Findings from Grand Valley State University in the Area of Managed Care and Specialty Pharmacy Described (Predicting pharmacy choice for managed care network design): Drugs and Therapies – Managed Care and Specialty Pharmacy

2026 MAY 28 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Data detailed on Drugs and Therapies - Managed Care and Specialty Pharmacy have been presented. According to news reporting from Grand Rapids, Michigan, by NewsRx journalists, research stated, “Pharmacy type selection is a key component of medication access and use. Prior studies have commonly used logistic regression to examine pharmacy type choice, but this approach may not fully capture complex relationships among patient characteristics.”

The news correspondents obtained a quote from the research from Grand Valley State University, “To examine patient characteristics associated with pharmacy type selection and to compare the performance of traditional logistic regression with ensemble machine learning models for predicting pharmacy type selection. We conducted a cross-sectional analysis of adults participating in the 2021 National Consumer Survey on Medication Experience (NCSME-PR; n = 1,502). The outcome was the pharmacy type selected when a prescription medication was needed, categorized as chain, independent, supermarket, mass merchandise, mail-order, or clinic-based pharmacy. Predictors included patient characteristics defined by the Andersen Behavioral Model of Health Services Use, encompassing predisposing, enabling, and need factors. We compared logistic regression with random forest and extreme gradient boosting (XGBoost) models using 5-fold cross-validation and held-out test data. Model discrimination was assessed using the area under the receiver operating characteristic curve. Across pharmacy types, ensemble models demonstrated higher discrimination than logistic regression, with XGBoost achieving the highest area under the receiver operating characteristic curve values. Prior mail-order pharmacy use, number of chronic conditions, income, and US region were consistently associated with the selection of pharmacy type. Ensemble models captured nonlinear patterns in these associations that were not fully reflected in logistic regression models. Pharmacy type selection varies by patient characteristics and pharmacy type, and predictive performance differs by analytic approach. Comparative modeling indicates that conclusions about pharmacy choice may depend on the modeling framework used.”

According to the news reporters, the research concluded: “These findings contribute to pharmacy choice research and highlight considerations for future studies using managed care administrative data.”

This research has been peer-reviewed.

For more information on this research see: Predicting pharmacy choice for managed care network design. Journal of Managed Care & Specialty Pharmacy, 2026;32(6):679-690. Journal of Managed Care & Specialty Pharmacy can be contacted at: Acad Managed Care Pharmacy, 100 N Pitt St, 400, Alexandria, VA 22314-3134, USA.

Our news journalists report that additional information may be obtained by contacting Suhila Sawesi, Health Informatics and Bioinformatics, Dept. of Information Sciences and Technologies, College of Computing, Grand Valley State University, Grand Rapids, MI. Additional authors for this research include Michael Irungu, Mohamed Rashrash, Pranitha Presingu, Kyle Kane, Jon Schommer and Lawrence Brown.

Publisher contact information for the Journal of Managed Care & Specialty Pharmacy is: Acad Managed Care Pharmacy, 100 N Pitt St, 400, Alexandria, VA 22314-3134, USA.

(Our reports deliver fact-based news of research and discoveries from around the world.)