Usman Omar A, Oshiro Connie, Chambers Justin G, Tu Samson W, Martins Susana, Robinson Amy, Goldstein Mary K
VA Palo Alto Health Care System, Palo Alto, CA.
Stanford University, Stanford, CA.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1046-1055. eCollection 2018.
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.
基于知识的临床决策支持系统的软件测试具有挑战性、劳动强度大且成本高昂；然而，由于临床应用的后果更为严重，所以测试是必要的。精心挑选测试用例可提高测试覆盖率，同时将测试负担降至最低。ATHENA-CDS是一个基于知识的系统，可为慢性疾病提供基于指南的建议。利用ATHENA-CDS糖尿病知识库，我们展示了一种通用方法，即使用规则/过滤器来选择测试用例，以创建一组模仿系统逻辑的路径。测试用例通过比例启发式方法分配到各个路径。利用电子健康记录中的数据，我们发现了1086例血糖控制高于目标值的病例。我们总共创建了48个过滤器和50条独特的系统路径，用于分配200个测试用例。我们表明，我们的方法生成了一组全面的测试用例，可为基于知识的CDS测试提供充分的覆盖。
