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Invited Commentary |

Variation Reduction to Reduce Readmission A Figment of Imagination or Reality of the Future?

Sharon M. Weber, MD1; Gregory D. Kennedy, MD, PhD1
[+] Author Affiliations
1Department of Surgery, University of Wisconsin–Madison, Madison
JAMA Surg. 2015;150(11):1049-1050. doi:10.1001/jamasurg.2015.2234.
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One of the principles of the Six Sigma methodology is variation reduction. Since the publication of the Institute of Medicine’s monograph, “To Err is Human: Building a Safer Health System,”1 health care systems and the Centers for Medicare and Medicaid Services have been interested in reducing variation through the application of standard process measures in an attempt to achieve the following 6 pillars of high-quality health care: safety, timeliness, effectiveness, efficiency, equity, and patient centeredness. The standardization of process measures and management pathways has clearly led to improved results in many clinical situations. However, we face a significant problem in medicine, referred to as common cause variation in the principles of the Six Sigma. This is the variation introduced by uncontrollable patient factors. The study by Gani et al2 addresses a gap in knowledge regarding the problem of readmission. This article tackles the issue of variability in readmission rates and reveals that variability is primarily due to the common cause of patient-related factors (including race/ethnicity, insurance status, comorbidity, complications, and length of stay) rather than surgeon- or surgical subspecialty–specific factors. There are limitations in this administrative claims–derived data set, including the lack of more robust complication data, which likely is reflected in the fact that only 30% of readmitted patients were reported to have a complication when the overwhelming data show that surgical readmissions are driven by complications, specifically those that occur after discharge35; the lack of knowledge regarding readmissions to other hospitals, which has been found to occur in about 15% of cases6; and a paucity of information on sociodemographic factors, which, at a minimum, likely influence the decision to readmit.7

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