Normal Labor
Conditions
Keywords
Normal Labor, Clinical Decision-Making in Labor, Artificial Intelligence
Brief summary
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. \[2\]
Detailed description
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.
Interventions
participants who actively use the AI application during labor management,
Sponsors
Delta University for Science and Technology
Study design
Allocation
NON_RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE (Subject)
Intervention model description
quasi-experimental study
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
Participants must meet the following conditions to be included in the study: 1. Healthcare providers (obstetricians and nurses) currently working in the Labor Kiosk, Obstetrics and Gynecology Department, or Outpatient Gynecology Clinics at Mansoura University Hospital. 2. Direct involvement in the care and supervision of women in active labor. 3. 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). 4. For the control group: no prior use of the AI-based application, following standard care practices. 5. Willingness to participate and provide informed consent.
Exclusion criteria
Participants will be excluded if they: 1. Are healthcare providers not directly involved in labor management (e.g., administrative staff or laboratory personnel). 2. Have less than the minimum required clinical experience in labor management (e.g., \<6 months). 3. Are on leave or unavailable during the study period. 4. Decline to participate or do not provide informed consent.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Primary Outcome | During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours). | 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
| Measure | Time frame | Description |
|---|---|---|
| Secondary Outcome | During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours). | 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. |
Countries
Egypt
Contacts
CONTACTBasma W Basma
Outcome results
None listed