Fig.2.b.) whose specialization classes describe the set of patients that represent the clinical guideline rule. For instance, as shown in Fig.3., an Hospitalized Patient is a Patient and is in the specific Hospitalization phase.

Fig. 2. (a) Main concepts; (b) Patient Specialization. Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455 1451

Fig. 3. Patient Specialization for rule definition

All the sub-classes of Patient can be specialized to describe the kind of patient that is relevant for the guideline rule. In addition, rules (e.g. sub-classes) can be created related to a specific class or a specific individual of a class, for example, Fig. 4. presents and specialization of Antecedent, where the antecedent is an individual and not a class (e.g. hasValue restriction in owl). This is very important considering, that it will not be practical to define all the possible values as classes. For instance, all the ICD10 diseases as classes [26].

Fig. 4. Specialization using value type restrictions

In order to evaluate the executable capacity of EXEMED, we create a specialization of Patient called RequiredPatient with the following characteristics (restrictions):

x Hospitalized Patient x Patient with DiabetesMellitus Diagnosis x Patient has the value of MedicalIntervention glycatedHemoglobin. In order to validate the execution of this “rule” represented by the class RequiredPatient we create two individuals of the class Patient. One of them (patient1), with all the characteristics and the other one, with only one characteristic (patient2). Finally, the Fact++ reasoned [28] was executed to check the inference results. Fig. 5. illustrates the inference result for patient1 who has all the characteristics. As it is showed, it satisfices the class definition. It is important to notice that RequiredPatient class and the individuals are not part of EXEMED and were only created to validate the execution. In summary, EXEMED allows defining the rules related to a clinical guideline as specializations of the concept Patient with restrictions on the relationship with the eight proposed concepts. The information contained in EHR repositories can be loaded as individuals of the class Patient in order to validate the concordance of each patient treatment with the rules defined in the guideline. 1452 Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455

Fig. 5. Reasoner Execution Results

4. Application of EXEMED in a Case Study

In order to define rules derived from clinical practice guidelines, as it was described above, a case study was carried out in a Hospital of Bogotá, Colombia. This study case was supported by a prototype; different iterations were executed with the stakeholders who analyzed three different cases associated with different specializations. A group of medical doctors provided the rules in narrative texts, based on a specific guideline. Another group of medical doctors defined the rules using the concepts provided by EXEMED. Table 3, shows some examples that were proved in Gastrointestinal Bleeding, Chronic Kidney and Hipokalemia guidelines, those are sentences previously selected by the Hospital to evaluate the adherence to the guide. The information that belongs to each indicator was loaded in EXEMED, allowing the definition of structured restrictions. Once a restriction is obtained, it can be used to evaluate the adherence of an electronic health record to it. It is important to notice that Table 3 represents the pre-defined elements of EXEMED using italics, text using bold represents specialization of the elements, and text using bold and italics represents special properties related to the Time concept as follows:

Table 3. Examples based on Gastrointestinal Bleeding, Chronic Kidney and Hipokalemia guidelines. Case of study. Ex1 Ex2 Gastrointestinal All digestive tract endoscopy indicated Gastrointestinal endoscopy indicated bleeding Bleeding bleeding and which has undergone which has already undergone endoscopic endoscopic therapy. therapy in 6 months. RULE Diagnosis (Indicated bleeding) AND Medical intervention (Gastrointestinal Medical intervention (Procedures endoscopy) AND Diagnosis (Indicated undergone endoscopic therapy) bleeding) Medical intervention (Prescribed exams Medical intervention (Delivered exams endoscopic therapy) endoscopic therapy frequency once and period between 0 and 6 months ) Ex3 Ex4 All patients receiving IBP pre endoscopy All patients pre Receiving IBP by endoscopic for bleeding of non-variceal upper non-variceal upper gastrointestinal tract gastrointestinal tract without clear bleeding in 6 months indication RULE Diagnosis (bleeding of non-variceal Diagnosis (upper gastrointestinal tract Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455 1453

upper gastrointestinal) bleeding) Medical intervention (Prescribed exams Medical intervention (Prescribed exams IBP IBP pre endoscopy) pre endoscopy frequency once and period between 0 and 6 months) Chronic Kidney Ex1 Ex2 Disease Hydrochlorothiazide is contraindicated Metformin is an antihyperglycemic that should when creatinine clearance is less than 30 not be used when FG is less than 30 m² - 50 ml / min. mL/minute/1.73 or serum creatinine greater than 1.5 mg / dL in men and 1.4 mg / dl in women. RULE Medical Intervention (Prescribed exams Medical Intervention (Prescribed exams crFG creatinine < 30 - 50 ml / min.) < 30 m² mL/minute/1.73) OR (Prescribed exams (Creatinine > 1.5 mg/dl) AND Risk Factor (Gender Men)) OR (Prescribed exams (Creatinine > 1.4 mg/dl) AND Risk Factor (Gender Women)) Medical intervention Medical intervention (Prescribed Medicine (Prescribed Medicine Metformin) Hydrochlorothiazide) Ex3 Ex4 If potassium is greater than 5.5 meq/l or All patients with impaired renal function and impairment creatinine is greater than 30% had been initiated treatment with IECAS from baseline, the medicine should be inhibitors or ARAs II, must monitor serum discontinued. potassium and creatinine in a period of 5-7 days RULE IF (Medical intervention (Delivered IF Diagnosis (Impaired renal function) AND exams Potassium Measurement) AND Medical intervention (Delivered exams Medical intervention (Delivered exams IECAS or ARAs II) Potassium > 5.5meq/l)) OR (Medical intervention (Delivered exams Creatinine) AND Medical interventions (Delivered exams (Creatinine > 30%)) Medical intervention (Prescribed Medical intervention (Prescribed exams Medicine (None)) serum potassium) AND Medical Intervention (Prescribed exams creatinine) frequency once and period between 5 and 7 days) Hipokalemia Ex1 Ex2 Patients with mild hypokalemia to whom All patients with mild hypokalemia (between 3 it was controlled serum potassium level and 3.5 mEq / h) whom tolerate log VO begin in the following 24 hours. diet rich in potassium KCL. RULE Diagnosis (hypokalemia) and Severity Diagnosis (hypokalemia) and Severity Level Level (Mild) (Mild) Medical intervention (Delivered exams Medical intervention (Education potassium serum potassium) frequency once and KCL diet) period between 0 and 24 hours) Ex3 Ex4 Patients on parenteral furosemide who Patients with severe hypokalemia, which 1454 Alexandra Pomares Quimbaya et al. / Procedia Technology 16 ( 2014 ) 1446 – 1455

underwent measurement of serum conducted replenishment potassium through a potassium levels at least once every 48 central line hours during hospitalization RULE Phase (Hospital follow- up (Floor No. 5 Diagnosis (hypokalemia ) and Severity Level Hospitalization)) AND Medical (Severe) intervention (Delivered medicine parenteral furosemide) Medical intervention (Procedures serum Medical intervention (Procedures potassium frequency once and period Replenishment potassium ) between 0 and 48 hours)

The application of EXEMED using this case study allows us to validate its usefulness to describe different kind of rules derived from different types of diseases. It is important to highlight that during the initial execution of this study case it was necessary to assist the medical doctors who create the rules using the prototype; however, once they received the initial training, they improved their ability to create rules considerably.

5. Conclusions and Future work

This paper presents EXEMED, a knowledge base created for the definition of executable rules that can be applied over Electronic Health Records (EHR) in order to measure the EHR compliance with respect to a specific clinical guideline. EXEMED provides the concepts and roles needed to express the sets of elements relevant for the definition of rules based on a clinical guideline. The main concepts included in EXEMED are Medical Intervention, Location, Risk Factor, Severity Level, Adverse Event, Diagnosis, Phase, Antecedent and Time. The implementation of EXEMED using description logic provides flexibility to create any kind of rule and to extend it as needed. EXEMED proved to be useful to describe different kind of rules of different type of diseases in a case study on three diseases. EXEMED was designed for the evaluation of rules over electronic medical records. The main future work is to develop a software component able to extract and transform the information stored in electronic health record systems to load them on the knowledge base to evaluate they concordance with the defined rules.

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