Identifying patients at risk of unplanned re-hospitalisation using statewide electronic health records

ABSTRACT: Preventing unplanned hospitalisations, including readmissions and re-presentations to the emergency department, is an important strategy for addressing the growing demand for hospital care. Significant successes have been reported from interventions put in place by hospitals to reduce their incidence. However, there is limited use of data-driven algorithms in hospital services to identify patients for enrolment into these intervention programs. Here we present the results of a study aiming to develop algorithms deployable at scale as part of a state government’s initiative to address rehospitalizations and which fills several gaps identified in the state-of-the-art literature. To the best of our knowledge, our study involves the largest-ever sample size for developing risk models. Logistic

SUBMITTER: Brankovic A 

PROVIDER: S-EPMC9534931 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature