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. |
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| 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] |
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| Database: | Education Research Complete |
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| 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] |
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| ISSN: | 0142159X |
| DOI: | 10.1080/0142159X.2025.2519645 |
