Smart Normal Labor: Healthcare Providers' Experience With an AI-Based Mobile App (Smart labor)

July 21, 2026 updated by: Basma Wageah Mohamed Mohamed Elrefay, Delta University for Science and Technology

Smart Normal Labor From Healthcare Providers' Perspective: Evaluating Clinical Decision-Making Speed, Diagnostic Accuracy, Satisfaction, and Experience Using an AI-Based Mobile Application

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]

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

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.

Study Type

Interventional

Enrollment (Estimated)

427

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Health Services Research
  • Allocation: Non-Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: AI-Based Smart Normal Labor Application
Healthcare providers use the AI-based Smart Normal Labor Application during the management of normal labor to support clinical decision-making, labor monitoring, and timely identification of labor-related conditions.
Other: The intervention group
participants who actively use the AI application during labor management,
No Intervention: Routine Care Group
Healthcare providers provide routine normal labor management according to standard institutional protocols without using the AI-based Smart Normal Labor Application.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Primary Outcome
Time Frame: 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.
During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Secondary Outcome
Time Frame: 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.
During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Delta University for Science and Technology

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 6, 2026

Primary Completion (Actual)

July 21, 2026

Study Completion (Estimated)

July 30, 2026

Study Registration Dates

First Submitted

April 4, 2026

First Submitted That Met QC Criteria

July 21, 2026

First Posted (Actual)

July 22, 2026

Study Record Updates

Last Update Posted (Actual)

July 22, 2026

Last Update Submitted That Met QC Criteria

July 21, 2026

Last Verified

January 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • Smart Normal Labor

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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