Utilizing Technology and AI Approaches to Facilitate Independence and Resilience in Older Adults

利用技术和人工智能方法促进老年人的独立性和适应能力

• 批准号: 10652012
• 负责人: Peter M. Abadir
• 金额: $ 24.11万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10652012
• 关键词: African American; Age; Alzheimer' s Disease; Area; Artificial Intelligence; Caregivers; Caring; Cause of Death; Communities; Computer software; Databases; Decision Making; Dimensions; Disease; Elderly; Electronic Health Record; Ethics; Ethnic Origin; Family; Goals; Instruction; Intervention; Minority; Minority Groups; Modeling; Outcome; Palliative Care; Patients; Personal Satisfaction; Persons; Population; Process; Provider; Quality of Care; Race; Racial Equity; Regimen; Resources; Slide; Technology; Technology Transfer; Therapeutic; Time; Training; Variant; advanced analytics; age group; analytical tool; augmented intelligence; base; care delivery; care systems; cohort; digital; end of life; health care delivery; health care service utilization; health plan; improved; patient population; population based; population health; predictive modeling; preference; psychosocial; resilience; service delivery; technology development; tool

Abstract: Palliative care (PC) is an interdisciplinary concept aimed at improving the wellbeing of persons with serious illness throughout their course of illness including end-of-life. PC enables care decisions that align with patient and caregiver preferences. For persons with Alzheimer’s Disease and Related Disorders (ADRD), PC is particularly challenging, as determining a patient’s current status on the ADRD disease course is often difficult. Successful PC services in patients with ADRD focuses on integrating therapeutic regimens with timely identification and alleviation of physical, psychosocial, and decision-making needs of patients and their families. PC can promote ethical, equitable, and efficient population health principles by achieving optimal healthcare utilization by avoiding overuse and underuse. Many challenges hinder appropriate levels of PC integration for persons with ADRD at both the patient- and population-levels. There is ongoing discussion on optimal timing of PC delivery in addition to variation in availability of well-trained teams and resources to deliver PC. One ubiquitous challenge is the care system’s ability to identify those persons that will benefit most from PC. Moreover, there are documented disparities in delivery of PC, such that minority race/ethnicity patients receive too little, too late care compared to their majority counterparts. Artificial intelligence (AI) predictive modeling techniques may enable accurate and timely identification of persons with ADRD who are likely to benefit from PC assessment. To achieve our goal of using advanced AI analytic tools to improve PC received by persons with ADRD, the project has the following objectives:1) To develop and validate advanced predictive models (PM) to identify persons with ADRD who are likely to benefit from PC assessment; 2) To evaluate the impact of PM based palliative care interventions on population-level healthcare utilization outcomes; 3) To assess the disparities in PC services delivery and healthcare utilization in African American and other minority populations with ADRD; 4) To initiate first stage of technology transfer of the advanced analytic tools we develop by undertaking initial pilots and developing both publicly accessible software and integration into the JHU “ACG” population-based platform. Dr. Chintan Pandya (PI) and team will develop machine learning prediction models to identify ADRD patients likely to benefit from PC assessment. These models will be developed using data captured in electronic health record (EHR) and other large electronic databases (e.g., insurance claims) of patients with ADRD cared for within a large patient/consumer population. In addition to sharing open architecture free-access tools at the conclusion of this project, we plan on integrating the software-based algorithms we develop into our widely used (reaching 250+ million patients in 20+ nations) Johns Hopkins ACG predictive modeling and disease stratification software. This will allow for rapid diffusion of new palliative care analytic technologies among a very large U.S. and global healthcare organizations client base. At the patient level, our ADRD PC decision support framework will promote interactive goals of care discussions between patients/caregivers and providers. The populationbased analytic tools we develop will help promote resource planning, quality, and equity assessment among ADRD cohorts within health delivery systems, health plans, and communities.

摘要：姑息治疗（PC）是一个跨学科概念，旨在改善 在整个疾病过程中患有严重疾病的人，包括生命终结。 PC启用 与患者和护理人员偏好保持一致的护理决定。适用于阿尔茨海默氏症的人 疾病和相关疾病（ADRD），PC特别具有挑战性，因为确定患者的 ADRD疾病病程的现状通常很困难。患者成功的PC服务 ADRD专注于将治疗方案与及时识别和缓解融合 患者及其家人的身体，社会心理和决策需求。 PC可以 通过实现最佳 通过避免过度使用和不足来利用医疗保健。许多挑战阻碍了适当的 患者和人口水平的ADRD患者的PC集成水平。那里 除了可用性的差异外，还在讨论PC交付的最佳时机 训练有素的团队和提供PC的资源。一个无处不在的挑战是护理系统的 能够识别那些将从PC中受益最大的人。此外，有记录 PC交付的差异，使得少数族裔/民族患者收到的差异太少，太晚了 与他们的多数同行相比。人工智能（AI）预测建模 技术可能可以使ADRD的人准确，及时地识别 来自PC评估的好处。为了实现我们使用先进的AI分析工具改进的目标 ADRD人员收到的PC，该项目具有以下对象：1）开发和 验证先进的预测模型（PM），以识别有可能受益的ADRD的人 来自PC评估； 2）评估基于PM的姑息治疗干预措施对 人口水平的医疗保健利用结果； 3）评估PC服务中的分布 非裔美国人和其他少数民族的交付和医疗保健利用 adrd; 4）启动我们开发的高级分析工具的技术转移的第一阶段 通过进行初始飞行员并开发公共访问的软件并集成到 JHU“ ACG”基于人口的平台。 Chintan Pandya博士（PI）和团队将发展 机器学习预测模型以识别可能从PC中受益的ADRD患者 评估。这些模型将使用电子健康记录（EHR）中捕获的数据开发 以及其他大型电子数据库（例如，ADRD护理患者的保险索赔） 在大型患者/消费者人群中。除了共享开放式建筑免费访问 该项目结束时的工具，我们计划整合基于软件的算法 发展到我们广泛使用的（在20多个国家中达到25万多名患者）Johns Hopkins ACG 预测建模和疾病分层软件。这将允许快速扩散新的 美国和全球医疗保健组织中的姑息治疗分析技术 客户群。在患者一级，我们的ADRD PC决策支持框架将促进 患者/看护者和提供者之间护理讨论的互动目标。我们开发的基于人群的分析工具将有助于促进资源计划，质量和权益 在健康分娩系统，健康计划和社区中的ADRD队列之间的评估。