Determining Barriers to Achieving Optimal Post-Acute Care Destinations

项目摘要 每年有1300万医疗保险受益人从急性护理医院出院，其中约42% 这些老年人被转介到急性后护理(PAC)服务，包括长期急性护理医院， 住院康复、熟练的护理设施和家庭保健。有效的转诊促进患者的进步 健康和防止负面后果有赖于协调的出院计划。然而，这种协调是 当跨专业的解雇计划团队经常面临时间限制时，团队很难实现 沟通问题、决策中的风险容忍度差异以及不一致的评估。因此， 个人和医院层面的出院计划实践存在显著差异，没有 这一常见但关键过程的临床指南。没有标准化的排放计划实践 患者出院后面临负面后果的风险，包括#年的社会和经济差距 PAC转诊位置、不必要的治疗、计划外的再次住院和增加的医疗保健 成本。临床决策支持系统(CDSS)为临床医生提供循证的、个性化的 关于其患者在护理点的信息，并解决标准化解决方案的迫切需要 完善排污规划决策。护理过渡出院转诊专家系统(DIRECT)是 最近开发的CDSS算法(RO1-2-NR007674)可以识别哪些患者需要PAC和 建议根据患者需求将护理级别设置为家庭健康护理或设施级别护理。使用Direct In 出院计划与减少再次入院有关，然而，医院临床医生 转介给PAC的患者比直接转介的患者少26%。这种不一致在历史上一直很难研究，因为 针对临床病历中出院计划数据的非结构化特点，进行数据抽象和分析 很难实现。建议的研究为申请者改进直接算法和扩展算法做好准备 通过两个具体目标达到新的临床环境：1)在没有PAC的出院患者中， 比较患者特征和30天再住院率与DIRECT确定的有需要的患者 PAC和那些直接医生和临床医生同意不转诊PAC的患者，以及2)确定原因 当直接CDSS推荐PAC时，与没有服务的出院相关。建议数 这项研究将把郊区社区医院开发的现有CDSS扩展到一个 并利用自然语言处理方法来提高对原因的理解 一些患者没有接受推荐水平的PAC。这项研究的发现将阐明 可能的实施和算法改进策略，用于未来的前瞻性研究，并与 申请者的长期研究目标是通过发展和发展改善老年人护理的过渡 实施CDSS。这项研究的结果表明，国家护理研究所的使命是 通过支持创新技术来改善老年人的健康。

Project Summary 13 million Medicare beneficiaries are discharged from acute care hospitals annually, and approximately 42% of these older adults receive referrals to post-acute care (PAC) services including long term acute care hospitals, inpatient rehabilitation, skilled nursing facilities, and home health care. Effective referrals that promote patient health and prevent negative outcomes rely on coordinated discharge planning. However, this coordination is difficult to achieve when interprofessional discharge planning teams frequently face time constraints, team communication issues, variance in risk tolerance in decision making, and inconsistent assessments. Therefore, significant variation in discharge planning practices exists at the individual and hospital level and there are no clinical guidelines for this common but crucial process. Without standardized discharge planning practices in place, patients are at risk for negative outcomes after discharge including social and economic disparities in PAC referral location, unnecessary treatments, unplanned hospital readmissions, and increased healthcare costs. Clinical decision support systems (CDSS) equip clinicians with evidence-based, individualized information about their patients at the point of care, and address the urgent need for standardized solutions to improve discharge planning decisions. The Discharge Referral Expert System for Care Transitions (DIRECT) is a recently developed CDSS algorithm (RO1-2-NR007674) that identifies which patients need PAC and suggests the level of care as home health care or facility-level care based on patient needs. Use of DIRECT in discharge planning is associated with a reduction in hospital readmissions, however, hospital clinicians referred 26% fewer patients to PAC than DIRECT. This discordance has been historically difficult to study due to the unstructured nature of discharge planning data in clinical notes, making data abstraction and analysis difficult to achieve. The proposed study prepares the applicant to advance the DIRECT algorithm and expand it to a new clinical setting through two specific aims: 1) Among patients discharged without PAC, compare patient characteristics and 30-day readmission rates between those identified by DIRECT as needing PAC and those where DIRECT and clinicians agreed on no referral for PAC and 2) Identify the reasons associated with discharge home without services when the DIRECT CDSS recommends PAC. The proposed study will expand an existing CDSS developed in a suburban community hospital to a new population in a large urban hospital and utilize natural language processing methods to advance the understanding of why some patients do not receive the recommended level of PAC. The findings from this study will illuminate possible implementation and algorithm refinement strategies for future prospective study, and align with the applicant’s long term research goals to improve transitions in care for older adults by developing and implementing CDSS. The results of this study address the National Institute of Nursing Research’s mission to improve the health of older adults by supporting innovative technology.