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Association Between the Patient-Centered Medical Home and Healthcare Utilization
Rainu Kaushal, MD, MPH; Alison Edwards, MStat; and Lisa M. Kern, MD, MPH
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Association Between the Patient-Centered Medical Home and Healthcare Utilization

Rainu Kaushal, MD, MPH; Alison Edwards, MStat; and Lisa M. Kern, MD, MPH
Use of specialist visits decreased by patients whose primary care physicians transformed their practices into patient-centered medical homes, 1 year after medical home implementation.


Objectives: The patient-centered medical home (PCMH) model of primary care is being implemented widely, with unclear effects on healthcare utilization. How much any effect is driven by electronic health records (EHRs), a core component of PCMHs, is unknown. Our objective was to determine any association between the PCMH model and healthcare utilization and to isolate that effect from any by the EHR alone.

Study Design: We conducted a prospective cohort study (2008-2010) of 275 primary care physicians and 230,593 patients in the Hudson Valley, a multi-payer region in New York state with predominantly small practices.

Methods: We considered 3 groups: physicians who implemented Level III PCMHs in 2009, as per the National Committee for Quality Assurance, all of whom also used EHRs (n = 92); physicians using paper medical records (n = 119); and physicians using EHRs without the PCMH (n = 64). We used negative binomial regression to determine associations between study group and change over time for each of 7 utilization measures, adjusting for 10 physician characteristics.

Results: For every 100 patients whose physicians transformed to PCMHs, there were 21 fewer specialist visits over time compared with patients whose physicians used paper records (P = .03), and 22 fewer specialist visits over time compared with patients whose physicians used EHRs without the PCMH (P = .05). There were no significant differences over time in primary care visits, radiology tests, laboratory tests, emergency department visits, admissions, or readmissions.

Conclusions: The PCMH was associated with a significant decrease in the rate of specialist visits, the most expensive type of ambulatory visit, 1 year after PCMH implementation.

Am J Manag Care. 2015;21(5):378-386

Take-Away Points
The patient-centered medical home (PCMH) is being implemented widely, with unclear effects on healthcare utilization. How much any effect is driven by electronic health records (EHRs), a core component of PCMHs, is unknown.
  • We considered 3 groups of primary care physicians in a multi-payer, multi-provider community: physicians who implemented the PCMH (all of whom also used EHRs), physicians who used paper medical records, and physicians who used EHRs without the PCMH.
  • The PCMH group experienced a 6% decrease in specialist visits compared with each of the other groups, but no change in other healthcare utilization, 1 year after PCMH transformation.

The US healthcare system is struggling with unsustainable costs and suboptimal quality.1 Experiments with transformative delivery of healthcare, combined with payment reform, are under way across the country. The patient-centered medical home (PCMH) is a model of primary care that seeks to transform healthcare delivery by improving coordination of care, leveraging the capabilities of electronic health records (EHRs), and restructuring physician reimbursement. This model aims to decrease healthcare costs while improving quality, physician experience, and patient satisfaction.

Whether the PCMH is achieving its goal of controlling healthcare costs is not clear,2-4 and previous studies have had mixed results. For example, several studies have found reductions in some but not all of the healthcare utilization outcomes considered,5-12 with reductions in emergency department (ED) visits being the most common finding.5,6,10,12 However, other studies have found no association between the PCMH and healthcare utilization or cost.13 Previous studies have typically been conducted in integrated delivery systems10,11 or with single health plans,5-11 and they do not generally reflect the more common multi-payer, small-practice, community-based setting.2

If the PCMH model controls costs, the extent to which this is actually attributable to EHRs is unknown, as EHRs alone have been shown to affect healthcare delivery by im-proving quality and safety, for example.14,15 Whether any financial effect of the PCHM model is driven by EHRs within the PCMHs or by the added transformation that the PCMH model entails such as changes to the roles and responsibilities of different physicians and staff members—has important implications for clinical practice and health policy. Studies that consider the PCMH model as a whole, without separating the effects of EHRs, might misattribute financial effects to one intervention or the other.

In a multi-payer community, we studied the effects of the PCMH on changes over time in 7 healthcare utilization outcomes: primary care visits, specialist visits, radiology and other diagnostic tests, laboratory tests, ED visits, hospital admissions, and 30-day readmissions. This kind of comprehensive assessment enables not only measurement of financial effects within 1 type of healthcare utilization, but also potential cost-shifting across types of utilization. We primarily compared PCMH practices with non-PCMH practices (either paper-based or EHR practices) and then further compared the PCMH with the EHR alone.



We conducted a longitudinal cohort study of primary care physicians in the Hudson Valley region of New York over 3 years (2008-2010). The PCMH was implemented in 2009; therefore, 2008 represents care prior to implemen-tation, and 2010 represents care approximately 1 year post implementation. The Institutional Review Boards of Weill Cornell Medical College and Kingston Hospital approved the protocol. The study was registered with the National Institutes of Health Clinical Trials Registry (NCT00793065).


Setting and Context

The Hudson Valley consists of the 7 counties immedi-ately north of New York City. This study evaluates part of the Hudson Valley Initiative, which seeks to transform healthcare delivery through health information technology, practice transformation, and value-based purchasing.16 This initiative is the combined work of THINC,17 a nonprofit, coalition-building organization; the Taconic Independent Practice Association (IPA),18 a nonprofit physician organization; and MedAllies,19 a for-profit health information services provider.

For this initiative, THINC convened 6 health plans (3 national commercial plans: Aetna, UnitedHealthcare, and Empire Blue Cross Blue Shield; 2 regional commercial plans: MVP Healthcare and Capital District Physicians’ Health Plan; and 1 regional Medicaid health maintenance organization, Hudson Health Plan), which together cover approximately 70% of the community’s commercially insured population. The health plans agreed to provide financial incentives, which amounted to $2 to $10 per patient per month, to practices that implemented Level III PCMHs, as defined by the 2008 National Committee for Quality Assurance (NCQA) standards.20,21

Practice Transformation

Practices that became PCMHs were assisted in their transformation by the Taconic IPA and 2 external consulting groups. Lead physicians from each practice met at least monthly as a medical council to share best practices. Practice transformation consisted of systematically reviewing the NCQA tool, documenting PCMH processes already in place, and implementing processes not initially in place. Practice-based needs assessments began in January 2009, and actual transformation began in March 2009. All practices submitted their applications to NCQA and were awarded Level III recognition (the highest level); the median submission date was December 2009. Three major themes emerged as central to the transformation process for these practices: changing the culture toward population management, building a team by clearly defining roles and responsibilities, and becoming accountable for performance.


We used the following physician characteristics, as collected by the IPA in 2008: age, gender, degree (MD vs DO), specialty, county, and adoption of a practice management system. The number of primary care physicians in the physician’s practice was obtained in 2008, 2009, and 2010. EHR status was derived from a comprehensive survey of physicians in the community in 2010. This survey, which predated the federal Meaningful Use program, confirmed the presence of an EHR by collecting data on the HER vendor and software version number.

Five of 6 health plans contributed claims for calendar years 2008, 2009, and 2010 to a third-party data aggregator, which ensured completeness and adherence to standardized specifications. The data aggregator attributed each claim to a specific patient and then attributed each patient to a primary care physician (eAppendix, available at All of the patient’s healthcare utilization was assigned to the primary care physician to whom the patient was attributed, regardless of who ordered the healthcare services. We captured 7 different categories of healthcare utilization: 1) primary care visits including visits to primary care physicians and nurse practitioners), 2) specialist visits, 3) radiology and other diagnostic tests, 4) laboratory tests, 5) ED visits, 6) hospital admissions, and 7) 30-day all-cause readmissions. These 7 were chosen because together, they represent the large majority of all healthcare utilization and because measuring them separately allows us to capture any shifts in healthcare utilization from 1 category to another.

The data aggregator generated 3 additional physician characteristics for each year: the total number of patients attributed to each physician from the participating health plans (panel size), case mix, and plan mix. Case mix was derived using DxCG software.22-24 Plan mix was a series of 5 physician-level variables—1 for each health plan—which expressed the proportion of the physician’s attributed patients covered by that plan.

Statistical Analysis

We considered the physician to be the unit of analysis. We selected this approach due to the structure of the data set received by the research team, which was aggregated at the level of the physician. We conducted a sensitivity analysis adjusting for clustering by practice, although the study was not powered to find an effect at that level.

We started with all providers in the Taconic IPA in 2008 and included only primary care physicians (general internists and family medicine physicians) practicing in the Hudson Valley who had any patients in the aggregated claims. We then required a minimum number of patients per physician to maximize reliability. Previous work, using only patients with diabetes, suggested that a minimum panel size of 100 yields reliability estimates of 0.80 or greater.25 Because the patients in this study reflected the full spectrum of health states and not just diabetes, we chose a minimum panel size of 200 attributed patients per physician per year. This strategy helped to ensure that the study sample would generate statistically reliable estimates and would yield results that could be generalized to other full-time clinicians.

We classified physicians into 3 study groups: those transforming into PCMHs (all of whom were also using EHRs), those using paper medical records, and those using EHRs (but not in PCMHs). We compared the groups’ characteristics using analysis of variance (ANOVA) for continuous variables and 
χ2 tests for categorical variables, except for the comparison for practice size, for which we used a Kruskal-Wallis test due to the non-normal distribution. Panel size was log-transformed for statistical tests.


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