Evaluating a Self-Care Innovation for Older Adults using Agent-Based Modeling

描述（由申请人提供）：大多数人，特别是老年人，大部分时间在医疗保健环境之外管理他们的健康-在家里，工作和社区。病人通常会给医生一些关于他们应该做的自我护理的建议，但是关于去哪里的信息质量很差。CommunityRx是由CMS医疗保健创新奖（12年7月1日至15年6月30日1C 1CMS 330997）资助的基于健康信息技术的创新，从患者-医疗保健提供者接触开始，促进患者，护理人员和提供者的自我护理协调。CommunityRx旨在显著改善健康和医疗保健，同时降低医疗保健成本，特别是在服务不足的医疗保健环境中。该应用程序旨在将临床实践范式从医疗保健系统转变为医学研究所所描述的真正的“健康系统”。它还试图通过整合准实验设计和成本效益分析来扩展传统的计划评估方法，以评估个人层面的结果，并基于代理人的建模来研究CommunityRx作为复杂适应系统的一部分。CMS HCIA资助提供了应用综合经验和系统科学研究的机会，以充分评估该计划对人口的影响，并预测该计划在其他环境中的表现和影响。拟议的研究对于确定CommunityRx系统针对的中年和老年人（45-74岁）的重要亚组的影响机制至关重要，并使用基于代理的建模来了解连接医疗保健和自我保健系统如何改善健康，改善医疗保健，降低成本并促进社区的经济实力。来自老年人的经验数据将与CMS资助下收集的数据、同行评议文献中的证据和专家意见一起使用，以模拟关于年轻参与者和一般人群的假设，从而执行基于代理的建模。我们假设CommunityRx的影响超出了个人计划参与者，因为有关自我保健资源的信息是根据简单的规则通过社交网络分发的。具体而言，本研究旨在1）与对照组相比，评估CommunityRx对计划参与者的医疗保健利用率，成本，健康和以患者为中心的结果的影响; 2）检查信息流向和传播的主要代理人，包括：计划参与者，社区健康信息专家，医疗保健提供者，社区服务提供者（提供自我保健资源的企业和组织）; 3）建立和使用基于主体的模型来测试分布式影响，包括经济影响，CommunityRx系统采用的示范区，并预测性能随着时间的推移进行实验，不同的假设代理，环境和人口水平的特点。

DESCRIPTION (provided by applicant): Most people, especially older adults, manage their health most of the time outside the healthcare setting - at home, work and in the community. Patients routinely leave the doctor with recommendations about things they should do for self-care, but with very poor quality information about where to go. CommunityRx is a health information technology-based innovation funded by the CMS Health Care Innovation Awards (7/1/12-6/30/15 1C1CMS330997) that, starting with the patient-health care provider encounter, facilitates self-care coordination for patients, caregivers, and providers. CommunityRx aims to measurably improve health and health care while reducing health care costs especially in underserved health care settings. This application seeks to shift the clinical practice paradigm from a health care system to a true "health system" as described by the Institute of Medicine. It also seeks to expand beyond traditional program evaluation approaches by integrating quasi- experimental design and cost-effectiveness analysis to assess individual-level outcomes with agent-based modeling to study CommunityRx as part of a complex adaptive system. The CMS HCIA funding presents the opportunity to apply integrated empirical and systems science research to fully evaluate the impact of the program on the population and to predict the performance and impact of the program in other environments. The proposed research is essential to determining the mechanisms of impact on the important subgroup of middle age and older adults (ages 45-74) targeted by the CommunityRx system and uses agent based modeling to learn how connecting health care and self-care systems can improve health, improve health care, reduce costs, and promote economic strength of communities. Empirical data from older adults will be used with data collected under the CMS funding, evidence from the peer-review literature, and expert opinion to model assumptions about younger participants and the general population to perform agent-based modeling. We hypothesize that the impact of CommunityRx extends beyond individual program participants as information about self-care resources is distributed through social networks according to simple rules. Specifically, this research aims to 1) evaluate the impact of CommunityRx on health care utilization, cost, health, and patient-centered outcomes for program participants compared to controls; 2) examine the flow and spread of information to and through primary agents including: program participants, community health information experts, healthcare providers, and community-based service providers (businesses and organizations providing self-care resources); and 3) build and use an agent-based model to test the distributed impact, including economic effects, of CommunityRx system adoption on the demonstration area and predict performance over time by conducting experiments that vary assumptions about agent, environment, and population-level characteristics.