Owoche Patrick O, Shisanya Morris Senghor, Mayeku Betty, Namusonge Lucy Natecho
School of Computing and Informatics, Kibabii University, Bungoma, Kenya.
School of Nursing, Kibabii University, Bungoma, Kenya.
PLoS One. 2025 May 14;20(5):e0323533. doi: 10.1371/journal.pone.0323533. eCollection 2025.
Maternal and newborn mortality remains a critical public health challenge, particularly in resource-limited settings. Despite global efforts, Kenya continues to report high maternal mortality rates of over 350 deaths per 100,000 live births and a neonatal mortality rate of 21 per 1,000 live births. Artificial Intelligence (AI)-enabled maternal healthcare interventions, such as Obstetric Point-of-Care Ultrasound (OPOCUS) and AI-driven SMS intervention on Promoting Mothers through Pregnancy and Postpartum (PROMPTS), offer innovative solutions to improve early detection, diagnosis, and maternal health-seeking behaviors. However, there is limited evidence on their usability, feasibility, and impact on maternal and neonatal outcomes.
This study aims to assess the implementation, user experiences, and impact of OPOCUS and PROMPTS on maternal and neonatal health outcomes in Kenya. Specifically, it evaluates their effectiveness in reducing maternal complications, improving antenatal and postnatal care utilization, and enhancing clinical decision-making while identifying potential barriers to adoption and scalability.
This mixed-methods, cross-sectional study will be conducted in ten counties in Kenya that have integrated AI-based maternal healthcare interventions. Quantitative data will be collected from health facility records, national health databases (KHIS), and structured surveys, while qualitative data will be gathered through key informant interviews (KIIs) with healthcare providers and policymakers, as well as focus group discussions (FGDs) with maternal health service users. Statistical analyses will include comparative pre- and post-AI implementation assessments, with thematic analysis for qualitative insights.
The study will generate empirical evidence on the feasibility, effectiveness, and barriers to AI integration in maternal health services. Findings will inform policy recommendations, enhance AI-assisted maternal healthcare design, and support the scaling of AI-driven interventions to improve maternal and neonatal health outcomes in Kenya and other low-resource settings.
AI-based maternal health interventions hold promise for reducing maternal mortality, improving diagnostic accuracy, and enhancing health-seeking behaviors. However, their success depends on user experiences, healthcare system readiness, and policy alignment. This study will provide critical insights for evidence-based scaling and policy integration of AI in maternal healthcare.
孕产妇和新生儿死亡率仍然是一项严峻的公共卫生挑战，在资源有限的环境中尤其如此。尽管全球都在努力，但肯尼亚的孕产妇死亡率持续居高不下，每10万例活产中有超过350例死亡，新生儿死亡率为每1000例活产21例。人工智能（AI）支持的孕产妇保健干预措施，如产科即时超声检查（OPOCUS）和通过孕期和产后促进母亲健康的人工智能驱动短信干预（PROMPTS），为改善早期发现、诊断以及孕产妇寻求保健行为提供了创新解决方案。然而，关于它们的可用性、可行性及其对孕产妇和新生儿结局的影响的证据有限。
本研究旨在评估OPOCUS和PROMPTS在肯尼亚对孕产妇和新生儿健康结局的实施情况、用户体验及影响。具体而言，它评估这些措施在减少孕产妇并发症、提高产前和产后保健利用率以及加强临床决策方面的有效性，同时确定采用和扩大规模的潜在障碍。
这项混合方法的横断面研究将在肯尼亚已整合基于人工智能的孕产妇保健干预措施的10个县进行。定量数据将从医疗机构记录、国家卫生数据库（KHIS）和结构化调查中收集，而定性数据将通过与医疗服务提供者和政策制定者进行关键信息访谈（KIIs）以及与孕产妇保健服务使用者进行焦点小组讨论（FGDs）来收集。统计分析将包括人工智能实施前后的比较评估，并对定性见解进行主题分析。
该研究将生成关于人工智能融入孕产妇保健服务的可行性、有效性和障碍的实证证据。研究结果将为政策建议提供依据，改进人工智能辅助的孕产妇保健设计，并支持扩大人工智能驱动的干预措施，以改善肯尼亚和其他资源匮乏地区的孕产妇和新生儿健康结局。
基于人工智能的孕产妇保健干预措施有望降低孕产妇死亡率、提高诊断准确性并增强寻求保健行为。然而，它们的成功取决于用户体验、医疗系统的准备情况以及政策的一致性。本研究将为基于证据的人工智能在孕产妇保健中的扩大应用和政策整合提供关键见解。
