Andrew Bazemore

Background Medical claims are widely used in workforce estimation and health services research but identifying primary care (PC) clinicians is challenging. Existing methods relying on specialty or activity-based metrics are imprecise and hard to duplicate. Objective We seek to develop a simple, accurate decision tree classifier using claims data, trained on survey responses from family physicians (FPs). And to use this classifier to estimate the PC workforce in Virginia. Design We linked 2016–2023 Virginia All-Payer Claims Database (APCD) data with responses from American Board of Family Medicine surveys to infer whether FPs provided PC. Using claims-derived features, we trained a decision tree to classify clinicians providing PC. We developed an enhanced version of the tree, adding exclusion criteria, and applied both classifiers to the entire APCD, including other PC clinician types. Subjects Virginia clinicians. Main Measures Estimated classifier accuracy by clinician type and workforce estimates with bootstrapping used to estimate 95% confidence intervals. Key Results The base decision tree correctly classified 93% of the training sample using three clinician-level features: percent of claims with diagnosis category of Z00, percent with place of service 19–23 or 31–32, and percent of patients having multiple visits with the clinician. We estimate the enhanced tree achieved at least 89% accuracy for every clinician type. We found that from 2016 to 2023, the percentage of Virginia PC clinicians who were nurse practitioners (NPs) grew from 17 to 32%, with NPs passing FPs as the most common PC clinician type. Conclusions This decision tree approach overcomes shortcomings of existing methods and offers a straightforward, scalable, interpretable tool for classifying PC clinicians, with applications in workforce planning and health services research.

Purpose: The data-generating mechanisms underlying health care data are infrequently considered, leading to inequitable equilibria being reinforced throughout the care continuum. As race-based criteria are reassessed, including in chronic kidney disease, the effect of those criteria on patterns of disease progression should also be reevaluated. We proposed a microsimulation model for attenuating societal bias in primary care chronic kidney disease data to study this. Methods: We developed a continuous-time, discrete-event individual-level simulation model of kidney function decline, measured by estimated glomerular filtration rate (eGFR). The model simulates individual eGFR trajectories over time and enables generating counterfactual outcome distributions that would have been observed in the absence of race-based diagnosis and treatment criteria. eGFR decline is accelerated by hypertension, diabetes, and reaching chronic kidney disease stage 3a and can be delayed by interventions, which are applied based on eGFR level, measured with or without an adjustment for Black race. A Bayesian calibration procedure was applied to identify rates of eGFR decline corresponding to stage distributions in the cohort. Results: Under the counterfactual scenario without a race adjustment, Black individuals qualify for diagnosis earlier, and non-Black individuals later, than under the reference scenario with race adjustment. The difference was largest for earlier stages and smaller at each consecutive stage. We do not observe differences in life expectancy between the 2 scenarios. Limitations: Large variability in the prevalence of treatment and heterogeneity in treatment effectiveness may affect our results. Conclusions: Beyond estimating the clinical consequences of the eGFR equation change, our work offers an alternative to previously proposed data-debiasing approaches. The simulated data can be used to inform future interventions and policy decisions. Highlights: We developed a microsimulation model of chronic kidney disease progression with primary care data that reflect the effect of removing race-based diagnostic and treatment criteria. The removal of race-based diagnostic criteria in our simulations changed the timing of qualification for chronic kidney disease diagnosis, ranging from 0.6 y to 9.6 y, with opposite effects for Black and non-Black patients. The simulated differences in expected survival after removing the race adjustment did not exceed 2 mo among individuals who developed chronic kidney disease. The explicit representation of the data-generation process can help anticipate the effect that policy changes can have on clinical data distributions.

Related to recent federal directives to strengthen physician nutrition education, this paper examines family medicine's leadership in nutrition counseling while identifying modifiable barriers limiting primary care's prevention potential. Family physicians, comprising over 109 000 certified clinicians, provide 20% of U.S. healthcare visits and deliver substantial nutrition counseling, particularly in underserved communities. The American Board of Family Medicine dedicates 5% of certification content to nutrition/obesity and 25% to chronic disease care. However, structural barriers significantly constrain implementation. Medicare only reimburses nutrition counseling for end-stage conditions (diabetes and kidney disease), frustrating key opportunities to help patients. Primary care receives <5% of national health spending and under 1% of federal research funding, despite handling half of all office visits. The workforce lacks integrated nutritionists and dietitians, with physicians 20 times more likely to address nutrition when services are covered. We propose federal actions to transform primary care's nutrition capacity: expanding Medicare coverage to earlier disease stages and obesity, increasing primary care investment, directing research investment to community practice settings, supporting workforce integration of nutrition professionals, and developing meaningful quality measures. These aligned policies could unleash primary care's prevention potential.

Primary care has long played an important role in vaccination in the US and around the world. However, the COVID-19 pandemic disrupted conventional administration patterns for many vaccines, in part through an increased emphasis on mass vaccination sites and pharmacies as a way to expedite the rollout of the COVID vaccine. In an era of increased vaccine hesitancy, though, the frequency of long-term, trusting relationships between patients and their primary care providers may bolster diminishing adherence to recommended vaccine schedules. In this study, we used the American Family Cohort – the US’s largest primary care clinical registry – to assess how patterns of vaccination in primary care have changed since 2017 for ten vaccines recommended on the basis of age. We also assessed how these trends have changed among practices with varying rates of care continuity to test our hypothesis that an ongoing patient-provider relationship improves vaccination rates. In a total of 61,766,185 visits for 6,943,589 patients conducted at 1,323 practices, we observed that the influenza vaccine was the most commonly administered vaccine, followed by pneumococcus. The rates of many vaccines peaked in 2019, though, mirroring national trends that suggest few vaccines are administered at the same rates as they were post-pandemic. We found little support for our hypothesis that care continuity is related to vaccination rates. The population health impact of payment policies and commercial initiatives that shift vaccine administration out of primary care is unclear at this point, but merits ongoing surveillance.

Purpose: Understanding primary care panel sizes is crucial as US policy makers and workforce planners wrestle with both primary care shortage and rising clinician burnout. We aimed to investigate physician and practice factors associated with variation in panel size. Methods: We analyzed data from the 2017 to 2023 American Board of Family Medicine (ABFM) Family Medicine Continuing Certification Questionnaire. The nationally-representative, mandatory (100% response rate) questionnaire included demographic, practice, and panel size data for family physicians (FPs). Bivariate analyses and generalized linear models with a log-linked γ distribution examined associations between panel size and physician, practice, and area characteristics. Results: 21,770 FP respondents reported estimated panel size averaging 1,933 patients/FP. On adjusted analyses, FPs in independently owned practices reported the largest panel sizes, while those in academic practices and federal clinics had the smallest panel sizes. FPs working in practices of 2 to 5 physicians had larger panel sizes than those in solo or larger group practices. FPs providing obstetric and inpatient care had smaller panel sizes than their counterparts. Working with nurse practitioners or physician assistants was associated with larger panel sizes. A greater proportion of vulnerable patients in a practice was associated with smaller panel sizes. Panel size decreased over time after adjusting for other predictors. Conclusions: Panel sizes among FPs vary significantly based on practice type and size, team composition, and scope of practice. These findings highlight the importance of considering both individual and practice-level factors in workforce planning and policy development to optimize primary care delivery and manage physician workloads effectively.

Objective: Continuity of primary care reduces costs and hospitalizations over 1-year periods, but its long-term effects remain unclear, a key concern for value-based payment. We examined associations between physician-level continuity and health care expenditures/utilization across single- and multi-year measurement periods. Design: Retrospective cohort study using 2011 to 2017 Medicare Fee-for-Service (FFS) claims. We constructed physician-level continuity measures over 1- to 5-year lookback periods. Generalized linear models estimated associations with total Medicare Part A & B expenditures, and logit models assessed hospitalization and emergency department (ED) visits, adjusting for patient and physician characteristics. Setting & Participants: Nationally representative sample of 4,940 primary care practices, including 1.1 to 2.5 million Medicare FFS beneficiaries seen by 6,758-14,949 physicians. Results: Beneficiaries in the highest continuity quintile had 7.4%-10.4% lower total expenditures than those in the lowest quintile, with the greatest difference in the 1-year lookback. Hospitalization and ED visit odds were 5.5%-8.6% and 4.9%-6.3% lower, respectively, for high-continuity physicians. Effects attenuated slightly with longer lookbacks. Conclusion: Physician continuity is consistently associated with lower costs, hospitalizations, and ED visits across lookback periods. Given this stability and the complexity of multi-year claims measurement, a 1-year assessment may be sufficient for physician continuity evaluation and value-based payment programs.

Introduction: Comprehensiveness in primary care is defined as managing most medical needs in a population while integrating the context of patient’s values, preferences, and beliefs. This study aims to synthesise validated measures for measuring comprehensiveness in primary care to facilitate its practical application. Aim: The objective of this study was to synthesise validated measures for measuring comprehensiveness in primary care, facilitating its practical application. Methods: A narrative-style literature review was employed to conduct a hierarchical review of relevant literature. The process involved several stages: initial term filtering, separation of primary care from specialist care using medical subject heading (MeSH) terms, incorporation of non-MeSH terminology, and a manual review of titles, abstracts, and full articles. Articles were included if they discussed the measurement, assessment, or application of comprehensiveness in primary care and were relevant to primary care and methodologically sound. A multistage PubMed search of ‘comprehensiveness’ (MeSH) with hierarchical sub-term filtering and snowball method gleaning of additional articles from literature-described terminology was conducted. Results: Thirteen studies met the inclusion criteria. Methodological strategies varied from claims-based approaches for cost and utilisation to surveys assessing the scope of clinical services and patient experience. Discussion: Thoroughly measuring comprehensiveness in primary care integrates methods that evaluate the effect of physician ranges of clinical services on the cost and utilisation of health care, and the impact on patient outcomes within the context of the patient experience. Implementing these methods pragmatically can assist communities and health systems in implementing, measuring, and capturing comprehensiveness in primary care.

Comprehensiveness in primary care is defined as managing most medical needs in a population while integrating the context of patient's values, preferences, and beliefs. This study aims to synthesise validated measures for measuring comprehensiveness in primary care to facilitate its practical application. The objective of this study was to synthesise validated measures for measuring comprehensiveness in primary care, facilitating its practical application. A narrative-style literature review was employed to conduct a hierarchical review of relevant literature. The process involved several stages: initial term filtering, separation of primary care from specialist care using medical subject heading (MeSH) terms, incorporation of non-MeSH terminology, and a manual review of titles, abstracts, and full articles. Articles were included if they discussed the measurement, assessment, or application of comprehensiveness in primary care and were relevant to primary care and methodologically sound. A multistage PubMed search of 'comprehensiveness' (MeSH) with hierarchical sub-term filtering and snowball method gleaning of additional articles from literature-described terminology was conducted. Thirteen studies met the inclusion criteria. Methodological strategies varied from claims-based approaches for cost and utilisation to surveys assessing the scope of clinical services and patient experience. Thoroughly measuring comprehensiveness in primary care integrates methods that evaluate the effect of physician ranges of clinical services on the cost and utilisation of health care, and the impact on patient outcomes within the context of the patient experience. Implementing these methods pragmatically can assist communities and health systems in implementing, measuring, and capturing comprehensiveness in primary care.

Many Americans received compounded semaglutide and tirzepatide when these drugs were in shortage, but there is little understanding of how and how often they were used, limiting our ability to ensure safety, coordination, and appropriate regulation. We analyzed retrospective cohort data from the American Family Cohort from January 2021 to December 2024. We identified patients with documented semaglutide or tirzepatide use, categorizing them by brand and/or compounded drug exposure. Brand prescriptions were extracted from structured data; compounded drug use was identified from clinical notes. Among 153,075 included patients (64.0% female, mean age 55.0 years), 8.3% used compounded formulations. Compounded drug users had longer therapy durations versus brand-only users. Documentation rates of compounded semaglutide and tirzepatide use in primary care appear lower than survey-reported usage, suggesting that many patients received these medications outside coordinated care. Improved data interoperability, regulatory oversight, and clinical guidance are needed.