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Surgeons are under enormous pressure from multiple healthcare stakeholders to measure and improve their performance. Government regulators are publicly reporting patient outcomes and satisfaction scores.1 Payers are reducing reimbursements based on quality measurements.2 Licensing boards and professional societies are revising member certification to increasingly include performance evaluation.3 Patients are now searching online for information about surgeon outcomes to guide where they seek care.4 Surgeons themselves have created quality collaboratives to share best practices and improve their own performance.5,6 In short, we are in an era of unprecedented focus on evaluating and reporting the work of surgeons.

Despite the widespread interest in measuring and improving surgical quality, little consensus exists on what measures to follow or which strategies to implement. In this chapter, we describe the general principles of performance measurement in surgery, including how to choose among measures. We then outline the benefits and drawbacks of different performance improvement strategies.


Establishing accurate measurement of surgical quality is essential to any attempt at improving performance. The following sections describe the key principles to understanding the underlying methodology and options for performance measurement.

Understanding Variation in Outcomes

Some hospitals and surgeons seem to simply do better than others, and this reality creates an opportunity to learn and improve from the best performers. However, reliably and fairly identifying high and low performers can be challenging. In addition to the quality of care provided, patient outcomes can also be highly influenced by chance and case mix. To understand how best to measure quality, it is important first to explore why outcomes vary across hospitals and surgeons.


Variation in outcomes across surgeons and hospitals may be the result of good or bad luck. The role of chance becomes important in low-volume procedures (eg, pancreatectomy) or when the event rate is low (eg, death after a cholecystectomy). Good or bad luck can result in either a type 1 or type 2 error.

Type 1 errors occur when extreme outcomes—good or bad—are attributed to quality when they actually are simply due to chance. Consider, for example, the “zero-mortality paradox” observed in Medicare claims data.7 A hospital with a 0% mortality rate 3 years in a row for pancreatic resection might be considered the highest quality; however, in a subsequent year, it might have a 30% higher mortality rate than other hospitals. The apparent paradox is explained by the fact that most hospitals with a 0% mortality rate simply have a low case volume and good luck, and thus, this rate does not accurately reflect the quality provided at these hospitals. In other words, the difference between a low-volume hospital (eg, 5 pancreatectomies a year) ...

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