Improving Hospital Efficiency: Predicting Post-Acute Care Facility Placement Using Machine Learning and Patient Mobility Scores from the Electronic Medical Record

项目摘要/摘要 每年，约有800万人从急性护理医院出院，进入急性后护理 设施，占所有医院出院的20%，占所有医疗保险出院的40%。急诊后护理 医疗机构经常为急性发作后出现功能障碍的患者提供康复服务 不能安全回家的病人。急诊医院的临床医生往往不能认识到医院- 在急性内科/外科问题解决之前，后天获得的功能限制。此故障会延迟 出院和在急性后护理机构开始康复，这可能会加剧 医院相关的功能限制。患者的活动状态是身体功能的一个组成部分， 决定对急性后护理设施的需求的一个重要因素。简单、经过验证的工具可用于 在急诊医院，例行评估病人的活动能力越来越普遍，但并不是常规的 预测急症后护理机构的出院需求。一种这样的工具，即急性后活动测量 护理住院患者移动性简表(AM-PAC IMSF)是一种有效且可靠的移动性测量方法，适用于 急救医院。AM-PAC IMSF在住院期间作为常规临床护理的一部分用于 所有在我们急救医院的病人。在一项先导研究中，我们证明了AM-PAC IMSF得分较低 入院与急性后护理机构的安置密切相关。我们的目标是 对我们的初步工作进行扩展，以开发一个正式的模型来预测哪些患者可能需要术后 急诊护理机构安置。这样的预测对于改进排污计划将是非常有价值的。 在急症后护理设施处理和加快康复服务的接收。我们的总体目标是 为了证明预测模型，利用电子病历中的大数据，可以帮助 优化医院出院流程。因此，我们提出了以下目标：1)确定基线是否 由AM-PAC IMSF在入院后48小时内测量的患者活动状态为 预测医院出院到特定的急性后护理水平；以及2)开发动态 预测模型，使用入院AM-PAC IMSF评分和后续 入院后每日评分轨迹，以预测出院至特定水平 急诊后护理。这项拟议的研究解决了AHRQ提高效率和质量的优先事项 通过改善医院出院流程提供医疗服务，并对患者进行相关改进 结果。

Project Summary / Abstract Annually, approximately 8 million people are discharged from an acute care hospital to a post-acute care facility, accounting for >20% of all hospital discharges and >40% of all Medicare discharges. Post-acute care facilities frequently provide rehabilitation services for patients experiencing functional limitations after acute illness who cannot return home safely. Clinicians in acute care hospitals often fail to recognize hospital- acquired functional limitations until after resolution of acute medical/surgical issues. This failure delays hospital discharge and the start of rehabilitation in a post-acute care facility, which can exacerbate hospital-associated functional limitations. Patients' mobility status, one component of physical function, is an important factor in determining the requirement for a post-acute care facility. Simple, validated tools for routinely evaluating patient mobility are increasingly common in acute hospitals but are not routinely used to predict the need for discharge to a post-acute care facility. One such tool, the Activity Measure for Post-Acute Care Inpatient Mobility Short Form (AM-PAC IMSF), is a validated and reliable mobility measure for patients in acute care hospitals. The AM-PAC IMSF is used, as part of routine clinical care throughout hospitalization, for all patients in our acute care hospital. In a pilot study, we demonstrated that lower AM-PAC IMSF scores at hospital admission were strongly associated with post-acute care facility placement. Our goal is to expand upon our preliminary work to develop a formal model to predict which patients are likely to require post- acute care facility placement. Such prediction would be invaluable for improving the discharge planning process and expediting receipt of rehabilitation services at a post-acute care facility. Our overall objective is to demonstrate that prediction models, leveraging `big data' from electronic medical records, can help optimize the hospital discharge process. Thus, we propose the following Aims: 1) To determine if baseline patient mobility status, measured by the AM-PAC IMSF within 48 hours of hospital admission, is predictive of hospital discharge to specific levels of post-acute care; and 2) To develop a dynamic prediction model, using both the hospital admission AM-PAC IMSF score and the subsequent trajectory of daily scores after hospital admission, to predict hospital discharge to specific levels of post-acute care. This proposed research addresses the AHRQ priority of improved efficiency and quality of healthcare delivery via improving the hospital discharge process, with associated improvement in patient outcomes.