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best for patients, there is a growing body of research suggesting a large gap between what is known to be the best and what is done in actual practice. There are numerous studies documenting that the quality of care provided varies widely across populations within the US health care system. Populations can be defined by patient characteristics such as race or socioeconomic status or by where care is provided – the institution or even the region of the country. Thus, whether patients receive high-quality care, consistent with best medical knowledge, is largely a function of the local system in which they receive care. This second domain we will consider, the local system of care, is comprised of the providers, resources, and systems that collectively provide care. There are several attributes of providers and processes that increase (or decrease) the likelihood that patients will receive appropriate care. We consider systems “high quality” if they are aligned to promote adherence to best practices and thereby achieve the best outcomes. Much of the research in this area has traditionally been descriptive – what might be considered patterns of care research – depicting racial disparities or documenting the relationship between hospital volume and outcome, or focused on defining ways to measure quality. As our understanding of variations in care and ability to quantify quality has become more sophisticated, we have recently moved into a more interventional approach to this type of work, focusing on disciplines such as quality improvement and implementation science.

Intellectual Disciplines and Research Tools

Disparities

A large body of research shows that certain racial and ethnic groups have worse surgical outcomes compared to others. Although for some diseases differences in outcomes can be explained in small part by differences in biology, a much larger component of the disparities problem in the

USA is due to the health care system, both the microand

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the macro-systems in which care is delivered. The importance of investigating and intervening at both local and national levels is illustrated by the depiction of Fig. 7.1 of this discipline at the intersection of the local microand macro-system level.

Disparities in access and quality of care exist within institutions, and there is growing evidence to support complicated social and cultural etiologies. For example, using data from the National Initiative for Cancer Quality Care to investigate breast cancer care, we found that disparities in rates of reconstruction based on age, race, and education were related to the frequency with which providers discussed reconstruction with their patients.4 Once the discussions take place, lower rates of reconstruction were observed in older and Hispanic patients and those who were born outside of the USA, suggesting either a cultural preference or language barriers. Issues such as trust and communication have been demonstrated to vary according to race and play a role in the patient–provider interactions in a number of diseases.

Additionally, a growing body of research suggests that our health care delivery system remains segregated, with referral patterns directing blacks and Hispanics to lowerquality hospitals. A recent study provides clear evidence of these segregated referral patterns. Liu and colleagues used the California hospital discharge database to investigate access to high-volume hospitals (as a proxy for quality). In this study published in the Journal of the American Medical Association, blacks were significantly less likely than whites to receive care at high-volume hospitals for 6 of the 10 operations (relative risk [RR] range, 0.40–0.72), while Hispanics were significantly less likely to receive care at high-volume hospitals for 9 of 10 operations (RR range, 0.46–0.88).5 In subsequent studies, this general finding of less access to high-quality providers among blacks & Hispanics vs. whites has been proven true for a broad array of surgical (and medical) conditions. Such entrenched referral patterns are an important, but often overlooked, attribute of our health care delivery system.

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Quality Measurement

Prior to discussing how to improve performance, we must first consider how to measure quality. Most often, quality is measured according to the Donabedian triad of structure, process, and outcome. Structure refers to fixed attributes of the system (e.g., hospital volume, surgeon specialty). Process refers to the details of care associated with good outcomes (e.g., adherence to recommended perioperative antibiotics). Outcomes represent the end results of care, most often morbidity and mortality, but they can also include functional status, quality of life, and patient satisfaction.

Perhaps the most important work evaluating structural aspects of quality was conducted by Dr. John Birkmeyer in a paper published in the New England Journal of Medicine in 2002.6 He used national Medicare claims data to study the relationship between hospital volume and risk-adjusted mortality for 14 high-risk surgical conditions. Although there were many prior studies demonstrating the volume–outcome relationship, this paper was the first to examine a broad range of procedures in a systematic way. Perhaps the greatest contribution of this study was the finding that the strength of the volume–mortality relationship varied across procedures. For some rare procedures,such as pancreatectomy and esophagectomy, the relationship was quite strong, with more than 10% mortality differences between highand low-volume hospitals. In contrast, for coronary artery bypass surgery and carotid endarterectomy, the differences were only approximately 1% between highand low-volume hospitals.

One recently published study evaluated the impact of the process measures used in the Center for Medicare and Medicaid Services (CMS) Surgical Care Improvement Program (SCIP).7 Stulberg and colleagues used an inpatient administrative database from Premier, Inc., to study the relationship between SCIP processes (e.g., appropriate selection, timing, and redosing of prophylactic antibiotics) and postoperative wound infections. In their paper published in the Journal of the American Medical Association, the authors found that none of the individual SCIP measures were independently associated

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with surgical infection rates. However, they did find that a composite process measure was associated with lower infection rates (14.2 vs. 6.8 per 1,000 discharges [adjusted odds ratio, 0.85; 95% confidence interval, 0.76–0.95]). This study was the first to demonstrate the relationship between SCIP measure adherence and outcomes in the real world.

Given the weak relationship shown in this study, along with similar findings from other studies, many have suggested that CMS should move from process measures to reporting risk-adjusted infection rates. However, measuring surgical quality with outcomes has its own limitations. The Achilles heel of hospitalor surgeon-specific outcome measurement is small sample size (i.e., the role of chance). For the large majority of surgical procedures, very few hospitals or surgeons have sufficient adverse events (numerators) and cases (denominators) for meaningful, procedure-specific measures of morbidity or mortality. As another example of research in quality measurement, a study by our group published in the

Journal of the American Medical Association examined seven surgical procedures, for which hospital mortality rates had been recommended as quality indicators by the Agency for Healthcare Quality and Research.8 For only one operation, coronary artery bypass graft (CABG), did the majority of US hospitals perform enough cases over a 3-year period to detect with statistical confidence mortality rates at least twice the national average. For the remaining six procedures, few hospitals had sufficient caseloads to meet this low bar of statistical power. This study shed light on the problem with small sample size and highlights the importance of being thoughtful about analyzing hospitaland surgeon-specific outcomes. There are newer statistical modeling techniques, discussed later, that can be used to address this problem.

There are several important research tools for quality measurement (Table 7.2). These include large database analyses, advanced statistical modeling techniques, and risk-adjustment techniques. There are a number of large administrative or clinical databases available. For example, more than 400 hospitals across the USA participate in the American College of

Surgeons National Surgical Quality Improvement Program