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.

项目摘要 认知障碍（CI）的患病率预计到2050年将三倍，导致质量降低 生活，增加医疗保健利用以及已经压力很大的初级保健系统的额外负担。 许多临床医生缺乏评估，诊断和管理CI的信心，并且超过50％的CI患者 未被诊断。为了解决这些重要问题，在该项目的第1阶段（R61）中，我们开发了并 使用简短的迷你库屏幕的结果验证了称为MC-Plus的机器学习模型 通常在年度医疗保健健康检查和电子健康记录（EHR）数据中识别患者 未来CI诊断的风险升高。我们还开发了，验证和驾驶CI临床决策支持 （CI-CD）与患者和临床医生有关CI风险升高的对话，并给予临床医生 他们诊断和管理CI所需的信心和工具。 MC-Plus和CI-CDS系统都是 添加到现有的基于Web的CDS平台中，该平台具有高使用率和高级医疗临床医生 满意度已经与Epic EHR无缝集成。 我们目前正在开始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配给 接受CI-CD或通常的护理（UC）。我们将评估CI诊断和临床医生对 与UC诊所的诊所相比，CI-CDS诊所的提供商中的CI诊断和管理。如果成功， CI-CDS系统将提高新的CI诊断率和狭窄的现有社会人口统计学 与UC相比 诊所。 CI-CDS系统每年将在研究地点向200万患者使用，并有可能 通过基于Epic EHR建立的现有非商业化CDS平台进行更广泛的传播。然而， CI-CD的设计需要从我们既定的遗产Epic EHR管道更新和现代化到现代化 确保其稳健性，可持续性，互操作性和可扩展性向更大的社区传播。 拟议的赠款补充剂旨在参与我们的IT（信息技术），软件工程和 内部Epic EHR IT团队将CI-CD架构现代化，以增强其便携性，可扩展性和 通过以下步骤影响：a）将CI-CD迁移到OpenShift平台； b）转换其史诗般的EHR- 基于快速医疗保健的互操作性资源（FHIR）的申请计划的特定集成 接口（API）; c）重新构建其患者数据提取和人工智能（AI）推断 从基于批处理到实时模型的MC-Plus模型的管道。这些活动将有助于更广泛 通过允许将其集成到许多不同的EHR中，对工具的影响。