Literature DB >> 29206678

Quality Improvement in Bariatric Surgery: The Impact of Reducing Postoperative Complications on Medicare Payments.

Brian T Fry1,2, Christopher P Scally1,3,4, Jyothi R Thumma1, Justin B Dimick1,3.   

Abstract

OBJECTIVE: To determine the temporal relationship between reducing surgical complications and costs, using the study population of bariatric surgery.
BACKGROUND: Understanding the relationship between quality and costs has significant implications for the business case of investing in performance improvement. An unprecedented focus on safety in bariatric surgery has led to substantial reductions in complication rates over time, making it an ideal patient population in which to examine this relationship.
METHODS: We performed a retrospective review of Medicare beneficiaries undergoing bariatric surgery in the years 2005 to 2006 and 2013 to 2014 (total N = 37,329 patients, 562 hospitals). Hospitals were ranked into quintiles based on their degree of improvement in risk and reliability-adjusted 30-day rates of serious complications across the time periods. Multivariable regression was used to calculate corresponding changes in average price-standardized payments for each quintile of hospitals.
RESULTS: We found a strong association between reductions in complications and decreased Medicare payments. The top 20% of hospitals had a decrease in average serious complication rate of 7.3% (10.0%-2.7%; P < 0.001) and an average per-patient savings of $4861 (95% confidence interval $3921-5802). Conversely, the bottom 20% of hospitals had smaller decrease in complication rate of 0.8% (4.4% to 3.6%; P < 0.001) and a smaller average savings of $2814 (95% confidence interval $2139-3490).
CONCLUSIONS: When analyzing Medicare patients undergoing bariatric surgery, hospitals with the largest reductions in serious postoperative complications had the greatest decrease in per-patient payments. This study demonstrates the potential savings associated with quality improvement in high-risk surgical procedures.

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Year:  2018        PMID: 29206678     DOI: 10.1097/SLA.0000000000002613

Source DB:  PubMed          Journal:  Ann Surg        ISSN: 0003-4932            Impact factor:   12.969


  1 in total

1.  Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database.

Authors:  Jacob Nudel; Andrew M Bishara; Susanna W L de Geus; Prasad Patil; Jayakanth Srinivasan; Donald T Hess; Jonathan Woodson
Journal:  Surg Endosc       Date:  2020-01-17       Impact factor: 3.453

  1 in total

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