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

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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许多支持者缺乏评估、诊断和管理认知能力的信心。不幸的是，研究表明超过 50% 的 CI 患者未被诊断出来。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，在本阶段 1 (R61) 中。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) 筛查我们还将开发用于识别未来痴呆症诊断 (AD/ADRD) 风险较高的患者的 (EHR) 数据。并验证基于网络和 EHR 集成的 CI 临床决策支持 (CI-CDS) 系统以吸引患者与参议员讨论痴呆症风险升高的问题，并给予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系统中。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，并且已经该 CDS 平台无缝集成到 EHR 中，改善了慢性病患者的治疗结果。如已发表的研究所示，我们将系统地研究糖尿病和高心血管风险等疾病。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。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 发现痴呆风险较高的成年人。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。可通过现有的非商业化 CDS 平台立即扩展到大量患者 CDS 系统已在 14 个州的护理系统中用于数百万患者。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循证 CI 指南转化为个性化 CI 护理的原型和初级保健内的指导。