Development of a case-based system for grouping diagnoses in general practice

Abstract Introduction This article describes the development of EPICON; an application to group ICPC-coded diagnoses from electronic medical records in general practice into episodes of care. These episodes can be used to estimate prevalence and incidence rates. Methods We used data from 89 practice...

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Published inInternational journal of medical informatics (Shannon, Ireland) Vol. 77; no. 7; pp. 431 - 439
Main Authors Biermans, Marion C.J, de Bakker, Dinny H, Verheij, Robert A, Gravestein, Jan V, van der Linden, Michiel W, de Vries Robbé, Pieter F
Format Journal Article
LanguageEnglish
Published Ireland Elsevier Ireland Ltd 01.07.2008
Subjects
Artificial intelligence
Classification
Computerised medical record
Decision Support Systems, Clinical - organization & administration
Diagnosis-Related Groups - organization & administration
Epidemiology
Episode of care
Family practice
Family Practice - methods
Family Practice - organization & administration
Internal Medicine
Medical Records Systems, Computerized - organization & administration
Netherlands
Other
Reproducibility of Results
Sensitivity and Specificity
Netherlands
Episode of care
Epidemiology
Artificial intelligence
Computerised medical record
Classification
Family practice
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Summary:Abstract Introduction This article describes the development of EPICON; an application to group ICPC-coded diagnoses from electronic medical records in general practice into episodes of care. These episodes can be used to estimate prevalence and incidence rates. Methods We used data from 89 practices that participated in the Dutch National Survey of General Practice. Additionally, we held interviews with seven experts, and studied documentation to establish the requirements of the application and to develop the design. We then performed a formative evaluation by assessing incorrectly grouped diagnoses. Results EPICON is based on a combination of logical expressions, a decision table, and information extracted from individual cases by case-based reasoning. EPICON is able to group all diagnoses in the selected 89 practices, and groups 95% correctly. Conclusion The results cautiously indicate that EPICONs performance will probably be adequate for the purpose of estimating morbidity rates in general practice.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1386-5056
1872-8243
DOI:10.1016/j.ijmedinf.2007.08.002