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.

项目总结