Files and links (1)
Published, Version of Record (VoR)CC BY-NC V4.0, Open
Journal article
Calibrating Readmission Risk Prediction Models for Determining Post-discharge Follow-up Timing
Journal of community hospital internal medicine perspectives, v 12(4), pp 24-27
04 Jul 2022
PMID: 36262913
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The soaring hospital readmission rates are straining the already limited financial resources in the US health system. Meanwhile, timely outpatient follow-up, an efficient and cost-effective intervention following hospital discharge, has been shown to reduce the readmission risk. However, the current and projected shortage of physicians in primary and specialty care poses a unique dilemma in transitional care planning: optimizing the utilization of post-discharge follow-up to reduce readmission rate while limiting the strain on the limited pool of outpatient physicians. The ideal solution would entail a strategy whereby patients at higher risk for readmission are stratified towards earlier outpatient follow-up and vice versa. This article explores the utility of Institution-specific readmission risk prediction algorithms for assessing patient population for diverse administrative, clinical and socioeconomic risk factors and further classifying the hospital’s patient population into high- and low-risk strata, so that appropriate risk-concordant timing of follow-up can be assigned at the time of hospital discharge, with earlier follow-up assigned to high readmission risk strata. This stratification shall help ensure judicious and equitable human resource allocation while simultaneously reducing hospital readmission rates.
Metrics
2 Record Views
Details
- Title
- Calibrating Readmission Risk Prediction Models for Determining Post-discharge Follow-up Timing
- Creators
- Subha Saeed - Crozer Chester Medical CenterRahul Patel - Crozer Chester Medical CenterRachel Odeyemi - Drexel University, College of Medicine
- Publication Details
- Journal of community hospital internal medicine perspectives, v 12(4), pp 24-27
- Publisher
- Greater Baltimore Medical Center
- Number of pages
- 4
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Medicine (Graduate); College of Medicine
- Web of Science ID
- WOS:001089570500005
- Other Identifier
- 991021860719904721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Medicine, General & Internal