Overlap In HMO Physician Networks

At the Intersection of Health, Health Care and Policy

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Cite this article as: Michael E. Chernew, Walter P. Wodchis, Dennis P. Scanlon and Catherine G. McLaughlin Overlap In HMO Physician Networks Health Affairs 23, no.2 (2004):91-101 doi: 10.1377/hlthaff.23.2.91

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Overlap In HMO Physician Networks by Michael E. Chernew, Walter P. Wodchis, Dennis P. Scanlon, and Catherine G. McLaughlin ABSTRACT: Health maintenance organizations’ (HMOs’) restrictions on the size of their physician networks may facilitate cost containment and quality improvement activities but may also impede access to care and impose barriers to those wishing to switch health plans or jobs. We examine the extent, variation, and predictors of overlap in HMO physician networks. We predict that people who switch HMOs have a reasonable likelihood (50 percent) of being able to retain their physician. Overlap ranges from an upper quartile of 69 percent to a lower quartile of 34 percent. Group/staff-model HMOs have little overlap, while younger plans, for-profit plans, and plans in small markets have greater overlap.

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n e o f t h e d e f i n i n g f e at u r e s of managed care plans is that they contract with a certain number of physicians in a market and thereby potentially limit enrollees’ access to their preferred physician. This is particularly true of health maintenance organizations (HMOs), although some HMOs offer plans that provide limited coverage for out-of-network use. In fact, one of the chief complaints about HMOs in the so-called managed care backlash debates centered on restricted physician choice.1 Concern over limited networks in the 1990s led some states to pass any-willing-provider laws.2 Debate over these laws was substantial in both the academic and industry press, eventually leading to the Supreme Court case, Kentucky Association of Health Plans, Inc. vs. Miller, in which the Supreme Court ruled in favor of these laws.3 Yet despite the great interest in any-willing-provider laws and, more generally, in the breadth of physician networks, we are aware of no national estimates regarding the extent to which restricted networks limit access to physicians. Specifically, we do not know to what extent the provider networks of different managed care organizations within the same market overlap.

Michael Chernew ([email protected]) is an associate professor in the University of Michigan School of Public Health, Department of Health Management and Policy, in Ann Arbor, where Catherine McLaughlin is a professor. Walter Wodchis is a research scientist at the Toronto Rehabilitation Institute and an adjunct scientist at the Institute for Clinical Evaluative Sciences in Toronto. Dennis Scanlon is an assistant professor of health policy and administration at Pennsylvania State University in University Park.

H E A L T H A F F A I R S ~ Vo l u m e 2 3 , N u m b e r 2 DOI 10.1377/hlthaff.23.2.91 ©2004 Project HOPE–The People-to-People Health Foundation, Inc.

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There is about a fifty-fifty chance that if a person were to switch health plans, he or she would not have to change physicians.

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Advantages of overlap. Overlap of physician networks within a market has both advantages and disadvantages. Many of the advantages arise because greater overlap reduces the need to change physicians when switching plans, which in turn facilitates employees’ switching among HMOs. Employers may also find it easier to shop among HMOs if there is a lot of network overlap because employees will be less opposed to plan changes if they do not have to switch physicians. Reductions in the costs of switching HMOs will likely increase HMOs’ premium competition, and the net effect could be a reduction in the margin between premiums and costs. In addition, greater overlap may increase flexibility in the labor market if people become more willing to switch jobs knowing that the HMOs offered by competing employers will allow them access to their same physicians.4 Finally, because plan switchers will find it easier to maintain established provider relationships, high degrees of overlap may promote continuity of patient care. n Disadvantages of overlap. Despite these advantages, many scholars are concerned about a potential negative “quality externality” associated with broad physician networks and substantial overlap.5 When plans offer broad networks, each plan typically accounts for a small share of any one physician’s practice. This may increase the costs and decrease the effectiveness of initiatives aimed at improving quality. Moreover, any given health plan has less incentive to promote improvements in physician practice styles because resulting practice efficiencies or quality enhancements would spill over to the care delivered to enrollees in competing plans, reducing the competitive advantage that could accrue from these improvements. From the perspective of plans, another disadvantage is a reduction in the bargaining position of plans relative to physicians. If the threat of exclusion from a plan’s network is weak, a health plan’s ability to constrain fees paid to physicians diminishes, and premiums may rise. Conceptually, the optimal amount of overlap would balance these advantages and disadvantages. Although theory suggests that overlap would tend to encourage price competition and decrease quality competition, research has not yet progressed to the point where we can quantify the relevant effects of overlap on prices and quality, so we cannot identify the optimal amount of overlap. A first step along this path is to quantify overlap and assess correlates of variation in overlap. That is the purpose of this paper. n Previous research limitations. Until now, data limitations have prevented systematic measurement of HMO provider overlap for all U.S. markets, much less an estimation of the predictors of overlap. Thus, existing research has only been able to use crude measures of overlap, such as the percentage of physician practice revenue from any one plan, or the number of insurance plan contracts held by physicians. In addition, often these data come from only one geographic market.6 Although we know that health plans have moved away from restricted networks in the past few years, we do not know how much overlap now exists or what covariates predict overlap.7

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Study Methods

H E A L T H A F F A I R S ~ Vo l u m e 2 3 , N u m b e r 2

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This study uses a unique data set to quantify the extent of provider overlap in U.S. metropolitan markets and relates that overlap to plan and market traits. We define overlap as the probability that a physician in any given plan is also in a competing plan. This measure approximates the probability that people will have to switch physicians if they switch their plans. Our measure of overlap between two plans (A and B) is computed as the fraction of physicians serving Plan A that also serve Plan B, which is equivalent to the probability that a physician in Plan A’s panel is also in Plan B’s panel. Our approach allows us to report the variation in overlap among plans and markets and examine predictors of overlap. It is not our intent to assess whether overlap is on balance beneficial or detrimental, although recent enrollment trends in HMO point-of-service (POS) options and preferred provider organizations (PPOs) suggest that consumers are demanding broader networks with more overlap.8 We seek only to assess and explain the range of overlap observed in HMO markets. n Data source. We used a data set of electronic provider lists for HMOs that were provided by health plans to a private firm, ProAct Health Pages. This firm describes itself as the most comprehensive source of plan and provider comparative information available. It contains detailed, continuously maintained information on more than 500,000 physicians in more than fifty categories, more than 6,000 hospitals, and more than 400 health plans. The data reflect each plan’s network as of January 2000.9 We defined markets as metropolitan statistical areas (MSAs). In each MSA we excluded all plans with fewer than ten physicians.10 The resulting database contains the affiliated physician lists for 974 plan-MSA observations, which represent 213 HMOs across the country in 229 markets, or 66 percent of the U.S. HMO enrollment in 2000. Our definition of HMO follows that used by InterStudy, and in general, PPOs and POS plans are not included. We compared our sample of plans to the full set of HMO plans listed in the InterStudy industry survey as of January 2000 to determine the representativeness of the data. Small plans, younger plans, and plans serving smaller MSAs were less likely to be included in our sample. Relative to the national average, we observed a greater fraction of enrollees in group/staff-model plans and a lower fraction in network-model plans. We compared the set of MSAs in our analysis to the sample of all MSAs with at least one HMO. MSAs not represented in our data have only one or two HMOs, and MSAs in the East South Central and West South Central regions were more likely to be excluded because of missing data. Our data represent nearly all MSAs in the New England, Mid-Atlantic, and Pacific regions. As with any data set, there are potential inaccuracies and limitations. Inaccuracies in this case can be generated in several ways. For example, the data themselves may be incomplete or outdated. Physicians may be listed by different plans under slightly different names, complicating the merging of lists. The firm that

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provided us with the data strives to minimize these errors, but some undoubtedly remain, in part because of changes over time in the underlying provider lists. To assess the level of accuracy, we performed several diagnostics on the data, including checking the affiliations of all listed physicians in two large plans. We provided our listing of affiliated physicians to the health plans, asking them to indicate all listed physicians with current affiliations and to confirm that all currently affiliated physicians were included in our listing. We found a combined error rate of less than 1.5 percent.11 n Measures of overlap. MSA measure. We assigned physicians to MSAs based on physicians’ office ZIP codes. We did not measure overlap in areas outside of MSAs. For each MSA, we calculated the degree of physician overlap between each pair of plans observed in our data. Because the number of physicians affiliated with each plan differs, the percentage of Plan A’s physicians affiliated with Plan B is not the same as the percentage of Plan B’s physicians affiliated with Plan A. Thus, we calculated two measures for each pair, one for the probability a physician in Plan A also contracted with Plan B, and the other for the probability that a physician in Plan B contracted with Plan A.12 Our results were based on 3,963 plan pairs arising from 974 distinct plan-MSA observations. We summarize the plan-pair measure with overlap estimates at the plan, market, and national levels. Health plan measure. The plan-level overlap measure is created by computing the weighted average of overlap between the plan and all competing plans. The weights are enrollments in competing plans, giving overlap with larger competitors (as measured by enrollment) more weight than overlap with smaller competitors. This measure allows us to answer questions such as, “If I am in an HMO with a given trait (for example, a network-model HMO) and I switch HMOs, what is the probability, on average, that my provider will be in the new plan?” Market measure. The market-level overlap measure is generated by taking the weighted average of the plan-level overlap measures for all plans in the MSA. The weights are plan market shares, which again weight larger plans more heavily. The MSA-level measure allows us to answer questions such as, “If I am in an HMO in an MSA with the given trait (for example, over one million residents) and I switch HMOs, what is the probability that my physician will be in the new plan?” National measure. The national-level measure of overlap is the broadest measure of overlap available. It is generated by taking the weighted average of MSA overlap measures, where the weights are the MSA-level HMO enrollment. This answers the following question: “If I am in an HMO and I switch to another HMO, what is the probability that my provider will be in the new plan?” We calculated these measures for all physicians, as well as separately for primary care and specialist physicians. We defined primary care physicians as those in family practice, general practice, internal medicine, pediatrics, obstetrics and gynecology, adolescent medicine, and geriatric medicine. Specialists are defined as all other physicians.

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Study Results Our national measure of overlap is 0.48, which indicates that the probability that a given physician in one plan is also in a competing plan is 48 percent. The unweighted average of overlap measures for the 3,963 plan pairs across all MSAs is 57 percent (range: 34–69 percent). 13 For primary care physicians, the mean (weighted) overlap is 52 percent (range: 38–72 percent). For specialists, the mean (weighted) overlap is slightly lower at 47 percent (range: 31–66 percent). n Plan attributes and overlap. Overlap results stratified by plan characteristics, combining primary care and specialist physicians, are presented in Exhibit 1. Not surprisingly, group/staff-model plans have much lower overlap than other plan types have, with most plans having virtually no overlap; other plan types have similarly high levels of overlap, and only the difference between IPA and mixed plans is


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Analysis. We analyzed the variation in overlap at the plan and MSA levels by several plan and market characteristics. We also examined the range of overlap among plans and within MSAs, reporting the upper and lower quartiles of the distribution by various plan and MSA attributes. Plan attributes examined are model type (group/staff, independent practice association or IPA, network, mixed), ownership type, plan age, and market share. MSA attributes are region, population, managed care penetration, and HMO market competition. We measured market competition using the Hirschman-Herfindahl Index (HHI), which is calculated as the sum of the squared HMO market shares. The HHI for a highly competitive market would be near zero; for a monopoly market, it would be equal to one. Plans and MSAs were divided into quartiles for attributes with cardinal values. Some categories (for example, MSA population) are rounded to the nearest meaningful value to facilitate the description of results. We report bivariate and multivariate analyses. The bivariate analysis was based on pairwise statistical tests of differences in means between strata. In each case, we report differences relative to a reference category. We also examined differences between all possible pairwise comparisons, using a Bonferroni correction to adjust inferences because of multiple comparisons. The latter results are discussed below but not shown in the exhibits. Statistical tests were based on weighted standard errors, although standard deviations are shown in the exhibits to better indicate the variability of overlap. The multivariate analysis was based on a least-squares regression of the overlap percentage on plan and market attributes. The unit of observation is a plan pair within an MSA. As above, the multivariate analyses used the plan-MSA enrollment as observation weights. The explanatory variables are attributes of Plan A or the MSA. More detailed explanatory variables incorporating attributes of both Plans A and B generated too many combinations to be tractable. Because multiple plan observations were present in the multivariate analyses, we estimated corrected (Huber-White) robust standard errors and used these for statistical tests.

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EXHIBIT 1 Descriptive Statistics: Health Plan Attributes And Overlap

Number

Weighted overlap mean (std. dev.)

25th percentilea

75th percentileb

Model type Group/staffc IPA Network Mixed

21 492 81 380

0.05 (0.14) 0.58 (0.17)*** 0.54 (0.20)*** 0.52 (0.21)***

0.00 0.48 0.35 0.35

0.01d 0.70 0.69 0.69

Ownership type Not-for-profitc For-profit

286 688

0.38 (0.30) 0.55 (0.18)***

0.01 0.39

0.65 0.69

Plan age Less than 10 yearsc 10–14 years 15–19 years 20 years or more

121 359 178 316

0.67 (0.16) 0.55 (0.16)*** 0.58 (0.18)** 0.37 (0.28)***

0.64 0.43 0.49 0.32

0.76 0.67 0.71 0.60

Plan market share Less than 2%c 2–9% 10–19% 20% or more

235 289 172 278

0.58 (0.17) 0.54 (0.18) 0.53 (0.20) 0.45 (0.27)

0.43 0.37 0.35 0.16

0.67 0.69 0.70 0.67

SOURCE: Authors’ tabulations based on data from ProAct Health Pages and InterStudy. a 25 percent of plan pairs have less overlap than the amount reported. For example, when the overlap between independent practice association (IPA)–model plans and all competitors is examined, 25 percent of plan pairs have less than 48 percent overlap. b 75 percent of plan pairs have less overlap than the amount reported. For example, when the overlap between IPA-model plans and all competitors is examined, 75 percent of plan pairs have less than 70 percent overlap. c Reference category for statistical comparison. d Although most group/staff-model plans have near-zero overlap, a small number of observations from group/staff plans have higher overlap. This skewness results in the mean exceeding the third quartile. Examination of the data suggests that these are observations from group- or staff-model plans that have expanded into new markets and built a physician network comprising community physicians, perhaps making them more like mixed-model plans in those markets. **p < .05 ***p < .01

significant. For-profit plans have significantly higher overlap than not-for-profit plans have. Plans in existence for less than ten years have the highest level of overlap, while the oldest plans have the lowest level. Overlap declines with plans’ market share, although the only significant difference is between plans with 10–19 percent market share compared with plans with 20 percent or more. n Market-level overlap. Market-level overlap results stratified by market characteristics are presented in Exhibit 2. Physician overlap is highest in the smallest markets and lowest in the largest markets. Overlap is weakly associated with competition (as measured by the HHI), with the most competitive markets (first quartile) having significantly lower overlap than markets in the second and fourth quartiles. MSAs with the highest level of managed care penetration (more than 40 percent) have significantly less overlap than all other MSAs. n Overlap by geographic region. Overlap also varies by region (Exhibits 1 and

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EXHIBIT 2 Descriptive Statistics: Market Attributes And Health Plan Overlap

Number

Weighted overlap mean (std. dev.)

25th percentile

75th percentile

MSA population Less than 250,000a 250,000–499,999 500,000–1,499,999 1,500,000 or more

252 237 248 237

0.64 (0.25) 0.57 (0.24) 0.61 (0.21) 0.42 (0.24)***

0.58 0.47 0.51 0.27

0.82 0.75 0.77 0.60

Market competition quartileb Firsta Second Third Fourth

248 239 249 238

0.43 (0.23) 0.51 (0.23)*** 0.47 (0.29) 0.59 (0.25)***

0.32 0.41 0.22 0.43

0.63 0.65 0.72 0.76

Managed care penetration Less than 20%a 20–29% 30–39% 40% or more

224 195 270 285

0.50 (0.23) 0.61 (0.16)* 0.56 (0.17) 0.40 (0.28)*

0.35 0.51 0.48 0.16

0.61 0.72 0.67 0.65

Geographic region Mid-Atlantica New England South Atlantic East South Central West South Central East North Central West North Central Mountain Pacific

134 180 113 28 19 59 197 170 74

0.65 (0.13) 0.65 (0.13) 0.59 (0.19) 0.56 (0.16) 0.60 (0.17) 0.60 (0.16) 0.46 (0.16)*** 0.25 (0.25)*** 0.55 (0.24)*

0.53 0.59 0.51 0.32 0.52 0.47 0.33 0.00 0.35

0.75 0.71 0.74 0.69 0.72 0.70 0.57 0.37 0.72

SOURCE: Authors’ tabulations based on data from ProAct Health Pages and InterStudy. NOTE: MSA is metropolitan statistical area. a Reference category for statistical comparison. b As measured by the Hirschman-Herfindahl Index (HHI), which assigns 0 to perfectly competitive markets and 1 to monopoly markets. *p < .10 ***p < .01

2). The Mid-Atlantic and New England regions have higher managed care penetration (and higher overlap), while the southern regions have lower levels of penetration and only moderate levels of overlap. In contrast, the western regions are home to the oldest HMO plans, many of which are group/staff models. n Overlap for primary care and specialist physicians. Results for primary care and specialist physicians followed a nearly identical pattern to the results presented here, except that the magnitude of overlap for all categories is relatively higher for primary care than for specialist physicians. n Factors associated with overlap. The preceding results represent bivariate comparisons without controlling for all plan and MSA attributes simultaneously. Exhibit 3 reports the results of the multivariate analysis conducted on plan pairs in each MSA. Huber-White robust standard errors are reported along with signifi-


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Market attribute

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EXHIBIT 3 Regression Analysis: Factors Associated With Health Plan Overlap Coefficienta

Variable Intercept

Standard errorb

0.30

0.07***

0.38 0.38 0.40

0.04*** 0.05*** 0.03***

0.05

0.02**

–0.02 –0.01 –0.05

0.03 0.03 0.03*

Plan market share (less than 2%)c 2–9% 10–19% 20% or more

–0.01 –0.01 –0.08

0.03 0.03 0.03**

MSA population (less than 250,000)c 250,000–499,999 500,000–1,499,999 1,500,000 or more

–0.05 –0.05 –0.12

0.03* 0.03* 0.03***

Market competition quartile (first)c Second Third Fourth

–0.06 –0.03 –0.03

0.02*** 0.02 0.03

Tax status (not-for-profit)c For-profit Plan age (less than 10 years) 10–14 years 15–19 years 20 years or more

c

Managed care penetration (less than 20%)c 19–29% 30–39% 40% or more Geographic region (Mid-Atlantic)c New England South Atlantic East South Central West South Central East North Central West North Central Mountain Pacific

0.06 0.05 0.07 0.04 –0.11 0.08 0.05 0.00 0.02 0.01 –0.02

0.04 0.04 0.04 0.04 0.04*** 0.04** 0.05 0.03 0.04 0.06 0.03

SOURCE: Authors’ tabulations based on data from ProAct Health Pages and InterStudy. NOTES: N = 3,897. Model Adj. R2 = 0.37. F-stat = 72.4***. d.f. (27,209). IPA is independent practice association. MSA is metropolitan statistical area. a The coefficient measures the percentage-point change in overlap associated with a unit change in the explanatory variable, holding all else constant. b Robust standard errors corrected for plan clustering. c Reference category for statistical comparison. *p < .10 **p < .05 ***p < .01

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Model type (group/staff) IPA Network Mixed

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“Future research needs to explore the ramifications of overlap on enrollment patterns, pricing, and quality.”

Implications For Policy n

Policy importance. The extent of overlap among physician networks has ramifications for many aspects of health policy. Overlap affects the extent to which employers’ decisions to limit access to various health plans translate into limited access to employees’ current physicians. It also influences employees’ willingness to change plans and jobs and the impact of switching plans on continuity of care. The degree of overlap can also affect the extent to which, and on what basis, plans compete as well as the incentives for health plans to invest in, and their ability to carry out, efforts to improve the quality of physician care. In this paper we report that, on average, there is 48 percent overlap in HMO physician networks. Thus, there is about a fifty-fifty chance, on average, that if a person were to switch plans, he or she would not have to change physicians. However, there is considerable variation in this probability (34–69 percent). Overlap varies with both plan and market attributes. Group/staff-model plans have very little overlap; younger plans and for-profit plans have greater overlap. Overlap declines with market size. Yet these systematic differences are relatively small when compared with the overall variation in overlap, which is likely driven by idiosyncratic aspects of plans and markets as well as perhaps by historical accident of


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cance values. The model explains 37 percent of the observed variance in overlap. The higher overlap for IPA, network, and mixed-model types compared with group/staff plans reinforces the bivariate results. On average, IPA plans have thirty-eight percentage points more overlap with other plans than group/staff plans have. For-profit plans have five percentage points more overlap than notfor-profit plans have. Plans more than twenty years old have five percentage points less overlap than plans less than ten years old, controlling for plan type. Finally, plans with more than 30 percent (third quartile) market share have less overlap than plans with smaller market shares. In multivariate results, MSA size appears to have a stronger relationship to overlap than in the bivariate analysis. The largest MSAs have twelve percentage points less overlap than the smallest MSAs have. Less competition is consistently associated with less overlap, although only the second most competitive markets have significantly less overlap than the most competitive markets. Managed care penetration is positively, although not significantly, related to overlap. Regional differences persist after other plan traits are controlled for. In the regression analysis, South Atlantic MSAs indicated lower overlap than the reference region (MidAtlantic), while South Central regions appear to have more overlap once other plan and market traits are controlled for.

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u t u r e r e s e a r c h n e e d s to g o b e y o n d d e s c r i b i n g the extent of and variation in physician overlap and explore the ramifications of overlap on enrollment patterns, pricing, and quality. In many ways, physicians are the core of health care delivery, and overlap in physician networks influences the most important outcomes in the system. A fuller understanding of the role of physician overlap is necessary to develop and evaluate policies aimed at improving the working of the system. The authors thank Hal Luft, Adams Dudley, and Jennifer Haas for helpful comments. Funding was provided by the Agency for Healthcare Research and Quality (AHRQ) under Grant no. P01-HS10771.

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how particular plan networks evolved. n Limitations. These findings are just a first step toward understanding the relationships among competing provider networks, and our analysis has several important limitations. First, we do not observe all plans and are less likely to observe physician networks from smaller plans. Because overlap does not vary dramatically by plan size, except for the largest plans, and because we weighted smaller plans less, we do not expect the bias from this omission to be very large. Second, the imperfect matching of physicians will tend to lead to underestimates of plan overlap. However, the firm collecting these data has contractual incentives to maintain the data set’s accuracy, and the data checks we performed suggest that this measurement error is small. Third, it is important to recognize that our measure of overlap, which is based on the probability that physicians in one plan are also in another, is not exactly the same as the probability that any given enrollee’s physician is in the other plan. If more popular physicians are more likely to contract with multiple health plans, we would underestimate the extent of overlap. Fourth, as is often the case, the average degree of overlap masks the fact that there are plans at each extreme. An overlap of 50 percent for a plan with two competitors could arise because the plan has 50 percent overlap with both competitors or because it has 100 percent with one competitor and no overlap with the other. Finally, we focus only on overlap in HMO networks. This is important because HMOs have been the most criticized and scrutinized regarding limits on physician choice. However, people may be able to retain access to their physician if they have access to a fee-for-service or PPO plan. However, neither of these plans are perfect substitutes for HMOs because they may be more costly, so understanding the degree of overlap among HMOs remains important. Moreover, much of the effort to measure health plan performance has focused on HMOs, and overlap may be an important determinant of that performance.

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NOTES 1. 2.

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5. 6.

7. 8.

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11.

12.

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J. White, “Choice, Trust, and Two Models of Quality,” Journal of Health Politics, Policy and Law 24, no. 5 (1999): 993–999. For evidence of the spread of these laws, see R.L. Ohsfeldt et al., “The Spread of State Any Willing Provider Laws,” Health Services Research 33, no. 5, Part 2 (1998): 1537–1562; and J.A. Marsteller et al., “The Resurgence of Selective Contracting Restrictions,” Journal of Health Politics, Policy and Law 22, no. 5 (1997): 1133–1189. Analysis of their effects can be found in C.A. Ambrose and J.M. Ambrose, “Any-Willing-Provider Laws: Their Financial Effect on HMOs,” Journal of Health Politics, Policy and Law 27, no. 6 (2002): 927–945; and M.G. Vita, “Regulatory Restrictions on Selective Contracting: An Empirical Analysis of ‘Any Willing Provider’ Regulations,” Journal of Health Economics 20, no. 6 (2001): 955–966. L.B. Benko, “Willing and Able: Supreme Court Ruling Forces HMOs to Open Networks to Any Willing Provider; Some Say It May Wound Managed-Care Industry,” Modern Healthcare 33, no. 14 (2003): 6–7. This effect is related to the literature on job lock, which examines the extent to which the lack of availability of coverage in some jobs deters people from switching jobs. See B.C. Madrian, “Employment Based Health Insurance and Job Mobility: Is There Evidence of Job Lock?” Quarterly Journal of Economics 109, no. 1 (1994): 27–54. N.D. Beaulieu, “Externalities in Overlapping Supplier Networks” (Unpublished manuscript, Harvard University, 2002). Ibid.; R.A. Berenson, “Beyond Competition,” Health Affairs (Mar/Apr 1997): 171–180; and A.E. Roussel et al., “Primary Care Physicians’ Participation in Managed Care Networks,” Journal of Ambulatory Care Management 22, no. 2 (1999): 27–40. D.A. Draper et al., “The Changing Face of Managed Care,” Health Affairs (Jan/Feb 2002): 11–23. See D.P. Scanlon et al., “Options for Assessing PPO Quality: Accreditation and Profiling as Accountability Strategies,” Medical Care Research and Review 58, Suppl. 1 (2001): 70–100; and D.G. Smith, “The Effects of Preferred Provider Organizations on Health Care Use and Costs,” Inquiry 34, no. 4 (1997–1998): 278–287. ProAct Health Pages collects and merges these lists to generate a comprehensive database of plan-physician combinations for employers who want an easy way for their employees to search for affiliated physicians or check if their physician is in any given plan. More information can be found at www.proactcorp .com. For additional detail, see ProAct, “Health Pages,” thehealthpages.com/articles/about.html (3 December 2003), which includes the following text: “Health Pages also incorporates the provider directories of over 300 managed care plans into our physician superdirectory, which allows consumers to determine what plans each doctor is affiliated with.” We made this exclusion because we were concerned that plans with fewer than ten physicians in a market were not really serving that market and that the data might be erroneous. The results are qualitatively similar using no exclusion criteria or strengthening it to require plans have at least a 5 percent share of the HMO market in the MSA. It should be noted that hospital-based physicians (radiologists, anesthesiologists, pathologists, and hospitalists) often do not appear on health plan provider lists. Our measure of overlap is thus best interpreted as a measure of overlap among community-based physicians. The share of physicians in Plan A that also serve Plan B will not, in general, be the same as the share of physicians in Plan B that serve Plan A. For example, plans with few physicians may have complete overlap with a larger plan, but the larger plan may have many physicians not offered by the smaller plan. We were concerned that this might be an underestimate of the effective amount of overlap if not all plans served all areas of the MSA. We therefore recomputed the unweighted overlap measure using the county as the definition of market and found that the increase in overlap was not substantial, rising four percentage points to 61 percent.