Normal LaborNCT07720531
Generative AI-Powered Intrapartum Clinical Decision Support Among Healthcare Providers
A study testing Generative AI-Powered Intrapartum Chatbot in people with Normal Labor, run by Delta University for Science and Technology at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Normal Labor
- What is being tested?
- Generative AI-Powered Intrapartum Chatbot
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 427 participants
- Where
- 1 siteEgypt
- Study dates
- 6 May 2026to 29 Oct 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- Obstetricians and nurses 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.
- At least six months of clinical experience in labor management. For the intervention group: completion of at least two months of training on the generative AI-powered Smart Normal Labor intrapartum chatbot before its use with clinical cases during the intervention.
- For the intervention group: use of the generative AI-powered Smart Normal Labor intrapartum chatbot during clinical labor management.
- For the control group: provision of routine care without prior use of the study chatbot.
- Willingness to participate and provide informed consent.
Who cannot join
- Healthcare providers not directly involved in labor management, including administrative staff and laboratory personnel.
- Less than six months of clinical experience in labor management.
- Being on leave or unavailable during the study period.
- Declining participation or not providing informed consent.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Generative AI-Powered Intrapartum Clinical Decision Support and Healthcare Providers' Decision-Making Speed, Diagnostic Accuracy, Satisfaction, and Clinical Experience: A Quasi-Experimental Study
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]
Read the detailed description
This single-center, non-randomized, parallel-group quasi-experimental study evaluated a generative AI-powered intrapartum clinical decision-support chatbot among healthcare providers involved in normal labor management.
The study included 427 obstetricians and nurses: 214 in the intervention group and 213 in the control group. Providers in the intervention group used the AI-based Smart Normal Labor application to support clinical assessment and decision-making during routine labor care. Providers in the control group followed conventional institutional care practices without using the application.
The evaluation combined assessments of clinical decision-making performance with provider-reported satisfaction, acceptance, clinical experience, and workflow. It also explored perceived barriers and facilitators to application use in clinical practice. The study focused on provider-level outcomes; maternal and neonatal clinical outcomes were not directly assessed.
- Conditions
- Normal Labor
- Being tested
- Generative AI-Powered Intrapartum Chatbot
- Keywords
- Normal Labor, Clinical Decision-Making in Labor, Artificial Intelligence, Generative Artificial Intelligence, Chatbot, Diagnostic Accuracy, Intrapartum Care
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Generative AI-Powered Intrapartum ChatbotOther
Also called Smart Normal Labor
Smart Normal Labor is a mobile application incorporating a generative AI-powered chatbot for healthcare providers. It provides interactive clinical decision support using information entered by providers about labor progress and clinical findings. Its intended functions include supporting interpretation of clinical information and formulation of a management plan.
| Study group | Role | What they receive |
|---|---|---|
| Generative AI-Powered Intrapartum Chatbot Decision Support Group | Experimental | Healthcare providers used the generative AI-powered Smart Normal Labor intrapartum chatbot to support clinical assessment, interpretation of labor progress, and clinical decision-making during normal labor management. The chatbot was used alongside routine institutional care practices. Receives: Other: Generative AI-Powered Intrapartum Chatbot |
| Routine Care Group | No intervention | Healthcare providers managed normal labor according to routine institutional care practices without using the generative AI-powered intrapartum chatbot. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Clinical Decision-Making Time (Minutes)
Elapsed time, measured in minutes, from admission of the woman in labor to confirmation of a clinical management plan by the healthcare provider. Shorter times indicate faster clinical decision-making.the management of normal labor, measured using a structured clinical decision-making assessment tool.
Measured: From admission to confirmation of the clinical management plan during the assessed labor episode.
Secondary outcomes (1)
Diagnostic Accuracy (%)
Diagnostic accuracy was assessed by comparing healthcare providers' diagnoses with the final diagnoses documented in the clinical records. Accuracy was expressed as a percentage, with higher values indicating greater agreement with the documented final diagnoses.
Measured: During the assessed labor episode, with comparison against the final diagnosis documented in the clinical record.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Jul 2026
- Study start6 May 2026
- Primary completion25 Oct 2026
- Study completion29 Oct 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Normal LaborStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- A single-center, non-randomized, parallel-group quasi-experimental study comparing healthcare providers using a generative AI-powered intrapartum clinical decision-support chatbot (intervention group, n = 214) with healthcare providers following conventional care practices without the chatbot (control group, n = 213).
- Masking
- None (open label)
- Enrolment
- 427 participants (actual)
Sponsor and investigators
- Lead sponsor
- Delta University for Science and Technology
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07720531
- Sponsor's protocol ID
- Smart Normal Labor
- Status last verified
- Jan 2026
- First submitted
- 4 Apr 2026
- Last updated
- 2 Nov 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Nov 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Generative AI-Powered Intrapartum Clinical Decision Support and Healthcare Providers' Decision-Making Speed, Diagnostic Accuracy, Satisfaction, and Clinical Experience: A Quasi-Experimental Study
- ClinicalTrials.gov ID
- NCT07720531
- Acronym / sponsor ID
- Smart Normal Labor
- Conditions
- Normal Labor
- Keywords
- Normal Labor, Clinical Decision-Making in Labor, Artificial Intelligence, Generative Artificial Intelligence, Chatbot, Diagnostic Accuracy, Intrapartum Care
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 427 participants (actual)
- Lead sponsor
- Delta University for Science and TechnologyUniversity, hospital or other
- Study start
- 6 May 2026 (actual)
- Primary completion
- 25 Oct 2026 (actual)
- Study completion
- 29 Oct 2026 (actual)
- First posted
- 22 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
This single-center, non-randomized, parallel-group quasi-experimental study evaluated a generative AI-powered intrapartum clinical decision-support chatbot among healthcare providers involved in normal labor management.
The study included 427 obstetricians and nurses: 214 in the intervention group and 213 in the control group. Providers in the intervention group used the AI-based Smart Normal Labor application to support clinical assessment and decision-making during routine labor care. Providers in the control group followed conventional institutional care practices without using the application.
The evaluation combined assessments of clinical decision-making performance with provider-reported satisfaction, acceptance, clinical experience, and workflow. It also explored perceived barriers and facilitators to application use in clinical practice. The study focused on provider-level outcomes; maternal and neonatal clinical outcomes were not directly assessed.
Study design
- Primary purpose
- Health services research
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- A single-center, non-randomized, parallel-group quasi-experimental study comparing healthcare providers using a generative AI-powered intrapartum clinical decision-support chatbot (intervention group, n = 214) with healthcare providers following conventional care practices without the chatbot (control group, n = 213).
| Arm | Type | Receives |
|---|---|---|
| Generative AI-Powered Intrapartum Chatbot Decision Support Group | Experimental | Other: Generative AI-Powered Intrapartum ChatbotHealthcare providers used the generative AI-powered Smart Normal Labor intrapartum chatbot to support clinical assessment, interpretation of labor progress, and clinical decision-making during normal labor management. The chatbot was used alongside routine institutional care practices. |
| Routine Care Group | No intervention | –Healthcare providers managed normal labor according to routine institutional care practices without using the generative AI-powered intrapartum chatbot. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (6 listed)
- Obstetricians and nurses 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.
- At least six months of clinical experience in labor management. For the intervention group: completion of at least two months of training on the generative AI-powered Smart Normal Labor intrapartum chatbot before its use with clinical cases during the intervention.
- For the intervention group: use of the generative AI-powered Smart Normal Labor intrapartum chatbot during clinical labor management.
- For the control group: provision of routine care without prior use of the study chatbot.
Key exclusion criteria (4 listed)
- Healthcare providers not directly involved in labor management, including administrative staff and laboratory personnel.
- Less than six months of clinical experience in labor management.
- Being on leave or unavailable during the study period.
- Declining participation or not providing informed consent.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Generative AI-Powered Intrapartum ChatbotAlso: Smart Normal LaborSmart Normal Labor is a mobile application incorporating a generative AI-powered chatbot for healthcare providers. It provides interactive clinical decision support using information entered by providers about labor progress and clinical findings. Its intended functions include supporting interpretation of clinical information and formulation of a management plan. | Other | Generative AI-Powered Intrapartum Chatbot Decision Support Group |
Primary outcome
Clinical Decision-Making Time (Minutes)
Time frame: From admission to confirmation of the clinical management plan during the assessed labor episode.
Elapsed time, measured in minutes, from admission of the woman in labor to confirmation of a clinical management plan by the healthcare provider. Shorter times indicate faster clinical decision-making.the management of normal labor, measured using a structured clinical decision-making assessment tool.
1 secondary outcome
Diagnostic Accuracy (%)
Time frame: During the assessed labor episode, with comparison against the final diagnosis documented in the clinical record.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Nov 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Generative AI-Powered Intrapartum Chatbot in people with Normal Labor, run by Delta University for Science and Technology at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 2 Nov 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Clinical Decision-Making Time (Minutes) (time frame: From admission to confirmation of the clinical management plan during the assessed labor episode.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Nov 2026.