Predictive Modeling for Electronic Medical Record Adoption Success in Low-Resource Healthcare Settings

Authors

  • Damilola Oluyemi Merotiwon  Independent Researcher, Texas. USA
  • Opeyemi Olamide Akintimehin  Department of Human Nutrition and Dietetics, University of Ibadan, Nigeria
  • Opeoluwa Oluwanifemi Akomolafe  Micmakin Nigeria Limited, Akure, Ondo, Nigeria

Keywords:

Predictive Modeling, EMR Adoption Success, Low-Resource Healthcare, Health Informatics, Implementation Readiness, Digital Transformation

Abstract

Electronic Medical Record (EMR) systems have emerged as transformative tools in modern healthcare delivery, offering improved data management, enhanced clinical decision-making, and greater operational efficiency. However, the adoption of EMR systems in low-resource healthcare settings remains inconsistent and frequently unsuccessful due to infrastructural, organizational, and socioeconomic barriers. This paper proposes a literature-based predictive modeling framework to assess the likelihood of successful EMR implementation in such environments. Drawing from over 100 peer-reviewed articles and case studies, the framework integrates technological readiness, institutional capacity, policy environment, workforce competencies, and sociocultural adaptability. The model aims to assist healthcare planners, policymakers, and donors in identifying key predictors of success and tailoring implementation strategies accordingly. This study does not involve primary data collection; rather, it synthesizes existing literature to propose a structured and adaptable approach to forecasting EMR adoption outcomes in resource-constrained settings.

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International Journal of Scientific Research in Science and Technology

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2022-04-30

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Volume 9, Issue 2, March-April-2022

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Research Articles

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[1]
Damilola Oluyemi Merotiwon, Opeyemi Olamide Akintimehin, Opeoluwa Oluwanifemi Akomolafe "Predictive Modeling for Electronic Medical Record Adoption Success in Low-Resource Healthcare Settings" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 2, pp.631-646, March-April-2022.