JOURNAL ARTICLE

Effects of Hospital-Acquired Conditions on Readmission Risk: The Mediating Role of Length of Stay.

  • Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2023, v. 25, n. 4. P. 1603 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Bichescu, Bogdan; Hilafu, Haileab 3 of 3

Abstract

This article examines the relationship between hospital-acquired conditions (HACs)—undesirable complications occurring during hospital stays—and their impact on length of stay (LOS) performance and 30-day readmission risk among patients hospitalized for acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PN) in Florida from 2010 to 2014. Using patient-level data and econometric analyses, the study finds that exposure to HACs increases the odds of readmission by 37% and lengthens LOS by 79% relative to the Centers for Medicare and Medicaid Services' (CMS) Geometric Mean LOS (GMLOS) standard. Importantly, the deviation of LOS from GMLOS mediates the HAC-readmission relationship, with longer LOS substantially reducing readmission risk for HAC patients—benefits that plateau when LOS exceeds about 65% above GMLOS. The findings suggest that LOS is a critical, hospital-controlled mechanism to mitigate the adverse effects of HACs on readmissions, offering practical guidance for clinicians on discharge timing and informing policy discussions on refining CMS reimbursement and quality measures.

Additional Information

  • Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2023/07, Vol. 25, Issue 4, p1603
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:1523-4614
  • DOI:10.1287/msom.2022.0088
  • Accession Number:164959429
  • Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.