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  1. Home
  3. Reclassifying Primary Care: A Decision Tree Approach To Improving Workforce Estimates And Research Using Claims Data.
  1. Home
  3. Reclassifying Primary Care: A Decision Tree Approach To Improving Workforce Estimates And Research Using Claims Data.

Related Experiment Video

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Published on: January 11, 2020

Reclassifying Primary Care: A Decision Tree Approach to Improving Workforce Estimates and Research Using Claims Data.

Zachary J Morgan1, Andrew W Bazemore2,3, Lars E Peterson2,4

  • 1American Board of Family Medicine, Lexington, Lexington, KY, USA. zmorgan@theabfm.org.

Journal of General Internal Medicine
|May 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new decision tree model accurately identifies primary care (PC) clinicians using claims data. Nurse practitioners (NPs) are increasingly becoming the most common PC providers in Virginia, surpassing family physicians (FPs).

Keywords:
algorithmsdecision treeshealth workforcephysicians, primary careprimary health care

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Health Services Research
  • Medical Informatics
  • Workforce Studies

Background:

  • Identifying primary care (PC) clinicians from medical claims data is crucial for workforce estimation but current methods lack precision and reproducibility.
  • Existing specialty or activity-based metrics for PC clinician identification are insufficient for accurate workforce analysis.

Purpose of the Study:

  • To develop a simple, accurate decision tree classifier using claims data to identify PC clinicians, trained on family physician (FP) survey responses.
  • To apply this classifier to estimate the PC workforce composition in Virginia.

Main Methods:

  • Linked Virginia All-Payer Claims Database (APCD) data (2016-2023) with American Board of Family Medicine survey responses to identify PC FPs.
  • Trained a decision tree classifier using claims-derived features (e.g., diagnosis codes, place of service, patient visit frequency) to classify PC providers.
  • Developed an enhanced classifier with exclusion criteria and applied both to the APCD to estimate the PC workforce, including nurse practitioners (NPs) and other clinician types.

    • Main Results:

    • The base decision tree achieved 93% accuracy in classifying PC clinicians using three key features.
    • The enhanced decision tree demonstrated at least 89% accuracy across all clinician types.
    • Between 2016 and 2023, the proportion of NPs in Virginia's PC workforce grew from 17% to 32%, surpassing FPs as the most common PC provider type.

    Conclusions:

    • The developed decision tree classification method offers a scalable, interpretable, and accurate tool for identifying PC clinicians from claims data.
    • This approach addresses limitations of previous methods and is valuable for workforce planning and health services research.
    • The findings highlight a significant shift in the PC workforce composition, with NPs becoming the predominant provider type in Virginia.