Medicare beneficiaries in California's HMOs admitted for acute myocardial infarction during 1994–1996 were younger and had fewer comorbidities than those in FFS. Thus, their lower observed 30-day mortality rate of 15.85 percent relative to 18.62 percent for FFS patients is not surprising. Accounting for patient differences in risk, the FFS rate was slightly worse, but given the large sample, statistically significantly so, than the rate for HMO enrollees in general.
One does not join “HMOs in general” however, and the results for individual pseudoplans suggest that it matters which plan one joins. Among the 17 pseudoplans examined, three had significantly better than expected mortality rates. One had worse than expected rates under one, but not both, risk models. Given a p=.05 significance level, one outlier observation would be expected out of 17 points. While this might account for one outlier, it is not likely to explain all three of the good outliers, two of which are significant, (p<.01) in both models, and the other had values of p<.01 and p<.03.
There is substantial controversy about the identification of outliers, which is sometimes due to chance. Chance may account for any of the selected mortality results, but there is strong circumstantial evidence presented in these figures that some of these HMOs have different practice or hospital use patterns that appear related to the differences in risk-adjusted mortality.
Two principal concerns with this type of analysis were addressed directly. Hospital payment by HMOs is often not dependent on diagnostic related group (DRG) category, as is the case for FFS patients. Thus, some hospitals might “undercode” diagnoses for HMO patients. If so, HMOs would receive a lower predicted mortality rate than they truly “deserved.” If the variation in expected rates across pseudoplans reflected variable undercoding, there would be a negative relationship between the expected rate and the observed/expected mortality rate. This was not observed; in the low outliers have among the lowest expected mortality rates, and the high outlier has an unremarkable expected rate.
The second major area of concern was that differences across HMOs might merely reflect the geographic areas they serve. There are, indeed, substantial differences in risk-adjusted mortality rates among patients in FFS. Some areas exhibited 10 percent more deaths than expected—an excess greater than in any of the pseudoplans. Given that these “FFS areas” merely reflect the experience in overlapping locales from which HMO enrollees are drawn, and are not mutually exclusive, the true geographic variation is likely to be larger. Local FFS outcomes, however, were unrelated to outcomes across pseudoplans. It is possible, however, that HMOs select the best hospitals and clinicians within a geographic area, or may otherwise alter usual practices.
The data on LOS and revascularization support this conclusion. Although risk-adjusted total stays (index admission plus any transfers) were 0.79 days shorter for HMO patients, there was no relationship between LOS in the pseudoplans and their local area. The pseudoplans with the better mortality outcomes exhibit a consistent pattern that differs from both FFS and the pseudoplans with results that are similar to or worse than expected. The former tend to have a lower proportion of their AMI patients initially admitted to hospitals with CABG capabilities and tend to rely on referrals to other hospitals for revascularization. The CABG-capable hospitals these pseudoplan patients initially use have much higher average CABG volumes and the volume difference is even greater among the hospitals used for subsequent admissions (not shown). While many AMI patients are brought by ambulance to the nearest hospital, some are not, and this may account for the differential admission patterns. Even when admitted to a CABG-capable hospital, only half the patients who ultimately get CABG in the better pseudoplans have their procedure during the index admission, in contrast to the pattern among the other pseudoplans and FFS of heavy reliance on the index hospital, even if it is low volume. The substantial literature on the volume–outcome relationship for revascularization suggests these patterns are purposeful (Dudley et al. 2000
Overall, these findings of marked plan differences are somewhat surprising. Kaiser is clearly different in its structure, financial incentives, and physician panel; the non-Kaiser HMOs in California contract in varying ways with physicians and hospitals or use intermediary medical groups (Robinson 1999
). Although the identities of the HMOs were concealed in the linked file sent for analysis, different HMOs are represented among the “good” outliers. Those pseudoplans with worse than expected mortality rates (the solid points) had mortality and revascularization patterns similar to FFS in their local areas (see and ). On the other hand, the open circle points (those with better than expected mortality), showed no relationship to local FFS patterns.
In many areas of the state, physician and hospital networks were very similar among non-Kaiser HMOs, so there was little reason to expect selective contracting to yield markedly different sets of providers. The HMOs do exercise some quality review, however, and some may exclude a small number of providers with quality problems and this would not be apparent in overall network breadth. The HMOs also develop practice guidelines, for example, with respect to use of beta-blockers after AMI, and may directly influence LOS, transfers, and revascularization. Some HMOs use hospitals similar to those used by FFS patients, and their outcomes appear similar. Other HMOs, through means as yet undetermined, apparently steer their patients to selected hospitals that are less likely to offer CABG surgery, but if they do, they have higher volumes. Furthermore, these same plans are much less likely than FFS and the other plans to have CABG surgery done in the index admission even if the hospital is capable. The fact that these different patterns of care are associated with different risk-adjusted mortality rates is worthy of further investigation.
One might expect a few points among 17 to be outliers just by chance. One might also expect an organization like Kaiser to have different practice patterns for its enrollees in terms of the hospitals they use, LOS, and revascularization. The HMO enrollees probably have better outpatient drug coverage than does the average FFS beneficiary. The surprising result from this analysis is that those observations with better outcomes also have different practice patterns with respect to gross measures, such as the frequency and location of revascularization. Moreover, these different patterns appear for several HMOs, so it cannot be just a “Kaiser effect.”
In marked contrast to these California results, Cutler, McClellan, and Newhouse (2000)
found little difference in the patterns of care for AMI patients in Massachusetts, and argue that nearly all the difference in HMO costs is due to lower charges. As indicated above, Kaiser does not account for all the observed differences in practice patterns. California, however, does have a much higher prevalence of group practices, and if some HMOs concentrate their patients in such groups, this may explain the differences in clinical patterns. It may also be the case that the presence of Kaiser has created a public acceptance of more coordination of care, and this may allow some of the more conventional independent practice associations to exercise more controls over where their patients will be treated.
These findings highlight the need to move beyond simple comparisons of FFS versus HMOs to deeper analyses of the reasons for performance differences. Remembering the substantial variation in practice patterns across the FFS “comparison plans,” all of which have FFS payments to physicians and DRG payments to hospitals, the variation across HMOs is probably not just due to financial arrangements. Instead, specific practice guidelines, quality review, and other features should be examined. The differential practice patterns and better outcomes in some plans may also be due to different physicians and hospitals. If so, one should learn how those providers were chosen.