McCoy Allison B, Thomas Eric J, Krousel-Wood Marie, Sittig Dean F
Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA ; Center for Health Research, Ochsner Clinic Foundation, New Orleans, LA.
Department of Internal Medicine, University of Texas Medical School at Houston, Houston, TX ; The University of Texas at Houston-Memorial Hermann Center for Healthcare Quality and Safety, Houston, TX.
Ochsner J. 2014 Summer;14(2):195-202.
Many healthcare providers are adopting clinical decision support (CDS) systems to improve patient safety and meet meaningful use requirements. Computerized alerts that prompt clinicians about drug-allergy, drug-drug, and drug-disease warnings or provide dosing guidance are most commonly implemented. Alert overrides, which occur when clinicians do not follow the guidance presented by the alert, can hinder improved patient outcomes.
We present a review of CDS alerts and describe a proposal to develop novel methods for evaluating and improving CDS alerts that builds upon traditional informatics approaches. Our proposal incorporates previously described models for predicting alert overrides that utilize retrospective chart review to determine which alerts are clinically relevant and which overrides are justifiable.
Despite increasing implementations of CDS alerts, detailed evaluations rarely occur because of the extensive labor involved in manual chart reviews to determine alert and response appropriateness. Further, most studies have solely evaluated alert overrides that are appropriate or justifiable. Our proposal expands the use of web-based monitoring tools with an interactive dashboard for evaluating CDS alert and response appropriateness that incorporates the predictive models. The dashboard provides 2 views, an alert detail view and a patient detail view, to provide a full history of alerts and help put the patient's events in context.
The proposed research introduces several innovations to address the challenges and gaps in alert evaluations. This research can transform alert evaluation processes across healthcare settings, leading to improved CDS, reduced alert fatigue, and increased patient safety.
许多医疗服务提供者正在采用临床决策支持(CDS)系统来提高患者安全并满足有意义使用要求。最常见的是实施计算机化警报,提示临床医生注意药物过敏、药物相互作用、药物疾病警告或提供给药指导。当临床医生不遵循警报给出的指导时发生的警报覆盖,可能会阻碍患者预后的改善。
我们对CDS警报进行了综述,并描述了一项基于传统信息学方法开发评估和改进CDS警报新方法的提议。我们的提议纳入了先前描述的预测警报覆盖的模型,这些模型利用回顾性病历审查来确定哪些警报具有临床相关性以及哪些覆盖是合理的。
尽管CDS警报的实施越来越多,但由于人工病历审查以确定警报和响应的适当性涉及大量劳动,很少进行详细评估。此外,大多数研究仅评估了适当或合理的警报覆盖。我们的提议扩大了基于网络的监测工具的使用,通过一个交互式仪表板来评估CDS警报和响应的适当性,该仪表板纳入了预测模型。该仪表板提供两种视图,警报详细信息视图和患者详细信息视图,以提供警报的完整历史记录,并帮助将患者的事件置于背景中。
拟议的研究引入了多项创新,以应对警报评估中的挑战和差距。这项研究可以改变整个医疗环境中的警报评估流程,从而改进CDS,减少警报疲劳,并提高患者安全性。