Harnessing predictive analytics to support high-risk learners in a one-year certification program in emergency medicine (CPEM) in Pakistan.

Journal: Medical teacher
Published Date: Jun 18, 2025

Abstract

INTRODUCTION: Predictive analytics and Machine Learning (PAML) are gaining traction in health professions education (HPE). Their utilization includes, but is not limited to, guiding student enrollment, identifying at-risk learners, enhancing educational decisions, and allocating proper resources through data-driven insights. This study explored the use of PAML to identify at-risk learners in a one-year Certification Program in Emergency Medicine (CPEM) at the Indus Hospital and Health Network (IHHN), Pakistan with the aim of providing targeted educational support for improved outcome.

Authors

  • Saima Ali
    Department of Emergency Medicine, Indus Hospital and Health Network, Pakistan.
  • Syed Ghazanfar Saleem
    Department of Emergency Medicine, Indus Hospital and Health Network, Pakistan.
  • Priya Arumuganathan
    Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA.
  • Sama Mukhtar
    Department of Emergency Medicine, Indus Hospital and Health Network, Pakistan.
  • Adeel Khatri
    Department of Emergency Medicine, Indus Hospital and Health Network, Pakistan.
  • Megan Rybarczyk
    Department of Emergency Medicine, Perelman School of Medicine, University of Pennsylvania, PA, USA.

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