Cost-effectiveness of the 3E model in diabetes management - a machine learning approach to assess long-term economic impact

Aug 5, 2026

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This original research evaluates the cost-effectiveness and clinical impact of the 3E model—education, empowerment, and economy—in diabetes management using advanced machine learning methods. An observational longitudinal descriptive analysis followed 320 patients across intervention and control groups for 24 months. The intervention achieved a 74.3% reduction in total costs versus 41.8% in controls while maintaining glycemic control. Models using random forest and clustering identified cost drivers and responsive patient subgroups; natural language processing extracted medication patterns linked to greater savings.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Background

Economic burden of diabetes

Need for cost management strategies

Methods

Study design and participants

Machine learning and NLP approaches

Results

Cost reductions and glycemic control

Key cost factors and patient segmentation

Medication patterns and long-term projections

Conclusion

Implications for healthcare policy

TYPE Original Research PUBLISHED 23 May 2025 DOI 10.3389/fpubh.2025.1571546 OPEN ACCESS EDITED BY Thanitsara Rittiphairoj, Harvard University, United States REVIEWED BY Mohammad Mobashir, Norwegian University of Science and Technology (NTNU), Norway Jiahe Li, University of Pittsburgh, United States Vedat Caner, Beykent University, Türkiye *CORRESPONDENCE Supriya Raghav 2020phdoddsupriya8972@ [poornima.edu.in](poornima.edu.in); [raghav.supriya@gmail.com](raghav.supriya@gmail.com)[ ](raghav.supriya@gmail.com)RECEIVED 05 February 2025 ACCEPTED 30 April 2025 PUBLISHED 23 May 2025 CITATION Raghav S, Kumar S, Ashraf H and Khanna P (2025) Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact. Front. Public Health 13:1571546 . doi: 10.3389/fpubh.2025.1571546 COPYRIGHT © 2025 Raghav, Kumar, Ashraf and Khanna. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact Supriya Raghav 1*, Santosh Kumar 1, Hamid Ashraf 2 and Poonam Khanna3 1 Department of Public Health, School of Public Health, Poornima University, Jaipur, India, 2 Rajiv Gandhi Centre for Diabetes and Endocrinology, J. N Medical College, Aligarh Muslim University, Aligarh, India, 3School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India Background: This study investigated the cost-effectiveness and clinical impact of the 3E model (education, empowerment, and economy) in diabetes management using advanced machine learning techniques. Methods: We conducted an observational longitudinal descriptive analysis involving 320 patients, who were grouped into intervention and control groups over a 24-month period. Results: The 3E model demonstrated significant cost reductions, with the intervention group achieving a 74.3% decrease in total costs compared to 41 .8% in the control group while maintaining the same level of glycemic control. Machine learning models, including random forest and K-means clustering, were used to identify key factors influencing treatment costs and to segment patient subgroups that were most responsive to the intervention. Natural language processing techniques revealed medication patterns associated with greater cost reductions. Long-term projections using ensemble methods (such as XG Boost, Exponential Smoothing, and Prophet) predicted that, on average, each year contributes approximately 20% to the total cumulative savings over 5 years. No significant correlations were observed between cost reduction and socioeconomic factors, gender, or age, suggesting the broad applicability of the 3E model. Conclusion: These findings demonstrate the potential of the 3E model to achieve significant reductions in diabetes management costs without compromising care quality, highlighting its value for healthcare policy and resource allocation in chronic disease management. KEYWORDS diabetes management, 3E model, cost-effectiveness, machine learning, patient empowerment, natural language processing, long-term projections, medication patterns Introduction Diabetes mellitus is a well-known non-communicable disease that poses a significant threat to the global community. It is characterized by high blood glucose levels that lead to various health challenges and represents a global health crisis. The International Diabetes Federation (IDF) estimated that in 2021, 537 million adults were living

FAQs

What is the 3E model in this study?

The 3E model consists of education, empowerment, and economy, applied to diabetes management to improve outcomes while reducing costs.

How was the study conducted and for how long?

The study used an observational longitudinal descriptive design with 320 patients, followed over a 24-month period with intervention and control groups.

Which machine learning techniques were used to analyze cost drivers and patient response?

Random forest and K-means clustering were used to identify key factors influencing treatment costs and to segment patient subgroups most responsive to the intervention.

Aug 5, 2026

Upload by Skyler