Competing Practice Guidelines: Using Cost-Effectiveness Analysis To Make Optimal Decisions

Wide variation in physician care patterns [1-5] in the setting of rapidly increasing health care spending [6, 7] has led to efforts to foster greater consistency and value. For example, numerous clinical practice guidelines, algorithms, critical pathways, and standards (hereafter referred to collectively as clinical guidelines) have been developed in attempts to enhance quality of care while reducing avoidable variation in the costs of providing that care [8, 9]. Making the best clinical decision for a given patient requires knowing the potential costs and outcomes of different choices about treatment [10, 11]. This allows a decision maker to prioritize options according to their value, or cost-effectiveness [12, 13]. Applying more valuable clinical strategies first and following them with strategies of successively decreasing value should achieve optimal allocation of limited clinical and financial resources at the level of the individual patient. At the level of a population of patients with multiple clinical conditions, how does one decide among numerous different, clinically acceptable, and ethically valid treatment options, all of which differ in effectiveness and cost? To efficiently manage scarce resources, planners in the industrial sector have constructed complex mathematical models to capture key relations between resource and output variables, such as the availability of financial resources, suppliers, raw materials, producers, and distribution channels and expected demand. After describing the values that certain variables are allowed to take, one can use the set of mathematical techniques collectively known as optimization (using linear or nonlinear programming) [14] to maximize or minimize one key variable (such as benefit or risk). For example, an airline carrier with routes connecting several dozen cities and a limited number of aircraft and crew members generally wishes to minimize total cost. Optimization enables efficient routing adjustments and aircraft and crew member deployment by taking into account such constraints as local costs of jet fuel and required rest time for crew members. The result is the best possible arrangement for delivering the best possible outcomes with limited resources. In health care, the use of optimization is still new and is limited to well-defined areas in which one can easily summarize pathophysiology with mathematical equations. These areas include ventilator management in critically ill patients, adjustment of oral anticoagulation, treatment planning in radiation therapy, and maintenance of proper dialysate content in hemodialysis [15-18]. In health care, an important implication of this industrial strategy is that choosing a slightly less costly and less clinically effective treatment for a prevalent condition may conserve enough resources to permit the purchase of more valuable treatments for other, less prevalent conditions. Whereas each choice may not be the most cost-effective option for an individual patient, the constellation of interventions could best improve overall public health. In this article, we use optimization, supported by existing clinical guidelines, to show 1) which group of clinical options maximizes overall benefit for a population of patients and 2) how this group of options differs from options that maximize benefit for individual patients. We also show how the group of selected options changes according to the extent of resource constraints. Finally, we suggest ways in which cost-effectiveness analysis should be used to allocate resources. Methods In this study, we used optimization to select the best clinical options that, taken together, maximized the number of years of life added to a hypothetical population of 100 000 persons with an age and sex distribution similar to that of the United States in 1991 [19, 20]. For clarity, we considered only a limited number of diseases in the model. (More current clinical guidelines and epidemiologic inputs could easily be used to update our study.) Selection of Interventions and Clinical Situations Using MEDLINE to search the clinical literature from 1986 to the present, we sought clinical practice guidelines that 1) addressed clinical situations in which guidelines have actually been used, 2) evaluated differences in outcomes and direct medical costs between or among two or more ways of providing care, 3) used added years of life per patient (unadjusted for quality of life) to measure outcomes of care [21, 22], 4) made recommendations on the basis of cost per unit of outcome [for example, per added year of life], and 5) discounted both costs and outcomes at 5% per year (the standard approach to discounting) [23, 24]. Table 1 lists the six interventions that we selected for the model [25-30]. We included examples of major categories of health activities: prevention (prevention of hepatitis B), screening (screening for colorectal cancer), diagnosis (diagnosis of stable angina), risk factor reduction (risk factor reduction for hypercholesterolemia and smoking), and treatment (treatment of recurrent ventricular arrhythmia). Table 1. Interventions, Clinical Options, and Clinical Subgroups for Decision Making* For each clinical intervention, we summarized the key, mutually exclusive directions that could be followed, listing them as clinical options (Table 1, column 2). For the sake of clarity, Table 1 shows only the relevant characteristics of each option; the original [25-30] may be consulted for specifics on such variables as age ranges and doses. Each clinical option is considered to be of some benefit and is part of the standard repertoire of options that competent physicians might have offered their patients in 1991. Our task was to choose, for a population of patients, a single best option for each clinical intervention. We based our selection on total population benefit rather than on benefit for the individual groups of patients for whom guidelines were developed. Finally, in addition to assuming the existence of a standard U.S. population in terms of age and sex, we accounted for the fact that several of the cost-effectiveness studies reported results for multiple, more specific types of patients. Columns three and four in Table 1 highlight the instances in which more than one selection per clinical intervention was needed. For example, where guideline data existed for several types of patients, the model was programmed to select the best option for each type of patient. Selecting a clinical option for colorectal cancer screening, for instance, required only one decision (among the five clinical options) because the reference that we used discussed the group of persons at 65 years of age. However, selecting an option for the diagnosis of stable angina required four decisions, one for each of the clinical subgroups, beginning with those 35 to 44 years of age and ending with those 65 to 74 years of age. We sought the choice of one specific clinical option for each of the 22 different clinical subgroups of patients. Derivation of Population Cost and Effectiveness Data For each of the 22 clinical subgroups that required a selection decision, we assigned (from a cluster of allowable options) one baseline clinical option to serve primarily as the standard against which the cost and effectiveness of each competing option could be compared (Appendix Figure 1). Each baseline strategy was considered to be the option most widely currently practiced. If the optimization program selected the baseline strategy option for the population-wide solution, no additional years of life were added to the population at no additional cost because the incremental cost and effectiveness of a clinical option compared with itself are zero. Appendix Figure 1. For each selection decision, we calculated the incremental cost and incremental effectiveness (in years of life) of each of the allowable alternative clinical options. In other words, we obtained cost and effectiveness data directly from each study in the literature but recalibrated the data, when necessary, to express cost and effectiveness in terms of the baseline option. To determine the incremental cost and effectiveness of each alternative option, we adjusted the data, expressed per patient as obtained from the literature, for the approximate demand for that option over the next 12 months in our 100 000-member population. For example, a paper might report that a specific alternative clinical option (compared with the baseline strategy) resulted in a gain of 1.5 more years of life per patient at an additional cost of $20 000 per patient. If the estimated incidence of that clinical condition in our population over the next year was 30 patients, the population-wide incremental cost and effectiveness entries would be $600 000 and 45 years of life, respectively. We expressed all costs in 1991 U.S. dollars [31]. Derivation of Individual Cost and Effectiveness Data We also identified the most cost-effective clinical option for individual patients within each of the 22 clinical subgroups compared with the baseline strategy, independent of any other selection decision. This option delivered the greatest improvement for each clinical subgroup in years of life per unit cost compared with baseline [32]. For several subgroups, the baseline option was the most cost-effective for those individual persons. For example, if no alternative clinical option provided better effectiveness (more years of life) than the baseline strategy, the baseline strategy was retained as being most cost-effective for the individual patient (Appendix). Running the Population Model A spreadsheet was used to summarize all key input variables for the model (Appendix Figure 1). We listed the cluster of clinical options available for each of the 22 clinical subgroups, and we listed the contribution that each alternative option would make to total population cost and effectiveness if it was selected