Steven B. Pierdon, MD

To determine whether a system of care with an all-or-none bundled measure for primary-care management of diabetes mellitus reduced the risk of microvascular and macrovascular complications compared with usual care.

A parallel pre-post observational design was used. In 2006, a system of care for diabetes was implemented for some members of the Geisinger Health Plan. A total of 4095 primary-care patients were in the Diabetes System of Care group (DS) and compared with a propensity score—matched cohort of 4095 primary care patients not in the system of care (non-Diabetes System of Care [NDS]).

Cumulative hazard rate was measured over a 3-year period for retinopathy, amputation, stroke, and myocardial infarction (MI).

The adjusted hazard ratios (HRs) for MI (HR, 0.77; 95% CI, 0.65- 0.90), stroke (HR, 0.79; CI, 0.65-0.97), and retinopathy (HR, 0.81; CI, 0.68-0.97) were all significantly lower among DS patients. The adjusted HR for major amputations (HR, 1.32; CI, 0.45-3.85) did not differ between groups, but only 17 major amputations occurred during the follow-up period. The necessary number of patients to treat in order to prevent 1 event over 3 years was 82 for MI, 178 for stroke, and 151 for retinopathy.

A system of care with an all-or-none bundled measure used in primary care for patients with diabetes may reduce the risk of MI, stroke, and retinopathy over a 3-year period.

A diabetes system of care using an all-or-none bundled measure can reduce the microvascular and macrovascular complications of diabetes in only 3 years.

		A system of care for diabetes can reduce myocardial infarction, stroke, and retinopathy in only 3 years.

		Translation into a workable care protocol in primary care is challenging. Workflows were developed that were measurable, scalable, reliable, and not dependent on the diligence of individual providers.

		Payers and health systems can innovate and invest together in the infrastructure to build similar systems of care, knowing that the benefit for their patients with diabetes will be prompt.

The prevalence of diabetes mellitus in the United States increased 40% between 2007 and 2010, to 11.9% of the population aged 20 years and older.

 Patients with diabetes are at increased risk for microvascular complications including retinopathy, nephropathy and amputation, and macrovascular complications including stroke and myocardial infarction (MI). Glycemic control has dramatically improved microvascular outcomes in patients with type 1 diabetes

 but has not consistently been associated with improved macrovascular outcomes, such as cardiovascular disease (CVD). Recent studies show that intensive glucose control is associated with increased risk or no benefit.

In contrast, lipid and blood pressure (BP) control have resulted in clear reductions in CVD in persons with diabetes

 Moreover, the combined control of these risk factors reduced the risk of CVD outcomes.

Notably, the time to onset of risk reduction can be relatively short. For example, smoking cessation reduces coronary events within 6 months for patients at high risk.

In spite of the evidence, translation of standards of care into a workable care protocol in primary care is challenging. The American Diabetes Association (ADA) has promulgated detailed standards of care for improving patient outcomes, including recommendations for medical care from physician-coordinated teams.

 However, providing state-of-the- art guideline care is rarely achieved.

 Although there is evidence that the use of an electronic health record (EHR) may improve process-of-care measures and intermediate outcome measures for diabetes

 even when guideline care is provided, it is unclear whether the benefits of reduced risk accrue and how much time is required before risk reduction is measurable.

A team-based model was designed to more consistently provide evidence-based care to patients with diabetes. This system of care used a 9-component bundle of diabetes measures, a workflow redesign, EHR tools, and financial incentives to improve the consistency and reliability of care, as previously reported.

A key aspect of this program is an all-or-none measure that determines physician incentives. We describe the impact of this system of care on risk of microvascular complications of retinopathy and amputation and macrovascular complications of stroke and MI over the first 3 years of implementation.

This research study was approved by the Geisinger Institutional Review Board. The evaluation of the diabetes system of care was completed among members of the Geisinger Health Plan (GHP). Approximately 45% of GHP members receive their care from Geisinger primary care physicians. GHP also contracts with non-Geisinger physicians who provide primary care to the other 55% of members. The diabetes system of care was implemented in all 38 Geisinger primary care sites, referred to hereafter as Diabetes System (DS). DS patients were compared with propensity score—matched primary care patients from other clinics in the same region, referred to hereafter as non-Diabetes System (NDS). One key part of the DS is that it was implemented in an integrated healthcare system. This allowed for many advantages in implementation, such as a shared EHR system across all providers, ease of reporting to all providers without conflict, and an equitable application of physician incentives to all providers. The NDS system had many nonintegrated care characteristics, yet had numerous diabetes improvement programs in effect during the study period that are discussed further in the discussion section.

The DS was designed by a multispecialty, multidisciplinary work group. The ADA Guidelines for the Standard of Medical Care for Diabetes were used as the basis for the system of care.17 The team model included an allor- none measure with physician incentives awarded only when a patient fulfilled all 9 components of the bundle (

). Previous research has examined compliance rates within the DS program and found significant increases in all measures of diabetes care within the first 12 months.

Beginning in January 2006, the all-or-none bundle was routinely measured in all adults with diabetes at Geisinger primary care sites. DS included delegated accountable responsibilities for each team member—physician, advanced practitioner, nurse, case manager, front office staff, and call center staff. Workflows were developed to be measurable, scalable, reliable, and not dependent on the diligence of individual providers. DS included EHR decision support, autogenerated patient report cards, and automated updating of a patient registry. During the office visit, licensed practical nurses and medical assistants were presented with decision support for all process-related diabetes care and had standing orders for influenza and pneumococcal immunizations, diabetes lab work, eye exam referrals, and diabetic foot screenings. Providers are presented with decision support for only those elements of diabetes bundle care that require complex medical decision making, including glycated hemoglobin (A1C), low-density lipoprotein cholesterol (LDL), and BP management. Patient report cards were generated during the office visit to be shared later with the patient. The individual metrics and all-or-none bundle results for each clinic team and for each individual provider were reported monthly. Each team had access to the individual reports and team reports of all other physicians/ teams within Geisinger.

Analysis of outcomes was based on claims data obtained from GHP. As previously noted, GHP members in the DS were compared with those in the NDS. The analysis was confined to GHP members because medical claims data on all eligible diabetes patients could be used to identify relevant documentation of micro- and macrovascular events. Analysis was limited to the period of services provided and paid by GHP from January 1, 2004, to December 31, 2008, and was confined to patients who were aged 40 years or older with a diabetes diagnosis. The 2 years of observations from 2004 to 2005 were used to establish baseline event rates in the two patient populations before the DS was implemented.

During the study period, individuals were included as diabetes cases if they met Healthcare Effectiveness Data and Information Set (HEDIS) criteria

 and had 4 or more encounters with a diabetes 

International Classification of Diseases, Ninth Revision

) code on different dates during the study period. The earliest encounter with a diabetes 

 code was taken as the diabetes baseline date. Patients had to have at least 180 days of follow-up after their diabetes baseline date to contribute to the analysis.

We identified all inpatient and outpatient claims during the 5-year study period related to the following 

 codes 89.5x, 89.6x, 89.7x, and 99.7x); MI (

 codes 430, 431.xx-438.xx). The following numbers of cases were identified: 2518 retinopathy; 138 amputation; 3824 MI; and 2112 stroke. Incident microvascular or macrovascular events were identified as the earliest documentation of 1 of the above codes if there was also at least 1 year of follow-up prior to the first documentation of the claims event. This “clean-out period” restriction was imposed to eliminate prevalent events among those who became new members of GHP. In sensitivity analysis, we also used a 2-year clean-out period. The findings were essentially the same. Using the 1-year criteria, we identified 519 retinopathy, 17 amputation, 714 MI, and 445 stroke incident events.

Chronic kidney disease, 1 of 3 serious microvascular diseases associated with diabetes, was not evaluated as an outcome because it is not reliably reported in insurance claims data unless a patient has end-stage disease.

Because patients were not randomized, we used a 2-step method to protect against inferential threats attributable to baseline differences in risk factors. First, logistic regression was used to calculate a propensity score

 for each eligible diabetes patient to balance disease burden for members of the DS and NDS groups at the beginning of intervention (January 1, 2006), which was denoted as the index date. If patients had pure postintervention exposure, then the index date was set to be the same as the baseline date. Claims data from the date of enrollment to the index date were used to derive the propensity score based on patient gender and age at index date, as well as claims documentation of comorbidities (mild liver disease, peripheral vascular disease, heart failure, cerebrovascular accident, pulmonary disease, severe liver disease, renal disease, malignant cancer, cardiovascular disease, and dialysis) up to the index date. The propensity score was used to match DS and to NDS diabetes patients (± 0.2 of SD of the propensity score, an optimal caliper usually used in propensity score—matching studies

) and to balance both groups on future risk of events of interest. Second, to account for differences in person-time of observation in the postsystem-of-care periods, we calculated the probability of being “exposed” to the post-system period. To this end, each patient’s age at diabetes baseline date, duration of person-time in the preintervention period, and comorbidities documented before the index dates were included in the conditional logistic regression model. Conditional logistic regression was used to control for matched pair variation. A score obtained for each subject represented the likelihood of postsystem-of-careexposure.

Person-time was excluded until a patient reached 40 years of age. Cumulative risk was estimated using the weighted Cox Proportional Hazards regression model, which combined a traditional Cox Proportional Hazard regression model with inverse probability weighting (IPW). The inverse of the previously described postsystem exposure score was used as a weight, along with age at the index date and gender. A patient was censored from observation if they discontinued as a GHP member, died as a GHP member, or incurred their first unique major microvascular or macrovascular event as previously described. The C statistic was used to measure discriminatory capability of the model and calculated as described by Pencina and colleague’s method.

 The cumulative hazard of each outcome was predicted by the model. Statistical analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina) for the time-to-event analysis and relative risk comparison.

The number of patients needed to treat (NNT) was calculated based on risk reduction, which was estimated from the above Cox proportional hazard model; that is, the 3-year survival rate was estimated from the model for DS and NDS group, respectively, and the difference represented the 3-year risk reduction. The NNT is the inverse of the risk reduction.

The initial study cohort of 16,086 GHP members met the criteria for diabetes diagnosis. (

). After applying exclusion criteria (<180-day follow-up, age <40, only preintervention exposure or prevalent outcome), the sample was reduced by 3421 of the 8355 patients in the DS group clinics and by 3634 of the 7734 in the NDS group clinics. After applying propensity score—matching criteria, 4095 DS subjects were matched to 4095 NDS subjects. Before matching (

), DS group members were significantly older, had proportionately more of their person- time in the postintervention period, and had a longer follow-up period than NDS members. After matching, DS and NDS members still differed on duration of follow-up time in the postintervention period. The 2 groups were similar on the risk score derived at baseline and up to the time of the index date. The adjusted hazard ratio (

) for MI was 0.77 (95% CI, 0.65-0.90); for stroke, 0.79 (95% CI, 0.65-0.97); and for retinopathy, 0.81 (95% CI, 0.68- 0.97). The risk of the macrovascular and microvascular events were significantly lower among DS patients compared with NDS patients (Figure 2 includes cumulative hazard plots). These hazard plots show that the reduction in macrovascular outcomes (MI and stroke) is most pronounced after year 1 of the DS, and that the microvascular outcome retinopathy is evident after approximately 18 months. The risk of developing retinopathy, MI, and stroke among DS patients was significantly less than that of NDS patients in the 3-year follow-up period. The NNT to prevent 1 event over 3 years was 82 (95% CI, 37-133) for MI; 178 (95% CI, 57-681) for stroke; and 151 (95% CI, 47- 512) for retinopathy.

This analysis of claims data from a propensity-matched observational design shows a statistically significant lower risk of macrovascular and microvascular disease end points in the first 3 years of a diabetes system of care that included an all-or-none bundled measure compared with primary care without this intervention. The impact was substantial, with only 82 patients needed to treat to prevent 1 MI event, 178 to prevent 1 stroke, and 151 to prevent 1 case of retinopathy. Perhaps the most notable finding is the apparent early impact of the care model. The findings suggest an impact in the first 3 years with the possibility that a reduction in risk began to emerge after the first year. This finding is consistent with prior randomized controlled trials indicating that reduction in risk of cardiovascular outcomes can be achieved.

 However, the early impact of an all-ornone diabetes system of care on microvascular and macrovascular conditions has not been previously described.

The finding of a reduction of MI, stroke, and retinopathy in only 3 years through a risk factor intervention is supported by prior studies on individual risk factors such as smoking cessation, BP control, and influenza immunization. 

 Multiple trials have also shown statistically significant improvements in microvascular and macrovascular outcomes in 3 to 5 years of follow-up in programs designed to reduce cardiovascular risk.

This study showed improvement in a shorter period of followup. Observing statistically significant differences strongly depends on sample size, and showing statistically significant differences does not indicate when the benefits of an intervention first emerge. These previous trials were designed to show that the intervention had an effect, not to determine the earliest point at which the effect occurred. The current observational study included multiple simultaneous interventions which could have amplified the early benefits in risk reduction when compared with other studies using a single intervention.

A limitation of this study is that the DS group intervention did not occur in isolation.After the initiation of the DS, other performance improvement projects were initiated, including reporting of diabetic foot examination rates, diabetic eye examination rates, aspirin use, and angiotensin- converting enzyme inhibitor/angiotensin II receptor blocker use. However, it is unlikely that these other initiatives contributed to the early separation in risk observed in the DS clinics compared with the NDS clinics. The NDS sites also had other ongoing care improvements sponsored by the health plan. These included Web-based registry tools and outreach programs based on HEDIS measures for diabetes, which could have resulted in improved diabetes outcomes, but in a less substantial manner than in the DS sites. The DS intervention included many interventions, including physician and staff monetary incentives. These were used at the DS sites to focus attention and promote team attainment of goals. NDS sites also received a Physician Quality Summary bonus from GHP, but the monetary incentives were less than what was provided the DS sites and not related to an all-or-none improvement. The extent to which these varying monetary incentives contributed to the success of the intervention was not studied.

Finally, patients in the DS care arm were more likely to be cared for in practices that are a part of an integrated health system and in sites that have an EHR. While we used propensity score matching at the time of the index date to balance the “intervention” and control arms, there is the possibility that the separation of the time-toevent curves for micro- and macrovascular disease risk was explained by unmeasured confounding attributable to a host of factors that are simply correlated with the effectiveness of delivering the diabetes care.

This diabetes system of care had multiple interventions, and the impact of each will require further research. Determining the best combination of bundled measures, work flow redesign, financial incentives, and the relative impact of each aspect of the system of care are ongoing.

 Analysis of the impact of the system of care on overall mortality and total cost of care is planned.

Prior evidence indicates that it is possible to improve process of care measures for diabetes in clinical practice, 

 and document small to modest improvements in glycemic control.

 There is a single report of reduced adverse events (amputations, retinopathy, and MI) using a diabetes disease management program.

 If substantiated, our findings that a DS reduces the microvascular and macrovascular complications of diabetes in only 3 years have potentially important implications for patients, providers, and payers. 

Geisinger Health System, Danville, PA (FJB, TRG, TA, DED, SBP, GDS); Sutter Health, Sacramento, CA (XY, WFS, JP).

Articles

Using a system for primary care management of patients with diabetes may reduce the risk of myocardial infarction, stroke, and retinopathy over a 3-year period.

Primary Care Diabetes Bundle Management: 3-Year Outcomes for Microvascular and Macrovascular Events

The primary care medical home has been promoted to integrate and improve patient care while reducing healthcare spending, but with little formal study of the model or evidence of its efficacy. ProvenHealth Navigator (PHN), an intensive multidimensional medical home model that addresses care delivery and financing, was introduced into 11 different primary care practices. The goals were to improve the quality, efficiency, and patient experience of care.

To evaluate the ability of a medical home model to improve the efficiency of care for Medicare beneficiaries.

Observational study using regression modeling based on preintervention and postintervention data and a propensity-selected control cohort.

Four years of claims data for Medicare patients at 11 intervention sites and 75 control groups were analyzed to compute hospital admission and readmission rates, and the total cost of care. Regression modeling was used to establish predicted rates and costs in the absence of the intervention. Actual results were compared with predicted results to compute changes attributable to the PHN model.

ProvenHealth Navigator was associated with an 18% (P <.01) cumulative reduction in inpatient admissions and a 36% (P = .02) cumulative reduction in readmissions across the total population over the study period.

Investing in the capabilities of primary care practices to serve as medical homes may increase healthcare value by improving the efficiency of care. This study demonstrates that the PHN model is capable of significantly reducing admissions and readmissions for Medicare Advantage members.

ProvenHealth Navigator (PHN), a multidimensional medical home model, was introduced into 11 Geisinger Health System primary care practices with the goal of improving the quality, efficiency, and patient experience of care for Medicare Advantage patients.

This transformed primary care model resulted in significantly fewer hospital admissions (18%) and readmissions (36%) when measured across the entire population.

Total care costs for the entire PHN population decreased 7%, but this decrease did not achieve statistical significance.

Medical home models seeking to change cost trends require a multidimensional transformation of primary care practice with intensive case management and a payer partnership.

Both policy makers and private payers in the United States have begun to recognize that improving care coordination across the fragmented healthcare delivery system is essential  to improve the quality and affordability of care. Related efforts include recent Medicare demonstrations examining the impact of external disease management programs and  payment reforms that reward integrated care organizations.

 An alternative approach—the patient-centered medical home&mdash;involves enhanced primary care practices as the locus  of integration and coordination of care. A version of the medical home model was originally described by the American Academy of Pediatrics and has more recently been refined   and delineated by a coalition of professional societies through a set of joint principles.

Elements of and antecedents to the current concept of the medical home have been shown to be associated with higher quality of care and patient experience.

Closely related work on Wagner’s Chronic Care Model suggests the potential of this approach to improve the quality of care for patients with chronic conditions and prevent costly acute care.

 However, empirical evidence is scant about whether efforts to transform practices into medical homes will improve quality or yield healthcare cost savings. Two recent studies  demonstrate both the challenges of practicing transformation in primary care and the potential benefits in terms of patient and provider experience as well as preventable acute care utilization.

In this study, we evaluate the impact of a medical home model, Proven-Health Navigator (PHN), introduced for Medicare Advantage enrollees in 11 practices owned by Geisinger  Health System (GHS) in Pennsylvania. ProvenHealth Navigator is a new model of care designed to improve the quality, efficiency, and patient experience of care. It functions as a  partnership between participating primary care practices and Geisinger Health Plan (GHP). Central to the model is the transfer of population management capabilities, including  nurse case managers, from the health plan to the practice sites. This report focuses on the impact of the PHN on hospitalization and healthcare spending compared with a matched  set of practices, using 2 years of preintervention and 2 years of postintervention data.

We assembled medical claims data for services provided from January 1, 2005, through December 31, 2008,and paid through June 30, 2009, and demographic information for 15,310 members of GHP’s Medicare Advantage product. Using these data, we identified all claims for enrollees who were cared for by physician practices that implemented the  PHN model and enrollees who were cared for by matched physician practices during the study period. Enrollees who switched physician practices during the study period were  excluded from the analyses. Continuous enrollment was not required for inclusion in the study sample; analyses conducted on the continuously enrolled subpopulation yielded  qualitatively similar results.

This analysis was approved by the GHS institutional review board.

Located in rural northeastern and central Pennsylvania, GHS is a not-for-profit, integrated healthcare organization comprised of the Geisinger Clinic, which has nearly 800 employed physicians; 2 acute tertiary/quaternary care hospitals; GHP, which serves 190,000 commercial and 38,000 Medicare Advantage members; and numerous other clinical programs and facilities. Geisinger Health Plan also utilizes a network of more than 18,000 non-GHS providers and 80 non-GHS hospitals.

Geisinger Health System has an electronic health record (EHR) implemented systemwide for all ambulatory and inpatient care. This EHR also is used by GHP case managers and patients. These EHR capabilities were operational in all participating practices for several years prior to the launch of the PHN. All Geisinger-owned primary care practices, including the PHN sites, participated in a preexisting, EHR-enabled quality initiative to improve preventive, diabetes, and coronary artery disease care.

The PHN model has 5 functional program components: (1) Patient-Centered Primary Care Team Practice, (2) Integrated Population Management, (3) Micro-delivery Systems, (4) Quality Outcomes Program, and (5) Value Reimbursement System (

). A more detailed description of each component, with a comparison to the National Committee for  Quality Assurance (NCQA) Physician Practice Connections and Patient-Centered Medical Home (PPC-PCMH)standards, can be found in the eAppendix at 

Many of the elements required under the PPC-PCMH standards are provided in the Patient-Centered Primary Care Team Practice component. Access criteria are met through close  monitoring of performance on appointment standards (NCQA standard 1). Tracking and registry capabilities for several chronic diseases are embedded into the Primary Care Team Practice component’s EHR (NCQA standard 2). Reminders for preventive and chronic disease care are part of the quality improvement initiative described above (NCQA standards 3 and 8). Self-management support has been a central theme in the team-based care approach the practices use; disease and case management were added as part of the PHN model (NCQA standard 4). Electronic prescribing as well as test and referral tracking also are available in the EHR (NCQA standards 5, 6, and 7). Advanced communication capabilities for patients and providers are available through the electronic portals portion of the EHR system (NCQA standard 9).

Although the PHN model was created prior to the publication of NCQA’s PPC-PCMH standards, it does address all of the capabilities described in those standards. However, because our goal was to impact the quality, patient experience, and efficiency of care across the full continuum of care, not just in the office of the primary care physician (PCP), we believed that additional components and activities were necessary. The activities included in the 5 components are described further in Table 1. Several are worth noting. First, many of GHP’s population management activities were moved to the practice site. Geisinger Health Plan provided case managers for each practice at a ratio of 1 nurse for every 800  Medicare patients to serve as the hub for population-based activities. Second, the model explicitly calls for the PCPs to develop systems of care for their patients when they are seen by other physicians or in other settings. Third, additional financial support was provided by GHP to pay for new services in the PCP office. An example is dedicated phone lines to allow high-risk patients to contact their case managers directly. Fourth, performance reports documenting the quality, utilization, and overall cost-of-care results were provided to the  practice. Finally, we added a shared savings incentive model to the GHP reimbursement arrangement. Qualityoutcomes were aligned with preexisting preventive and chronic disease care quality initiatives. Shared savings incentive payments then were based on improvement in bundled metrics for these services and other agreed-upon metrics.

Implementation was focused on the GHP Medicare Advantage population because the high prevalence of chronic illnesses and the resource use of this population provide the best opportunity to demonstrate and evaluate the impact of the interventions. In October 2006 and January 2007, the PHN model was introduced into 2 pilot GHS practice sites selected because of their large GHP Medicare Advantage population and because their locations made them easily accessible for our PHN management team. During 2007 and January of 2008, the PHN model was expanded to include the Medicare Advantage members in 9 additional practices (

). Prior to implementation at each site, all practice staff were  trained on the core components of the model.

Initially, the PHN teams focused on improving the management of the highest risk patients. The GHP-embedded case managers were integrated as part of the practice care team. They were provided with utilization and predictive modeling reports derived from GHP claims data. For the first time, these reports gave the practice teams a systematic way to identify relative risk for their GHP patients. The case managers then met with the highest risk patients to design patient-specific care plans. They also provided close follow-up for patients transitioning from hospital to home.This activity focused on reaching out to the patient within 48 hours of discharge, medication reconciliation, appropriate resources and social supports in the home, and timely follow-up with the patient’s PCP. Monthly team meetings that included PCPs, office staff, case managers, and GHP staff were held to evaluate results, discuss practice workflow and care access, and review hospital admissions for missed opportunities.

The case managers also formed partnerships with preferred home health agencies and nursing homes. Outreach and education regarding the PHN strategy were provided to  these agencies. Pharmacy management initiatives were developed to improve generic utilization, assist members approaching the Medicare Part D coverage gap, and provide  members with acute care protocols for treating exacerbations of chronic conditions.

As progress was made, expanded strategies focused on members at moderate and low risk. Patients with gaps in preventive or chronic care were identified by EHR registries and  health plan claims tools. Health plan nurses with training in disease management targeted moderate-risk members with hypertension, coronary artery disease, and diabetes for  selfmanagement education; worked with providers to ensure appropriate screenings; and assisted in optimizing medications. Site-based practice staff reached out to low-risk  members to coordinate preventive care screenings such as mammograms, colorectal screening, and influenza vaccinations.

Because we hypothesized that opportunities to reduce total healthcare spending through this model would relate to the ability of the practice to prevent hospitalizations and readmissions, we constructed monthly series of these events for each patient. Readmissions were defined as all medical—surgical patients admitted to acute care within 30 days from time of discharge for primary admission. Total healthcare spending (plan payment plus copayment) was computed for each member for each month by summing the allowed amount on medical claims. Pharmacy claims were not included in total spending because of variability in prescription drug coverage among members and over time because of the introduction of Medicare Part D in 2006. To protect the confidentiality of GHP payment information, we indexed spending so that the mean for patients in the nonintervention practices in January 2005 was set to $100.

We analyzed data at the patient-month level using multivariate linear mixed regression to measure the effect of thePHN intervention on hospital admissions per 1000 members, readmissions per 1000 members, and total nonprescription per member per month medical spending. Because we could access data from each clinic before and after PHN implementation along with concurrent data from non-PHN practices, our chosen approach was to model the expected incremental effect of PHN status on outcomes during the postintervention period, after adjusting for all covariates. The results reported in this article, therefore, represent the effect of PHN within a clinic and not simply a comparison of PHN clinics with non-PHN clinics. Following the approach of Berlin et al,

 we decomposed PHN status into 2 components: the percentage of the study period during which each practice implemented PHN (ie, clinic-exposure association) and whether PHN was in effect during each observation (ie, a time-varying component). This 2-variable approach was necessary because, although our primary interest was the impact of a clinic switching from non-PHN to PHN status, there could be confounding due to the selection of PHN clinics and variability of different clinics’ duration of exposure to PHN. The first variable was time invariant and thus could represent the incremental effect of the intervention itself. It could, however, absorb bias from clinic-exposure association as well as biases between clinics selected or not selected for PHN. By contrast, the second variable (ie, a binary indicator of current PHN status centered around the time-invariant component at each site) isolated the actual effect of the PHN intervention on a clinic population, which was the objective of the study. Other covariates in the model were Hierarchical Condition Category (HCC) risk score,

the year, and interactions between both PHN variables and year (to capture differences in trends within and among the PHN and non-PHN groups) and between PHN variables and HCC score. Age and sex were not included as separate regressors because they were captured in the HCC score. We also included a series of variables for calendar months to capture seasonal variation in utilization and spending.

Regression models were estimated using general estimating equations with exchangeable covariance. Consistent with the underlying nature of the data, we used a log-link function and quasi-Poisson distribution for monthly admissions and readmissions. To analyze monthly spending, we used a standard 2-part model modified to account for a small amount of capitation GHP pays uniformly for all members. The second part of the spending model was specified with a log-link function and normal distribution. These models allowed us to calcunlate the difference between the observed outcomes for active PHN participants and their expected outcomes if the PHN had not been implemented. To adjust for secular trends in the postintervention time period, we utilized data from a group of non-GHS practices that cared for GHP Medicare Advantage enrollees as a control cohort. We considered using either a GHS or non-GHS control cohort, but chose the non-GHS practices for 2 reasons: (1) the larger sample of non-GHS practices available for matching and (2) concerns about spillover effects of PHN on non-PHN practices within GHS. To ensure similarity between the intervention and nonintervention groups, we used propensity score matching to identify a subset of practices that were most similar to the intervention practices in terms of patient population and outcomes in 2005. Because the PHN model was implemented at the practice level, we sought to match the intervention and nonintervention cohorts at this level. We estimated propensity score models predating the adoption of the PHN model by using the following practicelevel variables, all measured for 2005: mean patient age, percentage of male patients, mean HCC score, total per member spending, inpatient admissions, and readmissions. Each practice that adopted the PHN model was matched to 10 practices that did not adopt the PHN model based on the proximity of their estimated propensity score. Only those practices with estimated log odds from the propensity model that were within 0.60 standard deviation of an intervention site were counted as matches. A sensitivity analysis (not shown) with a GHS control cohort yielded results that were within the statistical confidence intervals (CIs) of the results presented here.

Because of overlap between sites, the propensity score model yielded a total of 75 distinct non-Geisinger practices within the common support region that could be matchedwith 1 or more of the 11 intervention sites (data not shown). Data from each of these sites were weighted to appropriately reflect the number of intervention sites with which they were matched. At baseline (2005), there were no statistically significant differences in sex or HCC scores of patients treated in the 11 PHN sites compared with the propensity score—matched non-GHS practices (

). Patients treated in the intervention sites were approximately 6 months younger on average than those treated in comparison sites (

 <.001), but because all results were regression adjusted, any potential bias associated with this age difference should have been eliminated. Average monthly admissions and readmissions per 1000 patients also were similar between the 2 groups (

 =.74, respectively). Average spending per member per month was approximately 4% higher in the intervention cohort than in the comparison cohort (

present the cumulative percent differences in actual admissions, readmissions, and total spending for PHN members versus the expected outcomes if PHN had not been implemented. As described previously, these expected outcomes were calculated by setting the time-dependent PHN indicator variable to zero in each multivariable regression model while keeping all other covariates constant. Outcomes were expressed as estimated effects with bootstrapped 95% CIs and as P values. The PHN model wa associated with a total cumulative reduction of 56 admissions per 1000 members per year (18%; 95% CI, −30% to −5%; 

 <.01). The PHN model also was associated with a

cumulative effect of 21 fewer readmissions per 1000 members per year (−36%; 95% CI, −55% to −3%; 

 = .02). Finally, the regression model estimated that the PHN model reduced cumulative total spending by 7%, but this difference did not reach significance (95% CI, −18% to 5%; 

 = .21). Results were qualitatively similar if all non-GHS clinic sites, rather than the propensity-matched comparison group, were used as a control cohort (data not shown).

Introduction of a medical home care delivery model was associated with a significant reduction in hospital admissions and readmissions for a population of Medicare Advantage enrollees. Our findings present a contrast to the recently published results of the Medicare Health Support demonstration, a set of parallel, randomized controlled trials of traditional disease management delivered by third parties to disease-specific populations.

 Despite targeting sicker individuals, participating programs had little effect on healthcare utilization or spending. We hypothesize that the comparative success of the PHN model was partly due to its ability to leverage existing physician—patient and interprovider relationships to fundamentally change the way care is delivered rather than work outside the system to improve care.

Because the PHN model is a complex intervention, it is difficult to ascertain which elements are responsible for specific improvements in care. Our findings, coupled with qualitative observations, however, highlight the importance of placing nurse case managers directly into the practices and arming them with data and analytical capabilities. With timely information on emergency department (ED) and inpatient use, case managers are able to manage transitions of care to ensure early postdischarge follow-up and medication reconciliation. In addition, proactive identification of at-risk individuals provides an opportunity to use patient-specific action plans to implement timely interventions for acute exacerbations of chronic illnesses.

Our lack of findings on per member per month spending likely was due at least in part to the sample size and duration of the study, coupled with the skewed distribution of the healthcare spending data. We note that based on its own actuarial analysis, GHP found that the PHN practices did generate savings and triggered incentive payments under the quality-based shared savings incentive system.

Based on our understanding of the initial focus of PHN practices and the policy importance of understanding the impact on resource use, we focused the analyses on hospital utilization and total costs. In future work, it will be important to examine a broader scope of process and outcome measures, including ED visits, clinical quality measures, physician and nurse professional satisfaction, and patient experience. Tracking this larger set of outcomes will not only allow us to better answer questions of sustainability but also permit refinement of the model.

These conclusions are tempered by several study limitations. First, our measure of medical spending excluded the cost of prescription drugs. There are concerns that improved care coordination may increase the cost of prescription drugs, thereby decreasing or eliminating medical services savings.

 However, a separate analysis of the changes in drug expense over time for both groups of practices demonstrated no differential impact or erosion of savings in the PHN sites. Second, the PHN model is situated in an integrated payer—provider environment (ie, the payer and provider are part of the same corporate entity) with long-standing use of an ambulatory EHR, in a Medicare population with high baseline spending and relatively little patient turnover. These factors almost surely contributed to PHN’s success and may therefore limit generalizability to other settings. The PHN model, however, has subsequently been introduced into non-GHS practices. Moreover, implementation experience to date suggests that the key components of the PHN model are on-site case management, the use of population data, and the shared savings incentives, all of which could be implemented outside of an integrated delivery model. Third, as described above, while the PHN model is aligned with the NCQA PPC-PCMH standards, it also includes other components. In particular, PHNimplementation likely differs from many other medical home efforts that may not include robust case management programs, attention to care delivered outside of the PCP office, shared savings reimbursement, or direct health plan support. Although advocates of the medical home will see these findings as general support for that model, attention should be paid to the specific elements of the intervention and the limits to generalizability noted above. Finally, although every effort was made to account for secular trends and confounding factors, our study was observational in nature and the usual caveats with regard to causal inference apply.

The strengths of the study design also are significant and include the fact that we were able to analyze healthcare spending and utilization for an entire population before and after the intervention alongside a well-matched comparison group. This approach allowed for relatively robust causal inference while minimizing the potential confounding effects of regression to the mean and survivorship biases.

A patient-centered medical home with intensive case management and a payer partner can significantly improve hospital utilization and may decrease total medical costs for a Medicare population.


Steven B. Pierdon, MD

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