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Evaluating Increasing Hospital Closure Rates in U.S.: A Model Framework and Lean Six Sigma Deployment Approach for Quality Improvement Initiatives to Prevent Further Closures in Rural and Disadvantaged Locations

Authored by: Soma Sekar Balasubramanian , Erick C. Jones

Supply Chain Engineering and Logistics Handbook

Print publication date:  December  2019
Online publication date:  November  2019

Print ISBN: 9781138066519
eBook ISBN: 9781315159096
Adobe ISBN:

10.1201/9781315159096-11

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Abstract

Closures of hospitals occur from time to time. Several rural hospitals were in the U.S. recently in an unprecedented manner, and hundreds of others are at the risk of closing. The Patient Protection and Affordable Care Act (PPACA), which was amended in 2010, has brought sea of changes in the healthcare industry right from changing the way how hospitals receive reimbursements by introducing several new programs targeting hospitals’ quality of care to expanding health insurance for millions of poor and underprivileged populations in the country. The goal of this study is to understand the closure of rural hospitals in line with the recent reform changes and identify countermeasures to prevent further closures in the future. The first section of the research identifies and evaluates the factors behind the closures by creating a binary logistic model to predict closures of hospitals under study. Three models were created: first using market characteristics factors (financial importance), second using hospitals’ core processes’ measures (operational importance), and a final model combining both models. The second section of the research identifies and evaluates process improvement initiatives by deploying Lean Six Sigma projects in the hospitals. A Hospital Enterprise System (HES) that encompasses three hospitals is used for the study. A 0–1 Integer Linear Programming method (Knapsack Method) is proposed for the selection of projects. Two scenarios are created in which the first scenario optimizes the cost savings by selecting projects among three hospitals. In the second scenario, one among the three hospitals is assumed to be present in a disadvantaged location, and few core measure projects are mandatorily implemented to improve the performance of processes to avoid penalties, reimbursement cuts and achieve cost savings to prevent closures in the future.

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