Martins S B, Lai S, Tu S, Shankar R, Hastings S N, Hoffman B B, Dipilla N, Goldstein M K
GRECC, VA Palo Alto Health Care System, Palo Alto, CA, USA.
AMIA Annu Symp Proc. 2006;2006:539-43.
ATHENA-HTN is a clinical decision support system (CDSS) that delivers guideline-based patient-specific recommendations about hypertension management at the time of clinical decision-making. The ATHENA-HTN knowledge is stored in a knowledge-base (KB). Changes in best-practice recommendations require updates to the KB. We describe a method of offline testing to evaluate the accuracy of recommendations generated from the KB. A physician reviewed 100 test cases and made drug recommendations based on guidelines and the "Rules" (descriptions of encoded knowledge). These drug recommendations were compared to those generated by ATHENA-HTN. Nineteen drug-recommendation discrepancies were identified: ATHENA-HTN was more complete in generating recommendations (15); ambiguities in the Rules misled the physician (3); and content in the Rules was not encoded (1). Three new boundaries were identified. Three updates were made to the KB based on the results. The offline testing method was successful in identifying areas for KB improvement and led to improved accuracy of guideline-based recommendations.
ATHENA-HTN是一种临床决策支持系统（CDSS），它在临床决策时提供基于指南的针对特定患者的高血压管理建议。ATHENA-HTN的知识存储在知识库（KB）中。最佳实践建议的变化需要更新知识库。我们描述了一种离线测试方法，以评估从知识库生成的建议的准确性。一位医生审查了100个测试病例，并根据指南和“规则”（编码知识的描述）提出药物建议。将这些药物建议与ATHENA-HTN生成的建议进行比较。发现了19个药物建议差异：ATHENA-HTN在生成建议方面更完整（15个）；规则中的歧义误导了医生（3个）；规则中的内容未编码（1个）。确定了三个新的界限。根据结果对知识库进行了三次更新。离线测试方法成功地识别出知识库需要改进的领域，并提高了基于指南的建议的准确性。
