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Reducing Long-Term Cost by Transforming Primary Care: Evidence From Geisinger's Medical Home Model
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Reducing Long-Term Cost by Transforming Primary Care: Evidence From Geisinger's Medical Home Model

Daniel D. Maeng, PhD; Jove Graham, PhD; Thomas R. Graf, MD; Joshua N. Liberman, PhD; Nicholas B. Dermes, BS; Janet Tomcavage, RN, MSN; Duane E. Davis, MD; Frederick J. Bloom Jr, MD, MMM; and Glenn D. Steele Jr, MD, PhD
ProvenHealth Navigator, Geisinger's version of advanced patient-centered medical homes, is associated with significant savings in total cost of care over time.
As a sensitivity check, we also obtained a number of alternative estimates using different sample defi nitions. In particular, we obtained estimates with a restricted sample consisting of only those who did not switch their primary care clinics during the study period (about 80% of the full sample size). This allowed us to control for any clustering effects due to similarities among members enrolled in same clinics15 by including member-clinic fi xed effects in our regression model. The resulting estimates (available upon request) were similar to the reported estimates shown below.

RESULTS

As shown in Table 1, the fi nal sample consisted of 26,303 members over the 5-year period, corresponding to 1,053,445 member-month observations in 43 primary care clinics that were designated as PHN sites at different points in time between 2006 and 2010. The median age of the members was 76 years, and 42% were male. Slightly less than half of the member-month observations fell in the 0 member-level PHN exposure category; about 12% fell in the highest category of greater than 24 months of PHN exposure. Table 1 also indicates that greater PHN exposure is associated with higher total cost and age, as well as greater proportion of membermonths with prescription coverage. This suggests that the effect of PHN exposure on total cost is confounded by member age and presence of prescription drug coverage benefit.

On average, GHP members in our sample maintained their membership for about 40 months out of the maximum possible 60 months, suggesting a stable enrollment pattern. About 20% of the members in our sample switched from a non-PHN site to a PHN site during our study period, while only about 1% switched from a PHN site to a non-PHN site.

Table 2 shows the regression coeffi cients and their corresponding 95% CIs for the key covariates in the 2 models (with and without the interaction effects between drug coverage and PHN exposure). In both models, the coeffi cients on the PHN exposure variables are consistently negative and get increasingly larger as the length of exposure increases, suggesting that longer PHN exposure is consistently associated with lower total cost. However, in contrast, the coeffi cient estimates on the interaction terms between drug coverage and PHN exposure are consistently positive, suggesting that there is a significant interaction between them.

Table 3 shows the estimated percent savings and the corresponding bootstrapped 95% CIs as obtained from the regression model parameter estimates shown in Table 2. Table 3 clearly supports the hypothesis that a longer exposure to PHN is associated with lower total cost and therefore greater savings, regardless of whether the interaction effects were included in the model. The largest and statistically most signifi cant percent saving was achieved in the highest category of PHN exposure (>24 months) in both models.

Overall, the estimated total cumulative savings to Geisinger attributable to PHN from its inception in November 2007 through December 2010 is 7.1% using the model that includes the interaction effects and 4.3% using the model that does not include the interaction effects, both of which are statistically significant (ie, greater than 0). However, there is no statistically significant difference between these 2 estimates, as indicated by the large overlapping CIs around these estimates (the overlap in the intervals ranges from 2.6% to 8.3%).

Table 4 shows the return on investment (ROI) of the PHN intervention to Geisinger. Geisinger has invested considerable resources to support the PHN initiative over the years by hiring and training case managers to assist in patient care and administrative staff to provide data support, as well as by providing incentive payments and bonuses to the participating clinics and physicians. The return to Geisinger is the estimated cost savings as shown in Table 3. ROI is calculated by dividing the estimated total dollar savings by the actual dollar amounts invested in implementing PHN. Thus, an ROI fi gure greater/less than 1 suggests that the returns from PHN were greater/less than the investment. ROI of 1, therefore, indicates a break-even point.

Table 4 suggests that, because of the large 95% CIs around our estimates, we cannot conclude that the ROI has exceeded the break-even point at any point during the first 4 years of the PHN implementation. Nevertheless, the point estimate of ROI in each year shows a consistent upward trend. To the extent that higher returns from PHN depend on the length of members’ exposure to PHN, as shown in Table 3, it remains to be seen whether the cumulative ROI can eventually exceed the break-even point.

DISCUSSION

In this analysis, we have shown that over time, PCMHs as embodied in Geisinger’s PHN initiative can reduce cost by providing patients improved care coordination, enhanced access to primary care providers, and more effective and effi cient disease and case management. There may indeed be downstream benefi ts of PCMHs which manifest themselves at the individual level only after a considerable length of exposure. While the ROI estimates did not reach statistical significance during this study period, the results still suggest that as more members get longer exposure to PHN, the accrued savings to GHP will likely increase beyond the level shown in this analysis, and the net savings as demonstrated by ROI may eventually achieve statistical signifi cance. As such, in order to be able to detect any measurable success of PCMHs in terms of signifi cant and sustainable cost savings, a continued investment in PCMHs as well as patience is likely to be necessary.

Our findings are consistent with the “prevention hypothesis” of PCMHs—that the enhanced primary care delivered by PCMHs reduces the likelihood of exacerbation of chronic conditions or allows more effi cient management of these exacerbations and thus reduces future inpatient admissions and readmissions. While we were unable to directly confi rm this hypothesis in our study, the previous studies6,10 have shown that PHN produces signifi cant reductions in hospitalization and certain adverse outcomes. Furthermore, we have found an interaction effect between drug coverage and PHN exposure which suggests that, when a member obtains drug coverage, PHN exposure is associated with higher total cost. This is consistent with the hypothesis that prescription drugs may be complements to other healthcare services in producing improved patient outcomes, rather than substitutes.

There may have been changes other than drug coverage in the benefit design (eg, changes in participating provider network) that may have impacted each member’s total costs over time. Unfortunately, our claims data do not include detailed information on each member’s benefi t design other than the drug coverage status. This problem, however, is somewhat mitigated by the fact that our sample includes only the Medicare Advantage enrollees of a single managed care organization.

This study further supports the case for PCMHs as a key component in developing a new and comprehensive system of care aimed at achieving the “triple aim.”5,11 Future studies will examine whether PHN has led to signifi cant improvements in patient and provider satisfaction, a critical aspect of the quality of care rendered within this redesigned primary care system.

Acknowledgments

We acknowledge Earl Steinberg, MD, MPP, Jason Roy, PhD, and Walter Stewart, PhD, for their valuable comments and suggestions. We also acknowledge Richard Bitting, BBA, and Alison Star, MBA, for facilitating acquisition of the data used for this study.

Author Affiliations: From Geisinger Center for Health Research (DDM, JG, JNL), Geisinger Clinic (TRG, FJB), Geisinger Health Plan (NBD, JT, DED), Geisinger Health System (GDS), Danville, PA.

Funding Source: There was no external funding for this report.

Author Disclosures: All authors (DDM, JG, TRG, JNL, NBD, JT, DED, FJB, GDS) report employment with Geisinger, which offers consuling services to other organizations for medical home implementations. Dr Bloom also reports receiving lecture fees from Merck for patient-centered medical home topics.

Authorship Information: Concept and design (DDM, JG, TRG, NBD, JT, DED, FJB, GDS); acquisition of data (DDM, NBD); analysis and interpretation of data (DDM, JG, TRG, JNL, NBD, GDS); drafting of the manuscript (DDM, JG, TRG, JNL, JT, DED, GDS); critical revision of the manuscript for important intellectual content (DDM, JG, TRG, JNL, JT, DED, FJB, GDS); statistical analysis (DDM); provision of study materials or patients (FJB); administrative, technical, or logistic support (JT, DED, FJB); and supervision (JG, DED, FJB).

Address correspondence to: Daniel D. Maeng, PhD, Geisinger Center for Health Research, 100 N Academy Ave, M.C. 44-00, Danville, PA 17822. Email: ddmaeng@geisinger.edu.
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