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: 10685809
• 负责人: Leah R Hanson
• 金额: $ 120.05万
• 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/10685809
• 关键词: Accident and Emergency department; Address; Adult; Advocate; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Attention; Caregivers; Caring; Chronic Disease; Clinic; Clinical; Clinical Decision Support Systems; Data; Decision Making; Dementia; Detection; Diabetes Mellitus; Diagnosis; Distress; Documentation; Early Diagnosis; Electronic Health Record; Ethnic Origin; Family; Feeling; Foundations; Future; Gender; Guidelines; Health Benefit; Health Expenditures; Impaired cognition; Individual; Inpatients; Intervention; Interview; Investments; Machine Learning; Medical; Medicare; Newly Diagnosed; Office Visits; Online Systems; Patient Care Planning; Patient-Focused Outcomes; Patients; Persons; Phase; Predictive Value; Prevalence; Primary Health Care; Printing; Private Sector; Public Health Informatics; Public Sector; Publishing; Quality of life; Race; Randomized; Readiness; Recommendation; Reporting; Risk; Socioeconomic Status; Specialist; Standardization; Stress; Suggestion; Surveys; System; Technology; Telephone; Theory of Change; Translating; Visit; Work; base; behavior change; cardiovascular risk factor; care systems; clinical decision support; cost; dementia risk; evidence base; follow-up; health care service utilization; improved; improved outcome; indexing; intervention effect; machine learning method; machine learning model; mild cognitive impairment; personalized care; pilot test; pragmatic trial; predictive modeling; prototype; randomized trial; response; satisfaction; screening; sociodemographic disparity; success; support tools; tool; treatment as usual

Project Summary The prevalence of Alzheimer’s disease (AD) and AD-related dementias (ADRD) 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 cognitive impairment (CI), and more than 50% of patients with CI are undiagnosed. Unfortunately, studies show that even in settings with high rates of standardized CI screening, very few patients who screen positive have documentation of any clinician follow-up action. To address these important problems, in phase 1 (R61) of this project, we will develop and validate a machine learning model (called MC-PLUS) using results from brief Mini- Cog (MC) screens completed routinely at Annual Medicare Wellness exams and electronic health record (EHR) data to identify patients at elevated risk of a future dementia diagnosis (AD/ADRD). We will also develop and validate a web-based and EHR-integrated CI clinical decision support (CI-CDS) system to engage patients and clinicians in conversation about elevated dementia risk, and to give clinicians the confidence and tools they need to diagnose and manage CI. Both MC-PLUS and the CI-CDS system will be added into an existing web-based CDS platform that has high use rates and primary care clinician satisfaction, and is already seamlessly integrated within the EHR. This CDS platform improves outcomes for patients with chronic diseases such as diabetes and high cardiovascular risk as shown in published studies. We will systematically validate the CI-CDS system with expert champions prior to conducting a pilot test at one primary care clinic. After milestones for success are demonstrated, we will begin phase 2 (R33), a large pragmatic trial with 30 primary care clinics randomized to receive CI-CDS or usual care (UC). We will evaluate change in clinician confidence in CI detection and care management in CI-CDS compared to UC clinics. If successful, the CI-CDS system will improve rates of new CI diagnosis and narrow existing sociodemographic disparities in adults with elevated dementia risk identified by MC-PLUS at index visit in CI-CDS compared to UC clinics. We will evaluate the impact of the intervention on care management and care plans using EHR data and chart audits. We will assess determinants of clinician actions in response to the CDS system using behavior change theory and technology acceptance constructs, and conduct phone surveys of patient and caregiver dyads to evaluate intervention effects on feelings of preparedness for decision making and distress. The CI-CDS system is immediately scalable to large numbers of patients through the existing non-commercialized CDS platform already in use for millions of patients in care systems spanning 14 states. The CDS system implemented as described could maximize return on massive investments that have been made in EHR systems, and provide a prototype to rapidly and consistently translate evolving evidence-based CI guidelines into personalized CI care and guidance within primary care.

项目概要 到 2050 年，阿尔茨海默病 (AD) 和 AD 相关痴呆症 (ADRD) 的患病率预计将增加两倍， 导致生活质量下降，医疗保健利用率增加，并给已经存在的人带来额外负担 强调初级保健系统。许多临床医生缺乏评估、诊断和管理认知能力的信心 损伤（CI），超过 50% 的 CI 患者未被诊断。不幸的是，研究表明 即使在标准化 CI 筛查率较高的环境中，筛查呈阳性的患者也很少 任何临床医生后续行动的记录。为了解决这些重要问题，在本计划的第一阶段 (R61) 项目中，我们将使用简短的 Mini- 的结果来开发和验证机器学习模型（称为 MC-PLUS） 在年度医疗保险健康检查和电子健康记录中例行完成 Cog (MC) 筛查 (EHR) 数据，用于识别未来痴呆症诊断 (AD/ADRD) 风险较高的患者。我们还将开发 并验证基于网络和 EHR 集成的 CI 临床决策支持 (CI-CDS) 系统以吸引患者 和临床医生讨论痴呆症风险升高的问题，并为临床医生提供信心和工具 他们需要诊断和管理 CI。 MC-PLUS 和 CI-CDS 系统都将添加到现有的 基于网络的 CDS 平台，具有较高的使用率和初级保健临床医生满意度，并且已经 无缝集成到 EHR 中。该 CDS 平台可改善慢性病患者的治疗结果 已发表的研究表明，糖尿病和高心血管风险等疾病。我们将系统地 在一家初级保健诊所进行试点测试之前，与专家一起验证 CI-CDS 系统。 在展示成功的里程碑后，我们将开始第 2 阶段 (R33)，这是一项大型务实试验，涉及 30 初级保健诊所随机接受 CI-CDS 或常规护理 (UC)。我们将评估临床医生的变化 与 UC 诊所相比，CI-CDS 对 CI 检测和护理管理的信心更高。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差异 与 UC 诊所相比，CI-CDS 初次访视时 MC-PLUS 发现痴呆症风险较高的成年人。我们 将使用 EHR 数据和图表评估干预措施对护理管理和护理计划的影响 审计。我们将使用行为改变来评估临床医生针对 CDS 系统采取的行动的决定因素 理论和技术接受度构建，并对患者和护理人员进行电话调查，以 评估干预措施对决策准备感和痛苦的影响。 CI-CDS系统 可通过现有的非商业化 CDS 平台立即扩展到大量患者 已在 14 个州的护理系统中为数百万患者使用。 CDS 系统实施为 所描述的可以最大限度地提高 EHR 系统大规模投资的回报，并提供 快速一致地将不断发展的基于证据的 CI 指南转化为个性化 CI 护理的原型 和初级保健内的指导。