DEVELOPMENT OF AN ELECTRONIC CARE PLAN FOR PERSONS WITH MULTIPLE CHRONIC CONDITIONS (E-CARE)

通过卫生与公众服务部以患者为中心的成果研究信托基金（PCOR TF）提供的资金，NIDDK与医疗保健研究与质量局（AHRQ）合作，开发和测试了一套可互操作的电子(e)护理规划工具，以促进以患者为中心的关键数据在家庭、社区、针对多种慢性疾病（MCC）患者的临床和研究环境，包括慢性肾病（CKD）、2型糖尿病（T2D）、心血管疾病（CVD）、慢性疼痛和SARS-CoV-2感染的急性后后遗症（PASC）。护理计划是多方面护理协调干预措施的重要组成部分，可降低死亡率和住院率，改善疾病管理和满意度。此外，积极主动的护理计划促进以人为本，改善结果，并降低护理成本。基于标准化数据利用标准（如快速医疗互操作性资源（FHIR）和基于FHIR的可替代医疗应用程序、可重用技术（SMART） （https://smarthealthit.org/））开发的护理计划已被提议作为一种使电子系统能够自动和动态地将数据元素聚集在一起并共享的方法。这种汇总数据不仅将提供可采取行动的信息，以确定和实现MCC患者的健康和保健目标，而且还将减少缺失并提高护理点数据的质量，以便用于务实研究。NIDDK领导该项目的开发组件，而实际测试由AHRQ领导。

Through funding made available from the Department of Health and Human Services’ Patient-Centered Outcomes Research Trust Fund (PCOR TF), the NIDDK has partnered with the Agency for Healthcare Research and Quality (AHRQ) on the development and testing of a pilot suite of interoperable electronic (e-) care planning tools to facilitate aggregation and sharing of critical patient-centered data across home-, community-, clinic- and research-based settings for persons with multiple chronic conditions (MCC), including chronic kidney disease (CKD), type 2 diabetes mellitus (T2D), cardiovascular disease (CVD), chronic pain, and post-acute sequelae of SARS-CoV-2 infection (PASC). Care plans are a prominent part of multifaceted, care coordination interventions that reduce mortality and hospitalizations and improve disease management and satisfaction. In addition, proactive care planning promotes person-centeredness, improves outcomes, and reduces the cost of care. Development of care plans based on standardized data—leveraging standards such as Fast Healthcare Interoperability Resources (FHIR) and Substitutable Medical Apps, Reusable Technology (SMART) (https://smarthealthit.org/) on FHIR—has been proposed as a method for enabling electronic systems to pull together and share data elements automatically and dynamically. Such aggregated data would not only provide actionable information to identify and achieve health and wellness goals for individuals with MCC, but also would reduce missingness and improve quality of point-of-care data for use in pragmatic research. The NIDDK is leading the development component of the project, while real-world testing is being led by AHRQ.