All Scientific Councils

Implementation of the General Secretariat's Strategic Plan in Measurement and Evaluation

In line with the General Secretariat's strategic plan, the Arab Board of Health Specializations (ABHS) is committed to advancing assessment methodologies through digital transformation. This approach ensures standardized, objective, and high-quality evaluations across all specialties.

The scientific councils within the Arab Board of Health Specializations (ABHS) rely on digital item banks for knowledge-based exams and clinical assessments, where questions and clinical stations are categorized based on subjects, educational objectives, competencies, and difficulty levels. This structured approach enables the implementation of an exam blueprint aligned with the scientific council’s directives for evaluating trainees, allowing the council, through the examination committees, to assess the quality of exams with precision and objectivity.

The digital examination management system provides accurate and precise data. The General Secretariat, through the Assessment Unit and in coordination with the scientific councils' examination committees, analyzes this data using automation and artificial intelligence (AI).

Classical and advanced statistical models are applied to evaluate question performance and measure their ability to differentiate between candidates with varying competency levels. Additionally, difficulty and discrimination indices are used to assess the appropriateness of questions in fairly and accurately evaluating trainees' competencies.

Furthermore, statistical analysis evaluates the performance of both trainees and training centers, facilitating data-driven decision-making. Based on these insights, recommendations are provided to the Curriculum Development Committee to enhance training content and assessment tools, with a focus on addressing knowledge and skill gaps among trainees and improving the effectiveness of training programs.

This integration of psychometric assessment, statistical analysis, and AI contributes to greater transparency in evaluation, more precise decision-making, and the overall improvement of medical education and training quality, ensuring alignment with international standards for competency-based medical assessment.