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

提高医院效率：使用机器学习和电子病历中的患者流动性评分来预测急性后护理设施的安置

• 批准号: 10056338
• 负责人: Elizabeth Colantuoni
• 金额: $ 9.99万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-30 至 2022-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10056338
• 关键词: Improving; Hospital; Efficiency; Predicting; Post

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.

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。