This work was conducted in the context of the 4-year Comprehensive Primary Care (CPC) initiative launched in October 2012 by the Centers for Medicare & Medicaid Services (CMS), joined by 39 other private and public payers [10]. CPC tested whether providing financial and technical support for particular enhanced primary care activities reduced costs and improved quality in 502 practices across seven US regions. Additional details regarding the CPC initiative are included in the Appendix.
Measure of Practice Site Range of Services (Practice-ROS)
The Practice-ROS measure assesses the comprehensiveness of services that the PCPs at a practice site provided to Medicare fee-for-service (FFS) beneficiaries. We consulted several sources to identify the potential services (beyond face-to-face office visits) that practice sites should provide to Medicare beneficiaries. These sources included Primary Care Assessment Tool surveys developed by Starfield and colleagues [8], a refinement of this practice survey developed under technical expert guidance for the Agency for Healthcare Research and Quality [11], and a list of services documented in assessments of comprehensiveness of primary care practices in other developed countries [9]. From this combined list, we identified a subgroup of primary care services relevant to Medicare beneficiaries over age 65 that could be detected through Medicare claims. The 5 categories of services identified consisted of [1] immunizations, [2] counseling for behavioral or mental health problems, [3] treatment of a minor laceration, [4] cryotherapy and/or skin excision, and [5] joint or tendon injection. To identify the specific Healthcare Common Procedure Coding System (HCPCS) codes relevant to these 5 categories of services PCPs deliver, we identified the codes that represented at least 0.5% of the services in each category billed by PCPs in the observed practices. Appendix Table B presents details on the categories and HCPCS codes.
To calculate the Practice-ROS score for each primary care practice site, we then reviewed all the Medicare claims billed by each PCP (primary care physician, nurse practitioner [NP], physician assistant [PA], or certified clinical nurse specialist [CCNS]) practicing at that site during 2013. We used the practitioner billings under the Tax Identification Number (TIN) of the practice organization for the primary care practice site to avoid counting services practitioners provided in unrelated practice settings (such as PCPs “moonlighting” in emergency rooms). If any of the PCPs at the practice site’s TIN billed for at least one instance of providing a Medicare FFS beneficiary a specific service in a category, the practice site was scored as being able to provide services in that category. Thus, the Practice-ROS measure ranges from 0 to 5, with a 5 indicating PCPs at the practice site collectively provided at least one instance of each of the 5 categories of service (immunizations, counseling for behavioral or mental health problems, treatment of a minor laceration, cryotherapy and/or skin excision, and joint or tendon injection) to beneficiaries seen at that practice site that year. A score of 3 indicated the practice provided at least 1 instance of each of 3 service categories, and 0 indicated that practitioners at the practice site never billed for any of these service categories during the year.
Data Sources
Data
To construct our measure, we used Medicare FFS claims in the Research Identifiable Files from the CMS Virtual Research Data Center. To calculate the Practice-ROS, we analyzed 2013 Medicare Part B claims submitted by CPC and comparison practice sites included in the CPC evaluation. We observed 6050 primary care physicians, NPs, PAs, and CCNSs in 1383 CPC and comparison practices in 2013, using claims of all 1,232,940 beneficiaries they had seen. In all our analyses, we combined CPC and comparison practices since our focus was to measure development rather than investigate differences between these groups. To participate in the CPC initiative, practice sites needed to have at least 100 Medicare FFS beneficiaries and 1 primary care clinician.
We examined cost and utilization outcomes in 2014 for beneficiaries attributed to all these practices in 2013 and still alive and enrolled in fee-for-service Medicare in 2014. We constructed beneficiary-level outcomes using beneficiaries’ 2014 Medicare claims for services received from all providers.
Control variables for patient, practice, and market characteristics came from a range of sources, including the Medicare enrollment database (EDB), CPC application data, the Area Resource File, SK&A, the Medicare Data on Provider Practice and Specialty (MD-PPAS), the Health Resources and Services Administration (HRSA), and the National Committee for Quality Assurance (NCQA) (Table 4 provides details on data sources).
Primary Care Practitioners
We identified PCPs in practice sites in 2013 using the following practitioner taxonomy codes in the MD-PPAS: internal medicine, family practice, pediatric medicine, geriatric medicine, general practice, NPs, and PAs. We excluded hospitalists.
Beneficiary Attribution
For CPC, CMS attributed beneficiaries to CPC and comparison practices where they had received the largest share of their primary care in the prior 2 years. On a quarterly basis, attribution started with all Medicare FFS professional services and outpatient claims for office/outpatient evaluation and management visits, nursing home and home care visits, or Medicare and annual wellness visits as determined by the HCPCS codes on the claim. Using these claims, we identified the practice that accounted for the greatest share of these services for each beneficiary [6].
Statistical Analysis
Reliability Testing
We assessed reliability of the Practice-ROS measure using the split-half method to observe the repeatability and reproducibility for the same population at the same time [12]. We randomly split each practice site’s professional services claims in half, computed the measure score for each practice site in each split sample, and then assessed correlations between practice site scores for the two samples. We calculated Spearman’s rho to quantify the repeatability and reproducibility of the measured result across the two half-samples.
Associations with Practice Characteristics
We explored various practice site covariates potentially relevant to the range of services the site might offer. These included practice site size (1–3 practitioners, in contrast to larger sites). We observed the dominant primary care physician specialty at the practice site (if there was one), by constructing dummy variables indicating if more than 50% of physicians at the practice site were in a particular primary care specialty (such as internal medicine) as well as when no specialty represented more than 50% of the physicians (>50% of physicians in family medicine was the reference group). Because the ease of providing diverse services at a practice site might be mediated by the presence of advanced practice clinicians, we included a covariate indicating whether there were PAs or NPs at the practice site. Since the type of setting (and thus availability of resources, and incentives to refer) may vary based on the practitioner’s proximity to and affiliation with a hospital-based health system, we included an indicator for whether the physicians at the practice site billed at least 95% of outpatient claims (defined as those in either a hospital outpatient department or non-hospital-based office setting) from a hospital outpatient department (9% of the sample). Additional characteristics of interest included whether the practice site was owned by a health system, was recognized as a medical home, or met meaningful use criteria for electronic health records. In multivariate modeling, we also included a variable to control for whether the practice site was in the CPC intervention or comparison group for the CPC initiative (note the CPC intervention was not designed to specifically require or reward expanded practice site range of services). To better isolate associations with the covariates of interest, we also controlled for other demographic characteristics of the physician’s patient panel and for county-level characteristics of the practice site. Finally, we included two other measures of PCP physician comprehensiveness, New Problem Management and Involvement in Patient Conditions, [6] to assess the relationship of the Practice-ROS to these dimensions of primary care comprehensiveness.
For the bivariate comparisons of practice and market characteristics with Practice-ROS, we calculated Pearson correlations for the continuous variables and T-tests of independent group means for the categorical variables. To estimate the independent association between practice site-level covariates and Practice-ROS count for the site, we used multivariate Poisson regression analysis. Each model included the covariates of interest—physician specialty, service location, and characteristics of the practice site—as well as controls for Medicare claims per PCP, practice site physician demographics, the practice site patient panel, and county-level factors. We did not adjust p-values to account for multiple comparisons, but in our analyses, we used a conservative significance level of p ≤ 0.01 for all measures, to avoid type I errors.
Association with Beneficiary Cost and Utilization Outcomes
We used a lagged analysis to test for associations between the Practice-ROS measure (in year 2013) and 3 beneficiary-level cost and utilization outcomes (measured in year 2014): total Medicare FFS expenditures per beneficiary per month, and annualized measures of the number of hospitalizations and ED visits per 1000 beneficiaries. Hospitalizations and ED visits were modeled using zero-inflated negative binomial regression (to account for possible over-dispersion in utilization counts and the large percentage of zeroes for beneficiaries with no use during a year). Total Medicare expenditures were modeled using ordinary least squares regression. Values above the 99th percentile were Winsorized (reset) to the 99th percentile to avoid the potentially distorting effects of extreme outliers. Each regression model controlled for the same beneficiary, practice, and market characteristics identified in 2012, before the start of CPC. All models accounted for clustering of patient outcomes within practices. Each observation (beneficiary) was weighted to account for the percentage of the year the beneficiary was eligible for Medicare. For beneficiaries in the comparison practices, the weights were adjusted for practice-level matching. Analyses were conducted using STATA 14.
Beneficiary-level control variables included Hierarchical Condition Category score, race, age, sex, original reason for Medicare eligibility (disability or end-stage renal disease, versus age), and dual status (eligible for both Medicare and Medicaid). We also included control variables for the beneficiary’s practice and local market as described above.
To ease interpretation of the associations between practice comprehensiveness measures and beneficiary-level outcomes, we report both the magnitude and percentage difference in the adjusted mean outcomes for an increase in the comprehensiveness score from the 25th to 75th percentile among all practice sites in the analysis.