Learning to Deliver Better Health Care

Most Americans assume that the health care they receive is determined by firm medical evidence, that the practice of medicine is fundamentally scientific. But this is far from true. For over 30 years, John Wennberg and others have been documenting remarkable variations in both clinical practice and spending across U.S. regions. These variations highlight the serious challenges faced by scientists, clinicians, academic medicine and policymakers—and the opportunities to improve both the quality and costs of care.

To understand the problems, the promise, and a path forward, it helps to distinguish two distinct categories of care: biologically targeted interventions and care-delivery strategies. Biologically targeted interventions are focused on a specific anatomic problem or disease process. Examples include the decision about whether to adopt a specific screening test for cancer or whether to treat a patient with prostate cancer with surgery or radiation therapy. Such interventions can be well specified not only in terms of the underlying anatomic or physiologic problem to be addressed, but also in terms of the expected intermediate and long-term outcomes and how these vary across clinical subgroups. Many of the dramatic improvements in health achieved over the past decades are a result of advances in biomedical knowledge and the development of such biologically targeted interventions.

Care delivery strategies are rarely considered explicitly in the day-to-day practice of clinical medicine. This category refers not to what care is provided (what drug, what device, what surgical procedure), but to how a specific biologically targeted therapy is delivered. The questions include who should provide the care (the patient him- or herself, an advanced practice nurse, a primary care physician, or a specialist); where care should be delivered (home, outpatient facility, or hospital); and how often patients should be monitored and reevaluated. Questions about care delivery also encompass system- and policy-level issues, such as how care should be organized, what kinds of resources should be deployed, how care should be paid for and financed, and how to improve the quality of care.

A recent Institute of Medicine workshop report, The Learning Healthcare System, highlighted the many limitations of the current evidence base, focusing primarily on the challenges surrounding biologically targeted therapies. Such limitations include the lack of any evidence on the efficacy or effectiveness of many interventions, the difficulty of extrapolating from trials carried out on selected populations to others (such as the elderly or those with multiple chronic conditions), and the growing recognition that the benefits of interventions vary substantially in different subgroups of the population included in the trials, with many lower risk patients experiencing little or no benefit (or even harm) and a relatively small subset receiving most of the benefits.

The relative magnitude of the uncertainty surrounding the use of selected discrete, biologically targeted therapies can be illustrated in the regional variations in the rates of these services in the Medicare population. In this context, it is useful to distinguish effective care (treatments for which the evidence of benefit is strong and there are no tradeoffs among benefits and harms) from preference-sensitive care (treatments for which patients’ values about the different outcomes may vary). An example of the former would be hospitalizations for hip fracture: The diagnosis is straightforward and the therapy (inpatient surgical repair of the fracture) is required. Variations in utilization rates are due entirely to underlying variations in the incidence of the disease and are relatively modest across U.S. regions. Examples of the latter would include screening for prostate cancer (where patient attitudes toward the risks of treatment must be weighed against the still unproven benefits of screening) or percutaneous coronary interventions for stable angina (where the modest benefit in terms of angina relief must be weighed against the life-long need for antiplatelet therapy, among other risks). In looking at most common biologically targeted interventions, both diagnostic and therapeutic, dramatic variability across U.S. regions becomes apparent. Addressing these variations will require not only collecting better information about risks and benefits (comparative effectiveness research) but also ensuring that treatment decisions reflect the well-informed judgments of patients rather than simply the opinions of providers.

There also are marked differences across regions in how care is delivered. Although virtually all Medicare beneficiaries have access to care (defined as at least one physician visit during the year) and there is thus little regional variation in the age-sex-race adjusted rate of at least one physician visit, there is a marked variability in the use of other care delivery strategies. For example, there is moderate regional variation in the number of primary care visits and inpatient days and extensive variation in the number of specialist visits and days in intensive care for patients with chronic illness nearing the end of life. Because variations in the use of these services are associated with the local capacity of the delivery system (how many physicians, how many hospital beds), these services are often referred to as being supply sensitive.

One of the fundamental reasons for distinguishing care delivery strategies from the use of biologically targeted interventions is their distinct relationship to variations in spending. Higher spending is not associated with greater use of biologically targeted interventions: whether these are treatments that all patients should receive (effective care) or interventions where patients’ judgments about how they value the risks and benefits should determine the treatment choice (preference-sensitive care). Rather, higher spending is due largely to differences in care delivery: how frequently patients are seen (evaluation and management services), how much time they spend in the hospital, and the intensity with which they are monitored (diagnostic tests and imaging).

Benchmarks of efficiency

The critical question underlying the variations in practice and spending is their relationship to health outcomes. Over the past 10 years, a number of studies have explored the relationship between higher spending and the quality and outcomes of care.

Patients’ experiences and outcomes. Whether the study was carried out at the state level, across hospital referral regions, or across major academic medical centers, a consistent pattern has emerged: The quality of care as reflected in process measures of care is worse when spending and the intensity of care delivery are greater. Among patients hospitalized with hip fractures, colon cancer, and acute myocardial infarction who were followed for up to five years, mortality rates in higher-spending regions and hospitals were no better or slightly worse than in lower-spending delivery systems. In regions where spending growth was greatest, survival after myocardial infarction improved more slowly than in regions where spending growth was slower. Finally, Medicare beneficiaries’ overall satisfaction with care was no better in higher-spending regions, and their perceptions of the accessibility of care were somewhat worse.

Physician attributes, practice settings, and perceptions of care. On a per-capita basis, the highest-spending quintile of hospital referral regions have 65% more medical specialists per capita, 75% more general internists, and 25% fewer family practitioners than the lowest-spending quintile. In higher-spending regions, a substantially higher proportion of physicians are foreign medical graduates, fewer are board certified, and they are much more likely to practice in small groups than are physicians in lower-spending regions. When surveyed, physicians in higher-spending regions are more likely to report that the continuity of care with their patients, as well as the quality of communication with other physicians, is inadequate to support high-quality care. And in spite of the substantially greater per-capita supply of both beds and specialists in higher-spending regions, physicians in these regions are more likely to perceive scarcity: They are more likely to report that it is difficult to get a patient into the hospital and that it is hard to obtain adequate medical specialist referrals.

These findings are consistent with the hypothesis that the lower-spending regions represent a reasonable benchmark of efficiency. In fact, if all U.S. regions could safely adopt the organizational structures and practice patterns of the lowest-spending regions, Medicare spending would decline by about 30%, according to several studies. Although it may not be realistic to reduce spending by that amount (which would require that about one-third of those working in health care find employment in other sectors of the economy), the magnitude of the differences in practice, combined with the fact that the differences in spending are largely due to differences in care delivery, points to an important challenge: Improving efficiency will require attention not only to the comparative effectiveness of biologically targeted interventions, but also to addressing the underlying causes of the differences in care delivery across regions and systems.

Reaching a diagnosis

A number of studies have explored the underlying causes of the regional differences in spending and the intensity of care delivery. Patients’ preferences for care vary slightly across regions, but not enough to explain the observed magnitude of spending differences. For example, Medicare beneficiaries in high-spending regions are no more likely to prefer aggressive end-of-life care than those in low-spending regions. Differences in the malpractice environment are associated with differences in both practice and spending, but explain less than 10% of state-level differences in spending and have a comparably small impact on differences in the growth in spending across states. The role of capacity is clearly important, especially in a payment system that ensures that physicians stay busy and hospital beds stay full; but the hospital bed supply and physician supply combined explain less than 50% of the difference in spending across regions.

The most recent studies have focused on the use of clinical vignettes to explore how physicians’ judgments vary across regions of differing spending levels. These studies have found that physicians in higher-spending regions were no more likely to intervene in cases where evidence was strong (such as chest pain with an abnormal stress test), but much more likely to recommend discretionary treatments (such as more frequent visits, referral to a specialist, or use of imaging services) than those in low-spending regions.

These findings suggest a likely explanation for the dramatic differences in spending across regions and the paradoxical finding that higher spending seems to lead to worse quality and worse outcomes. Current clinical evidence is an important, but limited, influence on clinical decisionmaking. Most physicians practice within a local organizational context and policy environment that profoundly influence their clinical decisions, especially in discretionary settings. Hospitals and physicians each face incentives that will in general reward the expansion of capacity (especially for highly reimbursed services) and the recruitment of additional procedure-oriented specialists. When there are more physicians, relative to the size of the population they serve, physicians will see their patients more frequently. When there are more specialists or hospital beds available, primary care physicians and other specialists will learn to rely on those specialists and use those beds. (It is more efficient from the primary care physician’s perspective to refer a difficult problem to a specialist or admit that patient to the hospital than to try to manage the patient in the context of an office visit for which payments have become relatively constrained).

The consequence is that “reasonable” individual clinical and policy decisions (reasonable given the state of current evidence) lead in aggregate to higher utilization rates, greater costs, and, inadvertently, worse quality and worse outcomes. The latter is most likely because having more different physicians involved increases the likelihood of mistakes (too many cooks spoil the soup), and hospitals are dangerous places if one does not need to be there. The key element of this theory is that because so many clinical decisions are in the gray areas (how often to see a patient, when to refer to a specialist, when to admit to the hospital), any expansion of capacity will result in a subtle shift in clinical judgment toward greater intensity.

Such observations and their likely explanations point to the need for much better evidence. Physicians and patients need better evidence about the risks and benefits of discrete, biologically targeted interventions and how these risks and benefits vary across different subgroups of the population, especially those often excluded from current randomized trials. But there also is a critical need for much better evidence about care delivery. No matter how good the clinical evidence about specific interventions becomes, many, if not most, clinical decisions will still require judgment. And because such gray areas will always remain, there also will remain a need for evidence that can guide clinicians, administrators, and policymakers when they are making decisions about care delivery.

Although the need for evidence may appear overwhelming, an important opportunity lies in recognizing that the information systems and analytic approaches required to improve the evidence base for biologically targeted interventions and for improving care delivery are fundamentally the same. In the ideal world of improved information systems and electronic records that might allow relatively routine assessment of both short- and long-term health outcomes and effective follow-up of patients, the capacity to evaluate both care delivery and biologically targeted interventions would be critical, at least in part because lack of information on the local context (delivery-system attributes) would sharply limit the current ability to properly interpret studies of biologically targeted interventions.

Challenge for academic medicine

The critical importance of health care spending to the nation’s future financial health has become overwhelmingly evident. The capacity to provide affordable health care coverage to the population and to pay for the new biologically targeted interventions that are under development will clearly depend not only on the costs of the interventions but also on the costs of delivering those interventions. Academic medicine and the federal agencies that provide their research support have largely focused on improving our understanding of disease biology, while ignoring the need to understand and address the dramatic variations in care delivery that are observed both across the country and among academic medical centers themselves.

Table 1 points to the magnitude of the opportunity, and the challenge, for academic medicine. The upper portion of the table focuses on the degree to which each of the five members of the U.S. News & World Report’s honor roll of academic medical centers is able to deliver proven clinical interventions to eligible patients during an acute inpatient stay. The lower portion of the table highlights the differences in spending and overall intensity of care. The specific data focus on care provided in the last six months of life, but these patterns of practice are highly predictive of how these institutions treat other seriously ill patients. The differences are substantial. Each institution provides high-quality inpatient care. But patients at the University of California, Los Angeles, have twice as many visits, spend roughly 50% more time in the hospital, and cost the Medicare program about twice as much as those treated at the Mayo Clinic in Rochester, Minnesota, or at the Cleveland Clinic in Ohio.

If all U.S. delivery systems could achieve the apparent efficiency of a Mayo or Cleveland Clinic, the resources available to expand coverage to the uninsured or provide interventions of proven benefit to those who will not be able to afford them would be substantial. Failure to address this challenge would call into question not only the scientific integrity of the enterprise (are the leaders of academic medicine truly committed to asking important questions?), but also our moral authority as health care providers (how can we continue to ignore obvious opportunities to improve the quality and future affordability of care?).

Academic medicine has the opportunity to lead the development of a learning health care system. Such an effort should include a focus not only on exploring the science of disease biology and improving the evidence to support the use of biologically targeted interventions but also on promoting the sciences of clinical practice and the evidence to support improvement in care delivery.

This article and the preceding piece by Peter Orszag are derived from the forthcoming Institute of Medicine book Evidence-Based Medicine and the Changing Nature of Health Care (National Academy Press).