Characterizing Hospitalization Trajectories in the High-Need, High-Cost Population using Electronic Health Record Data

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

High utilization by a minority of patients accounts for a large share of healthcare 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 3,404 patients meeting eligibility criteria, following an initial “rising-risk” period of three hospitalizations in six months, growth mixture modeling discerned four 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 non-surgical service, full code status, ICU-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.
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
少数患者的高使用率占医疗保健费用的很大一部分,但这种使用率的动态仍然知之甚少。我们试图描述2017年至2023年学术医疗中心成年患者住院的纵向轨迹。在符合资格标准的3,404名患者中,在六个月内三次住院的初始“风险上升”期之后,生长混合模型识别出随后住院轨迹的四组:不再使用,长期低使用率,持续高使用率但增长缓慢,持续高使用率但增长速度快。与高阶住院轨迹相关的基线因素包括:接受非手术服务、完全编码状态、icu级护理、阿片类药物管理、出院、合并症心血管疾病、终末期肾脏或肝脏疾病或癌症。以这种方式描述住院轨迹及其相关因素为早期识别那些最有可能成为高需求、高成本患者奠定了基础。
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