A variety of activities are included under the general heading of outcomes assessment. The terms outcomes assessment and outcomes studies are sometimes used to describe classic clinical trials that include unconventional end points, such as quality of life and cost. However, the same terms may also describe cohort or observational studies, in which the treatment patients receive is not dictated by the study, but rather, patients are monitored in the course of routine clinical care to determine what outcomes they experience as a result of the treatment selected. Finally, outcomes assessment is increasingly used to refer to the process of managing patterns of care in routine clinical practice.
Although these are clearly three distinct activities, they share several important common features. In all cases, there is an emphasis on quality of life and economic outcomes, in addition to the more familiar measures of efficacy employed in traditional oncology studies. There is also an explicit consideration of the importance of patient characteristics in determining outcomes. For example, all of these approaches ask such questions as what is the right drug for this patient? or what particular types of patients are most likely to benefit from one treatment strategy versus another? Finally, all share an unusually broad definition of what constitutes cancer care, including not just pharmaceutical agents but also practice setting, type of provider, and follow-up strategy, for example.
Underlying all of these approaches is a basic model of the relationship between patient and provider characteristics, treatment strategies, and outcomes. As delineated in this model, the choice of interventions is influenced by both patient and provider characteristics. A providers knowledge, preferences, and biases, as well as his/her sociodemographic characteristics may influence therapeutic choices. For example, a number of studies have demonstrated marked geographic variability in patterns of care for common cancers, presumably related, at least in part, to geographic variability in physician beliefs and biases.[1-3] Similarly, patient characteristics, including clinical factors such as age, stage, and comorbidity, as well as patient preferences, are important determinants of treatment choice.
Treatment choice, in turn, influences outcomes. But so do patient characteristics, and it is critical to control for this in assessing the relationship between treatment and outcomes. For example, if one looks only at the relationship between treatment choice and outcome, one could mistakenly conclude that drug A is less effective than drug B, when, in fact, patients receiving drug A were older with more comorbid disease and thus likely to have poorer outcomes with any therapeutic approach. The need to control for this confounding by patient characteristics is the biggest challenge of outcomes research using observational study designs.
Outcomes data can also be used to evaluate providers. Assessments of the patterns of care and, in particular, providers compliance with guidelines, are increasingly being used to assess the quality of care. It is also critical to control for patient characteristics in this type of analysis.
Several different study designs may be employed to examine the effectiveness of procedures or interventions. The major advantage of the randomized, controlled trial is that it eliminates the need to control for confounding patient characteristics, which are assumed to be allocated equally to both arms by chance alone. If the array of outcomes in randomized clinical trials is broadened to include not only response and survival but also quality of life, patient preferences, and costs, the trial can provide invaluable information on the relationship between treatment choice and all of the outcomes of interest, unconfounded by other factors. It is well known, however, that patients who agree to participate in randomized cancer trials are highly selected, and tend to be younger, healthier, and better educated than the average cancer patient. Furthermore, care delivered in the course of a clinical trial may be more intensive than in the usual care setting. As a result, it may be difficult to generalize the results from such trials to all patients seen in the clinical practice setting. In contrast, observational studies examine the results of treatments in a wider array of patients within the usual care setting, but nearly always involve some degree of confounding by patient characteristics. These two study designs are best seen as complementary; each addressing the limitations of the other.
Cohort studies are observational studies in which the treatment is not specified by the study design. Cohort studies may rely on preexisting data bases, such as administrative or claims data sets generated by government or private health insurers. These data bases have been mined successfully to assess patterns of care and outcomes. The amount of clinical detail, including tumor stage and specific treatment types or doses, is often fairly limited in these data bases, however, so that carefully constructed, prospective, cohort studies may be required to answer many questions of interest.
Finally, decision analysis is a frequently used study design in outcomes assessments. These computerized models combine data from a variety of data sources, including randomized trials and cohort studies, to generate estimates of the likely impact of alternative strategies on an array of outcomes, including length of life, quality of life, and costs.
Physicians and the general public are increasingly seeing the results of outcomes studies used to measure the quality of care provided by physicians, hospitals and insurance plans. Physician and hospital report cards, listing severity-adjusted mortality or other outcomes are a good example. Both New York and Pennsylvania have conducted state-sponsored analyses of coronary artery bypass surgery mortality and the results, listed by provider, have been published in the lay press.[4,5] It is likely that such measures will be used with greater frequency in the coming years in other areas of medicine, including oncology, once the appropriate procedures for severity-adjustment are developed. The use of patterns of care and outcomes data for internal fiscal and administrative management by health care institutions and insurance plans is less visible to the public but much more widespread. Physician profiling, in which individual physicians practice patterns are assessed for appropriateness and cost, is increasingly common.
Outcomes management is a process that integrates outcomes research or assessment with monitoring of providers in an attempt to produce high quality, cost-effective medical care. As described by Ellwood in an influential paper published in The New England Journal of Medicine in 1988, outcomes management involves several components. The cornerstone of this approach is the implementation of guidelines. Ellwood stressed the importance of considering the impact of medical care on functioning, as well as disease-specific clinical outcomes, in generating these guidelines, and argued that comprehensive data on the full array of outcomes resulting from medical care should be collected in the course of routine clinical care. He also urged that findings from analyses of these data be disseminated and, in particular, used in an interactive fashion to improve the quality of the guidelines. Current outcomes management programs follow this general paradigm; however, with the recent dramatic changes in the financing of health care, the concept of outcomes management has evolved to include a heavier emphasis on considerations of cost.
The two goals of outcomes management are to maximize patient outcomes and to minimize cost. Inherent in this charge is the need to assess the value for money produced by any specific medical intervention. The standard mechanisms of outcomes management are guidelines for the management of common conditions, and the measurement of provider performance against a set of quality indicators derived from those guidelines.
This tight link between guidelines and outcomes data is increasingly being recognized as critical to the success of outcomes management programs. Achievement of institutional or organizational goals requires that outcomes data are used to monitor compliance with guidelines, and that feedback is given to providers about their level of adherence to those guidelines. Furthermore, it is essential that information on the impact of interventions on outcomes be fed back to guideline writers who must continually update and improve the guidelines.
Many physicians would argue that, in their role as the individual patients advocate, they should support any intervention that they believe will result in a net benefit for the patient. However, for most oncologists today, patient advocacy is just one of several roles in their professional lives. Any physician who is involved in setting policy through membership in a Pharmacy and Therapeutics Committee or participation in the formulation of guidelines, for example, faces the necessity of making decisions about the allocation of resources that are not unlimited. Similarly, physicians who provide care to capitated patients or who have assumed some financial risk for patient care must consider both the outcomes and the costs of an array of therapeutic strategies. Also, any physician practicing in a setting where job security depends on attracting patients in a competitive market must also consider costs. In the late 1990s it is a rare oncologist, indeed, who can ignore considerations of whether the outcomes of therapy justify the costs.
The dilemma for the clinician is how to reconcile the need to do what is best for patients with the need to practice cost-conscious medicine. High-quality outcomes data are at the heart of the solution. If we know how alternative choices influence all of the relevant patient outcomes, we have a better chance of successfully identifying when we can choose a lower cost option without meaningfully compromising the quality of care. Even more importantly, we can justify the decision to pursue high cost options based on compelling evidence of the value added for patients.