The Patient Centered Medical Home and Associations With Health Care Quality and Utilization
Context Long term effects of the patient centered medical home (PCMH) are unclear. Contribution This longitudinal study compared performance on health care quality and utilization measures for practices using paper records, those using electronic health records, and PCMH practices during a 5 year period (including 3 years after PCMH implementation) Performance on quality measures was similar across groups, but modest differences in utilization were observed. Caution Unexplained large changes that occurred in the final year may have driven the observed differences in utilization. Implication Long term studies may be necessary to understand the effects of the PCMH on health care utilization. The patient centered medical home (PCMH) attempts to shift the medical paradigm from care for individual patients to care for populations, from care by physicians to care by a team of providers, from a focus on acute illness to an emphasis on chronic disease management, and from care at a single site to coordinated care across providers and settings (1) This model has been disseminated widely and leveraged to change physician reimbursement (2, 3) However, the effects of the PCMH are unclear. Several systematic reviews have found insufficient evidence to determine its effectiveness (46) Some studies have found associations between the PCMH and modest improvements in health care quality (7) but others have found no association with quality (810) Recent studies have also found either no effect on health care utilization (9) or isolated effects on some utilization outcomes but not others (8, 10) Previous studies may have been limited by their duration of follow up, typically 1.5 to 2 years after PCMH implementation (8, 10) The PCMH is a complex intervention that usually involves implementation of electronic health records (EHRs) plus organizational changes, including changes to clinical workflow and responsibilities of providers and staff. These changes take time, and studies with relatively short follow up may have underestimated the effects of the intervention. Another limitation of previous studies is that they have not consistently accounted for the effects of EHRs (9) which may independently influence quality and utilization (7) Our objective was to evaluate the effects of the PCMH on health care quality and utilization, using extended follow up (3 years after implementation) We also sought to distinguish the effects of the PCMH from those of EHRs alone. Methods Overview We conducted a longitudinal cohort study of primary care physicians over a 5 year period (2008 to 2012) that included 3 years after PCMH implementation (2010 to 2012) This study builds on our previous evaluations, which included 1 year after transformation to the PCMH model (7, 11) 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, multipayer, multiprovider region north of New York City. The Taconic Health Information Network and Community (THINC) a coalition building organization, convened 6 health plans (Aetna, UnitedHealthcare, Empire Blue Cross Blue Shield, Capital District Physicians' Health Plan, MVP Health Care, and Hudson Health Plan) which covered 70% of the community's commercially insured patients (12, 13) These plans provided incentives totaling $2 to $10 per patient per month to practices that achieved level III PCMH (the highest level) as defined by the 2008 standards of the National Committee for Quality Assurance (NCQA) (1) THINC worked in conjunction with the Taconic Independent Practice Association (IPA) and MedAllies on this initiative and a previous initiative to promote adoption of EHRs in this community (1315) Participants We included all primary care physicians for adults (general internists and family medicine physicians) who were members of the Taconic IPA or who volunteered for the PCMH initiative. We included all adult patients (aged 18 years) who were attributed to the primary care physicians and were eligible for at least 1 quality measure (described in the Statistical Analysis section) Practice Transformation As described previously, some practices volunteered for the PCMH initiative, and 2 external consulting groups assisted with PCMH transformation (7) Needs assessments took place in January 2009, and transformation began in March 2009. Practices systematically implemented components of the PCMH not already in place (Appendix 1) The external consulting groups reported that transformation was shaped by 3 themes: changing culture toward population management, building a team by clearly defining roles and responsibilities, and becoming accountable for performance. Practices submitted applications to the NCQA between August 2009 and January 2010, and all achieved level III recognition. Data Sources The Taconic IPA provided data on which practices were using EHRs and which were participating in the PCMH initiative. The IPA provided baseline (2008) physician characteristics (age, sex, degree [MD vs. DO] specialty, and county) and the number of primary care physicians in each practice each year as a proxy for practice size. Five of the health plans, representing 60% of the community's commercially insured patients, contributed claims for each year. A third party data aggregator applied attribution logic yearly to assign patients to primary care physicians (7) All of a patient's health care utilization was attributed to the primary care physician, regardless of the ordering provider. The aggregator provided the number of patients attributed to each primary care physician (panel size) as well as several patient level variables: age; sex; codes from the International Classification of Diseases, Ninth Revision; deidentified health plan identifier; and quality and utilization outcomes (described in the Statistical Analysis section) We obtained data on which physicians received payments through the federal EHR Incentive (Meaningful Use) Program for 2011 (the first year of the program) and 2012 from the Centers for Medicare Medicaid Services Web site (for Medicare beneficiaries) and the New York State Department of Health (for Medicaid beneficiaries) (16, 17) Statistical Analysis We included primary care physicians who had attributed patients in each of the 5 years of the study. We then included all patients attributed to those physicians and allowed the patients to vary over time. We created 3 study groups: physicians who implemented the PCMH (all of whom used EHRs) those who used EHRs but did not implement the PCMH, and those who used paper records without the PCMH. We fixed the study groups at the physicians' 2010 status to isolate the longer term effects of the PCMHs we studied previously (7, 11) We used claims to capture 8 ambulatory quality measures: eye examinations, hemoglobin A1c testing, low density lipoprotein cholesterol testing, and nephropathy screening for patients with diabetes; breast cancer screening and chlamydia screening for women; colorectal cancer screening; and appropriate medications for patients with asthma. These measures had been selected from the Healthcare Effectiveness Data and Information Set by the participating health plans for this initiative. We also used claims for 7 health care utilization outcomes: primary care visits, specialist visits, laboratory tests, radiology and other diagnostic tests, emergency department (ED) visits, hospitalizations, and 30 day all cause rehospitalizations. We considered 7 potential physician level confounders (age, sex, degree, specialty, panel size, IPA membership, and receipt of Meaningful Use payments from Medicare or Medicaid) 2 potential practice level confounders (county and practice size) and 4 potential patient level confounders (age, sex, case mix index [18] and health plan) Descriptive statistics were used to characterize physicians and patients. We compared characteristics across study groups by using analysis of variance for continuous variables and chi square tests for categorical variables. We modeled quality as a binary dependent variable indicating whether an eligible patient had received recommended care. Patients were allowed to contribute to multiple quality measures. The models considered each patientmeasure combination as a separate "trial," yielding the overall likelihood of receiving care. To determine adjusted rates of absolute performance and the adjusted relative association between study group and quality, we used generalized linear models with a Poisson probability distribution and log link function, fit with generalized estimating equations (19) The full model used all 5 years of data and included the following independent variables: study group and year (each as a dummy variable) interaction between study group and year, patient characteristics, and physician characteristics (except for IPA membership because of too few nonmembers) Panel size, practice size, county (in case physicians moved their practices during the study) Meaningful Use status, and patient case mix indices were time dependent; all other characteristics were fixed. We used an independent working correlation structure with robust SEs to account for clusters of measures within patients. These clusters were nested within physician, and physicians were nested within practice. We had complete data on 97.9% of patients. We report absolute adjusted rates of receipt of recommended care, estimated using marginal standardization under a balanced design and stratified by quality measure, study group, and year. We also report group relative risks (RRs) with 95% CIs using the marginal differences from 2008 to 2012. In addition, we exponentiated the model coefficients to obtain the RRs and 95% CIs that reflect the relative difference in differences for quali
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