Project HoPe: Achieving Home Discharge for institutionally-bound Patients with PROMs, AI, and the EHR

从医院到熟练护理机构(SNF)的不必要的出院费用很高，而且可能会加速 患者的功能丧失和长期住院的需求。阿尔茨海默病患者 阿尔茨海默病相关痴呆(AD/ADRD)和其他类型的认知障碍是独一无二的 这种现状对他们不利，因为他们住院的可能性是前者的两倍，后者是后者的四倍。 只有不到50%的人返回家园，并被释放到国家安全部队。这种情况可以按实际情况处理 典型的仓促排放计划过程的产物，没有足够的时间来发现，更不用说寻址了， 病人出院的障碍。最近的报告表明，多达三分之一的患者被送往医院 SNF，包括患有AD/ADRD的人，如果他们的急性后护理(PAC)需要和 预料到并解决了障碍。几个关键缺陷阻碍了患者潜力的广泛实现 直接放回家，或自己家的PAC潜力(希望)。这些问题包括：1)量化能力有限 决定PAC需求的因素，2)确定和解决家庭出院的可补救障碍，以及3) 动员利益相关者推进个性化排污计划。总体而言，这些赤字阻碍了 及时启动急性护理服务，以实现患者出院的潜力，PAC作为 这是必要的。以康复为重点的医院-家庭医疗机构(HHA)合作伙伴关系已经建立 在患者和护理者的参与下，在住院早期制定的跨学科护理计划增加了 病人回家的可能性。我们的团队开发了一种基于Epic电子健康记录(EHR)的 出院计划系统，三角测量EHR、患者报告结果(PRO)和社会决定因素 利用健康数据来确定希望障碍和直接提供与需求匹配的康复服务。美国航空公司的飞行员 在358名患者中，系统使家庭出院率增加了25%以上，并显示出高使用率 可接受性。然而，试点也发现了改善认知障碍解决方案的必要性， 瞄准高产量的希望障碍，以及非临床利益相关者的参与。我们建议解决以下问题 这些局限性通过追求三个具体目标来实现：1)开发一种低负担的计算机化自适应测试程序 评估与安全家庭放电相关的功能认知领域；2)开发机器学习 算法，以确定可操作的希望障碍的优先顺序，并估计家庭出院所需的变化程度； 3)运用以用户为中心的设计原则，完善EHR出货计划系统，优化易用性 并增强了EHR门户患者、护理人员和HHA工作人员的访问权限。我们的目标是既整合又试点 将这些交付成果放在一个成熟的、可用的EHR排放计划系统中，并评估 其实施的可行性和可接受性。我们预计该系统将是可扩展的，并且是可服从的 以跨机构调剂进行测试的多点务实试验。

Unnecessary discharges from a hospital to a skilled nursing facility (SNF) are costly and may accelerate patients’ functional losses and requirement for long-term institutionalization. Patients with Alzheimer's Disease and Alzheimer's Disease Related Dementias (AD/ADRD) and other types of cognitive impairment are uniquely disadvantaged by this status quo in that they are twice as likely to be hospitalized, four times more likely to be discharged to SNFs with less than 50% returning to their homes. This situation can be addressed as it is the product of a typically rushed discharge planning process with inadequate time to discover, much less address, a patient’s barriers to home discharge. Recent reports suggest that as many as a third of patients dismissed to SNFs, including those with AD/ADRD, could return directly home if their post-acute care (PAC) needs and barriers were anticipated and addressed. Several key deficits prevent broad realization of a patients’ potential to discharge directly home, or their Home PAC Potential (HoPe). These include a limited ability to: 1) quantify factors that determine PAC needs, 2) identify and address remediable barriers to home discharge, and 3) mobilize stakeholders for advancement of individualized discharge plans. Collectively, these deficits prevent the timely initiation of acute care services that can realize a patient’s potential for home discharge, with PAC as necessary. Rehabilitation-focused, hospital-Home Healthcare Agency (HHA) partnerships have established that interdisciplinary care plans enacted early in a hospital stay with patient and caregiver involvement increase the likelihood of a patient’s return home. Our team developed an Epic electronic health record (EHR)-based discharge planning system that triangulates EHR, patient reported outcomes (PROs), and social determinants of health data to identify HoPe barriers and direct needs-matched rehabilitation service delivery. A pilot of the system among 358 patients increased the home discharge rate by over 25% and revealed high user acceptability. However, the pilot also identified the need to improve addressing of cognitive impairments, targeting of high-yield HoPe barriers, and engagement of non-clinical stakeholders. We propose to address these limitations by pursuing three Specific Aims: 1) Develop a low-burden computerized adaptive test PRO to assess the domains of functional cognition relevant to a safe home discharge; 2) Develop a machine learning algorithm to prioritize actionable HoPe barriers and estimate the degree of change needed for home discharge; and 3) Apply user-centered design principles to refine the EHR discharge planning system for optimal usability and enhanced EHR portal patient, caregiver, and HHA staff access. Our goal is to both integrate and pilot these deliverables in a mature and optimally usable EHR discharge planning system, and to evaluate the feasibility and acceptability of its implementation. We anticipate that the system will be scalable, and amenable to inter-institution transfer for testing in a multi-site pragmatic trial.