Scott S. Lee, Benjamin French, Francis Balucan, Michael D McCann, Eduard E Vasilevskis
{"title":"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","authors":"Scott S. Lee, Benjamin French, Francis Balucan, Michael D McCann, Eduard E Vasilevskis","doi":"10.1093/haschl/qxad077","DOIUrl":null,"url":null,"abstract":"\n 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.","PeriodicalId":94025,"journal":{"name":"Health affairs scholar","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Characterizing Hospitalization Trajectories in the High-Need, High-Cost Population using Electronic Health Record Data\",\"authors\":\"Scott S. Lee, Benjamin French, Francis Balucan, Michael D McCann, Eduard E Vasilevskis\",\"doi\":\"10.1093/haschl/qxad077\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n 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.\",\"PeriodicalId\":94025,\"journal\":{\"name\":\"Health affairs scholar\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-12-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Health affairs scholar\",\"FirstCategoryId\":\"0\",\"ListUrlMain\":\"https://doi.org/10.1093/haschl/qxad077\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health affairs scholar","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.1093/haschl/qxad077","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Characterizing Hospitalization Trajectories in the High-Need, High-Cost Population using Electronic Health Record Data
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.