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Related Experiment Video

Updated: Jun 18, 2026

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

Risk adjustment using administrative data-based and survey-derived methods for explaining physician utilization.

Lyn M Sibley1, Rahim Moineddin, Mohammad M Agha

  • 1Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. lyn.sibley@utoronto.ca

Medical Care
|November 21, 2009
PubMed
Summary

Administrative data effectively predicts physician use, outperforming survey health indicators. This supports using administrative data for healthcare planning and reimbursement.

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: September 16, 2022

Area of Science:

  • Health Services Research
  • Health Informatics
  • Epidemiology

Background:

  • Predicting physician utilization is crucial for healthcare planning, resource allocation, and ensuring equitable access.
  • Risk adjustment methods are essential for comparing healthcare providers and populations.
  • Administrative data offers a potentially valuable source for risk adjustment, but its predictive accuracy needs validation.

Purpose of the Study:

  • To evaluate an administrative data-based risk adjustment method for predicting physician utilization.
  • To assess the added value of survey-derived health status indicators to administrative data models.
  • To inform the use of administrative data for healthcare planning, reimbursement, and equity assessment.

Main Methods:

  • Linked Canadian Community Health Survey data with administrative physician claims data.
  • Developed explanatory models for family physician (FP) and specialist physician (SP) utilization using demographic information and the Johns Hopkins University Adjusted Clinical Groups (ACG) Case-mix System.
  • Assessed model performance using the coefficient of determination (R) and evaluated the impact of adding survey-based health status measures.

Main Results:

  • Models based solely on administrative data explained 16% to 35% of physician visit variation.
  • The addition of survey-derived health status measures resulted in less than a 2% increase in explanatory power across all outcomes.
  • The study sample included 25,558 individuals, representing approximately 7.8 million people.

Conclusions:

  • Administrative data-based morbidity burden measures are valid predictors of future physician utilization.
  • Survey-derived health status indicators did not significantly enhance the predictive power of administrative data models.
  • Findings support the use of administrative data and Adjusted Clinical Groups (ACG) for healthcare planning, reimbursement, and research.