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Reduced Acute Inpatient Care Was Largest Savings Component Of Geisinger Health System’s Patient-Centered Medical Home

By Daniel D. Maeng, Nazmul Khan, Janet Tomcavage, Thomas R. Graf, Duane E. Davis, and Glenn D. Steele

DOI : 10.1377/hIthaff.Z014.0855 HEALTH AFFAIRS 34, NO. 4 (2015): 636-644 o2015 Project HOPE— The People-to-People Health Foundation, Inc.

– Daniel D. Maeng (ddmaeng@ is a research investigator for the Center for Health Research at Geisinger Health System, in Danville, Pennsylvania.
– Nazmul Khan is national advisory manager at PricewaterhouseCoopers, in New York City.
– Janet Tomcavage is senior vice president and chief, value-based strategic initiatives, at Geisinger Health System.
– Thomas R. Graf is chief medical officer for population health and longitudinal care service lines at Geisinger Health System.
– Duane E. Davis is a consultant at xG Health Solutions, in Columbia, Maryland. At the time this research was done, Davis was a vice president and chief medical officer at  Geisinger Health Plan.
– Glenn D. Steele is president and CEO of Geisinger Health System.


Early evidence suggests that the patient-centered medical home has the potential to improve patient outcomes while reducing the cost of care. However, it is unclear how this care model achieves such desirable results, particularly its impact on cost. We estimated cost  savings associated with Geisinger Health System’s patient-centered medical home clinics by examining longitudinal clinic-level claims data from elderly Medicare patients attending the clinics over a ninety-month period (2006 through the first half of 2013). We also used these data to deconstruct savings  into  its main  components  (inpatient,  outpatient,  professional, and prescription drugs). During this period, total costs associated with patient-centered medical home esposure declined by approximately 7.9 percent, the largest source of  this savings was acute inpatient  care ($34, or 19 percent savings per member pet month), Which accounts for about 64 percent of the total estimated savings. This finding is further supported by the fact that longer exposure was also associated with lower acute inpatient admission rates. The results of this study suggest that ent-centered medical homes can lead to sustainable, long-term improvements in patient health outcomes and the cost of care.

The health care industry is facing increasingly complex challenges such as new regulatory require- ments, value-based purchasing, an aging population, increased complexity of care delivery, and heightened focus on consumer-directed care. Although industry responses have been multifaceted, there is a widespread agreement on the need to strengthen the primary care foundation of the health system by reorganizing the way in which primary care is delivered.

There is a growing body of literature suggesting that the patient-centered medical home (PCMH) offers significant promise as a method of both improving the patient experience and reducing cost. Conceptually, patient-centered medical home can be defined as the following: “Provision of comprehensive primary care services that facilitates communication and shared decision-making between the patient, his/her primary care providers, other providers, and the patient’s family.”

In principle, the patient-centered medical home is a reengineered primary care practice that seeks to achieve the “Triple Aim” of improved population health, improved care experience, and lower cost of care.‘ Its goal is not, however, to explicitly cut cost. Instead, it attempts to place greater emphasis on appropriate use of resources upstream in the care process through such measures as routine primary care office visits, enhanced care coordination, and appropriate preventive care. In turn, PCMH clin- ics are designed to reduce downstream care such as treatments needed for exacerbations that lead to acute hospital admissions and readmissions, thereby improving efficiency and reducing cost.

Geisinger Health System’s Proven Health Navigator‘ is an advanced patient-centered medical home that Geisinger targeted for the elderly Medicare population when it was launched in 2006. Two years later the Navigator was expanded to include the health system’s broader adult commercial population. Recent studies have shown that among the elderly Medicare population, Geisinger’s patient-centered medical home has been associated with improved patient experience of care and better outcomes as well as lower use of acute care and cost. Moreover, such desirable PCMH impacts were also observed in settings outside the Geisinger Health System ,which suggests that this model may indeed be an effective and replicable strategy to be implemented on a wider scale.

Geisinger Health System serves roughly three million residents living in central Pennsylvania. Geisinger Health Plan (GHP), a subsidiary of Geisinger Health System that provided health insurance coverage to more than 450,000 members in 2013, has played an integral part in conceptualizing, designing, and implementing the Proven Health Navigator (PHN), particularly around hiring and training of case managers embedded (that is, physically located) within every Navigator primary care clinic. A “PHN site,” therefore, refers to one of the primary care clinics that has undergone extensive changes in its management and operations in accordance with the Navigator practice redesign (Exhibit 1). Although the creation of the Navigator had preceded the release of the National Committee for Quality Assurance Physician Practice Connections and Patient-Centered Medical Home (PPC-PCMH) standards in 2009,” the Proven- Health Navigator has either met or exceeded those standards since 2006.‘

EXHIBIT 1 : The Five Core Components Of The Geisinger Health System Proven Health Navigator (PHN) Patient-Centered Medical Home

PHN componentDescription
Patient-centered primary care- Provider-led, team-delivered care
- Patient and family engagement
- Enhanced access and scope of services
- Optimized preventive and chronic care via electronic health records and claims data.
Population management- Use of claims-based predictive modeling tools to identify high-risk patients
- Case management for complex, comorbid conditions
- Disease management
- Preventive care
Medical neighborhood- Enhanced care coordination and communication across specialists and care sites outside primary care clinic.
- High-value specialty services.
- Comprehensive care systems including nursing homes, emergency departments, hospitals, home health, and pharmacies.
Performance management- Routine patient surveys to evaluate care experience and satisfaction.
- Automated evidence-based guidelines for chronic disease care at office visits.
- Guideline compliance statistics are regularly reported.
- Quality and performance metrics (including selected HED S and CAHPS measures) are regularly reported.
Value-based reimbursement model- Fee-for-service.
- Pay-for-performance based on quality outcomes.
- Shared savings model based on performance.

souaca Authors‘ analysis. iaozas HEDIS is Healthcare Effectiveness Data and Information Set. CAHPS is Consumer Assessment of Healthcare Providers and Systems

As shown in Exhibit 1, the Navigator has five functional program components: patient-centered primary care, population management, medical neighborhood, performance management, and value-based reimbursement model. As a part of patient-centered primary care, population management activities have been moved to the Navigator sites via embedded nurse case managers. These embedded case managers, for instance, receive lists of high risk patients from GHP, and they review these lists together with the primary care provider at their respective sites. The case manager, therefore, takes the clinic’s knowledge of the patients and couples it with the claims based intelligence (that is, predictive models and risk stratification software based on claims data) in order to target those most in need of intervention with the most intensive services. The Proven Health Navigator explicitly establishes a system of care that is, a “medical neighborhood ’particularly for the subpopulation identified as high risk via case management. High risk patients are typically seen by multiple health care providers in various settings outside of their primary care clinics (for example, home health, acute hospitals, skilled nursing facilities, and emergency departments) and, therefore, are prone to care coordination and communication problems. Under the Navigator model, each patient-centered medical home designs a care system that identifies acting physicians at other care sites and increases communication and coordination between them and the medical home.

Financially, while the Navigator sites continue to receive fee for service payments from GHP, the total reimbursement is linked to their performance via bonus payments and a shared savings program based on documented metrics of quality and utilization. These metrics include widely accepted measures such as the Healthcare Effectiveness Data and Information Set and the Consumer Assessment of Healthcare Providers and Systems.

Consistent with the PCMH principles, the Navigator is a site level intervention that affects potentially all patients treated by each practice, in part through the implementation of enhanced electronic medical records that enable population management, reengineered workflow, and team-based care. However, the embedded case management, claims-based advanced intelligence, and performance based bonus payments are specifically aimed at patients covered by GHP, who account for approximately a third of all patients who receive care at the Navigator sites, which also accept patients covered by other health insurers in the area. (Non-GHP members receive at least some but not all of the PCMH benefits.) This study thus specifically focuses on GHP members because this segment of the Proven Health Navigator patient population represents the Navigator experience in its fullest extent. More details of the Navigator design and implementation have been published else where.

To date, it has not been clear how patient-centered medical homes such as the Navigator have achieved their benefits, particularly with respect to the cost of care. Prior studies have shown an association between lower use of acute care (inpatient admissions and emergency department visits) and PCMH implementation. However, no study has yet explicitly examined how much of patient-centered medical homes’ cost savings if any exist are driven by reductions in inpatient care as opposed to reductions in other types of services. With the rising prominence and popularity of accountable care organizations, in which attribution of patients and of the cost of their care becomes a critical challenge,” understanding patient-centered medical homes’ potential influence on different types of care utilization and the corresponding cost impacts is valuable to policy makers and health care administrators.

To this end, using a set of multivariate regression models, we examined the Navigator experience by breaking down the total cost savings associated with the Navigator into its major components (outpatient, inpatient, professional, and prescription drugs) and establishing the associations separately between a clinic’s exposure to the Navigator and each of the cost components. In addition, we also examined the association between Navigator exposure and clinic-level acute inpatient admission rates to verify that the total cost reductions associated with the Navigator is attributable to corresponding reductions in acute inpatient care.

The Proven Health Navigator was rolled out in phases over a seven-year period from late 2006 through mid-2013. Phase 1, involving three primary care clinics, started in November 2006. Phase 2, involving ten additional primary care sites, started a year later. By June 2013 there were eight phases, expanding to include a total of eighty-six Navigator sites located throughout central Pennsylvania. One crucial advantage of this ‘phased” Navigator rollout was that it allowed for variation in the length of Navigator exposure across the sites. That is, while some sites remained non-Navigator (that is, Navigator exposure of zero), selected others became Navigator sites at different times, allowing for internal comparisons across the primary care clinics that eventually became Navigator sites. In the following analysis we focused on the eighty-six primary care sites that eventually became Navigator sites by June 2013. Forty-two (49 percent) of these sites are currently owned by Geisinger Health System. The remaining forty-four are private independent physician practices that adopted the model’s core elements.
This study focused on the impact of the Proven Health Navigator on the elderly Medicare patient population for two reasons: First, this population is more prone to multiple chronic conditions and high use of care than the general popu1ation, 16 and it is thus expected that patient-centered medical homes can have a dramatic impact on this particular population. Second, the Navigator has specifically targeted this population by design since its inception in 2005. As the PHN model expanded only recently to include the commercial population, it is the Medicare population for which the Navigator has accumulated the most experience and, therefore, may have had the greatest impact.


The data originated from GHP’s claims database covering the period between January 1, 2006, and June 30, 2013. To select the study sample, the following inclusion criteria were applied: Members must have had GHP’s Medicare Advantage plans and be age sixty-five or older during the study period and have the plan types that require each member to select a primary care provider within GHP’s provider network. Those who were not required to select primary care providers were excluded from the study sample because their primary care affiliation (even if identifiable) could not be ascertained. This exclusion criterion did not imply that these excluded patients were “PHN-naive”; instead, it was an effort to ensure a clean identification of those who were exposed to the Navigator and those who were not.

The main outcome variable of interest was the total cost of care, which was defined as per member per month “allowed” amount that is, the sum of payment to providers and members’ out of pocket expenses in the form of copayments, coinsurance, and deductibles. The total allowed amount was further broken down into four major components, as described above. Inpatient cost included services provided at all inpatient facilities, including skilled nursing facilities. Outpatient cost included services provided at outpatient hospitals, ambulatory surgical centers, and other ambulatory care facilities. Professional cost included payments to doctors, specialists, independent labs, and other health care professionals. Prescription drug cost refers to all costs associated with the member’s pharmacy benefits. Because not every GHP Medicare Advantage member has Part D coverage through GHP, our claims data did not capture all of the prescription drug costs of such members. To account for this, we calculated the percentage of members at each site who had Part D coverage through GHP in each month and included this variable as a covariate in our regression model. Strictly speaking, “cost of care” conceptually refers to the monetary value of all of the resources required to produce the care used by the member. For the purposes of this study, however, because our claims data do not contain such information, and to the extent that health plan reimbursements reflect the “price” upon which the provider and the payer have agreed, we used the reimbursement information as the proxy for the true cost and thus use the term “cost” interchangeably with “expenditure.” Additionally, allowed amounts reflect negotiated payment rates that are likely to vary by providers. If, for instance, Navigator sites systematically accepted lower payment rates than non-Navigator sites, this may have biased our results. Because our claims data do not contain information on the changing payment rates over time, this is a potential limitation. However, as shown below, our data suggest little evidence of this in fact, the unadjusted average total allowed amounts for the Navigator sites were actually higher than those for the non-Navigator sites.

The unit of our analysis was each primary care site observed in each month of the study period. That is, we aggregated the patient-level per member per month allowed amounts by calculating mean per member per month costs for each site. The mean per member per month costs for each site were obtained by summing up the per member per month allowed amounts across all members in the site in each month and dividing that amount by the total number of members in that site during the same month.

In addition, we calculated all cause acute inpatient admission rates per 1,000 members for each site in each month, using a similar formula as above. This variable was used as an additional dependent variable to examine whether the observed association between Navigator exposure and cost savings is consistent with the observed association between Navigator exposure and acute inpatient admission rates. To the extent that the Navigator is a site-level rather than a patient-level intervention, such a site-level aggregation method as described above is conceptually consistent with the way in which the Navigator was developed and implemented. One limitation is that because our data set contained claims data of only GHP members, the data represent the experience of only the GHP membership within each site. Since GHP membership accounts for only a subset of the patient population receiving care from these sites (approximately 30 percent), our method thus does not truly capture the experience of the entire site. The mean per member per month costs per site and the acute inpatient admission rates per 1,000 admissions per site per month as specified above were the dependent variables in a set of multivariate regression models that included site fixed effects (that is, binary indicator variable for each of the eighty-six sites). This exploited the over-time variation in the timing of the Navigator adoption and removed any unobserved confounding due to time-invariant factors (such as practice location). Thus, to the extent that the selection of the sites into each Navigator implementation phase was non-random and depended on some permanent or persistent characteristics of the sites, this site- level fixed effects model controlled for any potential bias stemming from the nonrandom selection of sites. In effect, our method compared the site-level claims experiences among the sites that had not yet become Navigator sites against the claims experiences of the sites that had become Navigator sites at around the same time.
The key explanatory variable was the length of Navigator exposure for a given site, measured in months. To allow for nonlinearity in the relationship between the Navigator exposure and the dependent variables, the Navigator exposure variable was broken into six-month intervals. These intervals were included as a set of ten binary indicator variables in the regression model. The estimated coefficients on these indicator variables were used to test the hypotheses listed above.
Other covariates in the models included the following: percentage of members who were female, percentage of members who had 
prescription drug coverage, mean Hierarchical Condition Category (HCC) risk scores, mean member age, number of GHP members in the site, ownership status (that is, Geisinger-owned or not), as well as year and month indicator variables to capture yearly secular trends and seasonality. The HCC is a risk-adjustment model implemented in 2004 by the Centers for Medicare and Medicaid Services to adjust capitation payments to Medicare Advantage plans to reflect the risk of their members. ‘A value of 1 implies average risk, while a value greater than 1 implies greater than average risk. Also note that our analysis did not explicitly consider members’ ethnicity, because more than 90 percent of Geisinger’s member population is considered Caucasian.
In total, we estimated six separate generalized linear models with log link and gamma distribution to account for the skewness of the dependent variables. For some sites with low GHP membership, there were some zero values in the dependent variables. To avoid dropping such observations from the analysis because of the use of log link function in our generalized linear model, a small positive constant (0.01) was added to all dependent variables.
To translate the estimated coefficients on the Navigator exposure variables into actual dollar values and inpatient admission rates, we obtained regression adjusted cost estimates with the Navigator exposure variables set to zero to simulate the counterfactual in which the Navigator had never been implemented. The differences between the regression adjusted cost estimates with the original data and the corresponding estimates with the Navigator exposure variables set to zero were then reported as the estimated cost savings. The same method was applied to obtain the estimated Navigator impact on acute admission rates. Bootstrapped standard errors with 200 replications were obtained to calculate 95 percent confidence intervals around the reported estimates.
Furthermore, in calculating the cost savings associated with each level of Navigator exposure, as shown in the following section, the year and month indicator variables were set to zero to adjust for yearly secular trends and seasonality. This was necessary because the “length of Navigator exposure” variable measured in months is necessarily confounded by seasonal utilization patterns and secular trends such as inflation and other time-dependent factors not related to Navigator implementation. Because we set the year and month indicator variables to zero, the dollar values used in the analysis were set to the values as of 5anuary 2006, the first calendar month of the study period, and the temporal confounding effects were removed.


EXHIBIT 2 : Basic Description Of The Analytic Sample Of Primary Care Clinics That Became Proven Health Navigator (PHN) Sites Between January 1, 2006, And June 30, 2013, By Degree Of PHN Exposure

Total samplePHN exposure = 0PHN exposure > 0
Number of observations: members per month3,181,9092,012,1121,169,797
Number of observations: sites per month6,4193,6892,730
Average number of members per site per month (SD)291 (353)189(248)428(421)
Average member age, years, in a given month (SD)76.1 (6.9)76.0 (6.9)76.3(7.0)
Average HCC score in a given month (SD)1.16 (0.96)1.13 (0.95)1.22 (0.99)
Members per month with diabetes24.8%24.3%25.7%
Members per month with asthma5.8+4.5+7.9%
Members per month with coronary artery disease31.1 %31.0%31.4%
Members per month, prescription coverage68.0%64.2%74.6%
Members per month, female57.5°+57.4%57.7°+
Unadjusted mean PMPM total cost per site (SD)s792 (390)S735 (438)S869 (297)
Unadjusted inpatient visits per 1,000 members per site per month (SD)23.6 (21.5)24.5 (25.3)22.4 (14.6)

souace Geisinger Health Plan. aozes N – 86 PHN sites. SD is standard deviation. HCC is Hierarchical Condition Category. PMPM is per member per month

Our data included more than three million member-month observations (Exhibit 2). The total sample size available for our analysis was 6,419 site-month observations, which is less than the maximum possible 7,740 (eighty-six sites multiplied by ninety months) site-month combinations because some new primary care sites were added to the GHP provider network after January 2006. In the most recent month available (June 2013), the average Navigator exposure was thirty-one months. The average number of GHP Medicare Advantage members represented for a given site in a given month was 291. This amount differed by Navigator exposure status: The average number of members per site per month was 189 when the sites were not yet Navigator sites but 428 after the sites were converted to the Navigator model. This reflects both the growth of the GHP Medicare Advantage membership and conversion of larger practices into Navigator sites over time.

As mentioned above, because our data did not include claims data from non-GHP members, we could not determine what portion of the sites’ total patient population was represented by our data. GHP members in each site were, on average, about seventy-six years old with an average HCC risk score of 1.16 (Exhibit 2). Two-thirds of members also had Medicare Part D prescription drug coverage through GHP, and more than half were female. These member population characteristics seemed to differ by Navigator exposure status: Members in the Navigator-converted sites were, on average, slightly older, had slightly higher risk scores, had a slightly higher prevalence of chronic conditions (diabetes, asthma, and coronary  artery disease),  and were more likely to have prescription drug coverage through  GHP, compared to members  in non-Navigator sites.These  estimates  likely reflect the temporal trends of the stagnant and aging member population that Geisinger serves, which also correlated with the length of Navigator exposure over time. Interestingly, the unadjusted mean total per member per month cost per site appears to be higher after Navigator conversion ($735 versus $865), even though the unadjusted inpatient acute admission rates per 1,000 members after Navigator conversion  seemed to be lower than before conversion (24.5 versus 22.4).

EXHIBIT 3 : Regression-Adjusted Cost Estimates, Proven Health Navigator (PHN) (Observed) Versus Non-PHN (Expected)

Observed’Expected°Dollars95•/o ClPercent95•x• CI
TotalS6I 7S670—53(—100, —6)—7.9(—14.9, —1.0)
Inpatient149183—34(—60, —9)—18.7(—33.4, —3.9)
Outpatient161170—9(—26, 9)—5.1(—15.7, 5.5)
Professional153158—4(—15, 7)—2.7(—9.8, 4.4)
Prescription103111—7(—18, 3)—6.8(—16.2, 2.6)

aouacE Geisinger Health Plan. aotEs Adjusted for secular yearly trends and seasonality (in 2006 dollars). “Observed” and “expected” costs are explained in the text. Cl is confidence interval. °Mean per member per month cost per site. 

Exhibit 3 summarizes the estimated mean per member per month cost savings per site associated with the Proven Health Navigator during the study period, obtained via the regression models. See the online Appendix for the full regression model coefficient  estimates.“  “Observed” refers to estimated mean per member per month costs per site with Navigator implementation as  observed in the data. “Expected” refers to the estimated mean per member per month costs per site with the Navigator exposure variable set to zero, which simulates the hypothetical counterfactual in which the Navigator had never been implemented. The differences between the observed and expected costs capture the savings associated with Navigator exposure. The estimates indicate that after we controlled for the differences in risk scores, prevalence of chronic conditions, and potential site-selection bias that confounded the unadjusted results in Exhibit 2, there was, on average, $53 savings in the per member per month total cost of care per site in regression-adjusted 2006 dollars). This translates to about 7.9 percent total cost savings, on average, across the ninety-month period. Breaking down the total cost savings into its four components, Exhibit 3 suggests that the largest source of savings was acute inpatient cost ($34, or 19 percent), which accounts for about 64 percent of the total estimated savings of $53.Other cost components also show some cost savings, but these estimates are not statistically significant.

Exhibits 4 and 5 illustrate the estimated impacts of Navigator exposure on total cost of care and acute inpatient admission rates, respectively. The exhibits suggest that longer Navigator exposure is associated with a greater magnitude of cost savings, and this pattern is consistent with what we observed in terms of the association between acute inpatient admission rates and Navigator exposure, as illustrated by Exhibit 4. As before, these estimates were adjusted for yearly secular trends and seasonality, as described above.


The results of this study confirm our hypotheses: that a primary care clinic’s exposure to the Navigator was associated with savings in total cost of care compared to non exposure; that the longer a primary care clinic was exposed, the greater the cost savings; and that the largest and most significant source of the total cost savings was reduction in acute inpatient care. These findings provide some useful insights into the potential impact of a patient-centered medical home trans- formation from the perspective of primary care providers and payers that may be considering PCMH adoption, particularly for their elderly Medicare patient populations. This group typically has greater prevalence of multiple chronic diseases and uses more health care than the general population. Elderly Medicare patients are more likely than others to be prone to avoidable hospitalization and duplicative care that may be reduced via better care coordination.

Interestingly, our results not only confirm the expectation that the longer a primary care clinic has been exposed to a patient-centered medical home transformation, the greater its impact on cost of care, but they also suggest that these long- term cost savings continue to get larger well into the seventh year of the Navigator transformation and even beyond. Moreover, as Exhibit 2 indicates, there is no evidence of ‘cost shifting that is, the cost savings in one area of care (in this case, acute inpatient care) did not lead to in- creased costs in other areas of care. Savings were observed in all four cost components but were statistically significant only for acute inpatient costs. This is consistent with the “prevention” hypothesis of the PCMH model: that enhanced focus on primary care via implementation of a patient-centered medical home is likely to prevent patients from needing acute and more expensive care later on. This is further supported by the finding that much of the cost savings is driven by significant reductions in acute inpatient cost.

EXHIBIT  A :  Impact Of Proven Health Navigator Exposure On Mean Per Member Per Month Total Cost Of Care Per Geisinger Health Plan Site

Ooueca Geisinger Health Plan. xoaa0 The midpoints in the bars (blue squares) represent the point estimates of the percentage differences between Navigator and non-Navigator sites at the given length of Navigator exposure, measured in months, while the ranges around the midpoints represent the bootstrapped 95 percent confidence interval around the corresponding point estimates.

EXHIBIT  B : Impact Of Proven Health Navigator (PHN) Exposure On Acute Inpatient Admission Rates In Geisinger Health Plan Sites

souncs Geisinger Health Plan. uozss The midpoints in the bars (purple squares) represent the point estimates of the percentage differences between Navigator and non-Navigator sites at the given length of Navigator exposure, measured in months, while the ranges around the midpoints represent the bootstrapped 95 percent confidence interval around the corresponding point estimates.

Obviously, such cost savings will not be sustained indefinitely. At some point, an incremental Navigator exposure will start to yield smaller returns (that is, the law of diminishing marginal returns) and eventually yield no additional savings. Our data show, however, that any diminishing return to additional Navigator exposures still had not been observed almost eight years since the initial Navigator conversion. This finding has an important implication for the sustain- ability of PCMH models in achieving lasting cost savings in larger contexts.

We believe that there are three main reasons for this apparent success of the ProvenHealth Navigator: First, it is truly a data driven payer provider partnership that goes beyond simply enhancing information technology infrastructure at practice sites and seeks to translate practice-specific data into meaningful care plans by clinical experts. Second, it is led by systemwide programmatic leadership that focuses on the entire care process instead of a single point in the process. Third, it seeks to extend value for patients and medical professionals beyond traditional primary care settings.
For example, GHP hires, trains, and manages the embedded case managers, partly because practices often lack resources to support such capabilities. This is in contrast with other clinic- based case management models in which additional case management duties are simply added on top of the existing workload of nurses who often lack training and resources. Another example is optimizing treatment settings for patients with certain conditions (for example, heart fail- ure, pneumonia, and atrial fibrillation) who are often treated in inpatient settings but can also be effectively and safely treated in outpatient clin- ics. Although not an explicitly stated feature of the patient-centered medical home in general, this is consistent with the medical home’s overall aim to improve health care value by revitalizing primary care. Therefore, strategies have been implemented within the Navigator to redesign the workflow that would support a comprehensive and coordinated approach to managing such patients in the clinic setting.
Our study was limited by the use of claims data originating from a single health plan. Also, our results maybe confounded by unobserved health status changes attributable to turnover in GHP Medicare Advantage membership over time. Although our analysis attempted to control for this via inclusion of HCC risk scores as a covariate in our regression models, we were unable to ascertain how well our model captured the changing risk in the population. If, for instance, sicker and older members drop out of membership over time (for example, because of death or switching to other health plans) and younger, healthier members join, this may systematically bias our results. However, because GHP Medicare Advantage membership tends to remain stable over time, and the population within the GHP service area is also stable and getting older, we believe that this is not likely to be a major source of bias in our estimates.
As mentioned above, GHP membership accounts for only a subset of the total patient population treated by the primary care practices included in this study. This also implies that the generalizability of our findings is unclear. We note, however, that the magnitude of the cost savings reported here is similar to the estimated PCMH cost savings reported in a study by
Michael Paustian and colleagues“ (7.7 percent lower per member per month adult cost), which suggests that our findings are not unique and are potentially replicable. On the other hand, another study by Robert Reid and colleagues” reported lower savings of approximately $10 or 2 percent per person per month, even though it included only twenty-one months of post-PCMH imple- mentation data. This suggests that there is likely to be significant variability in the patient- centered medical home’s ability to achieve cost savings depending on geographical and institutional contexts.


This study illustrates the potential dual benefits of patient-centered medical homes in terms of lowering costs while achieving improved quality of care. Geisinger’s Proven Health Navigator experience suggests that improving the quality of care does not necessarily mean higher cost of care. In fact, achieving higher quality of care can lead to significant and sustainable reductions in the cost of care over a long period. 

This research was previously presented as poster at the HM0 Research Network’s annual conference, in Phoenix, Arizona, April 1, 2014.

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  18. To access the Appendix, click on the Appendix link in the box to the right of the article online.

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