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Quantifying System and Data Readiness for Automated Clinical Decision Support

量化系统和数据准备情况以支持自动化临床决策

基本信息

批准号：
9231578
负责人：
RACHEL L RICHESSON
金额：
$ 40.84万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-02 至 2019-08-31
项目状态：
已结题

项目摘要

ABSTRACT Automated clinical decision support (CDS) tools (e.g., provider alerts and reminders, or context relevant treatment information), embedded within electronic health record (EHR) systems, have been shown to improve provider compliance with practice guidelines and improve patient outcomes. The routine use of automated CDS is a fundamental component of the national healthcare reform strategy endorsed by the Centers for Medicare and Medicaid Services, the Office of the National Coordinator for Health IT, and two presidents. Once an organization identifies a clinical “practice gap” and corresponding CDS application, such as a provider alert, a number of technical and social issues must be addressed to ensure that the CDS intervention fits well in current work flows, is acceptable to providers, and functions as intended. At present, there is no guidance for potential CDS implementers on how to align their local data structures with the patient data “input” requirements of formal algorithm-based guidelines, nor is there a model to quantify the readiness of an organization or the resources that will be needed to integrate different CDS applications into local EHR systems. This proposed research will quantify the alignment of CDS data requirements (“inputs”) with EHR data structures, the quality of the data collected, and provider preferences. We propose to combine these metrics into a feasibility assessment for CDS implementation that can be used by organizations to prioritize CDS projects and by disease advocacy organizations and professional societies to identify CDS opportunities with the broadest potential for implementation and impact.

摘要自动化临床决策支持（CDS）工具（例如，提供商警报和提醒，或与上下文相关的治疗信息），嵌入电子健康记录（EHR）系统，已被证明可以改善提供者遵守实践指南并改善患者结果。自动化的日常使用 CDS是国家医疗保健改革战略的一个基本组成部分，医疗保险和医疗补助服务，国家卫生信息技术协调员办公室和两位总统。一旦组织确定了临床“实践差距”和相应的CDS应用程序，例如提供者但是，为了确保CDS干预措施能够很好地适用，必须解决一些技术和社会问题在当前的工作流程中，供应商可以接受，并按预期发挥作用。目前，潜在的CDS实施者如何将其本地数据结构与患者数据“输入”对齐正式的基于算法的准则的要求，也没有一个模型来量化的准备，将不同的CDS应用程序集成到本地EHR中所需的组织或资源系统.这项拟议的研究将量化CDS数据要求（“输入”）与EHR的一致性数据结构、所收集数据的质量以及提供商偏好。我们建议将这些联合收割机将指标转化为CDS实施的可行性评估，组织可以使用这些指标来确定优先级 CDS项目和疾病宣传组织和专业协会，以确定CDS机会具有最广泛的实施和影响潜力。