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Towards Electronic Medical Record (EMR) utilization in developing countries: a conceptual model based on systematic
Abolfazl Rezaei1, Maryam Jahanbakhsh2, Hamid Reza Dehghan3
1Health Information Technology Research Center, Department of Health Information Technology Management, Faculty of Medical Management and Informatics, Isfahan University of Medical Sciences, Isfahan, Iran.
BMC Medical Informatics and Decision Making
|June 25, 2026
Summary
This study presents a flexible, iterative model for implementing Electronic Medical Records (EMR) in resource-limited settings. It addresses challenges by distinguishing core needs from optimization features for scalable adoption.
Area of Science:
- Health Informatics
- Information Systems in Healthcare
- Global Health Technology
Background:
- Paper-based medical records hinder healthcare efficiency in developing nations.
- Electronic Medical Records (EMR) promise improved care but face adoption barriers in resource-limited settings.
Purpose of the Study:
- To develop a conceptual model for designing, developing, and implementing EMR systems tailored for resource-limited environments.
- To provide a framework that addresses the unique infrastructural, financial, and contextual challenges in these settings.
Main Methods:
- A systematic review of five databases identified relevant studies on EMR.
- Qualitative analysis of eligible articles synthesized key components and challenges.
- A conceptual model was developed using the System Development Life Cycle (SDLC) as a framework.
Main Results:
- Analysis of 80 studies yielded a three-layered framework (Foundation, Core Cycle, Optimization) with seven categories.
- Key dimensions identified include strategic foundations, system architecture, user experience, interoperability, implementation, ethics, and emerging technologies.
- These findings outline critical aspects of EMR development in resource-constrained contexts.
Conclusions:
- The proposed iterative model facilitates constructive EMR implementation in resource-limited settings.
- Distinguishing core necessities from optimization features enables flexible and scalable adoption strategies.
- This approach better accommodates local infrastructural constraints compared to rigid, linear models.
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