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，在医疗保健环境之外的大部分时间--在家里、工作场所和社区--管理自己的健康。病人通常会带着他们应该做的自我护理的建议离开医生，但关于去哪里的信息质量非常差。社区处方是一项基于健康信息技术的创新，由CMS医疗创新奖(7/1/12-6/30/15 1C1CMS330997)资助，从患者与医疗服务提供者之间的接触开始，促进患者、护理人员和提供者的自我护理协调。Community Rx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。这一应用寻求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”。它还寻求超越传统的项目评估方法，将准实验设计和成本效益分析相结合，以评估个人水平的结果，并将基于代理的建模作为复杂适应系统的一部分来研究社区处方。CMS HCIA基金提供了应用综合实证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社区处方系统针对的中老年人(45-74岁)这一重要亚群的影响机制至关重要，并使用基于代理的建模来了解连接保健和自我保健系统如何改善健康、改善保健、降低成本和促进社区的经济实力。来自老年人的经验数据将与在CMS资助下收集的数据、同行审查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，以执行基于代理的建模。我们假设，由于有关自我护理资源的信息根据简单的规则通过社交网络分发，因此社区处方的影响超出了单个计划参与者的范围。具体地说，本研究旨在1)评估与对照相比，社区处方对项目参与者的医疗保健利用、成本、健康和以患者为中心的结果的影响；2)检查流向和通过主要代理的信息流和传播，包括：计划参与者、社区健康信息专家、医疗保健提供者和基于社区的服务提供商(提供自我保健资源的企业和组织)；以及3)建立和使用基于代理的模型来测试在示范区采用社区处方系统的分布式影响，包括经济影响，并通过进行实验来预测随着时间的推移的绩效，这些实验改变了对代理、环境和人口水平特征的假设。

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.