Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems.

Selecting Test Cases from the Electronic Health Record for Software Testing of Knowledge-Based Clinical Decision Support Systems. AMIA Annu Symp Proc. 2018;2018:1046-1055 Authors: Usman OA, Oshiro C, Chambers JG, Tu SW, Martins S, Robinson A, Goldstein MK Abstract Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing coverage while minimizing testing burden. ATHENA-CDS is a knowledge-based system that provides guideline-based recommendations for chronic medical conditions. Using the ATHENA-CDS diabetes knowledgebase, we demonstrate a generalizable approach for selecting test cases using rules/ filters to create a set of paths that mimics the system's logic. Test cases are allocated to paths using a proportion heuristic. Using data from the electronic health record, we found 1,086 cases with glycemic control above target goals. We created a total of 48 filters and 50 unique system paths, which were used to allocate 200 test cases. We show that our method generates a comprehensive set of test cases that provides adequate coverage for the testing of a knowledge-based CDS. PMID: 31019657 [PubMed - in process]
Source: AMIA Annual Symposium Proceedings - Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research