Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

作者：Mengting Ji, Georgi Z. Genchev, Hengye Huang, Xu Ting, Hui Lü, Guangjun Yu · 发表于：Journal of Medical Internet Research · 年份：2021 · DOI：10.2196/25929 · 被引用次数：60 · 研究领域：Artificial Intelligence in Healthcare and Education、Electronic Health Records Systems、Mobile Health and mHealth Applications

BACKGROUND: Clinical decision support systems are designed to utilize medical data, knowledge, and analysis engines and to generate patient-specific assessments or recommendations to health professionals in order to assist decision making. Artificial intelligence-enabled clinical decision support systems aid the decision-making process through an intelligent component. Well-defined evaluation methods are essential to ensure the seamless integration and contribution of these systems to clinical practice. OBJECTIVE: The purpose of this study was to develop and validate a measurement instrument and test the interrelationships of evaluation variables for an artificial intelligence-enabled clinical decision support system evaluation framework. METHODS: An artificial intelligence-enabled clinical decision support system evaluation framework consisting of 6 variables was developed. A Delphi process was conducted to develop the measurement instrument items. Cognitive interviews and pretesting were performed to refine the questions. Web-based survey response data were analyzed to remove irrelevant questions from the measurement instrument, to test dimensional structure, and to assess reliability and validity. The interrelationships of relevant variables were tested and verified using path analysis, and a 28-item measurement instrument was developed. Measurement instrument survey responses were collected from 156 respondents. RESULTS: =36.984; P=.08; comparative fit index 0.991; goodne...