Practice Expense Data Collection and Methodology: Phase II Final Report.

Each year, Medicare allocates tens of billions of dollars for indirect practice expense (PE) across services on the basis of data from the Physician Practice Information (PPI) Survey, which reflects 2006 expenses. Because these data are not regularly updated, and because there have been significant changes in the U.S. economy and health care system since 2006, there are concerns that continued reliance on PPI Survey data might result in PE payments that do not accurately capture the resources that are typically required to provide services. In this final phase of a study on PE methodology, the authors address how the Centers for Medicare & Medicaid Services (CMS) might improve the methodology used in PE rate-setting, update data that inform PE rates, or both. The authors conclude that this information is best provided by a survey; therefore, they focus on the advantages and disadvantages of survey-based approaches. They also describe the use of a lean model survey instrument, as well as partnering with another agency to collect data. Finally, the authors describe a virtual town hall meeting held in June 2021 to give stakeholders an opportunity to provide feedback on PE data collection and rate-setting. The system of data and methods that CMS uses to support PE rate-setting is complex; thus, CMS must take into account a number of competing priorities when considering changes to the system. With this in mind, the authors offer a number of near- and longer-term recommendations.

Propensity score matching for multilevel spatial data: accounting for geographic confounding in health disparity studies.

BACKGROUND: Diabetes is a public health burden that disproportionately affects military veterans and racial minorities. Studies of racial disparities are inherently observational, and thus may require the use of methods such as Propensity Score Analysis (PSA). While traditional PSA accounts for patient-level factors, this may not be sufficient when patients are clustered at the geographic level and thus important confounders, whether observed or unobserved, vary by geographic location. METHODS: We employ a spatial propensity score matching method to account for "geographic confounding", which occurs when the confounding factors, whether observed or unobserved, vary by geographic region. We augment the propensity score and outcome models with spatial random effects, which are assigned scaled Besag-York-Mollié priors to address spatial clustering and improve inferences by borrowing information across neighboring geographic regions. We apply this approach to a study exploring racial disparities in diabetes specialty care between non-Hispanic black and non-Hispanic white veterans. We construct multiple global estimates of the risk difference in diabetes care: a crude unadjusted estimate, an estimate based solely on patient-level matching, and an estimate that incorporates both patient and spatial information. RESULTS: In simulation we show that in the presence of an unmeasured geographic confounder, ignoring spatial heterogeneity results in increased relative bias and mean squared error, whereas incorporating spatial random effects improves inferences. In our study of racial disparities in diabetes specialty care, the crude unadjusted estimate suggests that specialty care is more prevalent among non-Hispanic blacks, while patient-level matching indicates that it is less prevalent. Hierarchical spatial matching supports the latter conclusion, with a further increase in the magnitude of the disparity. CONCLUSIONS: These results highlight the importance of accounting for spatial heterogeneity in propensity score analysis, and suggest the need for clinical care and management strategies that are culturally sensitive and racially inclusive.

Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research.

OBJECTIVE: To sample 40 physician organizations stratified on the basis of longitudinal cost of care measures for qualitative interviews in order to describe the range of care delivery structures and processes that are being deployed to influence the total costs of caring for patients. DATA SOURCES: Three years of physician organization-level total cost of care data (n = 156 in California) from the Integrated Healthcare Association's value-based pay-for-performance program. STUDY DESIGN: We fit total cost of care data using mixture and K-means clustering algorithms to segment the population of physician organizations into sampling strata based on 3-year cost trajectories (ie, cost curves). PRINCIPAL FINDINGS: A mixture of multivariate normal distributions can classify physician organization cost curves into clusters defined by total cost level, shape, and within-cluster variation. K-means clustering does not accommodate differing levels of within-cluster variation and resulted in more clusters being allocated to unstable cost curves. A mixture of regressions approach focuses overly on anomalous trajectories and is sensitive to model coding. CONCLUSIONS: Statistical clustering can be used to form sampling strata when longitudinal measures are of primary interest. Many clustering algorithms are available; the choice of the clustering algorithm can strongly impact the resulting strata because various algorithms focus on different aspects of the observed data.

Addressing geographic confounding through spatial propensity scores: a study of racial disparities in diabetes.

Motivated by a study exploring differences in glycemic control between non-Hispanic black and non-Hispanic white veterans with type 2 diabetes, we aim to address a type of confounding that arises in spatially referenced observational studies. Specifically, we develop a spatial doubly robust propensity score estimator to reduce bias associated with geographic confounding, which occurs when measured or unmeasured confounding factors vary by geographic location, leading to imbalanced group comparisons. We augment the doubly robust estimator with spatial random effects, which are assigned conditionally autoregressive priors to improve inferences by borrowing information across neighboring geographic regions. Through a series of simulations, we show that ignoring spatial variation results in increased absolute bias and mean squared error, while the spatial doubly robust estimator performs well under various levels of spatial heterogeneity and moderate sample sizes. In the motivating application, we construct three global estimates of the risk difference between race groups: an unadjusted estimate, a doubly robust estimate that adjusts only for patient-level information, and a hierarchical spatial doubly robust estimate. Results indicate a gradual reduction in the risk difference at each stage, with the inclusion of spatial random effects providing a 20% reduction compared to an estimate that ignores spatial heterogeneity. Smoothed maps indicate poor glycemic control across Alabama and southern Georgia, areas comprising the so-called "stroke belt." These results suggest the need for community-specific interventions to target diabetes in geographic areas of greatest need.

Estimating Surgical Procedure Times Using Anesthesia Billing Data and Operating Room Records.

OBJECTIVE: The median time required to perform a surgical procedure is important in determining payment under Medicare's physician fee schedule. Prior studies have demonstrated that the current methodology of using physician surveys to determine surgical times results in overstated times. To measure surgical times more accurately, we developed and validated a methodology using available data from anesthesia billing data and operating room (OR) records. DATA SOURCES: We estimated surgical times using Medicare 2011 anesthesia claims and New York Statewide Planning and Research Cooperative System 2011 OR times. Estimated times were validated using data from the National Surgical Quality Improvement Program. We compared our time estimates to those used by Medicare in the fee schedule. STUDY DESIGN: We estimate surgical times via piecewise linear median regression models. PRINCIPAL FINDINGS: Using 3.0 million observations of anesthesia and OR times, we estimated surgical time for 921 procedures. Correlation between these time estimates and directly measured surgical time from the validation database was 0.98. Our estimates of surgical time were shorter than the Medicare fee schedule estimates for 78 percent of procedures. CONCLUSIONS: Anesthesia and OR times can be used to measure surgical time and thereby improve the payment for surgical procedures in the Medicare fee schedule.

JOURNAL CLUB: Radiologists' Perceptions of Computerized Decision Support: A Focus Group Study From the Medicare Imaging Demonstration Project.

OBJECTIVE: The purpose of this study was to discern radiologists' perceptions regarding the implementation of a decision support system intervention as part of the Medicare Imaging Demonstration project and the effect of decision support on radiologists' interactions with ordering clinicians, their radiology work flow, and appropriateness of advanced imaging. SUBJECTS AND METHODS: A focus group study was conducted with a diverse sample of radiologists involved in interpreting advanced imaging studies at Medicare Imaging Demonstration project sites. A semistructured moderator guide was used, and all focus group discussions were recorded and transcribed verbatim. Qualitative data analysis software was used to code thematic content and identify representative segments of text. Participating radiologists also completed an accompanying survey designed to supplement focus group discussions. RESULTS: Twenty-six radiologists participated in four focus group discussions. The following major themes related to the radiologists' perceptions after decision support implementation were identified: no substantial change in radiologists' interactions with referring clinicians; no substantial change in radiologist work flow, including protocol-writing time; and no perceived increase in imaging appropriateness. Radiologists provided suggestions for improvements in the decision support system, including increasing the usability of clinical data captured, and expressed a desire to have greater involvement in future development and implementation efforts. CONCLUSION: Overall, radiologists from health care systems involved in the Medicare Imaging Demonstration did not perceive that decision support had a substantial effect, either positive or negative, on their professional roles and responsibilities. Radiologists expressed a desire to improve efficiencies and quality of care by having greater involvement in future efforts.

A spatiotemporal quantile regression model for emergency department expenditures.

Motivated by a recent study of geographic and temporal trends in emergency department care, we develop a spatiotemporal quantile regression model for the analysis of emergency department-related medical expenditures. The model yields distinct spatial patterns across time for each quantile of the response distribution, which is important in the spatial analysis of expenditures, as there is often little spatiotemporal variation in mean expenditures but more pronounced variation in the extremes. The model has a hierarchical structure incorporating patient-level and region-level predictors as well as spatiotemporal random effects. We model the random effects via intrinsic conditionally autoregressive priors, improving small-area estimation through maximum spatiotemporal smoothing. We adopt a Bayesian modeling approach based on an asymmetric Laplace distribution and develop an efficient posterior sampling scheme that relies solely on conjugate full conditionals. We apply our model to data from the Duke support repository, a large georeferenced database containing health and financial data for Duke Health System patients residing in Durham County, North Carolina.

The effect of North Carolina hospital payor mix on dental-related pediatric emergency room utilization.

OBJECTIVES: We examined the effect of hospital payor mix on the proportion of pediatric emergency department (ED) visits that were dental related. METHODS: We used the North Carolina (NC) Emergency Room Discharge Database from 2007 to 2009 to estimate the relationship between the percent of pediatric ED patients that were covered by Medicaid and the percent of pediatric ED visits that were dental related. Hospital-level fixed effects controlled for unobserved hospital-level characteristics. Discharge claims from 110 ED facilities in NC were analyzed over the 3-year study period. Claims were limited to individuals under 18 years old with dental disease-related International Classification of Diseases, Ninth Edition, Clinical Modification diagnostic codes, 520.00-530.00. RESULTS: Using 327 hospital-years of data, 62 percent of ED visits for pediatric dental reasons were covered by Medicaid, a proportion over two times greater than for pediatric reasons overall, 26 percent. Hospitals with a greater proportion of Medicaid payors had a greater proportion of pediatric dental ED visits (P < 0.01). CONCLUSIONS: Hospitals serving a large population of children on Medicaid should be prepared to provide emergency dental services. Public health administrators should prioritize oral health resources at hospital communities with a high proportion of Medicaid payors.

A Bayesian growth mixture model to examine maternal hypertension and birth outcomes.

Maternal hypertension is a major contributor to adverse pregnancy outcomes, including preterm birth (PTB) and low birth weight (LBW). Although several studies have explored the relationship between maternal hypertension and fetal health, few have examined how the longitudinal trajectory of blood pressure, considered over the course of pregnancy, affects birth outcomes. In this paper, we propose a Bayesian growth mixture model to jointly examine the associations between longitudinal blood pressure measurements, PTB, and LBW. The model partitions women into distinct classes characterized by a mean arterial pressure (MAP) curve and joint probabilities of PTB and LBW. Each class contains a unique mixed effects model for MAP with class-specific regression coefficients and random effect covariances. To account for the strong correlation between PTB and LBW, we introduce a bivariate probit model within each class to capture residual within-class dependence between PTB and LBW. The model permits the association between PTB and LBW to vary by class, so that for some classes, PTB and LBW may be positively correlated, whereas for others, they may be uncorrelated or negatively correlated. We also allow maternal covariates to influence the class probabilities via a multinomial logit model. For posterior computation, we propose an efficient MCMC algorithm that combines full-conditional Gibbs and Metropolis steps. We apply our model to a sample of 1027 women enrolled in the Healthy Pregnancy, Healthy Baby Study, a prospective cohort study of host, social, and environmental contributors to disparities in pregnancy outcomes.