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Measuring restrictiveness of Medicare Advantage networks: A claims-based approach.

Feyman Y, Pizer SD, Shafer PR, Frakt AB, Garrido MM. Measuring restrictiveness of Medicare Advantage networks: A claims-based approach. Health services research. 2024 Feb 1; 59(1):e14255.

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Abstract:

OBJECTIVE: To develop and validate a measure of provider network restrictiveness in the Medicare Advantage (MA) population. DATA SOURCES: Prescription drug event data and beneficiary information for Part D enrollees from the Center for Medicare and Medicaid Services, along with prescriber identifiers; geographic variables from the Area Health Resources Files. STUDY DESIGN: A prediction model was used to predict the unique number of primary care providers that would have been seen by MA beneficiaries absent network restrictions. The model was trained and validated on Traditional Medicare (TM) beneficiaries. A pseudo-Poisson and a random forest model were evaluated. An observed-to-expected (O/E) ratio was calculated as the number of unique providers seen by MA beneficiaries divided by the number expected based the TM prediction model. Multivariable linear models were used to assess the relationship between network restrictiveness and plan and market factors. DATA COLLECTION/EXTRACTION METHODS: Prescription drug event data were obtained for a 20% random sample of beneficiaries enrolled in prescription drug coverage from 2011 to 2017. PRINCIPAL FINDINGS: Health Maintenance Organization plans were more restrictive (O/E = 55.5%; 95% CI 55.3%-55.7%) than Health Maintenance Organization-Point of Service plans (67.2%; 95% CI 66.7%-67.8%) or Preferred Provider Organization plans (74.7%; 95% CI 74.3%-75.1%), and rural areas had more restrictive networks (31.6%; 95% CI 29.0%-34.2%) than metropolitan areas (61.5%; 95% CI 61.3%-61.7%). Multivariable results confirmed these findings, and also indicated that increased provider supply was associated with less restrictive networks. CONCLUSIONS: We developed a means of estimating provider network restrictiveness in MA from claims data. Our results validate the approach, providing confidence for wider application (e.g., for other markets and specialties) and use for regulation.





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