TRAJECTORIES OF REPEATED READMISSIONS OF CHRONIC DISEASE PATIENTS: RISK STRATIFICATION, PROFILING, AND PREDICTION
成果类型:
Article
署名作者:
Ben-Assuli, Ofir; Padman, Rema
署名单位:
Ono Academic College; Carnegie Mellon University
刊物名称:
MIS QUARTERLY
ISSN/ISSBN:
0276-7783
DOI:
10.25300/MISQ/2020/15101
发表日期:
2020
页码:
201-226
关键词:
acute myocardial-infarction
multiple chronic conditions
design-science research
HOSPITAL READMISSION
heart-failure
health-care
30-day readmissions
big data
analytics
models
摘要:
The problem of recurrent, unplanned readmissions, where some patients return shortly after discharge from the hospital and are readmitted for the same or a related condition, has become a challenge worldwide due to care quality, health outcomes, and financial concerns. Predicting frequent, preventable readmissions and understanding the contributing factors is a critical problem that is being widely studied. However, few studies have examined longitudinal risk stratification, profiling, and prediction of multi-morbid, heterogeneous patient populations. We examine how readmission risk may progress over multiple emergency department visits of chronic disease patients, their early stratification into distinct trajectories with related frequencies, and the relationship of these trajectories to patient characteristics. We further extend this analysis to investigate the impact of time-stable and time-varying covariates in predicting future readmission conditional on latent class membership. Results indicate that longitudinal risk stratification can enable early identification of specific patient groups following distinct trajectories based on their presentation for emergency care. Prediction models that incorporate latent classes perform well and demonstrate the promise of trajectory modeling methods combined with advanced prediction models for longitudinal risk assessment in addressing readmission challenges. The methodology and insights from this study are generalizable to other important Information Systems problems.