Leveraging Artificial Intelligence to Strengthen Nursing Workforce Management in Saudi Arabia: A Secondary Data Analysis Aligned with Vision 2030

nursing workforce, Saudi Arabia, artificial intelligence, Vision 2030, workforce planning, Saudization

Dr. Firoz Khan1*
Assistant Professor, Department of Healthcare
Management, Batterjee Medical College,
Khamis Mushait, Asser, Saudi Arabia
Email-firoz.khan@bmc.edu.sa, ORCID ID0000-0002-7935-2177

Dr. Faraj Zubaidi2
Assistant Professor, Department of Healthcare
Management, Batterjee Medical College,
Khamis Mushait, Asser, Saudi Arabia.
Email-faraj.zubaidi@bmc.edu.sa. ORCID IDhttps://orcid.org/0000-0001-6136-8755

Ms. Bedour Basalamah3
Lecturer, Department of Healthcare
Management, Batterjee Medical College,
Khamis Mushait, Aseer, Saudi Arabia. Email:
bedour.basalamah@bmc.edu.sa. ORCID ID:
0009-0005-6278-6218

Dr. Farheen Fatima4
Assistant Professor, Preparatory Department,
Batterjee Medical College, Khamis Mushait,
Aseer, Saudi Arabia. Email:
farheen.mohammed@bmc.edu.sa,
drfarheenkhizar344@outlook.com. ORCID ID0000-0003-1083-2612

Address for correspondence:

Dr. Firoz Khan,
Assistant Professor, Department of Healthcare
Management, Batterjee Medical College,
Khamis Mushait, Asser, Saudi Arabia
Email-firoz.khan@bmc.edu.sa,
ORCID ID-0000-0002-7935-2177

Received: 11-05-2026
Revised: 15-06-2026
Accepted: 25-06-2026

European Journal of Prosthodontics and Restorative Dentistry (2026) 34(5s), 28–37

Leveraging Artificial Intelligence to Strengthen Nursing Workforce Management in Saudi Arabia: A Secondary Data Analysis
Aligned with Vision 2030

Saudi Arabia's Vision 2030 health reforms position the nursing workforce as central to national healthcare transformation, yet workforce adequacy is often assessed through single, nationallevel indicators that may obscure sectoral and regional variation. This study conducted a secondary, retrospective analysis of Ministry of Health-derived nursing workforce data spanning 2017-2024, combining national sector-year trends with a 2024 regional dataset covering all 13 administrative regions. Descriptive trend analysis, inequality statistics, composite pressure indices, unsupervised clustering, and scenario-based projections to 2030 were applied. The national nursing and midwifery workforce grew from 185,693 to 249,475 personnel, driven disproportionately by private-sector expansion, which nonetheless recorded the lowest Saudization rate (10.7%) among sectors. Regional analysis revealed that high nurse headcounts did not consistently correspond to low workforce pressure; Al Baha, Qassim, and Makkah emerged as recurring high-pressure regions across independent weighting and clustering methods, although sensitivity testing indicated some rank instability. Projections to 2030 showed continued workforce growth across all scenarios, alongside rising service demand per nurse. These findings support a shift toward artificial intelligence-enabled, regional, sector-aware, and scenario-based workforce planning to strengthen forecasting, prioritisation, and targeted intervention in alignment with Vision 2030 objectives.

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