ArticleExpert review of pharmacoeconomics & outcomes research2025
Enhancing pharmacist intervention targeting based on patient clustering with unsupervised machine learning.
Article in Expert review of pharmacoeconomics & outcomes research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectivesAdherence to the American Diabetes Association (ADA) Standards of Medical Care is low. This study aimed to assist pharmacists in identifying patients for diabetes control interventions using unsupervised machine learning.
methodsThis study analyzed the 2021 Medical Expenditure Panel Survey and used a k-mode cluster analysis. Patient features analyzed were adherence to a select set of preventive measures from the ADA Standards of Medical Care (HbA1c test, foot examination, blood cholesterol test, dilated eye examination, and influenza vaccination) and some patient characteristics (age, gender, health insurance, insulin use, and diabetes-related complications).
resultsThe study included 1,219 patients with self-reported diabetes, and the adherence rate to the ADA standards was 33.72%. Five distinct clusters emerged: (A) moderate-complexity, privately insured male; (B) moderate-complexity, publicly insured female; (C) low-complexity, privately insured female; (D) high-complexity, publicly insured female; (E) moderate-complexity, publicly insured male. Groups B, C, and E exhibited nonadherence.
conclusionsPharmacists can target publicly insured elderly (Groups B and E) and privately insured middle-aged females (Group C) for interventions. For instance, pharmacists may help patients in Groups B and E locate existing resources in their insurance program and remind those in Group C of the importance of adequate diabetes care.
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Registered trials
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