Quality of care has been defined by the Institute of Medicine (IOM) as “the degree to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.” In its seminal 2001 report, Crossing the Quality Chasm, the IOM advanced six aims for a quality healthcare system (Table 3-1): patient safety, patient-centeredness, effectiveness, efficiency, timeliness, and equity.1 Note that this framework depicts safety as one of these six components, in essence making it a subset of quality. Note also that, though many clinicians tend to think of quality as being synonymous with the delivery of evidence-based care, the IOM’s definition is much broader and includes matters that are of particular importance to patients (patient-centeredness and timeliness) and to society (equity).

Although the IOM makes clear that quality is more than the provision of care supported by science, evidence-based medicine does provide the foundation for much of quality measurement and improvement. For many decades, the particular practice style of a senior clinician or a prestigious medical center determined the standard of care (this tradition is now sometimes called “eminence-based medicine,” with more than a hint of derision). Without discounting the value of experience and mature clinical judgment, the modern paradigm for identifying optimal practice has changed, driven by the explosion in clinical research over the past two generations (the number of randomized clinical trials has grown from less than 500 per year in 1970 to 20,000 per year in 2010). This research has helped define “best practices” in many areas of medicine, ranging from preventive strategies for a 64-year-old woman with diabetes to the treatment of the patient with acute myocardial infarction and cardiogenic shock.

Health services researcher Avedis Donabedian’s taxonomy is widely used for measuring the quality of care. “Donabedian’s Triad” divides quality measures into structure (how is care organized), process (what was done), and outcomes (what happened to the patient).2 When used to assess the quality of care, each element of the Triad has important advantages and disadvantages3 (Table 3-2). In recent years, as clinical research has established the link between certain processes and improved outcomes, process measures have often been used as proxies for quality. Examples include measuring whether hospitalized patients with pneumonia received influenza and pneumococcal vaccinations, and measuring glycosylated hemoglobin (hemoglobin A1c) at appropriate intervals in outpatients with diabetes.

There is a nuance in the last sentence that is worth highlighting. If the latter measure was of whether the physician checked the hemoglobin A1c (or the blood pressure or cholesterol; the point would be the same) at appropriate intervals, that would be a classic process measure. On the other hand, if the measure was of the actual value of the hemoglobin A1c (i.e., fraction of patients with hemoglobin A1c below 7%), that would be an outcome—more specifically, an intermediate outcome, as it is really a proxy for outcomes we care about, such as mortality, kidney function, or retinopathy.4 Although such intermediate outcomes may seem like attractive hybrids between process measures and true outcomes, care is warranted as intermediate outcomes may, like true outcome measures (see below), require case-mix adjustment to fairly assess the quality of care. More importantly, the medical literature is replete with cautionary reports on the results of interventions that produced beneficial effects on plausible intermediate outcomes (such as suppression of premature ventricular contractions or raising levels of HDL cholesterol) but no effect—or even harm—on the main outcomes of interest.5–7

When the science of case-mix adjustment is suitably advanced (e.g., in cardiac bypass surgery8), measures of outcomes such as mortality rates are often used. The caveat is crucial: if case-mix adjustment is not done well, the surgeon or hospital that accepts (or is referred) the sickest patients may appear to be worse than the lesser surgeon or institution that takes only easy cases.

Finally, when the processes are quite complex and the science of case-mix adjustment is immature, structural measures are often used as proxies for quality. As with process measures, using structural measures in this way assumes that good research has linked such elements to overall quality. Examples here include the presence of intensivists in critical care units, a dedicated stroke service, nurse-to-patient ratios, and computerized provider order entry (CPOE).

It is worth highlighting another special kind of outcome measure. As we’ve seen, the IOM appropriately considers the patient’s experience to be one of the key dimensions of quality. Accordingly, assessments of such experience—generally collected through patient surveys—have become important quality measures, both because they reflect an outcome that we intrinsically care about, and because patients may identify quality problems missed by other methods.9,10 In Medicare’s new “value-based purchasing” program, rolling out in 2012–2014, 70% of the weight is placed on clinical measures such as care processes (e.g., administration of aspirin and beta-blockers to patients with myocardial infarction) and outcomes (30-day readmission rates, case-mix adjusted mortality rates), and fully 30% is given to the results of patient experience surveys.11

Harvard Business School professor Michael Porter has argued that we should focus our attention on outcome measures, as they are what really matters to patients.12 Although I agree in theory, as the Chapter 1 discussion of using hospital mortality rates as quality/safety measures made clear (and at the risk of being obvious), good process measures are more helpful and valid than bad outcome measures.13

Moreover, even good outcome measures don’t obviate the need for process and structural measurement. Let’s say we find that our readmission rates for heart failure patients or our mortality rates for stroke admissions are higher than we would like. The next step, of course, is to examine our processes and structures and compare them to those of our peers or to known best-practice benchmarks. This is what we mean when we say that one of the downsides of outcome measures is that they are not directly actionable.3 In the end, it is clear that a thoughtful mix of all elements of the Donabedian Triad—process, structure, and outcome—is important to any sound program of quality measurement and improvement.

In a series of pioneering studies, Wennberg and colleagues demonstrated large and clinically indefensible variations in care from one city to another for the same problem or procedure.14 Other studies have demonstrated large variations in the quality of care for patients based on race, income, and gender (healthcare disparities).15 Together, these studies hint at a fundamental flaw in modern medical practice: we see such profound variations in common processes and procedures, as well as in the outcomes of comparable patient groups, that we can only conclude that care is often inconsistent with evidence.

Stimulated by these early studies on disparities and variations, researchers have more directly measured the frequency with which doctors and healthcare organizations provide care that comports with best evidence. McGlynn and colleagues studied more than 400 evidence-based measures of quality, and found that practice was consistent with the evidence only 54% of the time.16 Adherence to evidence-based processes generally correlates with ultimate clinical outcomes,17 although, soberingly, some studies have found a weaker relationship than one would anticipate.18,19 Nevertheless, these large differences between best and actual practice have caused patients, providers, and policymakers to seek methods to drive and support quality improvement (QI) activities.

The problems described above have exposed several impediments to the reliable delivery of high-quality care, including the lack of information regarding provider or institutional performance, the weakness of incentives for QI, the difficulty for practicing physicians to stay abreast of evidence-based medicine, and the absence of system support (such as information technology) for quality. Each will need to be addressed in order to make substantial gains in the quality of care.

The first step in QI begins with quality measurement. Fifteen years ago, there were only a handful of generally accepted quality measures, such as whether patients with acute myocardial infarction received aspirin or beta-blockers. More recently, scores of such measures have been promulgated by a variety of organizations, including payers (such as the Centers for Medicare & Medicaid Services [CMS]), accreditors and regulators (such as the Joint Commission), and medical societies (Table 3-3). (In the United States, an organization called the National Quality Forum exists largely to review proposed measures and endorse those that meet prespecified validity criteria.20) These measures have identified many opportunities for improvement among individual physicians, practices, and hospitals.

Given the enormous amount of new literature published each year, no individual physician can possibly remain abreast of all the evidence-based advances in a given field. Practice guidelines, such as those for the care of community-acquired pneumonia or for deep venous thrombosis prophylaxis, aim to synthesize evidence-based best practices into sets of summary recommendations. Although some providers deride guidelines as “cookbook medicine,” there is increasing agreement that standardizing best practices is ethically and clinically appropriate; in fact, so-called High Reliability Organizations “hard wire” such practices whenever possible (Chapter 15). The major challenges for guideline developers are the need to keep guidelines updated as new knowledge accumulates21 and the difficulties in developing guidelines that are relevant to the care of patients with multiple, potentially overlapping illnesses.22 Clinical pathways are similar to guidelines, but attempt to articulate a series of steps, usually temporal (on day 1, do the following; on day 2; and so on). They are generally more useful for stereotypical processes such as the postoperative management of patients after cardiac bypass surgery or hip replacement.

Although one could argue that professionalism should be sufficient incentive to provide high-quality care, our recent recognition that the unwavering provision of such care depends on a system organized to reliably translate research into practice means that it will take significant investments (i.e., in developing guidelines, physician education, hiring case managers or clinical pharmacists, building information systems, and more) to deliver optimal care. The traditional payment system, which compensates physicians and hospitals equally whether quality is superb or appalling, provides no incentive to make the requisite investments.