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.

抽象的自动临床决策支持（CD）工具（例如提供者警报和提醒或上下文相关嵌入电子健康记录（EHR）系统中的治疗信息）已被证明可以改善提供者遵守实践指南并改善患者预后。自动化的常规使用 CDS是由中心认可的国家医疗改革战略的基本组成部分医疗保险和医疗补助服务，国家卫生IT协调员办公室，两位总统。一旦组织确定临床“实践差距”和相应的CD应用程序，例如提供商警报，必须解决许多技术和社会问题，以确保CD干预良好在当前的工作流中，提供者可以接受，并且按预期运作。目前，没有指导潜在的CDS实施者如何将其本地数据结构与患者数据“输入”保持一致基于算法的指南的要求，也没有模型来量化组织或将不同的CD应用程序集成到本地EHR所需的资源系统。这项拟议的研究将量化CD数据要求（“输入”）与EHR的对齐数据结构，收集的数据质量以及提供商的偏好。我们建议将这些结合起来指标对CD实施的可行性评估，组织可以使用该指标来确定优先级 CDS项目以及疾病倡导组织和专业社会，以识别CD的机会具有最广泛的实施和影响力。