The Patient-Centered Medical Home, Electronic Health Records, and Quality of Care

Context The patient-centered medical home (PCMH) relies on health information technology, primarily electronic health records (EHRs), and a team-based approach to care. The effect of the PCMH on quality of care provided in nonintegrated health systems is unknown. Contribution This longitudinal study compared the quality of care provided by physicians practicing in PCMHs with that provided by physicians in non-PCMH practices using paper records or EHRs. Caution Information was not available on duration of EHR use or other changes made as practices transformed into PCMHs. Implication The PCMH was associated with improved quality of care over time that was distinct from the effects of the EHR alone. The Editors The patient-centered medical home (PCMH) is a model of primary care that is being implemented widely, with more than 100 demonstration projects across the United States (1). The PCMH model emphasizes care coordination and management of chronic disease in an effort to improve quality, cost, and patient experience (2). It relies heavily on the use of health information technology, including electronic health records (EHRs), which are considered a critical tool for improving quality (3). Although it uses EHRs to effect change, the PCMH model goes beyond technology to change not just information flow but the roles and relationships of providers and staff in practices (4). Several systematic reviews have found few published articles exploring the association between the PCMH model and quality (57). Studies that have found a positive association have largely come from integrated delivery systems (810). Less is known about the effects of the PCMH in communities without such systems. We sought to determine the effect of the PCMH on quality in a multipayer community. Our primary aim was to compare the quality of care provided by physicians in PCMHs (all of whom were also using EHRs) with that provided by physicians using paper medical records. Because EHRs have their own effects on quality (3, 11, 12), our secondary aim was to compare the quality of care provided by physicians in PCMHs with that provided by physicians using EHRs without a PCMH model, thereby isolating the effect of the PCMH itself. Methods We conducted a longitudinal study of primary care physicians from 2008 to 2010. The Institutional Review Boards at Weill Cornell Medical College and Kingston Hospital approved the protocol. The study was registered at ClinicalTrials.gov (NCT00793065). Setting and Context This study took place in the Hudson Valley, a 7-county region immediately north of New York City. Approximately 85% of the people in this region live in urban areas, and the remaining 15% live in rural areas (13, 14). Approximately 85% of residents have health insurance (13, 15), and approximately 10% live at or below the poverty level (13). This study evaluated part of the Hudson Valley Initiative, which seeks to transform health care delivery through health information technology, practice transformation, and value-based purchasing (16). This initiative is the combined work of the Taconic Health Information Network and Community (THINC) (17), a not-for-profit coalition-building organization; the Taconic Independent Practice Association (IPA) (18), a not-for-profit physician organization; and MedAllies (19), a for-profit health information services provider. THINC convened 6 health plans (3 national commercial plans [Aetna, UnitedHealthcare, and Empire Blue Cross Blue Shield], 2 regional commercial plans [MVP Health Care and Capital District Physicians' Health Plan], and 1 regional Medicaid HMO [Hudson Health Plan]), which together cover approximately 70% of the community's commercially insured population. The plans agreed on a set of 10 quality measures for pay-for-performance. They also agreed to provide financial incentives, which amounted to $2 to $10 per patient per month, to practices that implemented level 3 PCMHs (20), as defined by the 2008 National Committee for Quality Assurance (NCQA) standards (21). Level 3 is the highest level and requires earning at least 75 of 100 points in processes of care for 9 areas: access and communication, patient tracking and registries, care management, patient self-management support, electronic prescribing, test tracking, referral tracking, performance reporting and improvement, and advanced electronic communication. Participants We included all primary care physicians (general internists, pediatricians, and family medicine physicians) who were members of the Taconic IPA. We also included nonmember physicians who volunteered for the Hudson Valley Initiative. 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 that were already in place, and implementing processes that were not initially in place. 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. Practice-based needs assessments began in January 2009, and transformation began in March 2009. All practices submitted applications to NCQA and achieved level 3 recognition. The median submission date was December 2009 (range, August 2009 to January 2010). Data The Taconic IPA, which had conducted a comprehensive survey of physicians in the Hudson Valley community in 2010, provided data on which physicians had implemented the PCMH and which physicians had implemented EHRs or were using paper medical records. The IPA also provided data, which it had collected in 2008, on physician age, sex, degree (MD vs. DO), specialty, county, and adoption of a practice management system. In addition, the IPA collected and provided the number of primary care physicians in each practice in 2008, 2009, and 2010. Five of the health plans, which covered 60% of the community's commercially insured population, submitted claims data for calendar years 2008, 2009, and 2010 to a third-party data aggregator. The data aggregator ensured adherence to standardized specifications and applied attribution logic to assign patients to primary care providers (Appendix Figure). Attribution was recalculated each year. The data aggregator provided patient age; sex; deidentified health plan identifier; International Classification of Diseases, Ninth Revision (ICD-9), codes; and patient-level quality measures (described later in this section). The data aggregator also generated the number of patients attributed to each physician in the data set (panel size); patients were counted toward panel size even if they did not contribute quality data. Appendix Figure. Logic for attributing patients to providers. E&M = evaluation and management. * See the Figure for the derivation of the study sample. We used patient ICD-9 codes to calculate comorbidity based on the count of major aggregated diagnostic groups (ADGs), as generated by the Johns Hopkins Adjusted Clinical Groups System (22). Each ADG is a group of diagnosis codes that are homogeneous with respect to specific clinical criteria and demand on health care services (22). Patients with multiple diagnoses can be assigned multiple ADGs. The number of major ADGs is considered a valid measure of the burden of comorbidity (22). We considered the 10 quality measures that had been selected by the health plans, which were drawn from the Healthcare Effectiveness Data and Information Set (HEDIS): eye examinations, hemoglobin A1c testing, low-density lipoprotein cholesterol testing, and nephropathy screening for adult patients with diabetes; breast cancer screening; chlamydia screening; colorectal cancer screening; appropriate medications for patients with asthma; appropriate testing for children with pharyngitis; and appropriate treatment for children with upper respiratory infection (23). The first 8 measures were applied to general internists and family physicians, and chlamydia screening and the last 3 measures were applied to pediatricians and family physicians. We used measure definitions from NCQA, except that data were derived only from claims (not supplemented with manual chart review) because of the large sample size. Statistical Analysis We used descriptive statistics to characterize the patients in the sample and the primary care providers to whom they were attributed. We log-transformed panel size because of skewness. Using maps from the U.S. Census Bureau, we classified counties as rural versus urban or suburban. We created 3 study groups according to their status in 2010: physicians whose practices transformed into PCMHs (all of whom were also using EHRs), those using paper medical records, and those using EHRs (but not in PCMHs). Study groups were compared by using analysis of variance for continuous variables and chi-square tests for categorical variables. To measure absolute performance, we calculated the proportion of eligible patients who received recommended care for each measure, overall and stratified by study group within each year. Chi-square tests were used to compare absolute unadjusted performance across study groups within each year. We used generalized linear models fit with generalized estimating equations to determine associations between study group and quality of care (24). We used patient-level data, allowing patients to contribute to more than 1 quality measure, as applicable. The regression models considered each patientmeasure combination as a separate trial and expressed the result as the likelihood of receiving recommended care overall. Patients were included in each year for which they had quality data; they were not required to contribut