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

HoPe 项目：利用 PROM、AI 和 EHR 使住院患者出院回家

• 批准号: 10456362
• 负责人: Andrea Lynne Cheville
• 金额: $ 104.76万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-03 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10456362
• 关键词: Address; Advance Care Planning; Alzheimer' s Disease; Alzheimer' s disease related dementia; Area; Attention; Caregivers; Caring; Clinical; Cognition; Cognitive; Computers; Data; Decision Making; Detection; Disadvantaged; Discharge Plannings; Discipline of Nursing; Early identification; Electronic Health Record; Elements; Expenditure; Future; Goals; Health care facility; Home; Home Health Care Agencies; Hospitalization; Hospitals; Impaired cognition; Institution; Institutionalization; Length of Stay; Multi-Institutional Clinical Trial; Outcome; Outcome Measure; Pathway interactions; Patient Outcomes Assessments; Patients; Probability; Process; Provider; Rehabilitation therapy; Reporting; Risk; Savings; Services; Site; Skilled Nursing Facilities; Social isolation; Standardization; System; Testing; Time; Work; acceptability and feasibility; acute care; base; care systems; clinical decision support; cognitive rehabilitation; computerized; cost; experience; functional loss; health data; improved; machine learning algorithm; mortality; novel; patient portal; patient-level barriers; pragmatic trial; preference; premature; pressure; prevent; prototype; rehabilitation service; service delivery; social factors; social health determinants; usability; user centered design

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.

从医院到熟练的护理设施（SNF）的不必要出院昂贵，可能会加速 患者对长期制度化的功能损失和要求。阿尔茨海默氏病的患者 和阿尔茨海默氏病有关的痴呆症（AD/ADRD）和其他类型的认知障碍是独特的 这种现状不利，因为他们住院的可能性是两倍 将不到50％的人返回家园的SNF出院。可以解决这种情况，因为它是 典型的急速排放计划过程的产物，没有足够的时间发现地址，更不用说地址， 病人的出院障碍。最近的报告表明，多达三分之一的患者被解雇 SNF，包括AD/ADRD的SNF，如果他们的急性后护理（PAC）需要直接返回家园，并且 预计并解决了障碍。几个关键定义了防止患者潜力的广泛认识 直接出院，或者他们的家庭PAC潜力（希望）。其中包括有限的能力：1）数量 确定PAC需求的因素，2）识别和解决可补充的住房障碍，以及3） 动员利益相关者提高个性化的排放计划。这些缺陷共同阻止了 急性护理服务的及时启动，可以意识到患者的出院潜力，而PAC作为 必要的。以康复为重点的医院医疗保健机构（HHA）合作伙伴关系确定 跨学科护理计划在医院与患者和照料者的参与期间颁布的早期颁布 病人回家的可能性。我们的团队开发了史诗般的电子健康记录（EHR） 三角测量EHR，患者报告的结果（PRO）和社会决定者的出院计划系统 健康数据以确定希望障碍和直接需求匹配的康复服务。一名飞行员 358名患者中的系统将房屋放电率提高了25％，并显示出高用户 可接受性。但是，飞行员还确定需要改善认知障碍的解决方案， 针对高收益希望障碍和非临床利益相关者的参与。我们建议解决 通过追求三个具体目标来实现这些限制：1）将低负担的计算机自适应测试Pro开发到 评估与安全的家居放电相关的功能认知领域； 2）开发机器学习 算法优先考虑可行的希望障碍并估算出院所需的变化程度； 3）应用以用户为中心的设计原则来完善EHR排放计划系统以获得最佳可用性 并增强了EHR Portal患者，护理人员和HHA员工的访问权限。我们的目标是集成和飞行员 这些可交付成果以成熟且最佳可用的EHR排放计划系统，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，并且可以正常 在多站点实用试验中进行测试的机构转移。