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）是一个跨学科的概念，旨在改善患者的福祉。 在整个病程中（包括临终前）患有严重疾病的人。 符合患者和护理人员偏好的护理决策。 疾病及相关疾病 (ADRD)、PC 特别具有挑战性，因为确定患者的 ADRD 病程的现状通常很难为患者提供成功的 PC 服务。 ADRD 侧重于整合治疗方案以及及时识别和缓解 患者及其家人的身体、心理和决策需求。 通过实现最佳目标，促进道德、公平和高效的人口健康原则 通过避免过度使用和使用不足来实现医疗保健利用 许多挑战阻碍了适当的利用。 ADRD 患者在患者和人群层面的 PC 集成水平。 除了可用性的变化之外，我们还在持续讨论 PC 交付的最佳时间。 提供 PC 的训练有素的团队和资源是护理系统面临的一项普遍挑战。 识别那些将从 PC 中受益最多的人的能力此外，还有记录。 PC 的提供存在差异，导致少数族裔患者接受的治疗太少、太晚 与他们的大多数邻居相比，人工智能（AI）预测模型更加关心。 技术可以准确、及时地识别可能患有 ADRD 的人 受益于 PC 评估，以实现我们使用先进的人工智能分析工具来改进的目标。 ADRD 患者收到的 PC，该项目有以下目标：1) 开发和 验证高级预测模型 (PM)，以确定可能受益的 ADRD 患者 来自 PC 评估；2) 评估基于 PM 的姑息治疗干预措施对 人口层面的医疗保健利用结果；3) 评估 PC 服务的差异 非裔美国人和其他少数族裔人群的分娩和医疗保健利用 ADRD；4) 启动我们开发的先进分析工具的第一阶段技术转让 通过进行初步试点并开发可公开访问的软件并集成到 JHU“ACG”基于人群的平台将由 Chintan Pandya 博士（PI）和团队开发。 机器学习预测模型可识别可能从 PC 中受益的 ADRD 患者 这些模型将使用电子健康记录（EHR）中捕获的数据来开发。 以及其他接受治疗的 ADRD 患者的大型电子数据库（例如保险索赔） 除了共享开放架构免费访问之外。 在这个项目结束时，我们计划集成基于软件的算法 发展成为我们广泛使用的（覆盖 20 多个国家的 250 多百万患者）约翰霍普金斯 ACG 预测模型和疾病分层软件这将有助于新疾病的快速传播。 美国和全球大型医疗机构的姑息治疗分析技术 在患者层面，我们的 ADRD PC 决策支持框架将促进。 我们开发的基于人群的分析工具将有助于促进资源规划、质量和公平性。 对卫生服务系统、卫生计划和社区内的 ADRD 群体进行评估。