Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions
Heterogeneous length-of-stay modeling of post-acute care residents in the nursing home with competing discharge dispositions
复制标题
具有竞争性出院处置的疗养院急性后护理居民的异质住院时间模型
DOI：
10.1007/s42524-022-0203-7
复制
发表时间：
2022
影响因子：
7.4
通讯作者：
Li, Mingyang
中科院分区：
文献类型：
--
作者：
Sakib, Nazmus;Sun, Xuxue;Kong, Nan;Masterson, Chris;Meng, Hongdao;Smith, Kelly;Li, Mingyang
疗养院(NHS)的急性后护理(PAC)居民是最近住院的患者，他们的医学诊断复杂，从严重的骨科损伤到心血管疾病。NHS的主要作用是最大限度地恢复PAC居民在NH期间的康复，并获得理想的出院结果，例如更高的社区出院可能性和较低的再/住院风险。通过准确预测PAC居民的多项出院处理(例如，社区出院和再次/住院)的住院时间，将使医院管理小组能够根据患者的个性化风险对他们进行分层，以实现个性化和以居民为中心的医院护理服务。由于PAC居民的健康状况高度异质性，以及他们的多种相关放电处置，开发准确的预测模型变得具有挑战性。现有的预测分析方法，如基于分布/回归的方法和机器学习方法，要么不能全面地结合不同的个体特征，要么忽略了多个放电配置。在这项工作中，数据驱动的预测分析方法被认为是在存在不同的居民特征的情况下，随着时间的推移联合预测个性化再住院风险和社区出院可能性的方法。进一步开发了一种抽样算法，为NH中的PAC居民的不同群体生成准确的预测样本，并促进设施级的性能评估。通过与大量现有预测方法的综合比较，给出了一个使用大规模NH数据的真实案例研究，展示了所提出的工作在个人和设施层面上的优越预测性能。开发的分析工具将使NH管理小组能够通过为他们提供更积极主动和有重点的护理来确定风险最高的居民，以改善居民结果。
Post-acute care (PAC) residents in nursing homes (NHs) are recently hospitalized patients with medically complex diagnoses, ranging from severe orthopedic injuries to cardiovascular diseases. A major role of NHs is to maximize restoration of PAC residents during their NH stays with desirable discharge outcomes, such as higher community discharge likelihood and lower re/hospitalization risk. Accurate prediction of the PAC residents’ length-of-stay (LOS) with multiple discharge dispositions (e.g., community discharge and re/hospitalization) will allow NH management groups to stratify NH residents based on their individualized risk in realizing personalized and resident-centered NH care delivery. Due to the highly heterogeneous health conditions of PAC residents and their multiple types of correlated discharge dispositions, developing an accurate prediction model becomes challenging. Existing predictive analytics methods, such as distribution-/regression-based methods and machine learning methods, either fail to incorporate varied individual characteristics comprehensively or ignore multiple discharge dispositions. In this work, a data-driven predictive analytics approach is considered to jointly predict the individualized re/hospitalization risk and community discharge likelihood over time in the presence of varied residents’ characteristics. A sampling algorithm is further developed to generate accurate predictive samples for a heterogeneous population of PAC residents in an NH and facilitate facility-level performance evaluation. A real case study using large-scale NH data is provided to demonstrate the superior prediction performance of the proposed work at individual and facility levels through comprehensive comparison with a large number of existing prediction methods as benchmarks. The developed analytics tools will allow NH management groups to identify the most at-risk residents by providing them with more proactive and focused care to improve resident outcomes.
登录
查看更多内容
影响因子：
2.1
作者：
Zhang, Xu;Barnes, Sean;Smith, Paul
通讯作者：
Smith, Paul
影响因子：
3.5
作者：
New, Peter W.;Stockman, Keith;Stoelwinder, Johannes U.
通讯作者：
Stoelwinder, Johannes U.
影响因子：
3.5
作者：
Kramer AA;Zimmerman JE
通讯作者：
Zimmerman JE
影响因子：
4.5
作者：
Faddy, Malcolm;Graves, Nicholas;Pettitt, Anthony
通讯作者：
Pettitt, Anthony
影响因子：
5
作者：
Cole, Stephen R.;Chu, Haitao;Greenland, Sander
通讯作者：
Greenland, Sander