Clinical decision support alert malfunctions: analysis and empirically derived taxonomy
Clinical decision support alert malfunctions: analysis and empirically derived taxonomy
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DOI：
10.1093/jamia/ocx106
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发表时间：
2018-05-01
影响因子：
6.4
通讯作者：
Sittig, Dean F.
中科院分区：
文献类型：
--
作者：
Wright, Adam;Ai, Angela;Sittig, Dean F.
目的：为了开发一个临床决策支持（CDS）警报故障的经验性分类，材料和方法：我们使用定性和定量方法的混合来识别CDS警报故障：（1）现场访问与首席医疗信息官，CDS开发人员，临床领导人和CDS最终用户的访谈;（2）首席医疗信息官的调查;（3）CDS发射率的分析;（4）对CDS发射率的分析。（4）CDS覆盖分析。我们使用了多轮，手动，迭代卡排序开发一个多轴，经验得出的分类CDS faults.Results：我们分析了68 CDS警报故障的情况下，从14个网站在美国不同的电子健康记录系统。出现了四个主要的轴：故障的原因，它的发现模式，何时开始，以及它如何影响规则发射。构建错误、概念化错误和引入新概念或术语是最常见的原因。用户报告是主要的发现方式。我们的数据库中的许多故障导致规则对不应该有的患者（假阳性）进行触发，但相反的情况（假阴性）也很常见。挑战包括代码集和值的更新，系统升级时的软件问题，CDS内容在计算环境之间迁移的困难，以及正确概念化和构建CDS.Conclusion的挑战：CDS警报故障频繁。根据经验得出的分类法将导致这些故障的常见重复问题形式化，帮助CDS开发人员在CDS故障发生之前预测和预防CDS故障，或者方便地检测和解决它们。
Objective: To develop an empirically derived taxonomy of clinical decision support (CDS) alert malfunctions.Materials and Methods: We identified CDS alert malfunctions using a mix of qualitative and quantitative methods: (1) site visits with interviews of chief medical informatics officers, CDS developers, clinical leaders, and CDS end users; (2) surveys of chief medical informatics officers; (3) analysis of CDS firing rates; and (4) analysis of CDS overrides. We used a multi-round, manual, iterative card sort to develop a multi-axial, empirically derived taxonomy of CDS malfunctions.Results: We analyzed 68 CDS alert malfunction cases from 14 sites across the United States with diverse electronic health record systems. Four primary axes emerged: the cause of the malfunction, its mode of discovery, when it began, and how it affected rule firing. Build errors, conceptualization errors, and the introduction of new concepts or terms were the most frequent causes. User reports were the predominant mode of discovery. Many malfunctions within our database caused rules to fire for patients for whom they should not have (false positives), but the reverse (false negatives) was also common.Discussion: Across organizations and electronic health record systems, similar malfunction patterns recurred. Challenges included updates to code sets and values, software issues at the time of system upgrades, difficulties with migration of CDS content between computing environments, and the challenge of correctly conceptualizing and building CDS.Conclusion: CDS alert malfunctions are frequent. The empirically derived taxonomy formalizes the common recurring issues that cause these malfunctions, helping CDS developers anticipate and prevent CDS malfunctions before they occur or detect and resolve them expediently.