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: 10092423
• 负责人: Leah R Hanson
• 金额: $ 59.2万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10092423
• 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; Modeling; Newly Diagnosed; Office Visits; Online Systems; Patient Care Planning; Patient-Focused Outcomes; Patients; 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; Testing; 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; mild cognitive impairment; personalized care; pragmatic trial; predictive modeling; prototype; randomized trial; response; satisfaction; screening; sociodemographics; 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.

项目摘要 到2050年，阿尔茨海默病（AD）和AD相关性痴呆（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，以及对已经 强调基层医疗体系。许多临床医生缺乏信心来评估，诊断和管理认知 脑损伤（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筛查率高的环境中，也很少有筛查阳性的患者 记录任何临床医生随访措施。为了解决这些重要问题，在第一阶段（R61）， 项目，我们将开发和验证一个机器学习模型（称为MC-PLUS）使用的结果，从简短的迷你， 在年度医疗保险健康检查和电子健康记录中定期完成Cog（MC）筛查 (EHR)数据，以确定未来痴呆诊断（AD/ADRD）风险升高的患者。我们还将开发 并验证一个基于网络和EHR集成的CI临床决策支持（CI-CDS）系统，以吸引患者 与临床医生讨论痴呆症风险升高的问题，并给予临床医生信心和工具， 他们需要诊断和管理CI。MC-PLUS和CI-CDS系统将被添加到现有的 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的满意度，并且已经 与EHR无缝集成。该CDS平台改善了慢性胰腺炎患者的结局 如已发表的研究所示，糖尿病和高心血管风险等疾病。我们将系统地 在一个初级保健诊所进行试点测试之前，与专家冠军一起验证CI-CDS系统。 在成功的里程碑被证明后，我们将开始第2阶段（R33），这是一个大型的实用性试验，有30名 随机分配接受CI-CDS或常规治疗（UC）的初级保健诊所。我们将评估临床医生的变化 与UC诊所相比，CI-CDS中CI检测和护理管理的置信度。如果成功，CI-CDS 系统将提高新的CI诊断率，缩小现有的社会人口差异， 与UC诊所相比，在CI-CDS的首次访视时，MC-PLUS确定痴呆风险升高的成人。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评估临床医生对CDS系统做出反应的决定因素 理论和技术接受结构，并对患者和护理人员进行电话调查， 评估干预对决策准备和痛苦感的影响。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 已经在14个州的护理系统中为数百万患者使用。CDS系统实现为 所描述的可以最大限度地提高对EHR系统进行的大量投资的回报，并提供 原型，以快速和一致地将不断发展的循证CI指南转化为个性化CI护理 在基层指导下，