Pregnancy and childbirth are uniquely important events in women's lives because they are accompanied by major physical, emotional, and psychological changes. Maternal satisfaction, emotional well-being, and perceptions of childbirth are strongly influenced by the quality of labor management. A woman's childbirth experience is shaped by multiple factors, including communication, autonomy, and active participation in the decision-making process. These factors are widely recognized as important indicators of the quality of maternity care. \[1\]
Recent demographic changes and global population growth have placed increasing demands on healthcare systems, particularly maternal health services. High birth rates in some regions, combined with shortages of trained healthcare professionals, have created a need for scalable, adaptable, and innovative models of care. In response to these challenges, digital health technologies have emerged as promising tools to enhance the quality of maternity care and support both healthcare providers and pregnant women. \[2\]
Healthcare providers (obstetricians and nurses) currently working in the Labor Kiosk, Obstetrics and Gynecology Department, or Outpatient Gynecology Clinics at Mansoura University Hospital.
Direct involvement in the care and supervision of women in active labor.
For the intervention group: previous exposure to and use of the AI-based smart normal labor application for a minimum defined period (e.g., 1 month).
For the control group: no prior use of the AI-based application, following standard care practices.
Are healthcare providers not directly involved in labor management (e.g., administrative staff or laboratory personnel).
Have less than the minimum required clinical experience in labor management (e.g., <6 months).
Are on leave or unavailable during the study period.
Decline to participate or do not provide informed consent.
Parallel
The intervention group
Other interventionparticipants who actively use the AI application during labor management,
Primary Outcome
The time required for healthcare providers to make appropriate clinical decisions during the management of normal labor, measured using a structured clinical decision-making assessment tool.
Secondary Outcome
Clinical decision-making accuracy will be assessed using a validated Clinical Decision-Making Checklist for Normal Labor. Total scores range from \[minimum\] to \[maximum\], with higher scores indicating greater clinical decision-making accuracy.
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