Smart Normal Labor: Healthcare Providers' Experience With an AI-Based Mobile App (Smart labor)
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]
研究概览
详细说明
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.
研究类型
注册 (估计的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名：Basma W Basma
- 电话号码：01552602703
- 邮箱：Basma.wageh@deltauniv.edu.eg
学习地点
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Dakahlia Governorate
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Al Mansurah、Dakahlia Governorate、埃及、35712
- 招聘中
- Basma wageah Basma
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接触：
- Basma W Basma, phd
- 电话号码：01552602703
- 邮箱：Basma.wageh@deltauniv.edu.eg
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接触：
- Basma W Elrefay, phd
- 电话号码：01090412521
- 邮箱：Basma.wageh@deltauniv.edu.eg
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参与标准
资格标准
适合学习的年龄
- 孩子
- 成人
- 年长者
接受健康志愿者
描述
Inclusion Criteria
Participants must meet the following conditions to be included in the study:
- 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.
- Willingness to participate and provide informed consent. Exclusion Criteria
Participants will be excluded if they:
- 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.
学习计划
研究是如何设计的？
设计细节
- 主要用途：卫生服务研究
- 分配：非随机化
- 介入模型：并行分配
- 屏蔽：单身的
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
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实验性的：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.
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participants who actively use the AI application during labor management,
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无干预：Routine Care Group
Healthcare providers provide routine normal labor management according to standard institutional protocols without using the AI-based Smart Normal Labor Application.
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研究衡量的是什么？
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Primary Outcome
大体时间：During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).
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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.
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During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).
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次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Secondary Outcome
大体时间：During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).
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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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During labor management (from the onset of active labor until delivery, assessed up to 6-8 hours).
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (实际的)
研究完成 (估计的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
与本研究相关的术语
其他研究编号
- Smart Normal Labor
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