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

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Title: Harnessing predictive analytics to support high-risk learners in a one-year certification program in emergency medicine (CPEM) in Pakistan.
Authors: Ali, Saima1 (AUTHOR) saima.ali13@tih.org.pk, Saleem, Syed Ghazanfar1 (AUTHOR), Arumuganathan, Priya2 (AUTHOR), Mukhtar, Sama1 (AUTHOR), Khatri, Adeel1 (AUTHOR), Rybarczyk, Megan2 (AUTHOR)
Source: Medical Teacher. Jan2026, Vol. 48 Issue 1, p142-149. 8p.
Subject Terms: *Student assistance programs, *Predictive tests, *Educational outcomes, *Continuing medical education, *Professional licensure examinations, *Educational tests & measurements, *Retrospective studies, *Certification, *Longitudinal method, *Academic achievement, *Machine learning, *Learning strategies, *Learning disabilities, Prediction models, Receiver operating characteristic curves, Evaluation of human services programs, Questionnaires, Emergency medicine, Descriptive statistics, Structural equation modeling, Emergency medical services education, Medical records, Acquisition of data, Data analysis software, Sensitivity & specificity (Statistics), Regression analysis
Geographic Terms: Pakistan
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. Methodology: By leveraging data from prior CPEM cohorts (2018–2022, n = 91), regression tree and linear regression machine learning models were compared to predict the final examination performance of the CPEM 2023 learner cohort (n = 26). The models were prospectively applied to identify at-risk learners (n = 14/26). Extra learning support (ELS) was offered as an inclusive measure to everyone, not just the ones flagged by the models and was accepted by ten learners. Data were analyzed for model accuracy and the impact of the educational intervention. Results: Both models showed high accuracy (regression tree: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC)= 0.89; linear regression: AUC= 0.88), though the regression tree model demonstrated slightly better sensitivity and specificity. The models altogether predicted unsatisfactory performance for 14 learners scheduled to sit for the 2023 final examination. Following targeted intervention, eight learners showed improvement in their final scores. Regression tree model was comparatively better in making predictions; however, both models had their limitation. Conclusion: The study demonstrated the feasibility and utility of using PAML to identify at-risk learners and tailor support strategies for enhancing educational outcome in low-resource settings. This additional support can augment expert judgement and ensure equitable educational practices. However, model limitations and ethical concerns, such as algorithmic bias, overfitting, and data imbalance, must be actively addressed in high-stakes assessments. Practice points Predictive analytics and machine learning (PAML) can be used to generate early, data-informed signals that support proactive intervention. Machine learning algorithms capture complex, non-linear relationships between learner characteristics. Real time data integration and faculty feedback can enhance model accuracy. Ethical, inclusive, and targeted interventions provide tailored learning support. Bias mitigation and regular model validation ensure maximal utilization of limited resources. [ABSTRACT FROM AUTHOR]
Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Harnessing predictive analytics to support high-risk learners in a one-year certification program in emergency medicine (CPEM) in Pakistan.
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– Name: Abstract
  Label: Abstract
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  Data: 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. Methodology: By leveraging data from prior CPEM cohorts (2018–2022, n = 91), regression tree and linear regression machine learning models were compared to predict the final examination performance of the CPEM 2023 learner cohort (n = 26). The models were prospectively applied to identify at-risk learners (n = 14/26). Extra learning support (ELS) was offered as an inclusive measure to everyone, not just the ones flagged by the models and was accepted by ten learners. Data were analyzed for model accuracy and the impact of the educational intervention. Results: Both models showed high accuracy (regression tree: Area Under the Receiver Operating Characteristic (ROC) Curve (AUC)= 0.89; linear regression: AUC= 0.88), though the regression tree model demonstrated slightly better sensitivity and specificity. The models altogether predicted unsatisfactory performance for 14 learners scheduled to sit for the 2023 final examination. Following targeted intervention, eight learners showed improvement in their final scores. Regression tree model was comparatively better in making predictions; however, both models had their limitation. Conclusion: The study demonstrated the feasibility and utility of using PAML to identify at-risk learners and tailor support strategies for enhancing educational outcome in low-resource settings. This additional support can augment expert judgement and ensure equitable educational practices. However, model limitations and ethical concerns, such as algorithmic bias, overfitting, and data imbalance, must be actively addressed in high-stakes assessments. Practice points Predictive analytics and machine learning (PAML) can be used to generate early, data-informed signals that support proactive intervention. Machine learning algorithms capture complex, non-linear relationships between learner characteristics. Real time data integration and faculty feedback can enhance model accuracy. Ethical, inclusive, and targeted interventions provide tailored learning support. Bias mitigation and regular model validation ensure maximal utilization of limited resources. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Teacher is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/0142159X.2025.2519645
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 142
    Subjects:
      – SubjectFull: Student assistance programs
        Type: general
      – SubjectFull: Predictive tests
        Type: general
      – SubjectFull: Educational outcomes
        Type: general
      – SubjectFull: Continuing medical education
        Type: general
      – SubjectFull: Professional licensure examinations
        Type: general
      – SubjectFull: Educational tests & measurements
        Type: general
      – SubjectFull: Retrospective studies
        Type: general
      – SubjectFull: Certification
        Type: general
      – SubjectFull: Longitudinal method
        Type: general
      – SubjectFull: Academic achievement
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Learning strategies
        Type: general
      – SubjectFull: Learning disabilities
        Type: general
      – SubjectFull: Prediction models
        Type: general
      – SubjectFull: Receiver operating characteristic curves
        Type: general
      – SubjectFull: Evaluation of human services programs
        Type: general
      – SubjectFull: Questionnaires
        Type: general
      – SubjectFull: Emergency medicine
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Emergency medical services education
        Type: general
      – SubjectFull: Medical records
        Type: general
      – SubjectFull: Acquisition of data
        Type: general
      – SubjectFull: Data analysis software
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      – SubjectFull: Sensitivity & specificity (Statistics)
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      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Pakistan
        Type: general
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      – TitleFull: Harnessing predictive analytics to support high-risk learners in a one-year certification program in emergency medicine (CPEM) in Pakistan.
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              Text: Jan2026
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