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. \[2\]
General Objective To evaluate the impact of an artificial intelligence (AI)-based smart normal labor application on healthcare providers' clinical decision-making speed, diagnostic accuracy, satisfaction, and overall clinical experience during the management of normal labor. Specific Objectives To assess the effect of the AI-based smart normal labor application on the speed of clinical decision-making among obstetricians and nurses during the management of normal labor. To evaluate the effect of the AI-based smart normal labor application on diagnostic accuracy during the management of normal labor. To evaluate healthcare providers' satisfaction with the AI-based smart normal labor application. To assess healthcare providers' overall clinical experience while using the AI-based smart normal labor application during normal labor management. To identify barriers and facilitators associated with the adoption and usability of the AI-based smart normal labor application in clinical practice.
Study Type
INTERVENTIONAL
Allocation
NON_RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Enrollment
427
participants who actively use the AI application during labor management,
Basma wageah Basma
Al Mansurah, Dakahlia Governorate, Egypt
RECRUITINGPrimary 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.
Time frame: During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).
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.
Time frame: During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).
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