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

技术驱动的干预措施可改善认知障碍的早期检测和管理

• 批准号: 10838956
• 负责人: Leah R Hanson
• 金额: $ 23.7万
• 依托单位: HEALTHPARTNERS INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10838956
• 关键词: Address; Administrative Supplement; Adult; Advocate; Architecture; Artificial Intelligence; Attention; Award; Caregivers; Caring; Clinic; Clinical; Clinical Decision Support Systems; Code; Collaborations; Communities; Computer software; Custom; Data; Data Storage and Retrieval; Decision Support Model; Detection; Development; Diagnosis; Early Diagnosis; Ecosystem; Electronic Health Record; Ensure; Event; Fast Healthcare Interoperability Resources; Federally Qualified Health Center; Fostering; Future; Grant; Health Benefit; Health system; Impaired cognition; Information Technology; Intervention; Investments; Libraries; Marketing; Measures; Medical; Medicare; Modeling; Modernization; Monitor; Morphologic artifacts; Mumps; Office Visits; Online Systems; Parents; Patient Care; Patients; Performance; Persons; Phase; Prevalence; Primary Care; Private Sector; Process; Provider; Public Health Informatics; Public Sector; Publishing; Quality of Care; Quality of life; Randomized; Reaction Time; Recommendation; Retrieval; Risk; Science; Scientist; Services; Site; Software Engineering; Software Framework; Speed; Standardization; Stress; Technology; Testing; Time; Translating; United States Agency for Healthcare Research and Quality; Update; Visit; aged; application programming interface; care providers; care systems; clinical decision support; cost; design; directed attention; evidence base; improved; indexing; interoperability; machine learning method; machine learning model; migration; open data; operation; patient engagement; pilot test; portability; pragmatic trial; predictive modeling; primary care clinic; primary care clinician; primary care setting; randomized trial; real time model; repository; satisfaction; screening; sociodemographic disparity; success; support tools; tool; treatment as usual; vector

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.

项目概要 到 2050 年，认知障碍 (CI) 的患病率预计将增加两倍，导致认知质量下降 生活、医疗保健利用率的增加以及本已紧张的初级保健系统的额外负担。 许多临床医生对评估、诊断和管理 CI 缺乏信心，超过 50% 的 CI 患者 尚未确诊。为了解决这些重要问题，在该项目的第一阶段（R61）中，我们开发并 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 定期进行年度医疗保险健康检查和电子健康记录 (EHR) 数据，以识别患者 未来 CI 诊断的风险增加。我们还开发、验证并试点了 CI 临床决策支持 (CI-CDS) 系统，让患者和临床医生就 CI 风险升高进行对话，并为临床医生提供帮助 他们诊断和管理 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，该平台具有高使用率和高初级保健临床医生水平 满意度，并且已经与 Epic EHR 无缝集成。 我们目前正在开始第 2 阶段 (R33)，这是一项大型务实试验，有 30 个初级保健诊所随机分组 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 诊所的提供者相比，CI-CDS 诊所的提供者对 CI 进行诊断和管理。如果成功的话， 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范围 与 UC 相比，MC-PLUS 在指数访视时发现 CI-CDS 中 CI 风险升高的成年人存在差异 诊所。 CI-CDS 系统每年将向研究中心的 200 万患者提供服务，有潜力 通过基于 Epic EHR 构建的现有非商业化 CDS 平台进行更广泛的传播。然而， CI-CDS 设计需要从我们现有的传统 Epic EHR 管道进行更新和现代化 确保其稳健性、可持续性、互操作性和可扩展性，以便传播到更大的社区。 拟议的补助金旨在吸引我们的 IT（信息技术）、软件工程和 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 通过以下步骤产生影响： a) 将 CI-CDS 迁移到 OpenShift 平台； b) 转换其 Epic EHR- 与基于快速医疗互操作性资源 (FHIR) 的应用程序编程的特定集成 接口（API）； c) 重新构建患者数据提取和人工智能 (AI) 推理 我们的 MC-PLUS 模型从批量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 中来发挥该工具的影响。