Assessing patient, physician, and practice characteristics predicting the use of low‐value services

Abstract

Objective: To examine characteristics of beneficiaries, physicians, and their practice sites associated with greater use of low-value services (LVS) using LVS measures that reflect current care practices.Data Sources: This study was conducted in the context of a large, nationwide primary care redesign initiative (Comprehensive Primary Care Plus), using Medicare claims data in 2018. Study Design:We examined beneficiary-level total counts of LVS based on the existing 31 claims-based measures updated by excluding three services provided with diminishing frequency to Medicare beneficiaries and by replacing these with more recently identified LVS. We estimated hierarchical linear models with an extensive list of beneficiary, physician, and practice site characteristics to examine the contribution of characteristics at each level in predicting greater use of LVS. We also examined the proportion of variation in LVS use attributable to the set of characteristics at each level.Data Collection/Extraction Methods: The study included 5,074,642 Medicare feefor-service beneficiaries attributed to 32,406 primary care physicians in 11,009 primary care practice sites.Principal Findings: Patients with disabilities, end-stage renal disease, and those in regions with higher poverty rates receive 10 (standard error [SE] = 3.0), 80 (SE = 14.0), and 10 (SE = 1.0) more LVS per 1000 beneficiaries across all 31 measures combined than patients without such attributes, respectively. Greater physician comprehensiveness and an increase in the number of primary care practitioners at a practice were associated with 40 (SE = 20.0) and 20 (SE = 6.0) fewer LVS per 1000 beneficiaries, respectively. Yet, the explanatory variables we examined only account for 11 percent of the variation in LVS use, with most of the variation (87 percent) being due to unobserved differences at the beneficiary level.Conclusions: Unexplained residual variation, from underlying patient preferences and behavior of non-primary care providers, could be important determinants of LVS use.

Advancing healthcare equity through dissemination and implementation science

“…Examine provider‐ and organizational‐level characteristics that predict the use of low‐value care 94 and develop strategies to foster de‐implementation of low‐value care.…”

“…86 Address cultural relevance (i.e., attention to culture, values, and interactions) when examining fidelity of EBIs for historically marginalized populations. Examine provider-and organizational-level characteristics that predict the use of low-value care 94 and develop strategies to foster de-implementation of low-value care.…”

Shared Decision-making Lowers Medical Expenditures and the Effect Is Amplified in Racially-Ethnically Concordant Relationships

“…Clinician characteristics include biological sex, race-ethnicity, specialty, and region. Clinician characteristics have been shown to correlate with practice patterns 32,33 . Although the region is a broad measure, it is common for clinicians to interact with clinicians outside of their immediate geographical area via the Internet, conferences, and print media.…”

Association between physician age and patterns of end‐of‐life care among older Americans

“…Additionally, we adjusted for physician characteristics in the models: gender, geriatric training (because a previous study showed different patterns of EOL care by physicians' geriatric training status 38 ), health system affiliation (because a previous study indicated different care patterns by physicians' health system affiliation status [39][40][41][42] ), and the number of attributed beneficiaries (categorized into tertiles). We used physician-level data by Doximity, a professional networking platform for physicians in the U.S. 43,44 and defined physicians' geriatric training as (1) those with a self-reported specialty of geriatric medicine in the MD-PASS data and/or (2) those with board certification in geriatric medicine (both active and inactive) through the American Board of Internal Medicine (ABIM) in the Doximity data.…”