ArticleJournal of general internal medicine2026
Reclassifying Primary Care: A Decision Tree Approach to Improving Workforce Estimates and Research Using Claims Data.
Article in Journal of general internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMedical 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.
objectiveWe 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.
designWe 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
resultsThe 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.