A Technology-Driven Intervention to Improve Early Detection and Management of Cognitive Impairment

项目摘要 到2050年，认知障碍（CI）的患病率预计将增加两倍，这将导致 增加的医疗保健利用率，以及对已经紧张的初级保健系统的额外负担。 许多临床医生对CI的评估、诊断和管理缺乏信心，超过50%的CI患者 未确诊。为了解决这些重要问题，在该项目的第1阶段（R61），我们开发了 使用完成的简短Mini-Cog屏幕的结果验证了名为MC-PLUS的机器学习模型 定期在年度医疗保险健康检查和电子健康记录（EHR）数据中识别患者， 未来CI诊断的风险增加。我们还开发、验证并试用了CI临床决策支持 （CI-CDS）系统，使患者和临床医生参与有关CI风险升高的对话，并为临床医生提供 诊断和管理CI所需的信心和工具。MC-PLUS和CI-CDS系统均 添加到现有的基于网络的CDS平台中，该平台具有高使用率和高初级保健临床医生 它已经与Epic EHR无缝集成。 我们目前正在开始第2阶段（R33），这是一项大型实用性试验，30个初级保健诊所随机分配到 接受CI-CDS或常规护理（UC）。我们将评估CI诊断和临床医生信心的变化， 与UC诊所相比，CI-CDS诊所中的提供者诊断和管理CI。如果成功， CI-CDS系统将提高新的CI诊断率，并缩小现有的社会人口统计学 在CI-CDS中，与UC相比，MC-PLUS在索引访视时确定的CI风险升高的成人的差异 诊所。 CI-CDS系统每年将在研究中心向200万例患者提供， 通过建立在Epic EHR上的现有非商业化CDS平台更广泛地传播。然而，在这方面， CI-CDS设计需要从我们已建立的传统Epic EHR管道更新和现代化， 确保其健壮性、可持续性、互操作性和可扩展性，以便向更大的社区传播。 建议的补助金旨在让我们的资讯科技、软件工程及 内部Epic EHR IT团队对CI-CDS架构进行现代化改造，以增强其可移植性、可扩展性和 通过以下步骤影响：a）将CI-CDS迁移到OpenShift平台; B）将其Epic EHR- 与基于快速医疗保健互操作性资源（FHIR）的应用程序编程的特定集成 接口（API）;以及c）重新架构其患者数据提取和人工智能（AI）推理 我们的MC-PLUS模型从基于批处理的模型到实时模型的管道。这些活动将促进更广泛的 通过允许集成到许多不同的EHR中来提高工具的影响力。

Project Summary The prevalence of cognitive impairment (CI) is expected to triple by 2050, contributing to decreased quality of life, increased medical care utilization, and additional burden on an already stressed primary care system. Many clinicians lack confidence to assess, diagnose and manage CI, and more than 50% of patients with CI are undiagnosed. To address these important problems, in phase 1 (R61) of this project, we developed and validated a machine learning model called MC-PLUS using results from brief Mini-Cog screens completed routinely at Annual Medicare Wellness exams and electronic health record (EHR) data to identify patients at elevated risk of a future CI diagnosis. We also developed, validated, and piloted a CI clinical decision support (CI-CDS) system to engage patients and clinicians in conversation about elevated CI risk, and to give clinicians the confidence and tools they need to diagnose and manage CI. Both MC-PLUS and the CI-CDS system were added into an existing web-based CDS platform that has high use rates and high primary care clinician satisfaction and is already seamlessly integrated with the Epic EHR. We are currently beginning phase 2 (R33), a large pragmatic trial with 30 primary care clinics randomized to receive CI-CDS or usual care (UC). We will evaluate the change in CI diagnosis and clinician confidence in diagnosing and managing CI among providers in CI-CDS clinics compared to those in UC clinics. If successful, the CI-CDS system will improve rates of new CI diagnosis and narrow existing sociodemographic disparities for adults with elevated CI risk identified by MC-PLUS at index visits in CI-CDS compared to UC clinics. The CI-CDS system will be available to 2 million patients annually at the study sites with the potential to disseminate more broadly through the existing non-commercialized CDS platform built on Epic EHR. However, the CI-CDS design needs to be updated and modernized from our established legacy Epic EHR pipeline to ensure its robustness, sustainability, interoperability, and scalability for dissemination to the larger community. The proposed grant supplement aims to engage our IT (Information Technology), software engineering and internal Epic EHR IT teams to modernize the CI-CDS architecture to enhance its portability, scalability and impact through the following steps: a) migrating CI-CDS to the OpenShift platform; b) converting its Epic EHR- specific integration to Fast Healthcare Interoperability Resources (FHIR)-based application programming interfaces (APIs); and c) re-architecting its patient data extraction and artificial intelligence (AI) inference pipeline for our MC-PLUS model from batch-based to a real-time model. These activities will facilitate broader impact of the tool by allowing integration into many different EHRs.