School of Nursing, Columbia University, New York, New York, USA.
Department of Biomedical Informatics, Columbia University, New York, New York, USA.
J Am Med Inform Assoc. 2021 Mar 1;28(3):653-663. doi: 10.1093/jamia/ocaa296.
The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic health record data to assist nurses and physicians in prognostic and treatment decision making (ie, predictive CDSSs) in the hospital.
A systematic search of PubMed, CINAHL, and IEEE Xplore and hand-searching of relevant conference proceedings were conducted to identify eligible articles. Empirical studies of predictive CDSSs using electronic health record data for nurses or physicians in the hospital setting published in the last 5 years in peer-reviewed journals or conference proceedings were eligible for synthesis. Data from eligible studies regarding clinician involvement, stage in system design, predictive CDSS intention, and target clinician were charted and summarized.
Eighty studies met eligibility criteria. Clinical expert involvement was most prevalent at the beginning and late stages of system design. Most articles (95%) described developing and evaluating machine learning models, 28% of which described involving clinical experts, with nearly half functioning to verify the clinical correctness or relevance of the model (47%).
Involvement of clinical experts in predictive CDSS design should be explicitly reported in publications and evaluated for the potential to overcome predictive CDSS adoption challenges.
If present, clinical expert involvement is most prevalent when predictive CDSS specifications are made or when system implementations are evaluated. However, clinical experts are less prevalent in developmental stages to verify clinical correctness, select model features, preprocess data, or serve as a gold standard.
本研究旨在描述临床专家在开发、评估和实施利用机器学习分析电子健康记录数据以协助护士和医生进行预后和治疗决策的临床决策支持系统（CDSS）中的参与情况和性质，即预测性 CDSS）在医院中。
对 PubMed、CINAHL 和 IEEE Xplore 进行系统检索，并对手头相关会议论文集进行检索，以确定合格文章。合格的研究为使用电子健康记录数据为医院环境中的护士或医生开发的预测性 CDSS 的实证研究，在同行评议期刊或会议论文集中发表的过去 5 年的研究。对合格研究中关于临床医生参与度、系统设计阶段、预测性 CDSS 意图和目标临床医生的数据进行图表和总结。
80 项研究符合入选标准。临床专家的参与在系统设计的开始和后期阶段最为普遍。大多数文章（95%）描述了开发和评估机器学习模型，其中 28%描述了涉及临床专家，近一半的工作是验证模型的临床正确性或相关性（47%）。
在出版物中应明确报告预测性 CDSS 设计中临床专家的参与情况，并评估其克服预测性 CDSS 采用挑战的潜力。
如果存在，临床专家的参与在制定预测性 CDSS 规范或评估系统实施时最为普遍。然而，临床专家在验证临床正确性、选择模型特征、预处理数据或作为黄金标准方面的参与度较低。
