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A Method for Constructing a New Extensible Nomenclature for Clinical Coding Practices in Sub-Saharan Africa
MEDINFO 2017: Precision Healthcare through Informatics 965 A.V. Gundlapalli et al. (Eds.) © 2017 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/978-1-61499-830-3-965 A Method for Constructing a New Extensible Nomenclature for Clinical Coding Practices in Sub-Saharan Africa Sven Van Laere, Marc Nyssen, Frank Verbeke Department of Public Health (GEWE), Research Group of Biostatistics and Medical Informatics (BISI), Vrije Universiteit Brussel (VUB), Laarbeeklaan 103, 1090 Brussels, Belgium Abstract The word nomenclature has to be used with care in the context Clinical coding is a requirement to provide valuable data for of sub-Saharan Africa. By the word “nomenclature”, usually billing, epidemiology and health care resource allocation. In they understand: “a coding system to provide codes that are sub-Saharan Africa, we observe a growing awareness of the used in invoices, to specify the health services that were pro- need for coding of clinical data, not only in health insurances, vided for particular patients”. We define nomenclature in a but also in governments and the hospitals. Presently, coding broader way, also taking into account the clinical definition of systems in sub-Saharan Africa are often used for billing pur- health services. poses. In this paper we consider the use of a nomenclature to In this paper we will present a way to build this new nomen- also have a clinical impact. -
A Hybrid Static Type Inference Framework with Neural
HiTyper: A Hybrid Static Type Inference Framework with Neural Prediction Yun Peng Zongjie Li Cuiyun Gao∗ The Chinese University of Hong Kong Harbin Institute of Technology Harbin Institute of Technology Hong Kong, China Shenzhen, China Shenzhen, China [email protected] [email protected] [email protected] Bowei Gao David Lo Michael Lyu Harbin Institute of Technology Singapore Management University The Chinese University of Hong Kong Shenzhen, China Singapore Hong Kong, China [email protected] [email protected] [email protected] ABSTRACT also supports type annotations in the Python Enhancement Pro- Type inference for dynamic programming languages is an impor- posals (PEP) [21, 22, 39, 43]. tant yet challenging task. By leveraging the natural language in- Type prediction is a popular task performed by most attempts. formation of existing human annotations, deep neural networks Traditional static type inference techniques [4, 9, 14, 17, 36] and outperform other traditional techniques and become the state-of- type inference tools such as Pytype [34], Pysonar2 [33], and Pyre the-art (SOTA) in this task. However, they are facing some new Infer [31] can predict sound results for the variables with enough challenges, such as fixed type set, type drift, type correctness, and static constraints, e.g., a = 1, but are unable to handle the vari- composite type prediction. ables with few static constraints, e.g. most function arguments. To mitigate the challenges, in this paper, we propose a hybrid On the other hand, dynamic type inference techniques [3, 37] and type inference framework named HiTyper, which integrates static type checkers simulate the workflow of functions and solve types inference into deep learning (DL) models for more accurate type according to input cases and typing rules. -
Healthcare Terminologies and Classifications
Healthcare Terminologies and Classifications: An Action Agenda for the United States American Medical Informatics Association and American Health Information Management Association Terminology and Classification Policy Task Force Acknowledgements AHIMA and AMIA Terminology and Classification Policy Task Force Members Keith E. Campbell, MD, PhD The American Health Chair, AHIMA and AMIA Terminologies and Classifications Policy Task Force Information Management Chief Technology Officer, Informatics, Inc., and Assistant Clinical Professor; Association (AHIMA) is the Department of Medical Informatics and Clinical Epidemiology, Oregon Health and premier association of health Science University information management Suzanne Bakken, RN, DNSc, FAAN (HIM) professionals. AHIMA’s Alumni Professor of Nursing and Professor of Biomedical Informatics School of 51,000 members are dedicated to Nursing and Department of Medical Informatics, Columbia University the effective management of personal health information Sue Bowman, RHIA, CCS needed to deliver quality Director of Coding Policy and Compliance, American Health Information healthcare to the public. Management Association Founded in 1928 to improve the quality of medical records, Christopher Chute, MD, PhD AHIMA is committed to Professor and Chair of Biomedical Informatics, Mayo Foundation advancing the HIM profession in an increasingly electronic and Don Detmer, MD, MA President and Chief Executive Officer, American Medical Informatics Association global environment through leadership in advocacy, Jennifer Hornung Garvin, PhD, RHIA, CPHQ, CCS, CTR, FAHIMA education, certification, and Medical Informatics Postdoctoral Fellow Center for Health Equity Research and lifelong learning. To learn more, Promotion, Philadelphia Veterans Administration Medical Center go to www.ahima.org. Kathy Giannangelo, MA, RHIA, CCS, CPHIMS Director, Practice Leadership, AHIMA Gail Graham, RHIA The American Medical Director, Health Data and Informatics Department of Veterans Affairs Informatics Association (AMIA) Stanley M. -
ACDT: Architected Composite Data Types Trading-In Unfettered Data Access for Improved Execution
ACDT: Architected Composite Data Types Trading-in Unfettered Data Access for Improved Execution Andres Marquez∗, Joseph Manzano∗, Shuaiwen Leon Song∗, Benoˆıt Meistery Sunil Shresthaz, Thomas St. Johnz and Guang Gaoz ∗Pacific Northwest National Laboratory fandres.marquez,joseph.manzano,[email protected] yReservoir Labs [email protected] zUniversity of Delaware fshrestha,stjohn,[email protected] Abstract— reduction approaches associated with improved data locality, obtained through optimized data and computation distribution. With Exascale performance and its challenges in mind, one ubiquitous concern among architects is energy efficiency. In the SW-stack we foresee the runtime system to have a Petascale systems projected to Exascale systems are unsustainable particular important role to contribute to the solution of the at current power consumption rates. One major contributor power challenge. It is here where the massive concurrency is to system-wide power consumption is the number of memory managed and where judicious data layouts [11] and data move- operations leading to data movement and management techniques ments are orchestrated. With that in mind, we set to investigate applied by the runtime system. To address this problem, we on how to improve efficiency of a massively multithreaded present the concept of the Architected Composite Data Types adaptive runtime system in managing and moving data, and the (ACDT) framework. The framework is made aware of data trade-offs an improved data management efficiency requires. composites, assigning them a specific layout, transformations Specifically, in the context of run-time system (RTS), we and operators. Data manipulation overhead is amortized over a explore the power efficiency potential that data compression larger number of elements and program performance and power efficiency can be significantly improved. -
Evaluating Variability Modeling Techniques for Supporting Cyber-Physical System Product Line Engineering
This paper will be presented at System Analysis and Modeling (SAM) Conference 2016 (http://sdl-forum.org/Events/SAM2016/index.htm) Evaluating Variability Modeling Techniques for Supporting Cyber-Physical System Product Line Engineering Safdar Aqeel Safdar 1, Tao Yue1,2, Shaukat Ali1, Hong Lu1 1Simula Research Laboratory, Oslo, Norway 2 University of Oslo, Oslo, Norway {safdar, tao, shaukat, honglu}@simula.no Abstract. Modern society is increasingly dependent on Cyber-Physical Systems (CPSs) in diverse domains such as aerospace, energy and healthcare. Employing Product Line Engineering (PLE) in CPSs is cost-effective in terms of reducing production cost, and achieving high productivity of a CPS development process as well as higher quality of produced CPSs. To apply CPS PLE in practice, one needs to first select an appropriate variability modeling technique (VMT), with which variabilities of a CPS Product Line (PL) can be specified. In this paper, we proposed a set of basic and CPS-specific variation point (VP) types and modeling requirements for proposing CPS-specific VMTs. Based on the proposed set of VP types (basic and CPS-specific) and modeling requirements, we evaluated four VMTs: Feature Modeling, Cardinality Based Feature Modeling, Common Variability Language, and SimPL (a variability modeling technique dedicated to CPS PLE), with a real-world case study. Evaluation results show that none of the selected VMTs can capture all the basic and CPS-specific VP and meet all the modeling requirements. Therefore, there is a need to extend existing techniques or propose new ones to satisfy all the requirements. Keywords: Product Line Engineering, Variability Modeling, and Cyber- Physical Systems 1 Introduction Cyber-Physical Systems (CPSs) integrate computation and physical processes and their embedded computers and networks monitor and control physical processes by often relying on closed feedback loops [1, 2]. -
UML Profile for Communicating Systems a New UML Profile for the Specification and Description of Internet Communication and Signaling Protocols
UML Profile for Communicating Systems A New UML Profile for the Specification and Description of Internet Communication and Signaling Protocols Dissertation zur Erlangung des Doktorgrades der Mathematisch-Naturwissenschaftlichen Fakultäten der Georg-August-Universität zu Göttingen vorgelegt von Constantin Werner aus Salzgitter-Bad Göttingen 2006 D7 Referent: Prof. Dr. Dieter Hogrefe Korreferent: Prof. Dr. Jens Grabowski Tag der mündlichen Prüfung: 30.10.2006 ii Abstract This thesis presents a new Unified Modeling Language 2 (UML) profile for communicating systems. It is developed for the unambiguous, executable specification and description of communication and signaling protocols for the Internet. This profile allows to analyze, simulate and validate a communication protocol specification in the UML before its implementation. This profile is driven by the experience and intelligibility of the Specification and Description Language (SDL) for telecommunication protocol engineering. However, as shown in this thesis, SDL is not optimally suited for specifying communication protocols for the Internet due to their diverse nature. Therefore, this profile features new high-level language concepts rendering the specification and description of Internet protocols more intuitively while abstracting from concrete implementation issues. Due to its support of several concrete notations, this profile is designed to work with a number of UML compliant modeling tools. In contrast to other proposals, this profile binds the informal UML semantics with many semantic variation points by defining formal constraints for the profile definition and providing a mapping specification to SDL by the Object Constraint Language. In addition, the profile incorporates extension points to enable mappings to many formal description languages including SDL. To demonstrate the usability of the profile, a case study of a concrete Internet signaling protocol is presented. -
Data Types and Variables
Color profile: Generic CMYK printer profile Composite Default screen Complete Reference / Visual Basic 2005: The Complete Reference / Petrusha / 226033-5 / Chapter 2 2 Data Types and Variables his chapter will begin by examining the intrinsic data types supported by Visual Basic and relating them to their corresponding types available in the .NET Framework’s Common TType System. It will then examine the ways in which variables are declared in Visual Basic and discuss variable scope, visibility, and lifetime. The chapter will conclude with a discussion of boxing and unboxing (that is, of converting between value types and reference types). Visual Basic Data Types At the center of any development or runtime environment, including Visual Basic and the .NET Common Language Runtime (CLR), is a type system. An individual type consists of the following two items: • A set of values. • A set of rules to convert every value not in the type into a value in the type. (For these rules, see Appendix F.) Of course, every value of another type cannot always be converted to a value of the type; one of the more common rules in this case is to throw an InvalidCastException, indicating that conversion is not possible. Scalar or primitive types are types that contain a single value. Visual Basic 2005 supports two basic kinds of scalar or primitive data types: structured data types and reference data types. All data types are inherited from either of two basic types in the .NET Framework Class Library. Reference types are derived from System.Object. Structured data types are derived from the System.ValueType class, which in turn is derived from the System.Object class. -
Health & Coding Terminological Systems
Health & Coding Terminological Systems Hamideh Sabbaghi, MS Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Definition of Code Code is considered as word, letter, or number which has been used as passwords instead of words or phrases. Coding System 1. Classification system 2. Nomenclature system Classification System A well-known system of corrections for naming different stages of the disease. Nomenclature System A medical list of a specific system of preferred terms for naming disease. WHO- FIC • Main/reference classification • Related classifications to the main/ reference classification • Driven classification from the main/ reference classification Main/reference classification • ICD- 10 (1990, WHA) • ICD- 11 (2007, WHO) • http://who.int/classifications/apps/icd/icd10online/ ICD with a commonest usage can be used for: • Identification of health trend, mortality and morbidity statistics. • Clinical and health research purposes. Main/reference classification • ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) • ICF (International Classification of Function, Disability and Health) • ICHI (International Classification of Health Intervention) Related classifications to the main/ reference classification • ATC/DDD (The Anatomical Therapeutic chemical classification system with Defined Daily Doses) • ATCvet (The Anatomical Therapeutic Chemical Classification System for veterinary medicinal products) • ICECI (International Classification of External Causes -
Using LOINC with SNOMED CT 3 April 2017
Using LOINC with SNOMED CT 3 April 2017 Latest web browsable version: http://snomed.org/loinc SNOMED CT Document Library: http://snomed.org/doc 1. Using LOINC with SNOMED CT . 3 1 Introduction . 4 2 Short Introduction to SNOMED CT . 6 2.1 Features of SNOMED CT . 6 2.2 Benefits of SNOMED CT . 7 3 Short Introduction to LOINC . 9 3.1 Features of LOINC . 9 3.2 Benefits of LOINC . 13 4 Cooperative Work . 15 4.1 Cooperative Work Overview . 15 4.2 Release File Specifications . 16 4.2.1 LOINC Part map reference set . 16 4.2.2 LOINC Term to Expression Reference Set . 18 4.3 Benefits of Products of the Cooperative Work . 21 5 Guidance on Use of SNOMED CT and LOINC Together . 22 5.1 Use of SNOMED CT and/or LOINC in . 22 5.2 Practical Guidance on Uses of SNOMED CT and LOINC . 23 5.2.1 Guideline A - Vital Signs (Observation Names and Values) . 23 5.2.2 Guideline B - Laboratory Orders . 24 5.2.3 Guideline C - Laboratory Test Results (Observation Names and Values) . 26 5.2.4 Guideline D - Specimens . 30 5.2.5 Guideline E - Animal Species and Breeds . 32 5.2.6 Guideline F - Procedures (Laboratory Methods) . 33 5.2.7 Practical Uses of Part Maps and Expression Associations . 34 5.2 Terminology Scenarios - Summary . 40 6 References . 40 Using LOINC with SNOMED CT (3 April 2017) Using LOINC with SNOMED CT The Guide to Use of SNOMED CT and LOINC together provides advice on combined use of SNOMED CT and LOINC. -
DRAFT1.2 Methods
HL7 v3.0 Data Types Specification - Version 0.9 Table of Contents Abstract . 1 1 Introduction . 2 1.1 Goals . 3 DRAFT1.2 Methods . 6 1.2.1 Analysis of Semantic Fields . 7 1.2.2 Form of Data Type Definitions . 10 1.2.3 Generalized Types . 11 1.2.4 Generic Types . 12 1.2.5 Collections . 15 1.2.6 The Meta Model . 18 1.2.7 Implicit Type Conversion . 22 1.2.8 Literals . 26 1.2.9 Instance Notation . 26 1.2.10 Typus typorum: Boolean . 28 1.2.11 Incomplete Information . 31 1.2.12 Update Semantics . 33 2 Text . 36 2.1 Introduction . 36 2.1.1 From Characters to Strings . 36 2.1.2 Display Properties . 37 2.1.3 Encoding of appearance . 37 2.1.4 From appearance of text to multimedial information . 39 2.1.5 Pulling the pieces together . 40 2.2 Character String . 40 2.2.1 The Unicode . 41 2.2.2 No Escape Sequences . 42 2.2.3 ITS Responsibilities . 42 2.2.4 HL7 Applications are "Black Boxes" . 43 2.2.5 No Penalty for Legacy Systems . 44 2.2.6 Unicode and XML . 47 2.3 Free Text . 47 2.3.1 Multimedia Enabled Free Text . 48 2.3.2 Binary Data . 55 2.3.3 Outstanding Issues . 57 3 Things, Concepts, and Qualities . 58 3.1 Overview of the Problem Space . 58 3.1.1 Concept vs. Instance . 58 3.1.2 Real World vs. Artificial Technical World . 59 3.1.3 Segmentation of the Semantic Field . -
Unlocking the Mystery of Third Party Reimbursement in Kinesiotherapy
Unlocking the Mystery of Third Party Reimbursement In Kinesiotherapy Provided by the American Kinesiotherapy Association Written by Joanne Byron, LPN, BSNH, CHA, CMC, CPC, MCMC, PCS Edited & Contributions made by Carl Byron, III, ATC-L, EMT-I, CHA, CPC, MCMC Health Care Consulting Services, Inc. PO Box 572 Medina, Ohio 44258-0572 www.hccsincorp.com Page 2 Table of Contents Page Introduction – Bridging the Gap between KT and Payor 3 Key Elements to Reimbursement 4 Provider Enrollment 5 State of Health Insurance 9 Medicare Benefit Policy Manual 10 Reimbursement Basics 12 Documentation 13 Appropriate Coding 14 Procedure Coding – a 2 Tier System 15 CPT Assistant 16 Diagnosis Code Assignment ICD-9-CM 21 ICD-10-CM 22 Understanding the Health Insurance Industry 26 Types of insurance and reimbursement methodology 26 Managed Care Contracting Considerations 30 Administrative Simplification 33 NCQA 37 Managed Care Terminology (terms & definitions from HHS) 38 Provided by the American Kinesiotherapy Association (AKTA) & Written by Health Care Consulting Services, Inc. (HCCS) Copyright © 2010 AKTA & HCCS, All Rights Reserved This document is not intended to provide legal or consulting advice. It is for educational purposes only. Please retain an attorney or consultant for specific information for your practice. Page 3 Introduction Bridging the gap between KT and payer “How do we get paid?” . A challenging question every health care provider faces, regardless of specialty and health insurance company. The purpose of this reimbursement manual is to provide a basic understanding of health care reimbursement and compliance prior to filing claims for Kinesiotherapy services. Physical medicine budgets are getting ever tighter, demands on rehab specialists’ time and skills are increasing. -
Clinical Vocabularies for Global Real World Evidence (RWE) | Evidera
Clinical Vocabularies for Global RWE Analysis Don O’Hara, MS Senior Research Associate, Real-World Evidence, Evidera Vernon F. Schabert, PhD Senior Research Scientist, Real-World Evidence, Evidera Introduction significant volume of real-world evidence (RWE) analyses continue to be conducted with data A repurposed from healthcare administrative Don O’Hara Vernon F. Schabert databases. The range of sources represented by those databases has grown in response to demand for richer description of patient health status and outcomes. information for improved quality and coordination Data availability, including the range of available data of care, often using standardized messaging systems sources, has grown unevenly across the globe in response such as Health Level 7 (HL7). These messages are to country-specific market and regulatory dynamics. only as good as the standardization of codes between Nonetheless, as demand globalizes for RWE insights from message senders and receivers, which motivates the databases, those demands increase pressure on analysts encoding of facts using common code sets. The second to find ways to bridge differences between local data is the increased availability of common data models to sources to achieve comparable insights across regions. standardize the extraction and analysis of these data for RWE and drug safety purposes. While common data One of the challenges in bridging differences across models make compromises on the structure of tables databases is the codes used to represent key clinical and fields extracted from healthcare systems such as facts. Historically, RWE database studies have leveraged electronic medical records (EMR) and billing systems, local code sets for cost-bearing healthcare services they can improve consistency and replicability of analyses such as drugs, procedures, and laboratory tests.