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Determining the Value of Disease Management Programs
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Journal on Quality and Safety
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Determining the Value of Disease Management Programs s the population of the United States ages and life expectancy increases, the growing number of persons with chronic illness poses economic challenges to purchasers and providers of health care and to family members. Persons with one or more chronic illnesses account for nearly 75% of total health care expenditures.1 Despite a large body of evidence on effective therapies for these illnesses, widespread deficiencies in the application of these therapies persist.2,3 In response to increasing costs and persistent quality concerns, systematic approaches to chronic disease management have been developed and implemented by managed care organizations,4,5 disease management vendors, health care providers,6 and indemnity insurers in the fee-for-service sector.7,8 Disease management (DM) applies epidemiologic strategies to provide evidencebased care for entire populations with a chronic illness and includes some or all of the components listed in Table 1 (page 492). DM may represent a creative response to the challenges of chronic illness, but it also represents a significant additional health care investment. Expenditures for DM programs exceeded $1 billion in 1999.9 Health care systems vary in the size of investments made in DM, in the number and intensity of strategies implemented, and in the proportions of patient populations targeted. Some develop in-house programs; others contract with independent DM vendors (“carve-outs”) for some or all DM activities. Some systems employ nonphysician care managers; others emphasize training and support of primary care physicians. Yet there is relatively little evidence on whether DM programs have significantly affected either costs or quality of care. However, even if DM is generally
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Joe V. Selby, M.D, M.P.H. Dennis Scanlon, Ph.D. Jennifer Elston Lafata, Ph.D. Victor Villagra, M.D. Jeff Beich, M.H.A. Patricia R. Salber, M.D., M.B.A.
Article-at-a-Glance Background: Increasing prevalence, rising costs, and persisting deficiencies in quality of care for chronic diseases pose economic and policy challenges to providers and purchasers. Disease management (DM) programs may address these challenges, but neither purchasers nor providers can assess their value. The potpourri of current quality indicators provides limited insight into the actual clinical benefit achieved. A conference sponsored by the Agency for Healthcare Research and Quality (AHRQ) and held in October 2002 explored new approaches to measuring and reporting the value of DM for diabetes mellitus. Results: Quantifying the value of DM requires measuring clinical benefit and net impact on health care costs for the entire population with diabetes. If quality is measured with indicators that are clearly linked to outcomes, clinical benefit can be estimated. Natural history models combine the expected benefits of improvements in multiple indicators to yield a single, composite measure, the quality-adjusted life-year. Such metrics could fairly express, in terms of survival and complications prevention, relatively disparate DM programs’ benefits. Measuring and comparing health care costs requires data validation and appropriate case-mix adjustment. Comparing value across programs may provide more accurate assessments of performance, enhance quality improvement efforts within systems, and contribute generalizable knowledge on the utility of DM approaches. Conclusions: Conference attendees recommended pilot projects to further explore use of natural history models for measuring and reporting the value of DM.
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Table 1. Components of Disease Management Programs ■ ■ ■
■ ■ ■
Development and implementation of clinical practice guidelines Use of computerized databases to identify, monitor, and conduct outreach to patient populations Risk stratification of the patient population, with interventions tailored to each member’s disease severity or risk of complications Direct-to-physician interventions, such as newsletters, reminders, and performance feedback Direct-to-patient interventions, such as reminders and health education New forms of care delivery, including case management by nonphysicians and multidisciplinary and collaborative care teams
effective, the variations in investments and tactics are likely to result in variation in program benefits, resulting in differences in health care costs (or savings). Faced with average health maintenance organization (HMO) premium increases of 14% or higher in recent years, public and private health care purchasers are increasingly concerned with understanding economic as well as clinical effects of DM programs. Calls for return-oninvestment analyses of DM are widespread,10 as are calls to better align payment with quality improvement (QI).11 An optimal approach to monitoring DM programs would assess the dual aims of improving quality and controlling health care costs for patients with conditions. In this article, we define value as the clinical benefit achieved for given health care expenditures. This definition may be conservative in recognizing only clinical benefits and direct health care costs. Although these consequences are important to all stakeholders (purchasers, health plans, providers, patients), additional benefits, which may add value from some perspectives, have also been proposed, including improvements in patient quality of life, work productivity, and satisfaction with care. This article summarizes deliberations of a conference, “Valuing Diabetes Disease Management,” supported by the Agency for Healthcare Research and Quality (AHRQ) and organized by a committee including academic and
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health plan–based researchers, representatives of several major purchasers, and representatives from the Centers for Medicare & Medicaid Services (CMS). Attendees also included representatives of health plans, purchaser groups, and academic research. Conference objectives were to seek consensus on what constitutes “value” in diabetes DM and on a method for measuring value that could ultimately be incorporated into the request for information (RFI) process used by purchasers and purchasing coalitions in annual negotiations with plans. The evidence that DM can lead to improved clinical quality, cost savings, and other possible benefits was considered in depth. In discussing the objective of standardized measurement, emphasis was placed on availability and comparability of data across health systems. Attendees recognized that their discussions had implications beyond the immediate goal of designing a new RFI, including the potential to improve current performance assessment methods for chronic disease care, refine QI efforts within systems of care by shifting attention toward clinical benefit and cost-effectiveness, and generate new knowledge about the effectiveness of DM programs.
The Case of Diabetes To keep the focus on methodological issues rather than differences among conditions, conference discussion was limited to a single chronic condition—diabetes mellitus. Diabetes has been the most frequently targeted chronic condition for DM12 because of its high and increasing prevalence, its complexity and proven high costs,13,14 and the great potential for reducing complications. Of managed care organizations with DM programs, 75% have programs for diabetes.12 A large portion of the excess costs of care for diabetes is due to its complications,14 but patients with diabetes incur excess costs across a broad range of diagnoses and all sectors of health care (inpatient, outpatient, pharmacy, and diagnostic services). The number of clinical interventions proven through randomized clinical trials to prevent complications, reduce mortality, and improve health status in diabetes is remarkable. Controlling blood glucose prevents diabetic eye and kidney disease15,16; blood pressure
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control,17–24 lowering of low-density lipoprotein (LDL) cholesterol,25–26 raising of high-density lipoprotein (HDL) chololesterol,27 and regular use of low-dose aspirin28,29 lower risk of cardiovascular disease; periodic retinal screening and early treatment of retinopathy prevent visual loss and blindness30,31; and good foot care prevents lower-extremity amputations.32 These trials have generated a large number of quality indicators for diabetes care, although these indicators do not always reflect the effective intervention itself. For instance, testing glycohemoglobin (HbA1c; a commonly used indicator) does not directly improve patient outcomes, but lowering HbA1c does. These studies suggest that if diabetes DM programs implement proven interventions, they may eventually reduce health care costs by preventing complications. Early supporters of DM often pointed to the prospect of cost savings. More formal prediction models suggest that although these interventions may not always save money, they at least represent highly cost-effective means for reducing complications.33,34 None of the clinical trials that found reduced complications measured costs directly. None were conducted in typical clinical settings. Most focused on single interventions rather than the multifactorial efforts typical of DM programs. For these reasons, these trials do not prove that DM, as applied to entire populations in typical settings, would lower health care costs. Several small pilot trials35–38 have evaluated multifactorial interventions typical of DM in managed care populations and have consistently demonstrated improvements in glycemic control. Other benefits, such as improvements in blood pressure or LDL cholesterol levels, have not been reported, and these studies were not large or long enough to demonstrate actual reductions in complications. In two trials,35,37 surprisingly prompt decreases in hospitalization rates occurring well before expected complications reduction suggested possible short-term cost savings. Most of these studies, although randomized, were conducted in small samples with dedicated clinical providers. One larger observational evaluation39 has also suggested that a diabetes DM program led to prompt reductions in the number of hospital days, but two other larger studies did not observe short-term cost savings.40,41
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In summary, early empirical data further raise hopes that DM could lead to clinical benefit for diabetic patients and a financial return on investment within a relatively short period. Yet lingering uncertainty that real-world applications of DM will achieve this promise argues for close monitoring of both clinical and economic effects of DM programs over time.
Measuring the Clinical Benefits of Diabetes DM Programs Measurement of the net clinical benefit achieved for a population by a DM program is one essential ingredient in estimating value. Currently, diabetes DM programs are evaluated by comparing sets of quality indicators. Some of these indicators (for example, LDL cholesterol or HbA1c testing) have no clear link to any clinical benefit. Others, such as lowering blood pressure or LDL cholesterol levels or increasing aspirin use, are expected to save lives. Still others, such as retinal screening, aim to prevent complications that decrease the quality of life. Interventions such as blood pressure control may provide clinical benefit within 2 to 3 years; improving HbA1c levels may not yield detectable benefits for 8 to 10 years. The diversity of these indicators and their expected outcomes and time frames make it difficult to assess differences in total clinical benefit between programs. Consider the one-year changes in a set of performance indicators for commercial health plan enrollees shown in Table 2 (page 494).42 It is difficult to appreciate the net difference in overall quality or the clinical benefit that result from these changes. It is not obvious whether improvement in one measure confers more, less, or the same clinical benefit as improvement in another.
Introducing the Quality-Adjusted Life-Year (QALY) In light of the complexity of diabetes and the wealth of intervention data, many researchers have begun developing natural history models to project the benefits and costs of various diabetes interventions over time. These models incorporate data from relevant clinical trials and epidemiological studies to predict effects of interventions in terms of survival and complications. Several models of diabetes have now been described,33,43–47 and an active international community of diabetes modelers has begun to compare model
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Table 2. Changes in Performance Indicators, 1999–2000, HEDIS Health Plan Summary Data* Indicator Retinal screening (%) HbA1c testing (%) Poor HbA1c control (%) Monitoring nephropathy (%) LDL-C testing (%) LDL-C control (%)
1999 41. 4 72. 7 38. 1 28. 5 60. 7 29. 1
2000 45. 3 75. 1 44. 8 36. 1 69. 1 36. 7
*HbA1c, glycohemoglobin; LDL-C, low-density lipoprotein cholesterol. Source: Data listed for 1999 and 2000 by the National Committee on Quality Assurance: The Health Plan Employer Data and Information Set (HEDIS) http://www.ncqa.org/Programs/HEDIS (accessed Jun. 11, 2003).
structures, assumptions, and data, with the aim of improving predictive ability.48 Each of these models uses the QALY to express in a single measure the combined effects of various interventions (for example, blood pressure reduction, retinal screening) on survival (life-years gained) and on reductions in nonfatal complications (reduced morbidity with preserved quality of life). Through the use of QALYs, improvements that are known to prevent blindness or amputations (and accompanying decrements in quality of life) can be directly compared with improvements that reduce death due to heart disease. Historically, performance evaluation has focused heavily on processes of care, such as screening rates, because these were more readily measured in available data. Unlike aspirin use; flu shots; and HbA1c, LDL cholesterol, and blood pressure reduction,l these screening rates have not been linked directly to outcomes. Including them in a natural history model would therefore require a variety of assumptions about the clinical behavior that followed testing (for example, that HbA1c testing led to lower HbA1c levels). A QALYbased approach to measuring performance or clinical benefit will generally be most reliable if it is based on quality indicators that have been directly linked to improved outcomes in previous clinical trials. These may include both process measures that are clearly linked to outcomes49 and direct measures of intermediate outcomes (for example, blood pressure, HbA1c, LDL cholesterol levels).
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Moving toward QALY-based measurement would first require selection and refinement of a natural history model to incorporate all quality indicators of interest. A key question is whether and how to adjust for age, sex, clinical severity, socioeconomic, and other case-mix differences between program populations. The influence of case mix may be greater on intermediate outcomes than on process measures.50 Moreover, the impact on QALYs of improvements in various indicators may differ depending on case-mix variables, particularly age.45 The simplest approach would apply each program’s set of quality indicators, possibly with stratification on age, to a standard population. This approach, which essentially ignores the influence of most case-mix differences, amounts to a simple composite measure derived from a set of quality indicators. At the other extreme, each program’s population could be modeled separately, yielding QALY estimates tailored specifically to each population. The latter approach may be more informative to the program but introduces variation in the measure unrelated to quality or program effectiveness. Both approaches have value and could perhaps be used in tandem to meet the dual purposes of cross-program comparison and internal QI. A third strategy would compare longitudinal change in QALYs (for two years or longer) between programs, reducing influences of baseline case-mix differences and focusing more directly on the QI achieved. Although a composite QALY measure would best summarize program effectiveness, data on the component indicators would also be available and valuable for understanding composite differences. Time horizons for calculating QALYs must also be specified. Lifetime projections may be of most interest to patients or clinicians, but purchasers and health plans may prefer shorter intervals, such as duration of financial or clinical responsibility. Even shorter horizons may be preferable if there are concerns about high member or employee turnover or about sustaining clinical gains over many years. Use of shorter horizons, although generally “fair,” may penalize programs that focus on interventions with delayed benefit (which may include improvement in glycemic control). Conference attendees supported the concept of a composite, QALY-based measure of clinical benefit but cautioned that data collection needs for its calculation
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should not greatly increase the current data collection burden. The hope was expressed that this more evidence-based approach to performance assessment could eventually replace requests for information on structural features of programs.
Measuring the Cost Impact of Diabetes DM Programs The second component of the value formulation is program costs. Whether or not DM ultimately saves money—and there is reason to suspect that it may—the economic efficiency with which quality gains are achieved deserves monitoring. Unlike future complications, which must be predicted, present health care costs can, at least in theory, be measured empirically. Moreover, although prevention of complications can be predicted with some confidence from clinical trial data, predicting future costs is less certain because these trials did not directly measure costs. Finally, although DM necessarily focuses on preventing future complications, it aims to lower costs immediately or as soon as possible. For these reasons, measuring costs directly is preferable to predicting future costs and better positions programs and purchasers to detect changes in costs promptly. Standardized cost of care data have not been widely reported or compared across health plans. Concerns about data completeness, validity, and the comparability of cost accounting methods across programs will require significant attention. The simplest approach would capture total health care expenditures for patients with diabetes in a year and express these on a per-patient basis. Ideally, programs could identify and break out the incremental financial investments made to implement and operate the DM program (Table 3, page 496) from other health care costs. This may be relatively simple for programs that are “carved out” to vendors, but for DM programs developed and based within health plans or medical groups, it may prove impossible to distinguish program costs from the ongoing costs of delivering other medical care services (for example, hospitalizations, office visits, laboratory services, pharmacy services, diagnostic testing, supplies, durable medical equipment). Although it may seem preferable to focus on “diabetes-related” costs, it is often impossible to
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determine whether an expenditure is diabetes related. Diabetes leads to a doubling of total health care costs; nearly all elements of utilization are increased.13,14,51 The effect of diabetes on total costs is so large that it should not be difficult to detect a change due to DM if it occurs. Failure to monitor total costs could, in fact, underestimate cost savings of DM programs. Like comparisons of intermediate outcomes, cost comparisons are affected by patient case-mix differences. Programs with larger proportions of patients who are older or more sick would be expected to have higher per-person costs than other plans. In addition to accounting for age and sex differences, methods that capture and adjust for differences in severity of diabetes and presence of comorbidities are needed. Use of longitudinal comparisons (comparisons of change in costs for a fixed period) could also help to diminish the effects of case-mix differences between program populations. Assuming that differences between plans in case-mix, accounting systems, and missing data are relatively constant from year to year, comparing change in costs tends to adjust out these cross-sectional confounders. Once established, longitudinal analyses can be repeated annually using the same baseline year so that longer-term changes in costs as well as outcomes can be detected. The longitudinal approach resembles analyses published by Rubin et al.,39 who reported prompt reductions in costs (primarily in terms of hospitalization) after implementing diabetes DM in seven health plans. It is also consistent with the return on investment methodology recommended by Doxtator and Rodriguez10 and by Stone.52 Standard economic adjustments can be used to control for medical inflation when comparing costs over time. However, some secular events (for example, introduction of new and costly antidiabetic medications, increases in costs for a hospital bed-day) may affect costs of care to a degree far in excess of inflation. Conversely, greater use of cost sharing may appear to lower program costs, but it would be inappropriate to count as savings costs that are simply shifted to patients. Because these changes may differ across programs, they can bias longitudinal comparisons and will require scrutiny. Comparisons of costs and changes in costs will also need to account for program age. Newer programs will
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Table 3. Categories of Direct Disease Management (DM) Program Costs
Measuring and Comparing Value
Determining value requires simultaneous consideration of the clinical benefit provided by the Cost category Examples of costs program and the costs required to achieve that Start-up Costs benefit. (As proposed in this article, QALYs and Hardware, software Additional computers, software dedicosts can each reduce to a single number.) cated to population DM This approach borrows from both return-onTechnical support Staff salaries for building registries, investment and cost-effectiveness/cost-utility developing periodic reports analyses, in which ratios such as income/investMaterials Development of clinical management ed capital or dollars spent/QALY gained, respecforms, educational materials tively, are often used to further reduce the value Training For example, of case expression to a single measure of efficiency or managers value. However, information is lost if ratios are Ongoing Costs the only measures considered.52 The absolute Medical director, nurse case managers, Personnel values of costs and quality are needed to fully diabetes educators, clerical staff understand both investment and impact. A $1 Technical support Staff to maintain and update regmillion program that saves 1,000 QALYs and a istries and generate periodic reports $1,000 intervention that saves 1 QALY have the Reproduction or purchase of educaMember outreach same cost-effectiveness ratio—1 QALY/$1,000 tional materials, mailing costs, telespent—but very different impacts on the populaphone advice, and follow-up tion. Consideration of absolute costs and quality Continuing medical education, acaPhysician outreach may also help to detect and understand differdemic detailing ences in outcomes related to varying ages of proOffice space, equipment Overhead grams or to differences in case mix for which full adjustment cannot be made. be more likely to include one-time start-up costs (such Both clinical benefit and health care costs should be as investments in information technology) in one or measured and compared for the entire diabetic popuboth years, biasing comparisons of change. More lation under care for any portion of the year, not for mature programs may have already achieved reducpatient subgroups, and particularly not for the subgroup enrolled in DM programs. Subgroup approaches tions in costs and be in a flatter, “maintenance,” phase. Examination of the absolute values for costs in each risk measuring self-selection and regression to the mean52 rather than true savings. Analyzing subgroups year, as well as the changes between years, may help in understanding differences induced by variation in proreceiving special services may miss shifting of costs or gram age. complications to other diabetic patients. The basic An alternative to direct measurement of costs is to premise in directing DM services to “high risk” submeasure and report units of resource use, to which stangroups is that it efficiently lowers health care costs for dard prices are applied. This approach eliminates conall patients with diabetes. This can be tested only in cerns about the comparability of fully allocated cost the full population. data, inflation effects over time, and possible local Comparisons between plans should be expressed on effects on costs that are beyond the control of the plan a per-member-time basis (for example, costs per 1,000 or program. However, it may miss any savings due to members per year). In collecting and reporting program favorable contracting with suppliers or greater efficiendata, stratification, at least for age and sex, is required to cy. Ultimately, an approach will be needed that is agreeaccount for differences in quality indicators, utilization, able and feasible for plans and purchasers and that and costs related solely to these factors. Further collecemphasizes comparability and fairness across plans. tion of data on case mix, disease severity, and member
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turnover would be desirable. However, appropriate, available markers of each will have to be identified and agreed upon.
Other Potential Benefits of DM Diabetic complications greatly reduce one’s quality of life.53–55 Natural history models account for these effects by reducing the value of years survived after a complication. However, DM programs might also benefit quality of life in more general ways. Several small trials of diabetes DM35,36,38 found prompt improvements in average self-reported quality of life for enrollees. Improved glycemic control with relief of symptoms and enhanced self-management and self-efficacy skills were suggested as contributors to this effect. No large trial has yet confirmed these results. The U.K. Prospective Diabetes Study Group achieved substantial improvements in HbA1c levels, but did not demonstrate changes in self-reported quality of life.56 There is also no direct evidence that other diabetes DM program strategies such as blood pressure reduction and lipid lowering, affect quality of life. Given the limited evidence available at present, conference attendees agreed that there is not yet a rationale for routinely measuring or reporting quality of life data in enrolled populations. However, even small average improvements across all diabetic patients, if sustained over time, would greatly affect estimates of clinical benefit as measured by QALYs. More than 30% of total health care costs for diabetic employees of one self-insured Fortune 100 employer were attributable to medically related work absences and disability.57 Decreased absenteeism, improved work productivity, and increased patient and employee satisfaction have been suggested as consequences of improved diabetes DM. Several small DM evaluation trials found reductions in hospitalizations,37,38 a major reason for missed workdays.57 Testa and Simonson35 reported that intensive glucose lowering in patients with type 2 diabetes led to higher levels of retained employment, greater productivity and lower rates of absenteeism, fewer bed days, and fewer restricted activity days compared to usual care. Wagner et al.40 reported similar findings of decreased bed-days and some improvements in quality of life in a large-scale, randomized DM intervention program in a group-model HMO.
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If DM programs are shown to consistently decrease absenteeism and improve productivity among employed persons with diabetes, then they may create additional value from patient and employer perspectives. For example, the National Committee for Quality Assurance’s (NCQA’s) Quality Dividend Calculator attempts to translate improvements in risk factors directly to work days saved.58 Patient satisfaction is a key factor in employer selection of health plans and a component of most accreditation processes. Studies assessing the relationship between DM and patient satisfaction are limited, but those available consistently report high levels of satisfaction from program patients.36,38 Thus, DM programs may have positive financial implications for health plans and employers via improvements in patient satisfaction. However, the evidence that DM programs routinely achieve widespread improvement in satisfaction is not yet sufficiently clear to require measurement and reporting of value.
Conclusions The past decade has witnessed rapid advances in performance measurement, treatment of chronic illness, and the growth of DM programs. However, the gap between current performance assessment and what is known about measuring effectiveness and costeffectiveness remains wide. The methodology proposed here takes several steps toward closing that gap. It shifts the focus from simple process measures toward clinically relevant processes and intermediate outcomes that are tightly linked to clinical benefit. It proposes that a natural history model be refined to estimate a composite measure of clinical benefit, the QALY, related to differences or change in an agreed-upon set of quality indicators. It specifies measurement and reporting of health care costs. By participating in value measurement, purchasers should gain heightened appreciation of differences in both effectiveness and efficiency between DM programs; plans and programs should gain the skills needed to evaluate and understand the relative efficiency of both current and proposed programs. Because of the complexities of value measurement, the process should be collaborative and iterative. We
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recommend that initial pilot efforts involve a relatively small group of motivated plans, along with major purchasers and accreditation bodies, including the Joint Commission on Accreditation of Healthcare Organizations and NCQA. If a consensus model and standardized comparisons can be developed, the model would be maintained and supported by entities requesting the data (for example, a purchaser coalition or an accreditation body). A standardized RFI would collect agreed-upon inputs from each DM program. Anonymized, comparative results would be shared with programs and purchasers. If further evidence confirming the ability of DM to improve other outcomes (for example, quality of life, work productivity, satisfaction) becomes available, these outcomes should be incorporated into subsequent assessments of value. J
This report of a conference was supported by a grant from the Agency for Healthcare Research and Quality.
Joe V. Selby, M.D, M.P.H., is Director, Division of Research, Kaiser Permanente Northern California, Oakland, California. Dennis Scanlon, Ph.D., is Assistant Professor, Department of Health Policy & Administration, Pennyslvania State University, University Park, Pennsylvania. Jennifer Elston Lafata, Ph.D., is Director, Center for Health Services Research, Henry Ford Health System, Detroit. Victor Villagra, M.D., is President, Health & Technology, Vector, Inc, Farmington, Connecticut. Jeff Beich, M.H.A., is a Doctoral Candidate, Department of Health Policy & Administration, Pennsylvania State University. Patricia R. Salber, M.D., is Senior Medical Director, Center for Health Improvement, Blue Shield of California, San Francisco. Please address correspondence to Joe V. Selby, M.D, M.P.H., [email protected].
References 1. Hoffman C., Rice D., Sung H.Y.: Persons with chronic conditions: Their prevalence and costs. JAMA 276:1473–1479, Nov. 13, 1996. 2. Wagner E.H., et al.: Improving chronic illness care: Translating evidence into action. Health Aff (Millwood) 20:64–78, Nov.–Dec. 2001. 3. Saaddine J.B., et al.: A diabetes report card for the United States: Quality of care in the 1990s. Ann Intern Med 136:565–574, Apr. 16, 2002. 4. Ellrodt G., et al.: Evidence-based disease management. JAMA 278:1687–1692, Nov. 26, 1997. 5. Epstein R.S., Sherwood L.M.: From outcomes research to disease management: A guide for the perplexed. Ann Intern Med 124:832–837, May 1, 1996. 6. Krein S.L., et al.: Department of Veterans Affairs’ Quality Enhancement Research Initiative for Diabetes Mellitus. Med Care 38( suppl. I):I38–I48, Jun. 2000. 7. Heart failure program proves that DM works-—even under fee-forservice. Dis Manag Advis 8:117–121, Aug. 2002. 8. HCFA demo sites offer a smorgasbord of managed care innovation. Dis Manag Advis 7:52–57, Apr. 2001. 9. Weingarten S.R., et al.: Interventions used in disease management programmes for patients with chronic illness: Which ones work? Metaanalysis of published reports. BMJ 325:925–932, Oct. 26, 2002. 10. Doxtator R.F., Rodriguez D.J.: Evaluating costs, benefits, and return on investment for disease management programs. Dis Mgmt 1:185–192, 1998. 11. Institute of Medicine: Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press, 181–206, 2001. 12. Disease management growing, but lack of standardization creating challenges. Health Care Strategic Management 1:6, Jan. 18, 2000. 13. Rubin R.J., Altman W.M., Mendelson D.N.: Health care expenditures for people with diabetes mellitus. J Clin Endocrinol Metab 78:809A–812A, 1992. 14. Selby J.V., et al.: The excess costs of medical care for patients with diabetes mellitus in a managed care population. Diabetes Care 20:1396–1402, Sep. 1997.
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15. The Diabetes Control and Complications Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329:977–986, Sep. 30, 1993. 16. UK Prospective Diabetes Study Group: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352:837–853, Sep. 12, 1998. 17. The Hypertension Detection and Follow-up Program Cooperative Group: Implications of the Hypertension Detection and Follow-up Program. Prog Cardiovasc Dis 29(suppl. 1):S3–S10, Nov.–Dec. 1986. 18. Curb J.D., et al.: Effect of diuretic-based antihypertensive treatment on cardiovascular disease risk in older diabetic patients with isolated systolic hypertension. JAMA 276:1886–1892, Dec. 18, 1996. 19. Hansson L., Zanchetti A., Carruthers S.G.: Effects of intensive blood-pressure lowering and low-dose aspirin in patients with hypertension: Principal results of the Hypertension Optimal Treatment (HOT) randomised trial. Lancet 351:1755–1762, Jun. 13, 1998. 20. U.K. Prospective Diabetes Study Group: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 317:703–713, Sep. 12, 1998. 21. Tuomilehto J., et al.: Effects of calcium-channel blockade in older patients with diabetes and systolic hypertension. N Engl J Med 340:677–684, Mar. 4, 1999. 22. Estacio R.O., et al: The effect of nisoldipine as compared with enalapril on cardiovascular outcomes in patients with non-insulindependent diabetes and hypertension. N Engl J Med 338:645–652, Mar. 5, 1998. 23. Tatti P., et al.: Outcome results of the Fosinopril Versus Amlodipine Cardiovascular Events Randomized Trial (FACET) in patients with hypertension and NIDDM. Diabetes Care 21:597–603, Apr. 1998. 24. Hansson L., et al.: Effect of angiotensin-converting-enzyme inhibition compared with conventional therapy on cardiovascular morbidity and mortality in hypertension: The Captopril Prevention Project (CAPP) randomised trial. Lancet 353:611–616, Feb. 20, 1999.
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References, continued 25. Pyorala K., et al.: Cholesterol lowering with simvastatin improves prognosis of diabetic patients with coronary heart disease. Diabetes Care 20:614–620, Apr. 1997. 26. Sacks F.M., et al.: The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. Cholesterol and Recurrent Events Trial investigators. N Engl J Med 335:1001–1009, Oct. 3, 1996. 27. Koskinen P., et al.: Coronary heart disease incidence in NIDDM patients in The Helsinki Heart Study. Diabetes Care 15:820–825, Jul. 1992. 28. Collaborative overview of randomised trials of antiplatelet therapy—I: Prevention of death, myocardial infarction, and stroke by prolonged antiplatelet therapy in various categories of patients. Antiplatelet Trialists’ Collaboration. BMJ 308:81–106, Jan. 8, 1994. 29. Aspirin effects on mortality and morbidity in patients with diabetes mellitus. Early Treatment Diabetic Retinopathy Study report 14. ETDRS Investigators. JAMA 268:1292–1300, Sep. 9, 1992. 30. Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology 98(suppl.):S766–S785. May 1991. 31. Photocoagulation for diabetic macular edema. Early Treatment Diabetic Retinopathy Study report number 1. Early Treatment Diabetic Retinopathy Study research group. Arch Ophthalmol 103:1796–1806 Dec. 1985. 32. Davidson J.K., et al.: Assessment of program effectiveness of Grady Memorial Hospital–Atlanta. In Steiner G., Lawrence P.A. (eds.): Educating Diabetic Patients. New York: Springer-Verlag, 1981, pp. 329–348. 33. Eastman R.C., et al.: Model of complications of NIDDM. I. Model construction and assumptions. Diabetes Care 20:725–734, May 1997. 34. Herman W.H., Eastman R.C.: The effects of treatment on the direct costs of diabetes. Diabetes Care 21(suppl. 3):C19–C24, Dec. 1998. 35. Testa M.A., Simonson D.C.: Health economic benefits and quality of life during improved glycemic control in patients with type 2 diabetes mellitus: A randomized, controlled, double-blind trial. JAMA 280:1490–1496, Nov. 4, 1998. 36. Aubert R.E., et al.: Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. Ann Intern Med 129:605–612, Oct. 15, 1998. 37. Sadur C.N., et al.: Diabetes management in a health maintenance organization: Efficacy of care management using cluster visits. Diabetes Care 22:2011–2017, Dec. 1999. 38. Trento M., et al.: Group visits improve metabolic control in type 2 diabetes: A 2-year follow-up. Diabetes Care 24:995–1000, Jun. 2001. 39. Rubin R.J., Dietrich K.A., Hawk A.D.: Clinical and economic impact of implementing a comprehensive diabetes management program in managed care. J Clin Endocrinol and Metab 83:2635–2642, Aug. 1998. 40. Wagner E.H., et al.: Chronic Care Clinics for Diabetes in Primary Care: A system-wide randomized trial. Diabetes Care 25:695–700, Apr. 2001.
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41. Klonoff D.C., Schwartz D.M.: An economic analysis of interventions for diabetes. Diabetes Care 23:390–404, Mar. 2000. 42. National Committee for Quality Assurance: The Health Plan Employer Data and Information Set (HEDIS). http://www.ncqa.org/ Programs/HEDIS (accessed Jun. 11, 2003). 43. Vijan S., Hofer T.P., Hayward R.A.: Estimated benefits of glycaemic control in microvascular complications in type II diabetes. Ann Intern Med 127:788–795, Nov. 1, 1997. 44. Brown J.B., et al.: The global diabetes model: User friendly version 3.0. Diabetes Res Clin Pract 50 (suppl. 3):S15–S46, Nov. 2000. 45. Centers for Disease Control (CDC) Diabetes Cost-effectiveness Group: Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 287:2542–2551, May 15, 2002. 46. Sherwin R.S., et al.: The prevention or delay of type 2 diabetes. Diabetes Care 26 (suppl. 1):S62–S69, Jan. 2003. 47. Schlessinger L., Eddy D.: Archimedes: A new model for simulating health care systems-—The mathematical formulation. J Biomed Inform 35:37–50, Feb. 2002. 48. Brown J.B., et al.: The Mt. Hood challenge: Cross-testing two diabetes simulation models. Diabetes Res Clin Pract 50(suppl. 3):S57–S64, Nov. 2000. 49. Kerr E.A., et al.: Avoiding pitfalls in chronic disease quality measurement: A case for the next generation of technical quality measures. Am J Manag Care 7:1033–1043, Nov. 2001. 50. Hofer T.P., et al.: The unreliability of individual physician “report cares” for assessing the costs and quality of care of a chronic disease. JAMA 281:2098–2105, Jun. 9, 1999. 51. Druss B.G., et al.: Comparing the national economic burden of five chronic conditions. Health Aff (Millwood) 20:233–241, Nov.–Dec. 2001. 52. Stone R.E.: Playing disease management numbers games. Disease Management and Health Outcomes 6:343–348, Dec. 1999. 53. Songer T.J.: Disability in diabetes. In: National Diabetes Data Group: Diabetes in America, 2nd ed. Bethesda, MD: National Institutes of Health Pub. No. 95-1468, 1995, pp. 259–282. 54. Lloyd A., Sawyer W., Hopkinson P.: Impact of long-term complications on quality of life in patients with type 2 diabetes not using insulin. Value Health 4:392–400, Sep.–Oct. 2001. 55. Clarke P., Gray A., Holman R.: Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making 22:340–349, Jul.–Aug. 2002. 56. Quality of life in type 2 diabetic patients is affected by complications but not by intensive policies to improve blood glucose or blood pressure control (UKPDS 37). U.K. Prospective Diabetes Study Group. Diabetes Care 22:1125–1136, Jul. 1999. 57. Ramsey S., et al.: Productivity and medical costs of diabetes in a large employer population. Diabetes Care 25:23–29, Jan. 2002. 58. National Committee for Quality Assurance (NCQA): The NCQA Quality Dividend Calculator: Translating health care into productivity. http://www.ncqacalculator.com (accessed Jun. 11, 2003).
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Forum
Determining the Value of Disease Management Programs s the population of the United States ages and life expectancy increases, the growing number of persons with chronic illness poses economic challenges to purchasers and providers of health care and to family members. Persons with one or more chronic illnesses account for nearly 75% of total health care expenditures.1 Despite a large body of evidence on effective therapies for these illnesses, widespread deficiencies in the application of these therapies persist.2,3 In response to increasing costs and persistent quality concerns, systematic approaches to chronic disease management have been developed and implemented by managed care organizations,4,5 disease management vendors, health care providers,6 and indemnity insurers in the fee-for-service sector.7,8 Disease management (DM) applies epidemiologic strategies to provide evidencebased care for entire populations with a chronic illness and includes some or all of the components listed in Table 1 (page 492). DM may represent a creative response to the challenges of chronic illness, but it also represents a significant additional health care investment. Expenditures for DM programs exceeded $1 billion in 1999.9 Health care systems vary in the size of investments made in DM, in the number and intensity of strategies implemented, and in the proportions of patient populations targeted. Some develop in-house programs; others contract with independent DM vendors (“carve-outs”) for some or all DM activities. Some systems employ nonphysician care managers; others emphasize training and support of primary care physicians. Yet there is relatively little evidence on whether DM programs have significantly affected either costs or quality of care. However, even if DM is generally
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Joe V. Selby, M.D, M.P.H. Dennis Scanlon, Ph.D. Jennifer Elston Lafata, Ph.D. Victor Villagra, M.D. Jeff Beich, M.H.A. Patricia R. Salber, M.D., M.B.A.
Article-at-a-Glance Background: Increasing prevalence, rising costs, and persisting deficiencies in quality of care for chronic diseases pose economic and policy challenges to providers and purchasers. Disease management (DM) programs may address these challenges, but neither purchasers nor providers can assess their value. The potpourri of current quality indicators provides limited insight into the actual clinical benefit achieved. A conference sponsored by the Agency for Healthcare Research and Quality (AHRQ) and held in October 2002 explored new approaches to measuring and reporting the value of DM for diabetes mellitus. Results: Quantifying the value of DM requires measuring clinical benefit and net impact on health care costs for the entire population with diabetes. If quality is measured with indicators that are clearly linked to outcomes, clinical benefit can be estimated. Natural history models combine the expected benefits of improvements in multiple indicators to yield a single, composite measure, the quality-adjusted life-year. Such metrics could fairly express, in terms of survival and complications prevention, relatively disparate DM programs’ benefits. Measuring and comparing health care costs requires data validation and appropriate case-mix adjustment. Comparing value across programs may provide more accurate assessments of performance, enhance quality improvement efforts within systems, and contribute generalizable knowledge on the utility of DM approaches. Conclusions: Conference attendees recommended pilot projects to further explore use of natural history models for measuring and reporting the value of DM.
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Table 1. Components of Disease Management Programs ■ ■ ■
■ ■ ■
Development and implementation of clinical practice guidelines Use of computerized databases to identify, monitor, and conduct outreach to patient populations Risk stratification of the patient population, with interventions tailored to each member’s disease severity or risk of complications Direct-to-physician interventions, such as newsletters, reminders, and performance feedback Direct-to-patient interventions, such as reminders and health education New forms of care delivery, including case management by nonphysicians and multidisciplinary and collaborative care teams
effective, the variations in investments and tactics are likely to result in variation in program benefits, resulting in differences in health care costs (or savings). Faced with average health maintenance organization (HMO) premium increases of 14% or higher in recent years, public and private health care purchasers are increasingly concerned with understanding economic as well as clinical effects of DM programs. Calls for return-oninvestment analyses of DM are widespread,10 as are calls to better align payment with quality improvement (QI).11 An optimal approach to monitoring DM programs would assess the dual aims of improving quality and controlling health care costs for patients with conditions. In this article, we define value as the clinical benefit achieved for given health care expenditures. This definition may be conservative in recognizing only clinical benefits and direct health care costs. Although these consequences are important to all stakeholders (purchasers, health plans, providers, patients), additional benefits, which may add value from some perspectives, have also been proposed, including improvements in patient quality of life, work productivity, and satisfaction with care. This article summarizes deliberations of a conference, “Valuing Diabetes Disease Management,” supported by the Agency for Healthcare Research and Quality (AHRQ) and organized by a committee including academic and
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health plan–based researchers, representatives of several major purchasers, and representatives from the Centers for Medicare & Medicaid Services (CMS). Attendees also included representatives of health plans, purchaser groups, and academic research. Conference objectives were to seek consensus on what constitutes “value” in diabetes DM and on a method for measuring value that could ultimately be incorporated into the request for information (RFI) process used by purchasers and purchasing coalitions in annual negotiations with plans. The evidence that DM can lead to improved clinical quality, cost savings, and other possible benefits was considered in depth. In discussing the objective of standardized measurement, emphasis was placed on availability and comparability of data across health systems. Attendees recognized that their discussions had implications beyond the immediate goal of designing a new RFI, including the potential to improve current performance assessment methods for chronic disease care, refine QI efforts within systems of care by shifting attention toward clinical benefit and cost-effectiveness, and generate new knowledge about the effectiveness of DM programs.
The Case of Diabetes To keep the focus on methodological issues rather than differences among conditions, conference discussion was limited to a single chronic condition—diabetes mellitus. Diabetes has been the most frequently targeted chronic condition for DM12 because of its high and increasing prevalence, its complexity and proven high costs,13,14 and the great potential for reducing complications. Of managed care organizations with DM programs, 75% have programs for diabetes.12 A large portion of the excess costs of care for diabetes is due to its complications,14 but patients with diabetes incur excess costs across a broad range of diagnoses and all sectors of health care (inpatient, outpatient, pharmacy, and diagnostic services). The number of clinical interventions proven through randomized clinical trials to prevent complications, reduce mortality, and improve health status in diabetes is remarkable. Controlling blood glucose prevents diabetic eye and kidney disease15,16; blood pressure
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control,17–24 lowering of low-density lipoprotein (LDL) cholesterol,25–26 raising of high-density lipoprotein (HDL) chololesterol,27 and regular use of low-dose aspirin28,29 lower risk of cardiovascular disease; periodic retinal screening and early treatment of retinopathy prevent visual loss and blindness30,31; and good foot care prevents lower-extremity amputations.32 These trials have generated a large number of quality indicators for diabetes care, although these indicators do not always reflect the effective intervention itself. For instance, testing glycohemoglobin (HbA1c; a commonly used indicator) does not directly improve patient outcomes, but lowering HbA1c does. These studies suggest that if diabetes DM programs implement proven interventions, they may eventually reduce health care costs by preventing complications. Early supporters of DM often pointed to the prospect of cost savings. More formal prediction models suggest that although these interventions may not always save money, they at least represent highly cost-effective means for reducing complications.33,34 None of the clinical trials that found reduced complications measured costs directly. None were conducted in typical clinical settings. Most focused on single interventions rather than the multifactorial efforts typical of DM programs. For these reasons, these trials do not prove that DM, as applied to entire populations in typical settings, would lower health care costs. Several small pilot trials35–38 have evaluated multifactorial interventions typical of DM in managed care populations and have consistently demonstrated improvements in glycemic control. Other benefits, such as improvements in blood pressure or LDL cholesterol levels, have not been reported, and these studies were not large or long enough to demonstrate actual reductions in complications. In two trials,35,37 surprisingly prompt decreases in hospitalization rates occurring well before expected complications reduction suggested possible short-term cost savings. Most of these studies, although randomized, were conducted in small samples with dedicated clinical providers. One larger observational evaluation39 has also suggested that a diabetes DM program led to prompt reductions in the number of hospital days, but two other larger studies did not observe short-term cost savings.40,41
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In summary, early empirical data further raise hopes that DM could lead to clinical benefit for diabetic patients and a financial return on investment within a relatively short period. Yet lingering uncertainty that real-world applications of DM will achieve this promise argues for close monitoring of both clinical and economic effects of DM programs over time.
Measuring the Clinical Benefits of Diabetes DM Programs Measurement of the net clinical benefit achieved for a population by a DM program is one essential ingredient in estimating value. Currently, diabetes DM programs are evaluated by comparing sets of quality indicators. Some of these indicators (for example, LDL cholesterol or HbA1c testing) have no clear link to any clinical benefit. Others, such as lowering blood pressure or LDL cholesterol levels or increasing aspirin use, are expected to save lives. Still others, such as retinal screening, aim to prevent complications that decrease the quality of life. Interventions such as blood pressure control may provide clinical benefit within 2 to 3 years; improving HbA1c levels may not yield detectable benefits for 8 to 10 years. The diversity of these indicators and their expected outcomes and time frames make it difficult to assess differences in total clinical benefit between programs. Consider the one-year changes in a set of performance indicators for commercial health plan enrollees shown in Table 2 (page 494).42 It is difficult to appreciate the net difference in overall quality or the clinical benefit that result from these changes. It is not obvious whether improvement in one measure confers more, less, or the same clinical benefit as improvement in another.
Introducing the Quality-Adjusted Life-Year (QALY) In light of the complexity of diabetes and the wealth of intervention data, many researchers have begun developing natural history models to project the benefits and costs of various diabetes interventions over time. These models incorporate data from relevant clinical trials and epidemiological studies to predict effects of interventions in terms of survival and complications. Several models of diabetes have now been described,33,43–47 and an active international community of diabetes modelers has begun to compare model
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Table 2. Changes in Performance Indicators, 1999–2000, HEDIS Health Plan Summary Data* Indicator Retinal screening (%) HbA1c testing (%) Poor HbA1c control (%) Monitoring nephropathy (%) LDL-C testing (%) LDL-C control (%)
1999 41. 4 72. 7 38. 1 28. 5 60. 7 29. 1
2000 45. 3 75. 1 44. 8 36. 1 69. 1 36. 7
*HbA1c, glycohemoglobin; LDL-C, low-density lipoprotein cholesterol. Source: Data listed for 1999 and 2000 by the National Committee on Quality Assurance: The Health Plan Employer Data and Information Set (HEDIS) http://www.ncqa.org/Programs/HEDIS (accessed Jun. 11, 2003).
structures, assumptions, and data, with the aim of improving predictive ability.48 Each of these models uses the QALY to express in a single measure the combined effects of various interventions (for example, blood pressure reduction, retinal screening) on survival (life-years gained) and on reductions in nonfatal complications (reduced morbidity with preserved quality of life). Through the use of QALYs, improvements that are known to prevent blindness or amputations (and accompanying decrements in quality of life) can be directly compared with improvements that reduce death due to heart disease. Historically, performance evaluation has focused heavily on processes of care, such as screening rates, because these were more readily measured in available data. Unlike aspirin use; flu shots; and HbA1c, LDL cholesterol, and blood pressure reduction,l these screening rates have not been linked directly to outcomes. Including them in a natural history model would therefore require a variety of assumptions about the clinical behavior that followed testing (for example, that HbA1c testing led to lower HbA1c levels). A QALYbased approach to measuring performance or clinical benefit will generally be most reliable if it is based on quality indicators that have been directly linked to improved outcomes in previous clinical trials. These may include both process measures that are clearly linked to outcomes49 and direct measures of intermediate outcomes (for example, blood pressure, HbA1c, LDL cholesterol levels).
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Moving toward QALY-based measurement would first require selection and refinement of a natural history model to incorporate all quality indicators of interest. A key question is whether and how to adjust for age, sex, clinical severity, socioeconomic, and other case-mix differences between program populations. The influence of case mix may be greater on intermediate outcomes than on process measures.50 Moreover, the impact on QALYs of improvements in various indicators may differ depending on case-mix variables, particularly age.45 The simplest approach would apply each program’s set of quality indicators, possibly with stratification on age, to a standard population. This approach, which essentially ignores the influence of most case-mix differences, amounts to a simple composite measure derived from a set of quality indicators. At the other extreme, each program’s population could be modeled separately, yielding QALY estimates tailored specifically to each population. The latter approach may be more informative to the program but introduces variation in the measure unrelated to quality or program effectiveness. Both approaches have value and could perhaps be used in tandem to meet the dual purposes of cross-program comparison and internal QI. A third strategy would compare longitudinal change in QALYs (for two years or longer) between programs, reducing influences of baseline case-mix differences and focusing more directly on the QI achieved. Although a composite QALY measure would best summarize program effectiveness, data on the component indicators would also be available and valuable for understanding composite differences. Time horizons for calculating QALYs must also be specified. Lifetime projections may be of most interest to patients or clinicians, but purchasers and health plans may prefer shorter intervals, such as duration of financial or clinical responsibility. Even shorter horizons may be preferable if there are concerns about high member or employee turnover or about sustaining clinical gains over many years. Use of shorter horizons, although generally “fair,” may penalize programs that focus on interventions with delayed benefit (which may include improvement in glycemic control). Conference attendees supported the concept of a composite, QALY-based measure of clinical benefit but cautioned that data collection needs for its calculation
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should not greatly increase the current data collection burden. The hope was expressed that this more evidence-based approach to performance assessment could eventually replace requests for information on structural features of programs.
Measuring the Cost Impact of Diabetes DM Programs The second component of the value formulation is program costs. Whether or not DM ultimately saves money—and there is reason to suspect that it may—the economic efficiency with which quality gains are achieved deserves monitoring. Unlike future complications, which must be predicted, present health care costs can, at least in theory, be measured empirically. Moreover, although prevention of complications can be predicted with some confidence from clinical trial data, predicting future costs is less certain because these trials did not directly measure costs. Finally, although DM necessarily focuses on preventing future complications, it aims to lower costs immediately or as soon as possible. For these reasons, measuring costs directly is preferable to predicting future costs and better positions programs and purchasers to detect changes in costs promptly. Standardized cost of care data have not been widely reported or compared across health plans. Concerns about data completeness, validity, and the comparability of cost accounting methods across programs will require significant attention. The simplest approach would capture total health care expenditures for patients with diabetes in a year and express these on a per-patient basis. Ideally, programs could identify and break out the incremental financial investments made to implement and operate the DM program (Table 3, page 496) from other health care costs. This may be relatively simple for programs that are “carved out” to vendors, but for DM programs developed and based within health plans or medical groups, it may prove impossible to distinguish program costs from the ongoing costs of delivering other medical care services (for example, hospitalizations, office visits, laboratory services, pharmacy services, diagnostic testing, supplies, durable medical equipment). Although it may seem preferable to focus on “diabetes-related” costs, it is often impossible to
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determine whether an expenditure is diabetes related. Diabetes leads to a doubling of total health care costs; nearly all elements of utilization are increased.13,14,51 The effect of diabetes on total costs is so large that it should not be difficult to detect a change due to DM if it occurs. Failure to monitor total costs could, in fact, underestimate cost savings of DM programs. Like comparisons of intermediate outcomes, cost comparisons are affected by patient case-mix differences. Programs with larger proportions of patients who are older or more sick would be expected to have higher per-person costs than other plans. In addition to accounting for age and sex differences, methods that capture and adjust for differences in severity of diabetes and presence of comorbidities are needed. Use of longitudinal comparisons (comparisons of change in costs for a fixed period) could also help to diminish the effects of case-mix differences between program populations. Assuming that differences between plans in case-mix, accounting systems, and missing data are relatively constant from year to year, comparing change in costs tends to adjust out these cross-sectional confounders. Once established, longitudinal analyses can be repeated annually using the same baseline year so that longer-term changes in costs as well as outcomes can be detected. The longitudinal approach resembles analyses published by Rubin et al.,39 who reported prompt reductions in costs (primarily in terms of hospitalization) after implementing diabetes DM in seven health plans. It is also consistent with the return on investment methodology recommended by Doxtator and Rodriguez10 and by Stone.52 Standard economic adjustments can be used to control for medical inflation when comparing costs over time. However, some secular events (for example, introduction of new and costly antidiabetic medications, increases in costs for a hospital bed-day) may affect costs of care to a degree far in excess of inflation. Conversely, greater use of cost sharing may appear to lower program costs, but it would be inappropriate to count as savings costs that are simply shifted to patients. Because these changes may differ across programs, they can bias longitudinal comparisons and will require scrutiny. Comparisons of costs and changes in costs will also need to account for program age. Newer programs will
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Table 3. Categories of Direct Disease Management (DM) Program Costs
Measuring and Comparing Value
Determining value requires simultaneous consideration of the clinical benefit provided by the Cost category Examples of costs program and the costs required to achieve that Start-up Costs benefit. (As proposed in this article, QALYs and Hardware, software Additional computers, software dedicosts can each reduce to a single number.) cated to population DM This approach borrows from both return-onTechnical support Staff salaries for building registries, investment and cost-effectiveness/cost-utility developing periodic reports analyses, in which ratios such as income/investMaterials Development of clinical management ed capital or dollars spent/QALY gained, respecforms, educational materials tively, are often used to further reduce the value Training For example, of case expression to a single measure of efficiency or managers value. However, information is lost if ratios are Ongoing Costs the only measures considered.52 The absolute Medical director, nurse case managers, Personnel values of costs and quality are needed to fully diabetes educators, clerical staff understand both investment and impact. A $1 Technical support Staff to maintain and update regmillion program that saves 1,000 QALYs and a istries and generate periodic reports $1,000 intervention that saves 1 QALY have the Reproduction or purchase of educaMember outreach same cost-effectiveness ratio—1 QALY/$1,000 tional materials, mailing costs, telespent—but very different impacts on the populaphone advice, and follow-up tion. Consideration of absolute costs and quality Continuing medical education, acaPhysician outreach may also help to detect and understand differdemic detailing ences in outcomes related to varying ages of proOffice space, equipment Overhead grams or to differences in case mix for which full adjustment cannot be made. be more likely to include one-time start-up costs (such Both clinical benefit and health care costs should be as investments in information technology) in one or measured and compared for the entire diabetic popuboth years, biasing comparisons of change. More lation under care for any portion of the year, not for mature programs may have already achieved reducpatient subgroups, and particularly not for the subgroup enrolled in DM programs. Subgroup approaches tions in costs and be in a flatter, “maintenance,” phase. Examination of the absolute values for costs in each risk measuring self-selection and regression to the mean52 rather than true savings. Analyzing subgroups year, as well as the changes between years, may help in understanding differences induced by variation in proreceiving special services may miss shifting of costs or gram age. complications to other diabetic patients. The basic An alternative to direct measurement of costs is to premise in directing DM services to “high risk” submeasure and report units of resource use, to which stangroups is that it efficiently lowers health care costs for dard prices are applied. This approach eliminates conall patients with diabetes. This can be tested only in cerns about the comparability of fully allocated cost the full population. data, inflation effects over time, and possible local Comparisons between plans should be expressed on effects on costs that are beyond the control of the plan a per-member-time basis (for example, costs per 1,000 or program. However, it may miss any savings due to members per year). In collecting and reporting program favorable contracting with suppliers or greater efficiendata, stratification, at least for age and sex, is required to cy. Ultimately, an approach will be needed that is agreeaccount for differences in quality indicators, utilization, able and feasible for plans and purchasers and that and costs related solely to these factors. Further collecemphasizes comparability and fairness across plans. tion of data on case mix, disease severity, and member
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turnover would be desirable. However, appropriate, available markers of each will have to be identified and agreed upon.
Other Potential Benefits of DM Diabetic complications greatly reduce one’s quality of life.53–55 Natural history models account for these effects by reducing the value of years survived after a complication. However, DM programs might also benefit quality of life in more general ways. Several small trials of diabetes DM35,36,38 found prompt improvements in average self-reported quality of life for enrollees. Improved glycemic control with relief of symptoms and enhanced self-management and self-efficacy skills were suggested as contributors to this effect. No large trial has yet confirmed these results. The U.K. Prospective Diabetes Study Group achieved substantial improvements in HbA1c levels, but did not demonstrate changes in self-reported quality of life.56 There is also no direct evidence that other diabetes DM program strategies such as blood pressure reduction and lipid lowering, affect quality of life. Given the limited evidence available at present, conference attendees agreed that there is not yet a rationale for routinely measuring or reporting quality of life data in enrolled populations. However, even small average improvements across all diabetic patients, if sustained over time, would greatly affect estimates of clinical benefit as measured by QALYs. More than 30% of total health care costs for diabetic employees of one self-insured Fortune 100 employer were attributable to medically related work absences and disability.57 Decreased absenteeism, improved work productivity, and increased patient and employee satisfaction have been suggested as consequences of improved diabetes DM. Several small DM evaluation trials found reductions in hospitalizations,37,38 a major reason for missed workdays.57 Testa and Simonson35 reported that intensive glucose lowering in patients with type 2 diabetes led to higher levels of retained employment, greater productivity and lower rates of absenteeism, fewer bed days, and fewer restricted activity days compared to usual care. Wagner et al.40 reported similar findings of decreased bed-days and some improvements in quality of life in a large-scale, randomized DM intervention program in a group-model HMO.
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If DM programs are shown to consistently decrease absenteeism and improve productivity among employed persons with diabetes, then they may create additional value from patient and employer perspectives. For example, the National Committee for Quality Assurance’s (NCQA’s) Quality Dividend Calculator attempts to translate improvements in risk factors directly to work days saved.58 Patient satisfaction is a key factor in employer selection of health plans and a component of most accreditation processes. Studies assessing the relationship between DM and patient satisfaction are limited, but those available consistently report high levels of satisfaction from program patients.36,38 Thus, DM programs may have positive financial implications for health plans and employers via improvements in patient satisfaction. However, the evidence that DM programs routinely achieve widespread improvement in satisfaction is not yet sufficiently clear to require measurement and reporting of value.
Conclusions The past decade has witnessed rapid advances in performance measurement, treatment of chronic illness, and the growth of DM programs. However, the gap between current performance assessment and what is known about measuring effectiveness and costeffectiveness remains wide. The methodology proposed here takes several steps toward closing that gap. It shifts the focus from simple process measures toward clinically relevant processes and intermediate outcomes that are tightly linked to clinical benefit. It proposes that a natural history model be refined to estimate a composite measure of clinical benefit, the QALY, related to differences or change in an agreed-upon set of quality indicators. It specifies measurement and reporting of health care costs. By participating in value measurement, purchasers should gain heightened appreciation of differences in both effectiveness and efficiency between DM programs; plans and programs should gain the skills needed to evaluate and understand the relative efficiency of both current and proposed programs. Because of the complexities of value measurement, the process should be collaborative and iterative. We
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recommend that initial pilot efforts involve a relatively small group of motivated plans, along with major purchasers and accreditation bodies, including the Joint Commission on Accreditation of Healthcare Organizations and NCQA. If a consensus model and standardized comparisons can be developed, the model would be maintained and supported by entities requesting the data (for example, a purchaser coalition or an accreditation body). A standardized RFI would collect agreed-upon inputs from each DM program. Anonymized, comparative results would be shared with programs and purchasers. If further evidence confirming the ability of DM to improve other outcomes (for example, quality of life, work productivity, satisfaction) becomes available, these outcomes should be incorporated into subsequent assessments of value. J
This report of a conference was supported by a grant from the Agency for Healthcare Research and Quality.
Joe V. Selby, M.D, M.P.H., is Director, Division of Research, Kaiser Permanente Northern California, Oakland, California. Dennis Scanlon, Ph.D., is Assistant Professor, Department of Health Policy & Administration, Pennyslvania State University, University Park, Pennsylvania. Jennifer Elston Lafata, Ph.D., is Director, Center for Health Services Research, Henry Ford Health System, Detroit. Victor Villagra, M.D., is President, Health & Technology, Vector, Inc, Farmington, Connecticut. Jeff Beich, M.H.A., is a Doctoral Candidate, Department of Health Policy & Administration, Pennsylvania State University. Patricia R. Salber, M.D., is Senior Medical Director, Center for Health Improvement, Blue Shield of California, San Francisco. Please address correspondence to Joe V. Selby, M.D, M.P.H., [email protected].
References 1. Hoffman C., Rice D., Sung H.Y.: Persons with chronic conditions: Their prevalence and costs. JAMA 276:1473–1479, Nov. 13, 1996. 2. Wagner E.H., et al.: Improving chronic illness care: Translating evidence into action. Health Aff (Millwood) 20:64–78, Nov.–Dec. 2001. 3. Saaddine J.B., et al.: A diabetes report card for the United States: Quality of care in the 1990s. Ann Intern Med 136:565–574, Apr. 16, 2002. 4. Ellrodt G., et al.: Evidence-based disease management. JAMA 278:1687–1692, Nov. 26, 1997. 5. Epstein R.S., Sherwood L.M.: From outcomes research to disease management: A guide for the perplexed. Ann Intern Med 124:832–837, May 1, 1996. 6. Krein S.L., et al.: Department of Veterans Affairs’ Quality Enhancement Research Initiative for Diabetes Mellitus. Med Care 38( suppl. I):I38–I48, Jun. 2000. 7. Heart failure program proves that DM works-—even under fee-forservice. Dis Manag Advis 8:117–121, Aug. 2002. 8. HCFA demo sites offer a smorgasbord of managed care innovation. Dis Manag Advis 7:52–57, Apr. 2001. 9. Weingarten S.R., et al.: Interventions used in disease management programmes for patients with chronic illness: Which ones work? Metaanalysis of published reports. BMJ 325:925–932, Oct. 26, 2002. 10. Doxtator R.F., Rodriguez D.J.: Evaluating costs, benefits, and return on investment for disease management programs. Dis Mgmt 1:185–192, 1998. 11. Institute of Medicine: Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press, 181–206, 2001. 12. Disease management growing, but lack of standardization creating challenges. Health Care Strategic Management 1:6, Jan. 18, 2000. 13. Rubin R.J., Altman W.M., Mendelson D.N.: Health care expenditures for people with diabetes mellitus. J Clin Endocrinol Metab 78:809A–812A, 1992. 14. Selby J.V., et al.: The excess costs of medical care for patients with diabetes mellitus in a managed care population. Diabetes Care 20:1396–1402, Sep. 1997.
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15. The Diabetes Control and Complications Trial Research Group: The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329:977–986, Sep. 30, 1993. 16. UK Prospective Diabetes Study Group: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352:837–853, Sep. 12, 1998. 17. The Hypertension Detection and Follow-up Program Cooperative Group: Implications of the Hypertension Detection and Follow-up Program. Prog Cardiovasc Dis 29(suppl. 1):S3–S10, Nov.–Dec. 1986. 18. Curb J.D., et al.: Effect of diuretic-based antihypertensive treatment on cardiovascular disease risk in older diabetic patients with isolated systolic hypertension. JAMA 276:1886–1892, Dec. 18, 1996. 19. Hansson L., Zanchetti A., Carruthers S.G.: Effects of intensive blood-pressure lowering and low-dose aspirin in patients with hypertension: Principal results of the Hypertension Optimal Treatment (HOT) randomised trial. Lancet 351:1755–1762, Jun. 13, 1998. 20. U.K. Prospective Diabetes Study Group: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ 317:703–713, Sep. 12, 1998. 21. Tuomilehto J., et al.: Effects of calcium-channel blockade in older patients with diabetes and systolic hypertension. N Engl J Med 340:677–684, Mar. 4, 1999. 22. Estacio R.O., et al: The effect of nisoldipine as compared with enalapril on cardiovascular outcomes in patients with non-insulindependent diabetes and hypertension. N Engl J Med 338:645–652, Mar. 5, 1998. 23. Tatti P., et al.: Outcome results of the Fosinopril Versus Amlodipine Cardiovascular Events Randomized Trial (FACET) in patients with hypertension and NIDDM. Diabetes Care 21:597–603, Apr. 1998. 24. Hansson L., et al.: Effect of angiotensin-converting-enzyme inhibition compared with conventional therapy on cardiovascular morbidity and mortality in hypertension: The Captopril Prevention Project (CAPP) randomised trial. Lancet 353:611–616, Feb. 20, 1999.
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References, continued 25. Pyorala K., et al.: Cholesterol lowering with simvastatin improves prognosis of diabetic patients with coronary heart disease. Diabetes Care 20:614–620, Apr. 1997. 26. Sacks F.M., et al.: The effect of pravastatin on coronary events after myocardial infarction in patients with average cholesterol levels. Cholesterol and Recurrent Events Trial investigators. N Engl J Med 335:1001–1009, Oct. 3, 1996. 27. Koskinen P., et al.: Coronary heart disease incidence in NIDDM patients in The Helsinki Heart Study. Diabetes Care 15:820–825, Jul. 1992. 28. Collaborative overview of randomised trials of antiplatelet therapy—I: Prevention of death, myocardial infarction, and stroke by prolonged antiplatelet therapy in various categories of patients. Antiplatelet Trialists’ Collaboration. BMJ 308:81–106, Jan. 8, 1994. 29. Aspirin effects on mortality and morbidity in patients with diabetes mellitus. Early Treatment Diabetic Retinopathy Study report 14. ETDRS Investigators. JAMA 268:1292–1300, Sep. 9, 1992. 30. Early photocoagulation for diabetic retinopathy. ETDRS report number 9. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology 98(suppl.):S766–S785. May 1991. 31. Photocoagulation for diabetic macular edema. Early Treatment Diabetic Retinopathy Study report number 1. Early Treatment Diabetic Retinopathy Study research group. Arch Ophthalmol 103:1796–1806 Dec. 1985. 32. Davidson J.K., et al.: Assessment of program effectiveness of Grady Memorial Hospital–Atlanta. In Steiner G., Lawrence P.A. (eds.): Educating Diabetic Patients. New York: Springer-Verlag, 1981, pp. 329–348. 33. Eastman R.C., et al.: Model of complications of NIDDM. I. Model construction and assumptions. Diabetes Care 20:725–734, May 1997. 34. Herman W.H., Eastman R.C.: The effects of treatment on the direct costs of diabetes. Diabetes Care 21(suppl. 3):C19–C24, Dec. 1998. 35. Testa M.A., Simonson D.C.: Health economic benefits and quality of life during improved glycemic control in patients with type 2 diabetes mellitus: A randomized, controlled, double-blind trial. JAMA 280:1490–1496, Nov. 4, 1998. 36. Aubert R.E., et al.: Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. Ann Intern Med 129:605–612, Oct. 15, 1998. 37. Sadur C.N., et al.: Diabetes management in a health maintenance organization: Efficacy of care management using cluster visits. Diabetes Care 22:2011–2017, Dec. 1999. 38. Trento M., et al.: Group visits improve metabolic control in type 2 diabetes: A 2-year follow-up. Diabetes Care 24:995–1000, Jun. 2001. 39. Rubin R.J., Dietrich K.A., Hawk A.D.: Clinical and economic impact of implementing a comprehensive diabetes management program in managed care. J Clin Endocrinol and Metab 83:2635–2642, Aug. 1998. 40. Wagner E.H., et al.: Chronic Care Clinics for Diabetes in Primary Care: A system-wide randomized trial. Diabetes Care 25:695–700, Apr. 2001.
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41. Klonoff D.C., Schwartz D.M.: An economic analysis of interventions for diabetes. Diabetes Care 23:390–404, Mar. 2000. 42. National Committee for Quality Assurance: The Health Plan Employer Data and Information Set (HEDIS). http://www.ncqa.org/ Programs/HEDIS (accessed Jun. 11, 2003). 43. Vijan S., Hofer T.P., Hayward R.A.: Estimated benefits of glycaemic control in microvascular complications in type II diabetes. Ann Intern Med 127:788–795, Nov. 1, 1997. 44. Brown J.B., et al.: The global diabetes model: User friendly version 3.0. Diabetes Res Clin Pract 50 (suppl. 3):S15–S46, Nov. 2000. 45. Centers for Disease Control (CDC) Diabetes Cost-effectiveness Group: Cost-effectiveness of intensive glycemic control, intensified hypertension control, and serum cholesterol level reduction for type 2 diabetes. JAMA 287:2542–2551, May 15, 2002. 46. Sherwin R.S., et al.: The prevention or delay of type 2 diabetes. Diabetes Care 26 (suppl. 1):S62–S69, Jan. 2003. 47. Schlessinger L., Eddy D.: Archimedes: A new model for simulating health care systems-—The mathematical formulation. J Biomed Inform 35:37–50, Feb. 2002. 48. Brown J.B., et al.: The Mt. Hood challenge: Cross-testing two diabetes simulation models. Diabetes Res Clin Pract 50(suppl. 3):S57–S64, Nov. 2000. 49. Kerr E.A., et al.: Avoiding pitfalls in chronic disease quality measurement: A case for the next generation of technical quality measures. Am J Manag Care 7:1033–1043, Nov. 2001. 50. Hofer T.P., et al.: The unreliability of individual physician “report cares” for assessing the costs and quality of care of a chronic disease. JAMA 281:2098–2105, Jun. 9, 1999. 51. Druss B.G., et al.: Comparing the national economic burden of five chronic conditions. Health Aff (Millwood) 20:233–241, Nov.–Dec. 2001. 52. Stone R.E.: Playing disease management numbers games. Disease Management and Health Outcomes 6:343–348, Dec. 1999. 53. Songer T.J.: Disability in diabetes. In: National Diabetes Data Group: Diabetes in America, 2nd ed. Bethesda, MD: National Institutes of Health Pub. No. 95-1468, 1995, pp. 259–282. 54. Lloyd A., Sawyer W., Hopkinson P.: Impact of long-term complications on quality of life in patients with type 2 diabetes not using insulin. Value Health 4:392–400, Sep.–Oct. 2001. 55. Clarke P., Gray A., Holman R.: Estimating utility values for health states of type 2 diabetic patients using the EQ-5D (UKPDS 62). Med Decis Making 22:340–349, Jul.–Aug. 2002. 56. Quality of life in type 2 diabetic patients is affected by complications but not by intensive policies to improve blood glucose or blood pressure control (UKPDS 37). U.K. Prospective Diabetes Study Group. Diabetes Care 22:1125–1136, Jul. 1999. 57. Ramsey S., et al.: Productivity and medical costs of diabetes in a large employer population. Diabetes Care 25:23–29, Jan. 2002. 58. National Committee for Quality Assurance (NCQA): The NCQA Quality Dividend Calculator: Translating health care into productivity. http://www.ncqacalculator.com (accessed Jun. 11, 2003).
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