System Transformation in Patient-Centered Medical Home (PCMH): Variable Impact on Chronically Ill Patients' Utilization

Study Setting

In 2010, as a result of state health reform legislation enacted in 2008, the Minnesota Department of Human Services and the Minnesota Department of Health published standards for voluntary clinic certification as a Health Care Home (HCH), Minnesota's term for PCMHs. These Minnesota standards have a different focus than those published by NCQA in 2008 and 2011. Burton et al¹⁵ note that the NCQA standards are heavily weighted toward health information technology; by contrast, Minnesota standards focus little on health information technology because state law already requires that providers use e-prescribing (2011 and later) and have interoperable electronic health records (2015 and later). The Minnesota process focuses on quality improvement activities, continuous access, care coordination, a searchable electronic registry, and individualized care plans. Minnesota's certification process also includes meeting annually updated quality targets.

Our study setting focused on Minnesota clinics certified as HCHs by October 2011 that also participated in a separate research survey using the PPC-RS instrument to measure the presence of practice systems associated with PCMHs. These practice systems are grouped in 5 domains, shown in Table 1. “Health care organization” includes elements of performance measurement and quality improvement; “delivery system redesign” focuses on structural changes in staffing and managing patient visits; the “clinical information systems” domain captures disease registries, care checklists, and tracking of the care process; “decision support” measures the guidelines, reminders, and alerts available to the clinician; and “self-management support” assesses patient communication, education, and support in chronic disease management. The PPC-RS contains questions asking about the presence of systems in each of these domains, each with 3 answer options: “present and works well” (1 point), “present but needs improvement” (0.5 points), and “not present” (0 points). Some of these questions (indicated by an asterisk in Table 1) were asked separately by chronic condition: DM, CVD, depression, and asthma.

The survey process was completed in early 2011, close to the time of HCH certification for these clinics, which began in July 2010. Clinic medical leaders were asked about current practice systems in their clinic and the state of those systems 3 years earlier (before certification). Thus each clinic has 2 sets of scores, before and after certification. We rescaled the total scores so that the maximum response, across clinics and time, was 100. We captured the subscores for these 5 domains, rescaling in the same manner so that the maximum score within each domain was 100.

This study was reviewed and approved before the initiation of the survey, and was monitored throughout the project, by the HealthPartners Institutional Review Board.

Patient Population in Participating Clinics

The data were collected through a unique data-pooling effort, with assistance from Minnesota Community Measurement (MNCM), combining claims data for the large majority of patients in a typical primary care practice. MNCM, founded by 6 Minnesota health plans and the Minnesota Medical Association, provides transparent public reports of quality measures for all clinics in Minnesota. Their earliest public report (2004) summarized quality of care metrics for patients with specific chronic conditions and preventive care measures. By 2007, these chronic conditions included DM and CVD.

We leveraged the data collection process used by MNCM to identify the sample of patients for our study. Clinics participating in the MNCM initiative identified patients for whom they were actively managing chronic illnesses and provided payer information to MNCM. Thus MNCM had a list of patients with DM or CVD for the 87 clinics completing the PPC-RS. Based on this list, the 3 largest health plans in the region and the Minnesota Department of Human Services contributed to the research team de-identified administrative data for these patients from their commercial, Medicare, and Medicaid files for calendar year 2010. These data were merged with PPC-RS responses by the research team. The payers provided only complete claims to the research team; we have no information on missing data. Our experience is that incomplete claims are rare because of rigorous claims adjudication.

Study Design and Quantitative Variables

This is a cross-sectional observational study. While we had PPC-RS measures from 2 time points, we had patient utilization measures only for calendar year 2010. Total utilization was measured using patient encounters multiplied by total cost relative resource value (TCRRV) units. TCRRV units¹⁶ are based on Centers for Medicare and Medicaid Services' relative value units by Current Procedural Terminology 4 code, inpatient diagnosis-related groups, and ambulatory payment classification weights. The TCRRV units function as standardized fees for all procedures, hospital admissions, and prescriptions that are independent of place of service, type of insurance coverage, or year.

We also modeled utilization by type of care, including the number of primary care outpatient visits, the number of outpatient prescriptions, the probability of ≥1 inpatient visit, and the probability of ≥1 ED visit. Primary care visits were defined as outpatient visits with providers (including nurse practitioners and physicians assistants) in the following specialties: family medicine, internal medicine, general practice, geriatric medicine, and obstetrics/gynecology. Outpatient prescriptions were defined as a count of all medications prescribed and filled on an outpatient basis with a minimum of an initial 60-day or two 30-day fills.¹⁷

Our main independent variable of interest measured the maturity of the PCMH transformation process, based on normalized PPC-RS scores. We categorized each clinic's stage of PCMH transformation as early, intermediate, or late. Early stage clinics had a normalized PPC-RS score <50 in both time periods. Intermediate-stage clinics had a prior score <50 but a current score of ≥50. Late-stage clinics had a score ≥50 in both time periods. We selected the normalized score of 50 because, across time and clinics, this was close to the median normalized PPC-RS score. We created analogous transformation measures in each of the 5 domains. Variables used as controls included a patient's age, sex, number of additional comorbidities, and fixed effects for plan type (commercial, Medicare, Medicaid, special-needs Medicaid) and each of the payers. The comorbidity count includes DM (CVD only), chronic obstructive pulmonary disease, asthma, depression, congestive heart failure, and coronary artery disease (the latter 2 for DM only). These diagnoses were identified based on International Classification of Diseases, 9th Revision, code history.

Statistical Methods

We modeled each observed utilization measure Y_i as a function of the maturity of clinic transformation and the control variables. Specifically, for individual i treated in clinic j, we modeled The variables Intermediate_j and Late_j are binary indicators of the PCMH transformation stage for clinic j, relative to the omitted early stage. The vector x_i captures the characteristics of the patients, and δ_c and γ_p are the coverage type c and payor p fixed effects. The error terms ε_i are assumed to be normally distributed and are clustered within clinic. All models were estimated using StataMP 11.

The relationship between the observed Y_i and the modeled variable y_i depends on the type of utilization. Because total utilization, number of prescriptions, and number of outpatient visits are highly skewed, we used a log transformation and modeled y_i = ln(Y_i). The binary indicators of ≥1 inpatient admission or ≥1 ED visit were modeled using a probit regression, so y_i represents a latent variable that determines the observed Y_i. The probit model assumes that Y_i = 0 is observed when y_i < 0, and Y_i = 1 is observed when y_i ≥ 0.

Our parameters of interest are α₁ and α₂, associated with PCMH transformation. To enhance interpretation, we present marginal effects of transformation. For the log-transformed models (total utilization, number of scripts, number of outpatient visits), these marginal effects are simply the parameters, interpreted as an approximate percentage change in the outcome as a clinic moved from the early stage to either the intermediate or late stage. For example, if the outpatient visit regression estimated α₁ = −0.05 and α₂ = −0.10, on average we would expect to see a 5% decrease in visits when a clinic moved from the early to intermediate stage and a 10% decrease in visits when a clinic moved from the early to the late stage. For the probit models (inpatient admissions and ED visits), we used the regression results to predict the percentage-point change in the probability of ≥1 inpatient admission or ED visit when transformation maturity changed. For example, if we predict a 25% average probability of inpatient admission at the early stage and a 20% average probability of inpatient admission at the late stage, the marginal effect would be −0.05, or a 5 percentage-point decrease in average probability, when the clinic moved from the early to the late stage.

In addition to the main effect of clinic transformation stage, we also estimated how the impact of transformation maturity varied by the comorbidity status of the patient, by transformation domain, and by the combination of comorbidity and domain.