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The American Journal of Managed Care June 2018
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Physician Practice Variation Under Orthopedic Bundled Payment
Joshua M. Liao, MD, MSc; Ezekiel J. Emanuel, MD, PhD; Gary L. Whittington, BSBA; Dylan S. Small, PhD; Andrea B. Troxel, ScD; Jingsan Zhu, MS, MBA; Wenjun Zhong, PhD; and Amol S. Navathe, MD, PhD
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Physician Practice Variation Under Orthopedic Bundled Payment

Joshua M. Liao, MD, MSc; Ezekiel J. Emanuel, MD, PhD; Gary L. Whittington, BSBA; Dylan S. Small, PhD; Andrea B. Troxel, ScD; Jingsan Zhu, MS, MBA; Wenjun Zhong, PhD; and Amol S. Navathe, MD, PhD
This study demonstrates that variation reduction is an important, but not requisite, component of organizational success under orthopedic bundled payment.
Variables and Outcomes

Patient demographics and clinical data were calculated from Medicare claims, and patient illness severity was estimated using the Elixhauser Comorbidity Score.11-13 Medicare data were also used to calculate 2 quality of care measures, 30-day readmission and emergency department (ED) visit rates, based on CMS demonstration project specifications.14 Characteristics of operating physicians (gender, years of experience, allopathic vs osteopathic degree, graduation from US or non-US medical school, and board certification status) were obtained from publicly available information on consumer websites.

We evaluated variation in 3 outcomes of interest: implant costs, total episode payments, and institutional PAC provider utilization. Internal hospital cost data were used to quantify the costs of orthopedic implants (implant costs), while BHS used a time-driven activity-based costing approach15 to quantify other nonsupply hospital costs. Medicare data were used to calculate the sum of all Medicare payments for an episode of care (total episode payments) by combining claims for all acute hospital facility payments, physician fees during hospitalization, and total PAC payments through 30 days post discharge. Total PAC payments consisted of those corresponding to outpatient visits, ED visits, readmissions, physician fees, durable medical equipment, and utilization of PAC providers, such as skilled nursing facilities (SNFs), inpatient rehabilitation facilities (IRFs), and home health agencies (HHAs).

We used Medicare data to define a dichotomous variable (institutional PAC provider utilization) as discharge from hospitalization to either SNFs or IRFs as institutional PAC providers, compared with discharge home or home with HHA services. Patients were discharged to 1 of 4 destinations in nearly all (99.4%) episodes.

Finally, we defined several variables to evaluate associations between physician practice characteristics and our outcomes. We assessed practice quality by calculating the proportion of episodes for each physician with prolonged length of stay (PLOS), a validated measure of complications of orthopedic lower extremity joint replacement.16,17 PLOS evaluates the pattern of patient discharges as a function of length of stay (ie, number of days in the hospital) and identifies a point at which discharge is less likely than continued hospitalization, representing a complication. We assess practice volume using total case volume over the study period. Due to data skewness, we analyzed volume as a continuous variable, as well as a dichotomous variable with the top 10 physicians by volume (each with more than 100 cases and representing more than 70% of total cases) defined as high-volume physicians.

Statistical Analysis

Descriptive statistics were reported using means and SDs for continuous variables and percentages for categorical variables. Multilevel generalized linear models, adjusted for study year and patient and physician characteristics, were employed to evaluate the extent of physician variation in the 3 outcomes of interest. Models were clustered at the physician level and included a physician random effect. To account for multiple patients per operating physician and heteroscedasticity, all models utilized clustered standard errors with the Huber-White correction.

We utilized linear random effects models with a log link and gamma distribution for the outcomes of implant cost and total episode spending.18 To evaluate institutional PAC provider utilization, we used a random effects model with a logit link and binominal distribution to estimate the logit of the probability of discharge to institutional PAC providers as a linear function of covariates and operating physician random effect.19

The significance of between-physician variation was determined by testing whether the variance of the physician random effect differed significantly from 0 and was reported using the intraclass correlation (ICC). The ICC is a measure that reflects the proportion of overall variance explained by variation between, rather than within, clusters of individual observations. In this study, the calculated ICC corresponds to the proportion of variation explained by differences in practice patterns among physicians (ie, between-physician variation), as opposed to among cases for individual physicians (ie, within-physician variation). A linear threshold method was used to calculate ICC for institutional PAC provider utilization.20 To compare changes in the ICC across the study period, we used bootstrapping to calculate standard errors for ICCs in ACE year 1 and BPCI year 2 before using the z statistic and pairwise tests for each outcome to evaluate whether there were significant differences between the 2 years.

Random effects models were also used to calculate and compare between-physician variation and ICC for implant costs and total episode payments across our study period (ACE year 1 vs BPCI year 2). Because bundle definitions did not include PAC until the BPCI program period, we compared between-physician variation and ICC for PAC provider utilization among ACE year 1, ACE year 3, and BPCI year 2.

Separately, we used random effects models to test the associations among physician quality and case volume and our 3 outcomes of interest. All cost and spending figures were adjusted for inflation and reported in 2015 US$ equivalents. Implant cost data were aggregated at the level of individual physician per quarter and weighted by episode volume. Analyses were conducted using SAS version 9.4 (SAS Institute; Cary, North Carolina). All tests of significance were 2-tailed and considered statistically significant at an α of .05. The University of Pennsylvania Institutional Review Board approved the study.

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