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利用电子健康记录数据描述高需求、高成本人群的住院轨迹。

Characterizing hospitalization trajectories in the high-need, high-cost population using electronic health record data.

作者信息

Lee Scott S, French Benjamin, Balucan Francis, McCann Michael D, Vasilevskis Eduard E

机构信息

Section of Hospital Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.

出版信息

Health Aff Sch. 2023 Dec 6;1(6):qxad077. doi: 10.1093/haschl/qxad077. eCollection 2023 Dec.

Abstract

High utilization by a minority of patients accounts for a large share of health care costs, but the dynamics of this utilization remain poorly understood. We sought to characterize longitudinal trajectories of hospitalization among adult patients at an academic medical center from 2017 to 2023. Among 3404 patients meeting eligibility criteria, following an initial "rising-risk" period of 3 hospitalizations in 6 months, growth mixture modeling discerned 4 clusters of subsequent hospitalization trajectories: no further utilization, low chronic utilization, persistently high utilization with a slow rate of increase, and persistently high utilization with a fast rate of increase. Baseline factors associated with higher-order hospitalization trajectories included admission to a nonsurgical service, full code status, intensive care unit-level care, opioid administration, discharge home, and comorbid cardiovascular disease, end-stage kidney or liver disease, or cancer. Characterizing hospitalization trajectories and their correlates in this manner lays groundwork for early identification of those most likely to become high-need, high-cost patients.

摘要

少数患者的高利用率占医疗保健成本的很大一部分,但这种利用率的动态变化仍知之甚少。我们试图描述2017年至2023年期间一家学术医疗中心成年患者的住院纵向轨迹。在3404名符合资格标准的患者中,在最初6个月内有3次住院的“风险上升”期之后,增长混合模型识别出4组后续住院轨迹:不再使用、低慢性使用率、持续高使用率且增长率缓慢,以及持续高使用率且增长率快速。与高阶住院轨迹相关的基线因素包括入住非手术科室、完全代码状态、重症监护病房级护理、阿片类药物给药、出院回家,以及合并心血管疾病、终末期肾病或肝病或癌症。以这种方式描述住院轨迹及其相关因素为早期识别最有可能成为高需求、高成本患者的人群奠定了基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/87bd/10986247/fdcd81596712/qxad077f1.jpg

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