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Cost and quality trends under managed care: is there a learning curve in behavioral health carve-out plans?
Journal of Health Economics 18 Ž1999. 593–604 www.elsevier.nlrlocatereconbase
Cost and quality trends under managed care: is there a learning curve in behavioral health carve-out plans? Roland Sturm
)
RAND, 1700 Main Street Santa Monica, CA 90401, USA Received 1 September 1998; received in revised form 1 March 1999; accepted 1 April 1999
Abstract The paper studies the performance of network plans over time using data from 52 managed behavioral health plans. Costs exhibit a ‘learning curve’ with additional cost declines of 10–15% with every doubling of experience, which are independent from time trends and scale economies. Process-of-care measures show increased appropriateness of follow-up care and reduced 30-day rehospitalization, but the relationship to experience or time is not statistically significant. Possible causes of organizational ‘learning’ could be faster referrals to network clinicians, increased acceptance of network providers by patients, selection of more efficient providers, improved care management procedures, or better monitoring techniques. q 1999 Elsevier Science B.V. All rights reserved. JEL classification: I11; L84 Keywords: Learning; Managed care; Mental health; Carve-out
1. Introduction Improvements in performance with increasing experience have been found in a wide variety of industries, a phenomenon commonly known as an organizational learning or experience curve ŽYelle, 1979; Argote and Epple, 1990.. Organiza)
Tel.: q1-310-393-0411 ext. 6164; Fax: q1-310-451-7004; E-mail: [email protected]
0167-6296r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 6 2 9 6 Ž 9 9 . 0 0 0 1 1 - 9
594
R. Sturmr Journal of Health Economics 18 (1999) 593–604
tional learning curves shape the dynamics of an evolving industry and have direct implication for regulation ŽSpence, 1981; Mookherjee and Ray, 1993., an issue particularly relevant for health care, where large firms and organizations are relatively recent. Research on organizational learning curves started about 60 years ago and has since encompassed a wide range of industries, including the costs of manufacturing aircraft ŽAlchian, 1963., ships ŽRapping, 1965., power plants ŽJoskow and Rose, 1985. or nuclear power plant operations ŽJoskow and Rozanski, 1979; Sturm, 1993.. In medical literature, many studies have established that more experienced providers and hospitals have better health outcomes, measured by mortality in heart transplants ŽLaffel et al., 1992. or angioplasty ŽHannan et al., 1997.. However, the focus in the health literature differs in two dimensions. First, learning curve studies in health primarily focus on the technical skills of individuals or a small team in a specific clinical area, which is more individual learning or a ‘practice makes perfect’ concept than learning of a large organization. Second, the dependent variable is a clinical measure, such as the success or complication rates for specific surgical intervention. A Medline search on ‘learning curve’ found several hundred publications, but only one study that analyzed costs as the main dependent variable and focused on a larger organization as the unit of analysis ŽWoods et al., 1992.. In that case, researchers studied the costs of 71 consecutive heart transplant patients in one hospital. The general conclusion is that there are substantial improvements in performance or cost reductions with increasing experience. One area in health that has seen a particularly dramatic corporatization is mental health and substance abuse Ž‘behavioral health’., suggesting that there are some areas of increasing returns. Managed behavioral health organizations ŽMBHO. or ‘carve-outs’, which are organizations that specialize in administering behavioral health benefits that have been carved out of a comprehensive health care plan and which were virtually non-existent 15 years ago, are now responsible for the majority of privately insured Americans. Their membership has grown from 78 to over 150 million members just between 1992 and 1997 alone ŽOss et al., 1997., although some of the members are eligible only for limited benefits, such as employee assistance programs ŽEAPs.. As MBHOs have taken over the management of behavioral health benefits, employers generally experienced a substantial immediate drop in costs. While this cost decrease could have been a one-time drop associated with implementing managed care that does not affect long-run inflationary trends, recent longitudinal studies following the same employers found continuing costs decreases after the adoption of managed care ŽGoldman et al., 1998; Ma and McGuire, 1998; Sturm et al., 1998.. Continuing cost declines raise the concern that services are reduced below desirable levels of care, unless those declines are due to increased efficiencies in care delivery. If they exist, such efficiencies may not materialize immediately, but only over time—the organizational learning curve in managed care
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plans. Factors that could contribute to such an effect include a maturing of the network Žfaster referrals to network clinicians, selection of more efficient and cooperative providers., improved care management procedures, or better monitoring techniques. However, the existence of potential factors does not assure that they have in fact a meaningful impact on costs. There exist several examples of organizations which displayed a ‘forgetting’ curve ŽArgote and Epple, 1990; Sturm, 1993. and the relevance of organizational learning is therefore an empirical question. This paper estimates the amount of clinical cost reductions in behavioral health plans that can be attributed to organizational learning vs. general time trends or economies of scale. The data are from 52 employer-sponsored managed behavioral health plans offered by 22 employers that completed their first year of operations in 1991 or later. 2. Data We have annual data on insurance payments for 52 employer-sponsored managed behavioral health plans, which completed their first year of operations between 1991 and 1996, for a total of 144 plan years. Employers reflect a wide range of industries in 14 states. The average number of enrolled members per plan is around 21,000 and the data set is therefore based on over 3 million life years. A plan is defined as a benefit package offered by one employer and all members within a plan have identical benefits. Some employers offer multiple plans, usually to different types of employees, and such a group of plans shares the same care management team. There are 22 different groups in this data sets. The overall administration of the plans is by United Behavioral Health ŽUBH., the third largest MBHO in the country. Descriptive statistics for the dependent variables are provided in Table 1. The main variable is annual insurance payments per member and actual payments averaged about US$42 or about US$3.50 per insured member per month in 1992 dollars. These numbers are substantially lower than the ones in two recent case Table 1 Descriptive statistics for dependent variables Variables
Mean
Standard deviation
Minimum
Maximum
Insurance payment per member Follow-up care after discharge from acute in-patient care within 7 days 30 days Rehospitalization within 30 days
41.95
21.55
4.48
112.64
0.61 0.75 0.07
0.22 0.19 0.8
0 0 0
1 1 0.33
Unit of observation is plan year; ns144 Ž ns135 for follow-up care and rehospitalization rate..
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studies of high cost employers ŽGoldman et al., 1998; Ma and McGuire, 1998., but probably more representative for most employer-sponsored plans. An important feature of most managed behavioral health contracts is that they only include payments to facilities and providers. Pharmacy and general medical care are not part of the benefits managed by UBH and such claims are not in these data. Medication use for mental health conditions has grown with the rise of newer medications, especially for depression and psychotic disorders, and the analysis needs to control for this likely time shift from psychotherapy to drug therapy. Although not part of the main analysis, I also consider two processes of care indicators to provide a different angle on the interpretation of costs because there are concerns that cost decreases imply lower quality of care. 1 These two measures are part of the Health Plan Employer Data and Information System ŽHEDIS. administered by the National Committee for Quality Assurance ŽNCQA.. The first one is the proportion of patients who receive follow-up care after being discharged from an acute in-patient stay. Averaging across plans and years, 61% of in-patients receive any follow-up care within 7 days and 75% receive follow-up care within 30 days. The second variable is the proportion of in-patients who are readmitted within 30 days of a discharge, which average 7% across the plans. Table 2 provides descriptive statistics for explanatory variables. The primary goal of this paper is to relate cost changes to ‘experience’ and I consider three alternative measures for the latter. The first measure is simply plan age, which is 1 in the first full calendar year after start-up and has a maximum value of 7 in this data set Ž7 out of 22 groups contributed 4 or more years of data.. This measure would be most relevant if learning is specific to a plan Žor group, as all plans within a group have the same starting date. and there are no spill-overs from administering other plans Žgroups. simultaneously. The start up years differ by group, which provides a cross-sectional variation that differs from a time trend and which isolates a possible ‘learning’ effect that is not confounded by the secular trend to more effective medications. The second measure is the number of managed claims that were processed in the main state of a plan Ždetermined by the residency of the majority of plan members. and are on the original UBH data system Žwhich reflects the core managed care business, rather than insurance business that was added through acquisitions over time.. 2 This measure captures local spill-overs, such as the establishment of a contracted provider network in a geographic area, which benefits all plans in that area, but not plans elsewhere.
1 Thompson Ž1998. found that the impressive increases in productivity in ship-building ŽRapping, 1965. were associated with reductions in quality and that taking quality into account reduces the effect of learning. 2 Until 1997 when bought by United Health, UBH was known as US Behavioral Health. Our data come from the US Behavioral Health main data system, which reflects the experience of the company, whereas many of the other data bases now part of UBH contain claims that were never managed by US Behavioral Health.
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Table 2 Descriptive statistics for explanatory variables Variables
Mean
Standard deviation
Minimum
Maximum
Plan Year Cumulative claims in main state Cumulative UBH claims Integrated EAP Limit on lifetime insurance payments Year of Plan Start Ždata available from first full calendar year after start until end of 1997.
2.3 131,209 826,196 0.278 601,303 1993.87
1.4 185,255 358,222 0.45 420,755 1.6
1 185 63,650 0 50,000 1991
7 678,220 1,439,479 1 1,000,000 1996
Unit of observation is plan year; ns144. Unlimited lifetime benefits are set to US$1,000,000 for analysis.
Defining the geographic area as state is the compromise between data that are aggregated at the plan level Žand therefore need a measure that applies to as many plan members as possible. and the fact that care is delivered locally. On average, more than 75% of plan members reside in one state, but less then 50% reside in one county. The correlation between cumulative state claims and calendar year is 0.1, thus the effect of this measure would not be confounded with possibly more effective medication use Žand pharmacy costs are not included in these data.. The third measure is the cumulative number of claims processed by UBH, again limited to claims on the original management data system. This would be the most relevant experience measure if the development of management guidelines or data-based monitoring tools were the factors driving learning. However, the correlation with calendar year is 0.9, making it very difficult in such a small data set to separate this effect from secular time trends with any precision. The current Žrather than cumulative. value of claims processed by UBH provides a measure of the scale of operations and will be used to control for scale economies. The next set of variables is to control for other factors affecting costs. 3 The indicator variable for integrated EAP is zero if the plan only provides care management, but no EAP benefits. EAP benefits, often provided on the premises of an employer, provide counselling and referrals for various types of personal problems Žlegal, financial, family.. EAP benefits are generally free without copayments, but very limited, such as 3–5 sessions of counselling. Imposing low limits on lifetime benefits could reduce the number of severely ill patients in a plan as they are ineligible or select into other plans. Thus, higher limits should result in higher costs per member. 3 The initial working paper version of this paper Žusing an earlier version of the data. considers some alternative statistical specifications that allow to estimate the effect of additional variables, including risk sharing and benefit design, which are not reported here ŽSturm, 1998..
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Finally, I include a linear time trend or indicator variables for each year to control for unusual year effects or secular trends in the industry that would not be limited to a plan. Of particular importance are changes in practice styles, such as an increased availability and use of more effective medication. Another factor is case-mix: If more expensive patients select out of the plan or become ineligible, it could lead to a spurious ‘learning’ curve that would not be controlled for by other time variables Žin contrast to the trend towards medications.. Such an effect cannot be estimated from the aggregated plan data and I did a preliminary analysis for this paper to consider whether this was an important effect. Using on micro data from a large employer with substantially lower benefits than the average plan in this study, case-mix changes caused a decrease in plan costs of about 1% per year for this employer.
3. Learning curves and estimation The conventional form of the learning curve is a power function relating experience Ž x . to performance Ž y ., y s ax b , which after taking logs can be estimated as a linear regression model. Learning rates are typically expressed in terms of the progress ratio p: every time experience doubles, costs Žor another measure of performance. is reduced to p% of its previous level, or expressed in the terms of the parameters of the power function p s 2 b. While progress ratios can vary widely, a review of over 100 industry studies found that more than 3r4 of the progress ratios were between 70 and 90% and the mode Žabout one in six studies. was 81 or 82% ŽArgote and Epple, 1990.. This empirical approach to estimating learning effects could theoretically Žbut not practically. be embedded in a more theoretical production function framework. However, the concept of production functions suggests important other variables that need to be considered in this reduced form estimation. Particularly important are economies of scale because most managed care organizations have seen a major expansion in the size of their operation. If scale economies are present and the size of the operation is unmeasured, this increase in size of managed care organizations would lead to an overestimate of learning effects. Similarly, technological improvements in either treatment Žshift to medications. or care management that are generally available Žand not internal to an organization. could give rise to an organizational learning effect unless one controls for their existence. The results reported here correspond to a fixed effects models Žbased on deviations from group means. and therefore exclude variables that are constant over time within a group of plans Žcontractual arrangements regarding risk sharing, start year of plans.. There was no indication of heteroscedasticity, which is particularly problematic for models in log form ŽManning, 1998., and the only variance correction is the robust HuberrWhite variance estimates with group membership as a cluster indicator. Discussion of functional forms and specifica-
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tions, as well as results for random effects and other specifications for this analysis, are available in the earlier working paper ŽSturm, 1998.. 4. Results Fig. 1 shows descriptive results for the three dependent variables, measured as changes from the first plan year Žit plots the coefficients of a regression on indicators for group and each plan year ) 1.. The scale are $ changes per member for the cost variable and percentage point changes for 30-day readmission rates and 30-day follow-up care after an in-patient discharge. There is a substantial drop from year 1 to years 2 and 3 in per member costs, but there is a continuing decline. There is no clear increase in follow-up care after an in-patient discharge during the first plan years, although later plan years have higher Žmore appropriate. follow-up rates over time. There appears no trend for rehospitalization rates. Table 3 shows the main results corresponding to a fixed effects model for group for two measures of experience Žcumulative state claims and plan year.. The other regressor variables are EAP benefits, the logarithm of lifetime maximums, and a time trend. The first row of Table 3 summarizes the estimated progress ratios, which range from 86 to 92% Ž82 to 95% considering all alternative specifications, including random effects models, that were estimated but are not reported here.. EAP benefits and higher lifetime limits are consistently and significantly associated with higher costs. Initially, I tried to determine which experience measure Žplan year, state cumulative claims, total cumulative claims. is most relevant and it appeared that state experience has the strongest and most robust effect, but to disentangle these measures, a larger sample size would be needed. All three measures Žcumulative
Fig. 1. Change in three performance measures with plan experience.
600
Independent variables
Cumulative state claims, plan age and scale of operations
Cumulative state claims and scale of operation
Plan age and scale of operation
Follow-up care
Readmission rate
Progress ratio Cumulative state claims Plan Year Current UBH claims ŽScale of operations. Integrated EAP Limit on lifetime insurance payments Žnatural logarithm. Calendar Year R2
92.1 y0.216UU Ž0.080. 0.097 Ž0.203. y0.161 Ž0.331.
87.1 y0.199UU Ž0.068. – y0.189 Ž0.320.
85.7 – y0.223 Ž0.223. y0.409 Ž0.327.
100.2 0.014 Ž0.030. y0.011 Ž0.084. y0.176 Ž0.144.
94.1 0.001 Ž0.017. y0.088 Ž0.053. y0.070 Ž0.041.
0.952UU Ž0.207. 0.647U Ž0.260.
0.935UU Ž0.202. 0.590U Ž0.219.
0.743UU Ž0.218. 0.168 Ž0.192.
0.044 Ž0.099. 0.174 Ž0.208.
y0.003 Ž0.041. y0.052 Ž0.095.
0.028 Ž0.125. 0.573
0.064 Ž0.099. 0.595
0.098 Ž0.130. 0.545
0.068 Ž0.052. 0.170
0.049 Ž0.021. 0.167
Standard errors in parentheses. Significance test: U , significant at p- 0.05; UU , significant at p- 0.01. N s144 for cost, N s135 for quality measures.
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Table 3 Regression estimates
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state claims, plan year, and cumulative UBH claims. are highly significant in individual regressions without a time trend. The estimated effect for cumulative state claims and plan year is robust to changes in specifications and including a time trend, dummy variables, or current UBH claims with a time trend to control for scale of operations. However, plan year Žwhile continuing to show a progress ratio in the range of 85–90%. becomes statistically insignificant with calendar time variables. In contrast, cumulative UBH claims are so highly correlated with calendar time that the resulting estimates are very sensitive to specifications of the model when calendar time is included and I cannot separate this learning effect from time trends that may include shifts to effective medications. Table 3 therefore does not report results for cumulative UBH claims. The specification in Table 3 includes a linear time trend, but the effect sizes for the cost specification are almost identical with year dummy variables Žand without current claims, which are collinear with year dummies.. The results are also not affected whether or not one controls for scale economies. The point estimate for scale economies suggests that costs decline with increasing size, but the estimate is never statistically significant. The lack of scale economies also suggests that buying power of MBHO vis-a-vis providers does not increase with size of the MBHO Žwhich would be pecuniary scale economies that could exist in this application in addition to technological scale economies.. The reason for this is may be that markets are local and a large national presence has little impact, something that could differ from the medical system where large national provider organizations exists. To test whether the continuing cost decline was correlated with adverse effects on processes-of-care, I analyzed 30-day follow-up rates after an in-patient discharge and 30-day rehospitalization rates. However, in contrast to the strong effects on costs Žall cost regressions in Table 3 are significant at p - 0.0001., there are no significant relationships between experience measures, time trends, or other explanatory variables and the two quality measures Žfollow-up rates and 30-day readmission.. None of the regressions is significant Ž p ) 0.20., nor is any individual coefficient. The point estimate of the progress ratio for follow-up care shows no change at all and there is a slight Žand non-significant. decline in readmission rates Žand this could be a consequence of case-mix changes.. Thus, the continuing cost decline is not causing changes in care patterns as measured by those two variables. As limited as these measures may be, they are the main mental health quality measures currently being used by NCQA.
5. Discussion It may be surprising to find such strong organizational learning affecting costs in an activity that is not near the edge of scientific advances or new treatment technologies. The decline of about 10–15% in clinical costs with each doubling of
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experience Žmeasured by managed care claims in a plan’s state or plan year. is in addition to the effects of time trends towards reduced costs Žsuch as an increased reliance on medications that are not part of these costs. or scale economies. To be sure, the progress ratios are higher than in most previous industrial applications, where cost reductions are about twice the size Ža higher progress ratio means smaller cost reductions.. However, this study focused only on clinical costs and excluded administrative costs, which may experience a steeper decline. The medical cost study by Woods et al. Ž1992. of heart transplant costs also found much larger cost reductions as a function of experience, but it applied to a narrow and new medical activity, where one would expect learning to be more important than across the wide range of more traditional care in this application. The aggregate data used here do not allow a decomposition of the cost reductions into quantity and unit price changes, but we have done this decomposition for one of the larger employers in this data set, which contributes 6 years of observations ŽGoldman et al., 1998.. Over a 5-year period, clinical costs per member for this employer fell by about 17% Žwhich is a little below the estimated average in this data set.. Although the percentage of users increased by 15%, the number of sessions fell by 21%, with only a very minor decline in costs per session Žy1.4%.. The number of hospitalizations among users of outpatient services fell by 24% over 5 years and the average length of stay fell by 43%, although the costs per hospital day increased by 15%. Research in manufacturing and service industries attributes much of the total productivity growth to the slow and often almost invisible accretion of individually small improvements ŽRosenberg, 1982.. The emphasis on gradual quality improvement toward the goals of zero defects is a central tenet of high quality production philosophies ŽHayes, 1981.. The impact goes beyond the competitiveness of an individual firm and economic historians regard differing organizational capabilities of firms as the main factor that determines the shape and success of markets in a specific industry ŽChandler, 1990.. In this application, ‘learning’ was limited to costs and did not extend to quality improvements, however. While UBH’s internal guidelines call for follow-up care after an acute in-patient discharge within a few days, about 1 out of 3 patients is not seen within a week Žor 1 out of 4 within a month.. This is still a long way from ‘zero defects’ even if follow-up rates appear to be increasing despite falling costs and even though the follow-up rate is higher than the national average among HMOs for depression reported by NCQA Ž67.3% in 1997, NCQA, 1998.. Looking beyond behavioral health care, MBHOs are also interesting as a window into the future of managed care because advanced managed care techniques, such as concurrent utilization review by clinical care managers, guideline implementation, and disease management systems, are standard in MBHOs, whereas these techniques have only started diffusing in other areas of medicine. If similar organizational learning were to apply in those areas, significant cost savings could be realized in contrast to the current predictions of quickly escalat-
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ing costs ŽLevit et al., 1998.. While the learning curves could reflect an increasing improvement and sophistication of data based monitoring techniques to influence costs, similar techniques should be available to improve follow-up rates, but may not have been implemented. The organizational learning curve is a complex phenomenon affected by many processes. This paper demonstrates that their impact can be substantial in managed behavioral health care, but we cannot determine the sources of this learning, which can involve information systems, care management procedures, guidelines, provider selection, patient education, or provider education. The multitude of potential factors also suggests that organizations can vary considerably in the rates at which they learn. In other industries, the productivity differences between organizations that learn and improve their performance and organizations that deteriorate can be dramatic over longer periods of time even if performance is initially comparable and organizations use similar or even identical technologies ŽGhemawat, 1985; Argote and Epple, 1990; Sturm, 1993.. This clearly raises the question to which extent the results of this study apply to other managed care organizations and the answer to this question would shed light on the long-run shape of this industry.
Acknowledgements I want to thank Carole Gresenz, Will Manning, Juergen Unutzer, Vivian Ho, ¨ Ingo Vogelsang, and William Goldman for comments on an earlier draft and United Behavioral Health for providing the data. Joanie Chung, Jin Son, and Winnie Zhang provided research and programming assistance. This research was supported by the Robert Wood Johnson Foundation and by NIMH grants MH54147 and MH54623.
References Alchian, A., 1963. Reliability of progress curves in airframe production. Econometrica 31 Ž4., 679–693. Argote, L., Epple, D., 1990. Learning curves in manufacturing. Science 247, 920–924. Chandler, A., 1990. Scale and Scope. Harvard Univ. Press, Cambridge. Ghemawat, P., 1985. Building strategy on the experience curve. Harvard Business Review 63 Ž2., 143–149. Goldman, W., McCulloch, J., Sturm, R., 1998. Costs and use of mental health services before and after managed care. Health Affairs 17 Ž2., 40–52. Hannan, E.L., Racz, M., Ryan, T.J., McCallister, B.D., Johnson, L.W., Arani, D.T., Guerci, A.D., Sosa, J., Topol, E.J., 1997. Coronary angioplasty volume–outcome relationships for hospitals and cardiologists. J. Am. Med. Assoc. 7 Ž11., 892–898. Hayes, R., 1981. Why Japanese factories work. Harvard Business Review, 56–66. Joskow, P.L., Rose, N.L., 1985. The effects of technological change, experience, and environmental regulation on the construction cost of coal-burning generating units. Rand Journal of Economics 16 Ž1., 1–27.
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Joskow, P.L., Rozanski, G.A., 1979. The effects of learning by doing on nuclear plant operating reliability. Review Economics and Statistics 61 Ž2., 161–168. Laffel, G.L., Barnett, A.I., Finkelstein, S., Kaye, M.P., 1992. The relation between experience and outcome in heart transplantation. N. Engl. J. Med. 327, 1220–1225. National Health Accounts Team, Levit, K.R., Lazenby, H.C., Braden, B.R., 1998. National health spending trends in 1996. Health Affairs 17 Ž1., 35–51. Ma, C.A., McGuire, T.G., 1998. Costs and incentives in a mental health carve-out. Health Affairs 17 Ž2., 53–69. Manning, W.G., 1998. The logged dependent variable, heteroscedasticity, and the retransformation problem. Journal of Health Economics 17, 283–296. Mookherjee, D., Ray, D., 1993. Learning-by-doing and industrial structure: an overview. In: Dutta, B., Mookherjee, D., Ray, D. ŽEds.., Theoretical Issues in Development Economics. Oxford Univ. Press, Oxford. NCQA, 1998. The state of managed care quality. Oss, M.E., Drissel, A.B., Clary J., 1997. Managed behavioral health market share in the United States, 1997–1998. Behavioral Health Industry News, Gettysburg, PA. Rapping, L., 1965. Learning and World War II production function. Review of Economics and Statistics 47 Ž1., 81–86. Rosenberg, N., 1982. Inside the Black Box. Cambridge Univ. Press, Cambridge. Spence, A.M., 1981. The learning curve and competition. Bell Journal of Economics 12, 49–70. Sturm, R., 1993. Nuclear power in eastern europe: learning or forgetting curves?. Energy Economics 15 Ž3., 183–189. Sturm, R., 1998. Cost and quality trends under managed care: is there a learning curve in behavioral health carve-out plans? Working Paper No. 138, UCLArRAND Center on Managed Care for Psychiatric Disorders. Sturm, R., Goldman, W., McCulloch, J., 1998. Mental health and substance abuse parity: a case study of Ohio’s state employee program. Journal of Mental Health Policy and Economics 1, 129–134. Thompson, P., 1998. How much did the Liberty shipbuilders learn? New evidence for an old case study, unpublished manuscript. University of Houston. Woods, J.R., Saywell, R.M. Jr., Nyhuis, A.W., Jay, S.J., Lohrman, R.G., Halbrook, H.G., 1992. The learning curve and the cost of heart transplantation. Health Services Research 27, 219–238. Yelle, L., 1979. The learning curve: historical review and comprehensive survey. Decision Science 10, 302–328.
Cost and quality trends under managed care: is there a learning curve in behavioral health carve-out plans? Roland Sturm
)
RAND, 1700 Main Street Santa Monica, CA 90401, USA Received 1 September 1998; received in revised form 1 March 1999; accepted 1 April 1999
Abstract The paper studies the performance of network plans over time using data from 52 managed behavioral health plans. Costs exhibit a ‘learning curve’ with additional cost declines of 10–15% with every doubling of experience, which are independent from time trends and scale economies. Process-of-care measures show increased appropriateness of follow-up care and reduced 30-day rehospitalization, but the relationship to experience or time is not statistically significant. Possible causes of organizational ‘learning’ could be faster referrals to network clinicians, increased acceptance of network providers by patients, selection of more efficient providers, improved care management procedures, or better monitoring techniques. q 1999 Elsevier Science B.V. All rights reserved. JEL classification: I11; L84 Keywords: Learning; Managed care; Mental health; Carve-out
1. Introduction Improvements in performance with increasing experience have been found in a wide variety of industries, a phenomenon commonly known as an organizational learning or experience curve ŽYelle, 1979; Argote and Epple, 1990.. Organiza)
Tel.: q1-310-393-0411 ext. 6164; Fax: q1-310-451-7004; E-mail: [email protected]
0167-6296r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 7 - 6 2 9 6 Ž 9 9 . 0 0 0 1 1 - 9
594
R. Sturmr Journal of Health Economics 18 (1999) 593–604
tional learning curves shape the dynamics of an evolving industry and have direct implication for regulation ŽSpence, 1981; Mookherjee and Ray, 1993., an issue particularly relevant for health care, where large firms and organizations are relatively recent. Research on organizational learning curves started about 60 years ago and has since encompassed a wide range of industries, including the costs of manufacturing aircraft ŽAlchian, 1963., ships ŽRapping, 1965., power plants ŽJoskow and Rose, 1985. or nuclear power plant operations ŽJoskow and Rozanski, 1979; Sturm, 1993.. In medical literature, many studies have established that more experienced providers and hospitals have better health outcomes, measured by mortality in heart transplants ŽLaffel et al., 1992. or angioplasty ŽHannan et al., 1997.. However, the focus in the health literature differs in two dimensions. First, learning curve studies in health primarily focus on the technical skills of individuals or a small team in a specific clinical area, which is more individual learning or a ‘practice makes perfect’ concept than learning of a large organization. Second, the dependent variable is a clinical measure, such as the success or complication rates for specific surgical intervention. A Medline search on ‘learning curve’ found several hundred publications, but only one study that analyzed costs as the main dependent variable and focused on a larger organization as the unit of analysis ŽWoods et al., 1992.. In that case, researchers studied the costs of 71 consecutive heart transplant patients in one hospital. The general conclusion is that there are substantial improvements in performance or cost reductions with increasing experience. One area in health that has seen a particularly dramatic corporatization is mental health and substance abuse Ž‘behavioral health’., suggesting that there are some areas of increasing returns. Managed behavioral health organizations ŽMBHO. or ‘carve-outs’, which are organizations that specialize in administering behavioral health benefits that have been carved out of a comprehensive health care plan and which were virtually non-existent 15 years ago, are now responsible for the majority of privately insured Americans. Their membership has grown from 78 to over 150 million members just between 1992 and 1997 alone ŽOss et al., 1997., although some of the members are eligible only for limited benefits, such as employee assistance programs ŽEAPs.. As MBHOs have taken over the management of behavioral health benefits, employers generally experienced a substantial immediate drop in costs. While this cost decrease could have been a one-time drop associated with implementing managed care that does not affect long-run inflationary trends, recent longitudinal studies following the same employers found continuing costs decreases after the adoption of managed care ŽGoldman et al., 1998; Ma and McGuire, 1998; Sturm et al., 1998.. Continuing cost declines raise the concern that services are reduced below desirable levels of care, unless those declines are due to increased efficiencies in care delivery. If they exist, such efficiencies may not materialize immediately, but only over time—the organizational learning curve in managed care
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595
plans. Factors that could contribute to such an effect include a maturing of the network Žfaster referrals to network clinicians, selection of more efficient and cooperative providers., improved care management procedures, or better monitoring techniques. However, the existence of potential factors does not assure that they have in fact a meaningful impact on costs. There exist several examples of organizations which displayed a ‘forgetting’ curve ŽArgote and Epple, 1990; Sturm, 1993. and the relevance of organizational learning is therefore an empirical question. This paper estimates the amount of clinical cost reductions in behavioral health plans that can be attributed to organizational learning vs. general time trends or economies of scale. The data are from 52 employer-sponsored managed behavioral health plans offered by 22 employers that completed their first year of operations in 1991 or later. 2. Data We have annual data on insurance payments for 52 employer-sponsored managed behavioral health plans, which completed their first year of operations between 1991 and 1996, for a total of 144 plan years. Employers reflect a wide range of industries in 14 states. The average number of enrolled members per plan is around 21,000 and the data set is therefore based on over 3 million life years. A plan is defined as a benefit package offered by one employer and all members within a plan have identical benefits. Some employers offer multiple plans, usually to different types of employees, and such a group of plans shares the same care management team. There are 22 different groups in this data sets. The overall administration of the plans is by United Behavioral Health ŽUBH., the third largest MBHO in the country. Descriptive statistics for the dependent variables are provided in Table 1. The main variable is annual insurance payments per member and actual payments averaged about US$42 or about US$3.50 per insured member per month in 1992 dollars. These numbers are substantially lower than the ones in two recent case Table 1 Descriptive statistics for dependent variables Variables
Mean
Standard deviation
Minimum
Maximum
Insurance payment per member Follow-up care after discharge from acute in-patient care within 7 days 30 days Rehospitalization within 30 days
41.95
21.55
4.48
112.64
0.61 0.75 0.07
0.22 0.19 0.8
0 0 0
1 1 0.33
Unit of observation is plan year; ns144 Ž ns135 for follow-up care and rehospitalization rate..
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studies of high cost employers ŽGoldman et al., 1998; Ma and McGuire, 1998., but probably more representative for most employer-sponsored plans. An important feature of most managed behavioral health contracts is that they only include payments to facilities and providers. Pharmacy and general medical care are not part of the benefits managed by UBH and such claims are not in these data. Medication use for mental health conditions has grown with the rise of newer medications, especially for depression and psychotic disorders, and the analysis needs to control for this likely time shift from psychotherapy to drug therapy. Although not part of the main analysis, I also consider two processes of care indicators to provide a different angle on the interpretation of costs because there are concerns that cost decreases imply lower quality of care. 1 These two measures are part of the Health Plan Employer Data and Information System ŽHEDIS. administered by the National Committee for Quality Assurance ŽNCQA.. The first one is the proportion of patients who receive follow-up care after being discharged from an acute in-patient stay. Averaging across plans and years, 61% of in-patients receive any follow-up care within 7 days and 75% receive follow-up care within 30 days. The second variable is the proportion of in-patients who are readmitted within 30 days of a discharge, which average 7% across the plans. Table 2 provides descriptive statistics for explanatory variables. The primary goal of this paper is to relate cost changes to ‘experience’ and I consider three alternative measures for the latter. The first measure is simply plan age, which is 1 in the first full calendar year after start-up and has a maximum value of 7 in this data set Ž7 out of 22 groups contributed 4 or more years of data.. This measure would be most relevant if learning is specific to a plan Žor group, as all plans within a group have the same starting date. and there are no spill-overs from administering other plans Žgroups. simultaneously. The start up years differ by group, which provides a cross-sectional variation that differs from a time trend and which isolates a possible ‘learning’ effect that is not confounded by the secular trend to more effective medications. The second measure is the number of managed claims that were processed in the main state of a plan Ždetermined by the residency of the majority of plan members. and are on the original UBH data system Žwhich reflects the core managed care business, rather than insurance business that was added through acquisitions over time.. 2 This measure captures local spill-overs, such as the establishment of a contracted provider network in a geographic area, which benefits all plans in that area, but not plans elsewhere.
1 Thompson Ž1998. found that the impressive increases in productivity in ship-building ŽRapping, 1965. were associated with reductions in quality and that taking quality into account reduces the effect of learning. 2 Until 1997 when bought by United Health, UBH was known as US Behavioral Health. Our data come from the US Behavioral Health main data system, which reflects the experience of the company, whereas many of the other data bases now part of UBH contain claims that were never managed by US Behavioral Health.
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Table 2 Descriptive statistics for explanatory variables Variables
Mean
Standard deviation
Minimum
Maximum
Plan Year Cumulative claims in main state Cumulative UBH claims Integrated EAP Limit on lifetime insurance payments Year of Plan Start Ždata available from first full calendar year after start until end of 1997.
2.3 131,209 826,196 0.278 601,303 1993.87
1.4 185,255 358,222 0.45 420,755 1.6
1 185 63,650 0 50,000 1991
7 678,220 1,439,479 1 1,000,000 1996
Unit of observation is plan year; ns144. Unlimited lifetime benefits are set to US$1,000,000 for analysis.
Defining the geographic area as state is the compromise between data that are aggregated at the plan level Žand therefore need a measure that applies to as many plan members as possible. and the fact that care is delivered locally. On average, more than 75% of plan members reside in one state, but less then 50% reside in one county. The correlation between cumulative state claims and calendar year is 0.1, thus the effect of this measure would not be confounded with possibly more effective medication use Žand pharmacy costs are not included in these data.. The third measure is the cumulative number of claims processed by UBH, again limited to claims on the original management data system. This would be the most relevant experience measure if the development of management guidelines or data-based monitoring tools were the factors driving learning. However, the correlation with calendar year is 0.9, making it very difficult in such a small data set to separate this effect from secular time trends with any precision. The current Žrather than cumulative. value of claims processed by UBH provides a measure of the scale of operations and will be used to control for scale economies. The next set of variables is to control for other factors affecting costs. 3 The indicator variable for integrated EAP is zero if the plan only provides care management, but no EAP benefits. EAP benefits, often provided on the premises of an employer, provide counselling and referrals for various types of personal problems Žlegal, financial, family.. EAP benefits are generally free without copayments, but very limited, such as 3–5 sessions of counselling. Imposing low limits on lifetime benefits could reduce the number of severely ill patients in a plan as they are ineligible or select into other plans. Thus, higher limits should result in higher costs per member. 3 The initial working paper version of this paper Žusing an earlier version of the data. considers some alternative statistical specifications that allow to estimate the effect of additional variables, including risk sharing and benefit design, which are not reported here ŽSturm, 1998..
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Finally, I include a linear time trend or indicator variables for each year to control for unusual year effects or secular trends in the industry that would not be limited to a plan. Of particular importance are changes in practice styles, such as an increased availability and use of more effective medication. Another factor is case-mix: If more expensive patients select out of the plan or become ineligible, it could lead to a spurious ‘learning’ curve that would not be controlled for by other time variables Žin contrast to the trend towards medications.. Such an effect cannot be estimated from the aggregated plan data and I did a preliminary analysis for this paper to consider whether this was an important effect. Using on micro data from a large employer with substantially lower benefits than the average plan in this study, case-mix changes caused a decrease in plan costs of about 1% per year for this employer.
3. Learning curves and estimation The conventional form of the learning curve is a power function relating experience Ž x . to performance Ž y ., y s ax b , which after taking logs can be estimated as a linear regression model. Learning rates are typically expressed in terms of the progress ratio p: every time experience doubles, costs Žor another measure of performance. is reduced to p% of its previous level, or expressed in the terms of the parameters of the power function p s 2 b. While progress ratios can vary widely, a review of over 100 industry studies found that more than 3r4 of the progress ratios were between 70 and 90% and the mode Žabout one in six studies. was 81 or 82% ŽArgote and Epple, 1990.. This empirical approach to estimating learning effects could theoretically Žbut not practically. be embedded in a more theoretical production function framework. However, the concept of production functions suggests important other variables that need to be considered in this reduced form estimation. Particularly important are economies of scale because most managed care organizations have seen a major expansion in the size of their operation. If scale economies are present and the size of the operation is unmeasured, this increase in size of managed care organizations would lead to an overestimate of learning effects. Similarly, technological improvements in either treatment Žshift to medications. or care management that are generally available Žand not internal to an organization. could give rise to an organizational learning effect unless one controls for their existence. The results reported here correspond to a fixed effects models Žbased on deviations from group means. and therefore exclude variables that are constant over time within a group of plans Žcontractual arrangements regarding risk sharing, start year of plans.. There was no indication of heteroscedasticity, which is particularly problematic for models in log form ŽManning, 1998., and the only variance correction is the robust HuberrWhite variance estimates with group membership as a cluster indicator. Discussion of functional forms and specifica-
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tions, as well as results for random effects and other specifications for this analysis, are available in the earlier working paper ŽSturm, 1998.. 4. Results Fig. 1 shows descriptive results for the three dependent variables, measured as changes from the first plan year Žit plots the coefficients of a regression on indicators for group and each plan year ) 1.. The scale are $ changes per member for the cost variable and percentage point changes for 30-day readmission rates and 30-day follow-up care after an in-patient discharge. There is a substantial drop from year 1 to years 2 and 3 in per member costs, but there is a continuing decline. There is no clear increase in follow-up care after an in-patient discharge during the first plan years, although later plan years have higher Žmore appropriate. follow-up rates over time. There appears no trend for rehospitalization rates. Table 3 shows the main results corresponding to a fixed effects model for group for two measures of experience Žcumulative state claims and plan year.. The other regressor variables are EAP benefits, the logarithm of lifetime maximums, and a time trend. The first row of Table 3 summarizes the estimated progress ratios, which range from 86 to 92% Ž82 to 95% considering all alternative specifications, including random effects models, that were estimated but are not reported here.. EAP benefits and higher lifetime limits are consistently and significantly associated with higher costs. Initially, I tried to determine which experience measure Žplan year, state cumulative claims, total cumulative claims. is most relevant and it appeared that state experience has the strongest and most robust effect, but to disentangle these measures, a larger sample size would be needed. All three measures Žcumulative
Fig. 1. Change in three performance measures with plan experience.
600
Independent variables
Cumulative state claims, plan age and scale of operations
Cumulative state claims and scale of operation
Plan age and scale of operation
Follow-up care
Readmission rate
Progress ratio Cumulative state claims Plan Year Current UBH claims ŽScale of operations. Integrated EAP Limit on lifetime insurance payments Žnatural logarithm. Calendar Year R2
92.1 y0.216UU Ž0.080. 0.097 Ž0.203. y0.161 Ž0.331.
87.1 y0.199UU Ž0.068. – y0.189 Ž0.320.
85.7 – y0.223 Ž0.223. y0.409 Ž0.327.
100.2 0.014 Ž0.030. y0.011 Ž0.084. y0.176 Ž0.144.
94.1 0.001 Ž0.017. y0.088 Ž0.053. y0.070 Ž0.041.
0.952UU Ž0.207. 0.647U Ž0.260.
0.935UU Ž0.202. 0.590U Ž0.219.
0.743UU Ž0.218. 0.168 Ž0.192.
0.044 Ž0.099. 0.174 Ž0.208.
y0.003 Ž0.041. y0.052 Ž0.095.
0.028 Ž0.125. 0.573
0.064 Ž0.099. 0.595
0.098 Ž0.130. 0.545
0.068 Ž0.052. 0.170
0.049 Ž0.021. 0.167
Standard errors in parentheses. Significance test: U , significant at p- 0.05; UU , significant at p- 0.01. N s144 for cost, N s135 for quality measures.
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Table 3 Regression estimates
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601
state claims, plan year, and cumulative UBH claims. are highly significant in individual regressions without a time trend. The estimated effect for cumulative state claims and plan year is robust to changes in specifications and including a time trend, dummy variables, or current UBH claims with a time trend to control for scale of operations. However, plan year Žwhile continuing to show a progress ratio in the range of 85–90%. becomes statistically insignificant with calendar time variables. In contrast, cumulative UBH claims are so highly correlated with calendar time that the resulting estimates are very sensitive to specifications of the model when calendar time is included and I cannot separate this learning effect from time trends that may include shifts to effective medications. Table 3 therefore does not report results for cumulative UBH claims. The specification in Table 3 includes a linear time trend, but the effect sizes for the cost specification are almost identical with year dummy variables Žand without current claims, which are collinear with year dummies.. The results are also not affected whether or not one controls for scale economies. The point estimate for scale economies suggests that costs decline with increasing size, but the estimate is never statistically significant. The lack of scale economies also suggests that buying power of MBHO vis-a-vis providers does not increase with size of the MBHO Žwhich would be pecuniary scale economies that could exist in this application in addition to technological scale economies.. The reason for this is may be that markets are local and a large national presence has little impact, something that could differ from the medical system where large national provider organizations exists. To test whether the continuing cost decline was correlated with adverse effects on processes-of-care, I analyzed 30-day follow-up rates after an in-patient discharge and 30-day rehospitalization rates. However, in contrast to the strong effects on costs Žall cost regressions in Table 3 are significant at p - 0.0001., there are no significant relationships between experience measures, time trends, or other explanatory variables and the two quality measures Žfollow-up rates and 30-day readmission.. None of the regressions is significant Ž p ) 0.20., nor is any individual coefficient. The point estimate of the progress ratio for follow-up care shows no change at all and there is a slight Žand non-significant. decline in readmission rates Žand this could be a consequence of case-mix changes.. Thus, the continuing cost decline is not causing changes in care patterns as measured by those two variables. As limited as these measures may be, they are the main mental health quality measures currently being used by NCQA.
5. Discussion It may be surprising to find such strong organizational learning affecting costs in an activity that is not near the edge of scientific advances or new treatment technologies. The decline of about 10–15% in clinical costs with each doubling of
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experience Žmeasured by managed care claims in a plan’s state or plan year. is in addition to the effects of time trends towards reduced costs Žsuch as an increased reliance on medications that are not part of these costs. or scale economies. To be sure, the progress ratios are higher than in most previous industrial applications, where cost reductions are about twice the size Ža higher progress ratio means smaller cost reductions.. However, this study focused only on clinical costs and excluded administrative costs, which may experience a steeper decline. The medical cost study by Woods et al. Ž1992. of heart transplant costs also found much larger cost reductions as a function of experience, but it applied to a narrow and new medical activity, where one would expect learning to be more important than across the wide range of more traditional care in this application. The aggregate data used here do not allow a decomposition of the cost reductions into quantity and unit price changes, but we have done this decomposition for one of the larger employers in this data set, which contributes 6 years of observations ŽGoldman et al., 1998.. Over a 5-year period, clinical costs per member for this employer fell by about 17% Žwhich is a little below the estimated average in this data set.. Although the percentage of users increased by 15%, the number of sessions fell by 21%, with only a very minor decline in costs per session Žy1.4%.. The number of hospitalizations among users of outpatient services fell by 24% over 5 years and the average length of stay fell by 43%, although the costs per hospital day increased by 15%. Research in manufacturing and service industries attributes much of the total productivity growth to the slow and often almost invisible accretion of individually small improvements ŽRosenberg, 1982.. The emphasis on gradual quality improvement toward the goals of zero defects is a central tenet of high quality production philosophies ŽHayes, 1981.. The impact goes beyond the competitiveness of an individual firm and economic historians regard differing organizational capabilities of firms as the main factor that determines the shape and success of markets in a specific industry ŽChandler, 1990.. In this application, ‘learning’ was limited to costs and did not extend to quality improvements, however. While UBH’s internal guidelines call for follow-up care after an acute in-patient discharge within a few days, about 1 out of 3 patients is not seen within a week Žor 1 out of 4 within a month.. This is still a long way from ‘zero defects’ even if follow-up rates appear to be increasing despite falling costs and even though the follow-up rate is higher than the national average among HMOs for depression reported by NCQA Ž67.3% in 1997, NCQA, 1998.. Looking beyond behavioral health care, MBHOs are also interesting as a window into the future of managed care because advanced managed care techniques, such as concurrent utilization review by clinical care managers, guideline implementation, and disease management systems, are standard in MBHOs, whereas these techniques have only started diffusing in other areas of medicine. If similar organizational learning were to apply in those areas, significant cost savings could be realized in contrast to the current predictions of quickly escalat-
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ing costs ŽLevit et al., 1998.. While the learning curves could reflect an increasing improvement and sophistication of data based monitoring techniques to influence costs, similar techniques should be available to improve follow-up rates, but may not have been implemented. The organizational learning curve is a complex phenomenon affected by many processes. This paper demonstrates that their impact can be substantial in managed behavioral health care, but we cannot determine the sources of this learning, which can involve information systems, care management procedures, guidelines, provider selection, patient education, or provider education. The multitude of potential factors also suggests that organizations can vary considerably in the rates at which they learn. In other industries, the productivity differences between organizations that learn and improve their performance and organizations that deteriorate can be dramatic over longer periods of time even if performance is initially comparable and organizations use similar or even identical technologies ŽGhemawat, 1985; Argote and Epple, 1990; Sturm, 1993.. This clearly raises the question to which extent the results of this study apply to other managed care organizations and the answer to this question would shed light on the long-run shape of this industry.
Acknowledgements I want to thank Carole Gresenz, Will Manning, Juergen Unutzer, Vivian Ho, ¨ Ingo Vogelsang, and William Goldman for comments on an earlier draft and United Behavioral Health for providing the data. Joanie Chung, Jin Son, and Winnie Zhang provided research and programming assistance. This research was supported by the Robert Wood Johnson Foundation and by NIMH grants MH54147 and MH54623.
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