A Generalized Assessment of the Impact of Regionalization and Provider Learning on Patient Outcomes. (November 2016)

• Record Type: Journal Article
• Authors: Zhang, Yun; Lee, Shoou-Yih D.; Gilleskie, Donna B.; Sun, Yepeng; Padakandla, Arun; Jacobs, Bruce L.; Montgomery, Jeffery S.; Montie, James E.; Wei, John T.; Hollenbeck, Brent K.
• Is Part Of: Medical decision making. Volume 36:Number 8(2016)
• Page Start: 990
• Page End: 998
• Publication Date: 2016-11
• Subjects: volume-outcome relationship -- learning -- care regionalization -- binary integer programming; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X15593282
• Languages: English
• ISSNs: 0272-989X

Abstract

We present a generalized model to assess the impact of regionalization on patient care outcomes in the presence of heterogeneity in provider learning. The model characterizes best regionalization policies as optimal allocations of patients across providers with heterogeneous learning abilities. We explore issues that arise when solving for best regionalization, which depends on statistically estimated provider learning curves. We explain how to maintain the problem's tractability and reformulate it into a binary integer program problem to improve solvability. Using our model, best regionalization solutions can be computed within reasonable time using current-day computers. We apply the model to minimally invasive radical prostatectomy and estimate that, in comparison to current care delivery, within-state regionalization can shorten length of stay by at least 40.8%.