Ravi Chawla, MBA

The impact of primary care practices adopting the patient-centered medical home (PCMH) model is analyzed by comparing per member per month (PMPM) costs and utilization among commercial HMO members with chronic illnesses in PCMH and non-PCHM practices in the Philadelphia area. Transforming primary care practices to conform to the PCMH model has shown early promise in reducing costs and improving outcomes, and chronically ill patients’ frequent contact with the healthcare system and costly care make them ideal targets for such health system reforms.

The impact of the PCMH model on PMPM costs was analyzed using a generalized linear regression model to adjust for age, gender, and baseline cost. The impact of the PCMH model on utilization per 1000 rates was analyzed with the Poisson regression model, adjusting for baseline differences in age, gender, and risk score.

After accounting for differences at baseline, PCMH practices achieved lower total, inpatient, and specialist PMPM costs, as well as lower relative utilization of hospital admissions and specialist visits.

These findings suggest that policy makers should maintain or expand incentives to adopt PCMH reforms and that targeting chronically ill patients may be the most effective way to leverage the benefits of PCMH adoption.

Chronically ill patients enrolled in nonpediatric primary care practices that adopted the patient-centered medical home (PCMH) model had lower total, inpatient, and specialist per member per month costs compared with non-PCMH practices after adjusting for baseline characteristics.

		PCMH cost reductions appear to have been driven by lower utilization rates of hospital admissions and specialist visits.

		Inpatient admissions per 1000 patients were significantly lower for chronically ill patients in PCMH practices in each of the 3 program years analyzed.

Despite extensive evidence that quality primary care has the effect of improving health and controlling costs,

 the field is described as facing a crisis. One oft-cited source of the problem is the current fee-for-service reimbursement system, which does not adequately compensate the “cognitive” services that lie at the core of primary care.

The American College of Physicians (ACP) position paper distinguishes “cognitive” from procedural-driven services, and refers to the evaluation and management of patients by primary care physicians.

 The fee-for-service reimbursement model is credited in part with driving medical school graduates to enter better compensated specialty fields, creating the potential for a shortage in primary care practitioners.

Other concerned voices note that beyond the debate about an adequate supply of doctors, there is a need for primary care practitioners to take a more proactive role in the coordination of care across multiple sites and providers.

Poor coordination of care, as can occur when a patient lacks a usual source of care, has been shown to lead to increased costs, diminished care quality, reduction of access to preventive services, and increased usage of high-intensity care.

This relationship is of particular concern for the treatment of chronically ill patients. Primary care doctors are encountering increasingly complex patients, with the average family practice office encounter addressing 2.7 medical problems.

This complexity has very real implications for costs in the healthcare system: one study found that nearly all of the cost growth from 1987 to 2002 occurred among patients being treated for 5 or more conditions.

 Another noted that among Medicare beneficiaries, 10% of patients—typically those with multiple chronic illnesses that require numerous prescription medications, and who experience frequent hospitalization&mdash;account for 70% of healthcare costs.

The presence of chronic illness makes accurate, readily accessible medical records essential to providing quality care, and effective management may require ongoing patient contact, even outside of the doctor’s office.

The patient-centered medical home (PCMH) model offers a promising alternative to the current paradigm in primary care. The concept of the “medical home” originated in 1967 with the American Academy of Pediatrics (AAP), was initially presented as a model for addressing special healthcare needs among children. The medical home focuses on a team-based approach to primary care whereby a physician-leader coordinates care received from other providers across multiple sites and specialties. This methodology encourages increased access in terms of expanded practice hours and enabling new ways to engage with patients.

Information technology, such as electronic health records, is part of this approach, which can facilitate better identification of patient needs, more effective care management, and more efficient tracking of health outcomes. Incentivebased payment structures to better reward evaluation and management are also included. This concept gained wider attention following the 2007 statement of Joint Principles by the American Academy of Family Physicians, AAP, ACP, and American Osteopathic Association, which offered a unified vision of the central components of the PCMH model.

Some preliminary research has shown promising, though mixed, early results for practices adopting the PCMH model in terms of patient experience

 and in reducing utilization of high-cost medical interventions, such as emergency department (ED) visits and hospitalizations.

 This was corroborated by a recent review of studies of the PCMH model which showed that while individual programs have yielded successes, the overall evidence is more mixed. Adoption of the PCMH model appears to have positive effects on patient experience and provision of preventive services to patients, but its effect on ED utilization, an important driver of healthcare costs, was limited to older patients. Moreover, this review found no consistent reduction in hospital admissions or overall expenditures for patients in practices that adopted the PCMH model.

Given inconsistent definitions of the “medical home,"

 this review and others point to the difficulty in assessing the impact of the PCMH model. As much of the work to date has been inconclusive, this analysis will focus on the relevant subpopulation of chronically ill patients for whom the PCMH model would be most likely to yield benefits.

This paper aims to contribute to the growing body of literature and evidence on the PCMH model. Previous work, such as the 2010 Group Health Cooperative (GHC) study by Reid et al

 study on the Pennsylvania Chronic Care Initiative (PACCI) have evaluated the effects of the medical home on costs and utilization for all patients. This study focuses on the PCMH effect on patients with chronic illness(es). This subpopulation requires complex care and frequent contact with the healthcare system, which makes them more likely to benefit from improved coordination of care and enhanced access and communication.

 We hypothesize that implementing PCMH practices will result in lower total, inpatient, and specialist per member per month (PMPM) costs, and will lower relative utilization of hospital admissions and specialist visits over the 3-year study period.

This study evaluates early implementation of the PCMH model using data collected from the PACCI. This initiative was convened as a collaborative effort by the state’s largest commercial health plans (led by Independence Blue Cross), all 3 Medicaid managed care plans, and 32 medical practices, and was organized by the Governor’s Office of Health Care Reform to promote the adoption of the PCMH model and improve the quality of primary care for patients with chronic illnesses. Engagement by multiple stakeholders allowed for the appropriate resources, scale, and support to be provided, creating a framework for education, training, and data submission, as well as allowing for consistency in monitoring outcomes across the program.

To facilitate transition to the PCMH model, practices received supplemental financial incentives and were required to participate in a Learning Collaborative based on Wagner’s Chronic Care Model,

 working toward recognition from the National Committee for Quality Assurance (NCQA) as a PCMH by the end of the first year of participation. Each practice enrolled in the initiative was required to a) have at least 1 physician, plus a practice support leader, in attendance at each of 7 full-day sessions and to b) participate in weekly meetings. Furthermore, practices were paired with practice coaches responsible for providing additional support and training.

Data from practices in the PACCI pilot were used to determine the program’s effect on patients with chronic illness(es), a subpopulation requiring complex care. This was a longitudinal observational study conducted to assess the impact of adopting the PCMH as a model for improving primary care, focusing on PMPM healthcare costs and utilization per 1000 patients over 3 years of follow-up data.

A cohort of chronically ill members—defined as patients having asthma, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, diabetes, and/or hypertension&mdash;was created from administrative medical claims in the baseline year, 2008. Chronic illness was identified using 

International Classification of Diseases, Ninth Revision, Clinical Modification 

) codes from medical claims data. Additionally, all patients must have been continuously enrolled in the same practice from 2008 through 2011. We also limited our analysis to practices located within Philadelphia city limits in order to limit the effect of urban-suburban disparities on our findings

 and because the majority of initial PCMH practices were located within the city.

Defining the Patient-Centered Medical Home

To provide an operational definition of a “medical home,” the PACCI relied on the definition provided by the NCQA as of 2008.26 Practices were assigned PCMH status if they received at least Level 1 NCQA recognition in 2009 and maintained recognition through at least 2011. Level 1 recognition required that practices meet 5 of 10 possible “must pass” criteria and accumulate 25 total “points.” The complete list of potential PCMH improvements recognized by the NCQA appears in

. Practices designated “non-PCMH” did not join the PCMH program at any point during years 2008 to 2011 and were used as controls. The resulting study population consisted of 2674 patients enrolled in 11 PCMH practices and 19,546 patients in 393 non-PCMH practices, all of which were located within Philadelphia city limits.

This study was conducted as a longitudinal observational study to best assess the impact of adopting the PCMH model as a vehicle for improving primary care of chronically ill members. The outcomes of interest were PMPM healthcare utilization and costs. Costs were reported separately as inpatient, ED, specialist, and outpatient care costs, as well total PMPM costs. Utilization was reported in terms of inpatient admissions, ED visits, specialist visits, and outpatient visits per 1000 patients over 3 years of follow-up. Data on costs and utilization were extracted from medical claims; patient-level covariates of age and gender were recorded from enrollment data. To control for differences between the PCMH and non-PCMH groups at baseline, patient-level covariates (age, gender, and costs at baseline) were included in regression models.

At baseline, a number of differences were observed between case and control practices in terms of patient age and risk profile, socioeconomic characteristics, and comorbid conditions, which translated into significant differences in terms of costs and utilization even before the introduction of PCMH changes in the cases. The Figure presents some of these baseline comparisons. Patients in practices adopting PCMH reforms were generally younger (aged 39.2 years vs 45.4 years; 

 <.0001), but had lower estimated risk scores (1.61 vs 1.81; 

<.05) and significantly different case mix in terms of illness profiles.

To control for variation at baseline, statistical models were employed to generate adjusted figures for the program effects on costs and utilization for each of the follow-up years. PMPM costs were adjusted using a generalized regression model, which controlled for age, gender, and costs at baseline. For utilization per 1000 patients, Poisson regression models were used to estimate utilization counts after adjustment for baseline differences in age, gender, and risk score. These methods parallel those used in the GHC study. Risk scores were calculated using Verisk Health DxCG Risk Solutions version 3.

 a commercial population risk adjustment package. The risk score used patient age, gender, and claims information from all medical encounter and enrollment data to estimate annual total medical resource use. Once adjusted cost and utilization figures were imputed for each of the program years, differences between PCMH practices and controls were compared using the t test for dependent means, using Satterthwaite or unequal variances. The results appear in 

Controlling for baseline differences, PCMH practices maintained significantly lower utilization for hospital admissions (

<.01) for each year in the follow-up period. PCMH practices also saw 0.3 fewer admissions per patient in 2009, and 0.2 fewer admissions per patient in both 2010 and 2011. Specialist visits were reduced by 12.3 visits per 1000 patients in 2009, and by more than 10 visits per 1000 patients in 2010 and 2011. However, PCMH practices observed significantly higher utilization in ED and outpatient visits, though the adjusted difference in ED visits shrank over the period from 2009 to 2011 (Table 2).

Predictably, the observed differences in utilization translated into lower adjusted total PMPM costs. Total costs were significantly lower in PCMH practices during all 3 follow-up years (

<.05). Relative to baseline, overall PMPM costs were $16.50 lower in 2009, a difference of 5.5%. Total PMPM costs were $13.00, and $13.70 lower in subsequent years as well. This reduction was driven by significantly lower inpatient (

<.0001) costs among PCMH practices over all 3 program years. The relative reduction in specialist costs was particularly pronounced: in 2009, adjusted costs for PCMH were 17.5% lower than those in non-PCMH practices. While significant relative increases in ED PMPM costs (

<.0001) partially offset these reductions, PCMH practices did not experience a significant increase in outpatient costs despite the observed increase in outpatient utilization (Table 3).

This study adds to the growing body of literature, including prior work by the GHC team, which indicates that the initial investments required for primary care practices to adopt PCMH reforms can yield successes in controlling growth in cost and utilization of high-cost, high-intensity medical services.14 GHC is a nonprofit health insurance and care delivery system based in Seattle. That study looked at cost and utilization results between 12 and 21 months after implementation in an attempt to track progress in quality improvement, in creating a sustainable work environment, and in lowering costs. The data source on utilization and costs was the GHC’s costing system, which allocates use and costs for all services provided at GHC facilities and from external claims. Utilization and costs from 7018 continuously enrolled adults at the prototype clinic were compared with those of 200,970 adults enrolled at other clinics in the Puget Sound area.

Three particularly important differences exist between the work by the GHC and this study: this study explores only cost and utilization, without commenting on patient or provider satisfaction; the end points do not match exactly (the GHC study measured cost and utilization after 12, 18, and 21 months, whereas the Pennsylvania study measured results annually over 3 years). Most importantly, this study focuses on chronically ill patients, whereas GHC analyzed all patients. Data comparing the GHC study results with this study’s results appear in 

. Variables discussed include differences in inpatient admissions, ambulatory care utilization, and ED and specialty care costs and utilization. For this study, there is also discussion on possible reasons for gaps in cost and utilization between PCMH and non-PCMH practices.

Regarding inpatient admissions, the GHC study showed that differences in all-cause inpatient admissions did not reach significance until the 21-month follow-up, whereas the differences in inpatient admissions per 1000 patients were significant in all 3 program years of the Pennsylvania study. This gap between the 2 studies, in terms of utilization, was also reflected in PMPM costs: the GHC study found a consistent relative reduction of more than $10 PMPM in total costs, but this did not meet statistical significance at α = .05. By comparison, we found statistically significant relative reductions amounting to $16.50 PMPM in the first program year and $13.00 and $13.70 PMPM in subsequent years.

In the case of ambulatory care—sensitive treatment, the medical home practices in the GHC study were able to achieve significant relative reductions in admissions when the analysis was limited to inpatient admissions for ambulatory care–sensitive conditions (

<.001). By controlling for the presence of chronic illness—either by limiting the population to such patients, as in the case of the Pennsylvania study, or by framing the outcomes to focus only on treatment for chronic illness&mdash;significant differences become apparent in the outcomes achieved by PCMH and non-PCMH practices. This suggests that, with respect to costs and utilization, the PCMH may lead to meaningful improvements only when applied to relevant subpopulations, such as chronically ill patients.

In the Pennsylvania study, the increased utilization of outpatient care is actually suggestive of further success for the PCMH model. By improving coordination of care, doctors may have been appropriately directing their patients to lower-cost, lower-intensity services, which acted as substitutes for costlier hospital admissions and other services. This finding is especially promising, given that the increased utilization in terms of visits per 1000 patients did not lead to a significant increase in PMPM costs. Similar findings of increased primary care intensity leading to lower costs via substitution have been reported elsewhere.

Also, Wang et al report using a case-control matched longitudinal cohort study in patients with type 1 and type 2 diabetes mellitus, and found that adoption of the PCMH reduced overall medical cost for diabetic members by 21% in the first year.29 This result largely reflects a drop in inpatient costs, which fell by 44%.

The 2 studies also report disparate findings concerning costs and utilization of specialty care. Patients enrolled in the GHC study’s PCMH practices exhibited fewer ED visits and more specialty care visits, whereas the opposite was true of the Pennsylvania PCMH patients. This difference may be attributable in part to the Pennsylvania study’s focus on chronically ill patients. For example, increasing focus on patient needs and satisfaction may identify unmet healthcare needs across all patients, whereas among patients with chronic illness who are likely to have frequent and ongoing contact with the healthcare system, improved disease management may reduce the need for specialized care. Alternatively, physicians in the Pennsylvania managed care practices may be under-referring patients to specialist services, but these differences in specialty care may simply reflect regional variation in practice.

Finally, with respect to the Pennsylvania study, it is worth noting that in most cases, the gap in cost and utilization between PCMH and non-PCMH practices was seen to decrease over the 3 follow-up years. Although the control practices were required to have completed all 3 program years without formally receiving NCQA recognition, it is possible that some of the innovations adopted as a package by PCMH practices were embraced piecemeal by non-PCMH practices during follow-up years, leading to a gradual flattening of the results across practice type. One possible explanation stems from the fact that non-PCMH practices tended to be in the medium- / average-practice size category, whereas PCMH practices were typically larger group practices. The scale advantages of larger practices might have made information technology or staffing improvements more affordable, with average-size practices lagging behind on the trend. Previous research has indicated that larger practice size is positively correlated with the presence of elements of the PCMH infrastructure.

Additionally, because the PCMH practices began adopting elements of the NCQA guidelines before obtaining formal recognition, the process of improving outcomes may have started ahead of the medical home pilot program. This self-selection bias may have reduced the apparent impact of PCMH adoption.

The PCMH impact may extend to smaller practices as well. A recent PCMH study that focused on small, independent primary care practices in Rhode Island found that 5 pilot practices increased their NCQA recognition score from 42 to 90 points over a 2-year study period.

 There was a significantly lower rate of ambulatory care— sensitive ED visits in the pilot practices compared with 34 nonpilot practices.

Both the GHC experience and the results of the PACCI study suggest that the introduction of medical home innovations into primary care practices may help to curb utilization of high-intensity medical services as well as overall cost growth, despite initial investments in staffing changes and technology infrastructure. The differences in results observed when analyzing the primary care population as a whole, as opposed to the chronically ill cohort tracked in the Pennsylvania study, stresses the importance of quality risk stratification of patients, not only for research purposes, but also for making fair assessments of practice quality and for targeting of resources by medical practices.

A study by the Commonwealth Fund has suggested that with the exception of information technology infrastructure, incremental costs do not rise with increasing “medical homeness” of primary care practices.

 However, both the GHC medical home practices and those in the Pennsylvania pilot made staffing changes as part of their transition, adding physicians and nurse practitioners to allow enhanced patient contact.

With this in mind, financial incentives to defray the costs of introducing care managers, electronic medical records, registries, and related improvements may be particularly crucial to promoting adoption of the medical home as a model. Cost savings may eventually make the PCMH self-sustaining, with cost savings redirected to improving compensation for primary care doctors5 and to other practice improvements.

Implications for Future Research and Limitations

Although we employed the same rigorous adjustments used by the GHC study, some differences at baseline were not specifically accounted for in the regression adjustments used. For example, while all patients in the Pennsylvania study satisfied the criterion of displaying some chronic illness or illnesses, PCMH and non-PCMH practices may have had a different case mix. Though patient-level variation was accounted for, at least in part, by use of the DxCG risk score in the regression adjustments, future research might consider identifying the most useful practice changes for specified conditions or assessing whether practice case mix drives the decision to adopt PCMH innovations.

Furthermore, observed differences may be driven in part by self-selection on the part of primary care practices; practices with a trajectory toward electronic health records, enhanced case management, and so forth may have been more likely to seek NCQA certification early in the process. Future work based on finer practice-level variation could account for these baseline differences and could also assess the relative impact of higher levels of NCQA certification and within-level variation in order to distinguish the contributions of various components of the PCMH model to improving care and controlling costs and utilization. One promising method for follow-up studies would be to pair difference-in-difference regression analysis with case-control matching in order to get equal samples from PCMH and non-PCMH practices, and to achieve improved control of practice-level differences such as size and affiliation.

This study joins a growing body of research which suggests that the PCMH model offers a promising mechanism for addressing primary care’s pressing concerns with respect to delivering quality care and facing potentially unsustainable cost growth. By supporting patient self-management and improving coordination of care, practices adopting the PCMH model were able to achieve relative decreases in cost and utilization at the highest levels of care intensity (inpatient admissions) by chronically ill patients. In light of these findings, policy makers and healthcare thought leaders should continue to emphasize the PCMH as a vehicle for improving primary care, particularly for patients with complex, chronic conditions.

The authors thank Susannah Higgins, Manu Tyagi, Sara Fritz, Qiuyan Cindy Wang, Aaron Smith-McLallen, and Pam Eckardt of Independence Blue Cross, Philadelphia, for their assistance in data collection, analysis, and review. The authors acknowledge the assistance of CTI Clinical Trial & Consulting Services, Inc, Cincinnati, a paid consultant to Independence Blue Cross, for their technical support and editing during the manuscript preparation.

 Independence Blue Cross (JN, RC, CMC, RLS, SN), Philadelphia, PA.

This study was funded by Independence Blue Cross, an independent licensee of the Blue Cross and Blue Shield Association.

Articles

A longitudinal case-control design was used to evaluate the effects of the patient-centered medical home model on medical costs and utilization among chronically ill patients.

Medical Homes: Cost Effects of Utilization by Chronically Ill Patients

 Evaluate the effects of the patient-centered medical home (PCMH) model on medical costs and utilization in the nonpediatric population, particularly among high-risk patients.

 Longitudinal case-control design, comparing per member per month (PMPM) cost and utilization per 1000 patients for members enrolled in PCMH and non-PCMH practices from 2009 to 2011.

 Commercial health maintenance organization members in nonpediatric practices that adopted the PCMH model in 2009 were matched to patients in nonpediatric practices that did not adopt the model until 2011 or later. Propensity score matching was used to identify a pool of similar controls, and difference-in-differences regression analysis was used to compare PCMH and non-PCMH patients relative to baseline. Analysis was conducted using the complete pool of matched patients (N = 6940 cases and 6940 controls), then using the 10% of patients with the highest DxCG risk scores (N = 654 cases and 734 controls).

 There were no significant cost or utilization differences for the overall population. Total cost decreased significantly more for the PCMH group than for controls in the high-risk group in years 1 and 2 (reductions of $107 and $75 PMPM), driven by lower inpatient costs. The PCMH group experienced a significantly greater reduction in inpatient admissions in all 3 years (61, 48, and 94 hospitalizations per 1000).

 PCMH practices had significantly reduced costs and utilization for the highest risk patients, particularly with respect to inpatient care. As high-risk members represent a high-cost group, the most benefit can be gained by targeting these members.

High-risk patients enrolled in nonpediatric primary care practices which adopted the patient-centered medical home (PCMH) model had significantly lower per member per month medical costs and utilization per 1000 members compared with non- PCMH practices after adjusting for baseline characteristics.

		Costs and utilization did not significantly differ between PCMH and non-PCMH groups among all patients, suggesting that the benefits of the PCMH model are concentrated among high-risk, high-cost patients.

		PMCH cost reductions appear to have been driven by lower rates of hospitalization, and total costs fell even though utilization of specialist care saw significant increases in the first 2 years.

The patient-centered medical home (PCMH) has been advanced as a promising framework for transforming primary care. In 2007, the American Academy of Family Physicians, American Academy of Pediatrics, American College of Physicians, and American Osteopathic Association issued the “Joint Principles of the Patient-Centered Medical Home,” which outlined the PCMH model. The medical home model emphasizes a team-based approach to primary care, in which a physician-leader coordinates care by other providers across multiple sites and specialties. It encourages increased access, both in terms of expanding practice hours and opening new channels of communication with patients. Organizations such as the Patient-Centered Primary Care Collaborative have initiated numerous pilot programs aimed at studying the impact of PCMH adoption,

 and the PCMH model was written into the Patient Protection and Affordable Care Act of 2010 as an area for study.

A number of previous studies have shown early promise for the PCMH model as a vehicle for controlling costs and improving the quality of healthcare delivered by primary care practices,

 including for targeting subpopulations such as children with special health needs.

 However, reviews often point to the incomplete nature of this work, citing methodological concerns,

 insufficient time for practices to implement reforms, and inadequate policy support beyond the level of individual practices.

 This study aims to contribute to this literature by comparing the effects of adopting the PCMH model on the healthcare cost and utilization in the nonpediatric population, using propensity score matching in order to reduce variability in the PCMH and non- PCMH groups studied. Additionally, the analysis employs difference-indifferences regression analysis in order to further control for remaining differences in patients’ characteristics as well as cost and utilization at baseline.

This study aims to assess the impact of PCMH adoption on the patients identified as having the greatest health risks. While the Joint Principles envision the PCMH model as being applicable to all patients, other pilots have targeted only high-risk patients with complex needs.

 The high cost of care associated with relatively few individuals makes such targeting a potentially powerful mechanism: one study noted that virtually all of the growth in expenditures for Medicare over the period from 1987 to 2002 occurred among beneficiaries with 5 or more chronic illnesses

; another paper noted that only 10% of Medicare beneficiaries accounted for 70% of healthcare costs.

 In order to evaluate whether the benefits of medical homes are limited to such high-risk, high-cost subpopulations, this study reports cost and utilization comparisons 2 ways: first, for all matched patients; and subsequently, limited to the patients with risk scores in the 90th percentile or above for the study population.

The majority of the PCMH practices (15/17) included in this study were part of Pennsylvania’s Chronic Care Initiative, a multi-stakeholder effort to improve primary care, which launched a 3-year pilot project that started in May 2008 in the Philadelphia area. The largest commercial health plans— led by Independence Blue Cross, all 3 Medicaid managed care plans, and 32 practices caring for 250,000 members&mdash;were convened by the governor to establish the first of several regional PCMH programs. Practices received significant additional payments to participate in a learning collaborative which supported practice transformation and provided the care management and coordination services required by patients with chronic conditions. The learning collaborative was based on Wagner’s Chronic Care Model

 and led by the MacColl Institute, with Edward H. Wagner, MD, MPH, facilitating the first session.

Practices were required to send a physician and a practice support leader to 7 days of sessions. Practice coaches provided ongoing support and education on installing and using a registry to track and monitor patients and implementing a team-based approach involving care managers, health educators, and other nonphysician healthcare personnel to promote self-management skills and patient engagement.

 Additionally, clinical guidelines were used to create evidence-based standing orders to optimize patient care. Practices used their electronic systems to identify and stratify patients in need of care management, selfmanagement support, and goal monitoring. Patient engagement was promoted through the use of report cards, on which concrete data from each visit are logged for patients so they can monitor their progress.

To provide a definition of the PCMH model, the authors employed the recognition standards published by the National Committee for Quality Assurance (NCQA). These standards list 9 categories encompassing 30 possible practice improvements (including 10 “must pass” items), each of which has a point value. Three levels of certification are possible, with each defined by a minimum number of “must pass” items achieved and specified point totals. The full list of recognition criteria appears in 

 Practices qualified for PCMH status by achieving NCQA Level 1 recognition or higher during 2009, and the control group was limited to practices which did not receive recognition until 2011 or later.

This study employs a longitudinal, case-control design to compare members of commercial health maintenance organizations (HMOs) enrolled in nonpediatric PCMH and non-PCMH practices with respect to healthcare costs and utilization during the period from 2009 to 2011. All figures are reported first for the full matched cohort of cases and controls (N = 6940 cases and 6940 controls), and then using the 10% of patients with highest risk scores, drawn from the pooled population of cases and controls (N = 654 cases and 734 controls).

DxCG risk score refers to the concurrent medical risk score calculated using the commercial risk adjustment model developed by Verisk Health DxCG Risk Solutions, version 3.1 (Verisk Health, Cary, North Carolina).

 The DxCG models use linear, additive formulas obtained from ordinary least squares (OLS) regressions to combine expenses associated with clinical groupings (Condition Categories and Rx- Groups) and demographic factors (age, gender) to generate predictions. Hierarchies are imposed on Condition Categories before regression models are run to decrease their sensitivity to variations in coding and to strengthen the predictive power of the models across a wide range of benefit designs and pricing arrangements.

 The concurrent risk score in this analysis used each individual’s age, gender, and claims information from all medical encounters and enrollment data as inputs to predict annual total medical resource use for the patient.

Patients evaluated were enrolled in 17 PCMH practices, 15 of which were participating in the Pennsylvania Chronic Care Initiative pilot (cases), or 1 of 103 non-PCMH comparison practices in PA (controls). The high-risk patients were slightly more concentrated, appearing in only 14 PCMH practices (12 of which were part of the pilot) and 69 non-PCMH practices. Costs were assessed as per member per month (PMPM) costs and utilization was measured as encounters per 1000 patients per year.

Criteria In order to qualify for inclusion, each member must have been enrolled with the same primary care physician for at least 3 months and have been associated consistently with a PCMH or non-PCMH practice for 6 months or more during each year of the study period, including baseline (2008). Only nonpediatric practices located in Pennsylvania were included in the analysis. Pediatric practices were excluded to reduce the variability in the age of members in the study and minimize the impact of any potential unobserved differences between pediatric practices and nonpediatric practices. Patients with end-stage renal disease or extremely high medical costs (

$100,000 per year) were excluded to limit the impact of extreme outliers on the cost analysis.

The pool of eligible controls was limited to later adopters of the PCMH model in order to reduce potential practice self-selection issues. Patients enrolled in control practices that received NCQA recognition in 2011 were included in analyses for the first 2 program years, but were removed from the final year’s analysis to avoid contaminating the non-PCMH group with late adopters, along with their matched cases. This reduced the numbers included in program year 3 to 1141 cases and 1141 controls in the overall analysis, and 109 cases and 131 controls in the highrisk subgroup. While the guidelines remained largely similar from 2008 to 2011, some changes were made to the NCQA requirements, and a crosswalk table detailing the differences is available from the NCQA.

 An attrition diagram detailing all exclusion steps appears in 

Propensity score matching was used to select a sample of controls which were similar to the PCMH cases with respect to both practice- and patient-level demographics and characteristics. The propensity score included practice size, practice location, age, gender, DxCG risk score, chronic conditions (including asthma, diabetes, congestive heart failure, chronic obstructive pulmonary disease, and coronary artery disease), and median income for member’s zip code of residence. Matching successfully controlled for the significant differences at baseline between the group of cases and pool of potential controls, both for the overall comparison and among the cohort of highest risk patients (

Differences in PMPM cost and utilization per 1000 patients between PCMH and non-PCMH practices for the 3 follow-up years were compared using regression analysis. This method provides an estimate of the differences between treatment and control groups before and after treatment. Since the members included in this study are the same in each time period (ie, panel data), the difference-in-differences model can be simplified and is more statistically powerful. The difference between baseline and each time period can be modeled as follows (separate model for each time period difference):

is the difference between the repeated outcome measure for each observation, δ is the effect of time on all units, X 

 is the treatment indicator, and β is the treatment effect.

 Cost and utilization regressions controlled for concurrent risk in the comparison year and having a chronic condition in the baseline year, factors that could influence both cost and utilization in the comparison year. For example, when comparing 2010 with baseline, the 2010 risk score was added to the model to adjust for any new diagnoses a member might have acquired in 2010. Presence of a chronic condition at baseline was used as a covariate because chronic members often continue to incur costs, or incur even higher costs, over time. In order to test for the treatment effect for just the high-risk members, a high-risk indicator was introduced as covariate (ie, as a main effect and interaction term with the treatment indicator) into the model using the whole population. Additionally, a random intercept term was used in the model to account for any practice-level effects.

When comparing utilization and PMPM cost for all matched cases and controls during the 3 follow-up years with respect to baseline characteristics, no difference-in-differences comparison reached significance at α = .1 (

). The regression results limited to the 10% of pooled cases and controls with the highest DxCG risk scores resulted in clear differences between PCMH and non-PCMH. PCMH practices achieved statistically significant decreases in utilization of inpatient medical services in all 3 program years, amounting to reductions of 61, 48, and 94 hospitalizations per 1000 patients. In the first 2 program years, these reductions were accompanied by statistically significant adjusted savings in inpatient costs ($115 PMPM in 2009 and $62 PMPM in 2010). The decline in utilization of inpatient hospital services contributed to lower total medical costs for patients enrolled in PCMH practices during the first 2 study years as well. Cases had an adjusted total savings of $107 PMPM in 2009, representing an 11.2% reduction from baseline. For 2010, the difference was $75 PMPM, corresponding to a 7.9% reduction.

While total medical costs decreased, there were areas in which high-risk patients in PCMH practices experienced increases in costs and utilization. This cohort saw a statistically significant increase in specialist visits in 2009, and corresponding increases in PMPM spending for specialist care in 2009 and 2010 (

This study offers particular methodological contributions to the PCMH literature. The approach of combining propensity score matching to create comparable case and control pools with difference-in-differences regression analysis to adjust for practice- and patient-level variation and baseline cost and utilization characteristics allows improved control for factors which might otherwise confound efforts to study the effects of PCMH adoption. Inclusion of a non-PCMH control group and assessing differences over time allows the analyses to control for trends which may have affected all practices, regardless of PCMH status. Along with controlling for practice size, excluding practices which transitioned to the PCMH model in 2010 helped to limit the impact of self-selection on results. Because adoption of the PCMH reforms does not occur instantaneously, some non-PCMH practices may have incorporated aspects of the medical home without having yet risen to the level of eligibility for NCQA recognition. Confining the control group to later adopters diminishes this issue.

The findings of this study add new texture to the existing PCMH literature. After controlling for baseline differences, no statistically significant differences between patients enrolled in PCMH and non-PCMH practices were observed. However, when looking at the patients with highest risk scores in the pool of matched patients and practices, PCMH model adoption was shown to lead to a significant relative reduction in total costs in years 1 and 2, and significantly lower numbers of inpatient admissions in all 3 years. This suggests that the average patient may not be the relevant unit of observation for evaluating the impact of PCMH adoption. Rather, high risk patients with multiple comorbidities are the most logical targets for interventions aimed at supporting self-management, conveying test results in a timely and clear fashion, and coordinating follow-up and specialist care. Researchers may miss cost and utilization improvements if they confine their analyses to the typical patient, since healthcare costs are primarily driven by relatively rare events concentrated in few individuals. For example, during the baseline year, all cases and controls had 73 and 78 admissions per 1000 patients, respectively; but among the high-risk pool, these numbers increased to 566 and 540.

The observation that relative cost reductions are driven by decrease in inpatient admissions among high-risk patients suggests that the PCMH model is having its intended effect— improvements to information technology permit physicians to better assess patient needs, and better coordination of care means that ongoing issues can be managed at lower levels of care intensity (self-management, primary care) rather than after manifesting as crises requiring hospitalization. Even the observation of increased spending for specialist care can be interpreted as being consistent with this conceptualization: as providers gain access to better quality information about patient needs through improved medical record-keeping and care is coordinated across multiple sites, the patients with the highest medical risk may be appropriately directed to more frequent contact with specialists. This could drive up the cost of 1 component of care, while the use of appropriate early interventions helps control costs overall.

Despite extensive controls through matching, regression analysis, and study design, some self-selection issues may persist. A number of the initial pool of control practices received NCQA recognition in 2011, meaning that there may have been some “wind up” time in the prior year for practices identified as controls. However, this should have had the effect of reducing the apparent difference between PCMH and non- PCMH practices for the 2010 analyses.

Additionally, other studies of the medical home model have focused on patient satisfaction and quality improvement measures, which are essential aspects of the PCMH model and central to providing exceptional primary care, but these attributes are not the focus of this study. Over a sufficient time frame, improved integration of care may lead to better outcomes for patients with lower levels of health risk as well, so ongoing assessments of PCMH adoption should be conducted with these considerations in mind.

While no differences were observed when assessing patients from the full distribution of risk scores, this study demonstrated that adoption of the PCMH model was associated with significantly reduced costs and utilization for those members at highest risk, particularly with respect to inpatient care. High-risk members are the most costly to health plans, so it logically follows that the most benefit can be gained by targeting these members with programs such as the medical home. When evaluating PCMH programs, it is important to focus both interventions and evaluations on relevant populations

 Independence Blue Cross, Philadelphia, PA (SH, RC, CC, RS, SN).

This study was funded by Independence Blue Cross, which is an independent licensee of the Blue Cross and Blue Shield Association. All of the authors were employed by the Independence Blue Cross during the course of the study.

A longitudinal case-control design was used to evaluate the effects of the patient-centered medical home model on medical costs and utilization among high-risk patients.

Ravi Chawla, MBA

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