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]
調査の概要
詳細な説明
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.
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前：Basma W Basma
- 電話番号：01552602703
- メール：Basma.wageh@deltauniv.edu.eg
研究場所
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Dakahlia Governorate
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Al Mansurah、Dakahlia Governorate、エジプト、35712
- 募集
- Basma wageah Basma
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コンタクト：
- Basma W Basma, phd
- 電話番号：01552602703
- メール：Basma.wageh@deltauniv.edu.eg
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コンタクト：
- Basma W Elrefay, phd
- 電話番号：01090412521
- メール：Basma.wageh@deltauniv.edu.eg
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-
参加基準
適格基準
就学可能な年齢
- 子
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
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.
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：ヘルスサービス研究
- 割り当て：非ランダム化
- 介入モデル：並列代入
- マスキング：独身
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
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実験的：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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介入なし：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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この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Primary Outcome
時間枠：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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二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
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Secondary Outcome
時間枠：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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協力者と研究者
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (実際)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
その他の研究ID番号
- Smart Normal Labor
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
この情報は、Web サイト clinicaltrials.gov から変更なしで直接取得したものです。研究の詳細を変更、削除、または更新するリクエストがある場合は、register@clinicaltrials.gov。 までご連絡ください。 clinicaltrials.gov に変更が加えられるとすぐに、ウェブサイトでも自動的に更新されます。