Personalized Clinical Decision Support to Improve Participation in Hospital at Home

项目总结 住院费用昂贵--美国每年的医疗支出为1.1万亿美元 --而且与高发病率和高死亡率风险有关。家庭医院(HAH)是另一种选择 护理团队在病人家中提供急诊医院级护理的护理模式。尽管有几十年的数据 支持卫生保健与以患者为中心的关键结局相关的疗效，参与卫生保健的障碍限制了其 广泛采用和对人口健康的影响。我们的中心假设是提供者和患者 需要临床决策支持(CDS)集成来自不同来源的数据并共享决策制定 (SDM)框架，以帮助提供有关卫生保健和克服低参与率的护理点决策。 我们工作的总体目标是通过帮助患者参与决策来改善价值驱动的护理 哪种急性级护理选项最能满足他们的需求。这项研究的目的是评估一个 医疗信息技术(IT)支持的SDM解决方案包含预期的患者结果和 首选项和部署在医疗点改善了患者和提供者在卫生保健服务中的参与度 模特。为了实现这一目标，我们将：1)描述患者、护理者和提供者对风险的认知 HAH的权衡、需求和护理偏好；2)与患者、护理人员和提供者合作，反复 设计医院级门诊管理评估和决策支持(HOME-DS)，一个健康IT- 启用SDM解决方案，该解决方案整合了风险模型概率以及患者和护理人员的偏好；以及3) 评估在急诊护理中实施家庭护理服务的可行性，并建立家庭护理服务接受率。我们 将重点放在18岁及以上因疑似肺炎住院的成年人，这是一种流行的 通常包含在HAH模型中的情况。我们将广泛应用之前经过验证的 接受肺炎严重性指数(PSI)作为HOME-DS的量化危险分数输入。用户角色 最初的HOME-DS原型的需求将通过与患者的关键线人访谈来确定 因肺炎住院，他们的照顾者和提供者(Aim1)；以用户为中心的设计原则将进一步 指导HOME-DS原型的迭代开发(目标2)；我们将测试实施的可行性 急诊护理中的Home-DS，以指导患者和护理人员在选择医院级护理时的决策 家庭或传统医院(目标3)。我们假设HOME-DS在提供商内实施是可行的 入院工作流程，可产生50%的卫生保健参与率。拟议的项目将 让患有肺炎的异类人群参与进来，因为这是一个拥有大量急性护理的人群 使用成本和在理解实施挑战方面的巨大差距，以解释为什么替代 传统的住院治疗并没有得到更广泛的使用。结果将证明医疗信息技术的价值 将临床数据与患者偏好相结合，促进有效的SDM，增强以患者为中心的急性 护理选项。

PROJECT SUMMARY Inpatient hospitalization is costly – accounting for $1.1 trillion in health care spending annually in the United States – and is associated with high morbidity and mortality risks. Hospital at Home (HaH) is an alternative care model where care teams provide acute hospital-level care in a patient’s home. Despite decades of data that support HaH efficacy relevant to key patient-centered outcomes, barriers to HaH participation limit its widespread adoption and population health impact. Our central hypothesis is that providers and patients require Clinical Decision Support (CDS) integrating data from disparate sources and a Shared Decision Making (SDM) framework to help inform point-of-care decisions regarding HaH and surmount low participation rates. The overarching goal of our work is to improve value-driven care by helping patients engage in the decision of which acute-level care option best meets their needs. The objective of this study is to evaluate whether a Health Information Technology (IT)-enabled SDM solution incorporating expected patient outcomes and preferences and deployed at the point-of-care improves patient and provider participation in HaH as a care model. To achieve this objective, we will: 1) characterize patient, caregiver, and provider perceptions of the risk tradeoffs, needs, and care preferences for HaH; 2) partner with patients, caregivers, and providers to iteratively design Hospital-level Outpatient Management Evaluation and Decision Support (HOME-DS), a Health IT- enabled SDM solution that incorporates risk-model probabilities and patient and caregiver preferences; and 3) evaluate the feasibility of implementing HOME-DS in acute care and establish the acceptance rate of HaH. We will focus HOME-DS on adults aged 18 and older hospitalized with suspected pneumonia, a prevelant condition that has been commonly included in HaH models. We will apply the previously validated and broadly accepted Pneumonia Severity Index (PSI) as the quantitative risk score input for HOME-DS. User personas and needs for the initial HOME-DS prototype will be defined through key informant interviews with patients hospitalized with pneumonia, their caregivers, and providers (Aim1); user-centric design principles will further guide iterative development of the HOME-DS prototype (Aim 2); and we will test the feasibility of implementing HOME-DS in acute care to guide patient and caregiver decision making in selecting hospital level care in the home or traditional hospital (Aim 3). We hypothesize HOME-DS is feasible to implement within the provider workflow for hospital admission and can yield participation rates in HaH of 50%. The proposed project will engage a heterogeneous population with pneumonia, as this is a population with substantial acute care utilization costs and a large gap in understanding implementation challenges to explain why alternatives to traditional hospitalization are not used more widely. Results will demonstrate the value of Health IT that integrates clinical data with patient preferences to promote effective SDM enhancing patient-centered acute care options.