Managing Guest Editor: Dr. Wilson Tumuhimbise
Email: [email protected]; [email protected]
Affiliation: Researcher, Faculty of Computing and Informatics, Mbarara University of Science and Technology, Mbarara, Uganda.
Research interest: mHealth, Health Informatics
Co-Guest Editor: Dr. Teddy Ebimene Kurokeyi
Email: [email protected]
Affiliation: Bayelsa Medical University, Bayelsa State, Nigeria.
Research interest: Health Information Management
Co-Guest Editor: Dr. Tusar Kanti Dash
Email: [email protected]
Affiliation: Electronics & Communication Engineering, C V Raman Global University, Bidyanagar, Mahura, Janla Bhubaneswar, India.
Research interest: Health Informatics, Artificial Intelligence & Machine Learning
Submission Deadline: 30 July 2026
Note: When submitting your manuscript to a special issue, please identify the special issue's name on top of the title.
Special Issue Information：
Safe and efficient childbirth remains a major issue in most low-resource areas, especially in situations where maternal and neonatal outcomes are often undermined by inconsistent monitoring and delayed interventions. The World Health Organization (WHO) Labor Care Guide offers a systematic approach to labor management, but its generalized design restricts the flexibility to local practices and facility limitations. In Uganda, there is a high patient load in facilities, staffing and inconsistent compliance with standardized procedures, which compound the inability to make timely clinical decisions. The enhancement of labor monitoring has become a priority in the world and international agencies focus on data-driven methods to decrease maternal morbidity and mortality. Current systems tend to be based on paper-based documentation, disjointed electronic tools and manual observation, which are subject to error, incomplete documentation and delayed escalation of critical events.
In addition, traditional methods do not reflect dynamic labor developments in real time and they are not combined with predictive analytics, early-warning systems or cross-platform interoperability. Technical issues are the lack of digitization, inadequate infrastructure and staff training, while ethical issues are patient data privacy and equitable access. Scalability is also an obstacle in trying to establish the same monitoring in different types of facilities. Emerging technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT) have a transformative potential: AI algorithms can process constant physiological and labor data to predict complications and IoT-enabled devices can automatically and in real-time measure maternal and fetal parameters. The combination of AI and IoT or blockchain can be used to improve traceability, compliance and rapid clinical decision-making, which will effectively close gaps in monitoring and reporting. Tailoring the WHO Labor Care Guide to the specifics of the Ugandan contexts, facilities will be able to implement customized workflows, predictive insights and automated alerts, improving the safety, workflow efficiency and patient outcomes.
The special issue seeks contributions that show innovative methods of labor monitoring by context-aware adaptation of standardized protocols. Researchers, clinicians, healthcare technologists and policy practitioners are encouraged to submit their work, pilot projects or implementation reports that demonstrate practical, scalable and ethical solutions. The anticipated contributions are AI-driven decision support, IoT-based monitoring systems, digital workflow optimization and plans to introduce automated tools into the daily clinical practice. Submissions that emphasize computable changes in maternal or neonatal outcomes, staff productivity or patient safety will be particularly appreciated.
Scope of the Special Issue:
Keywords:
WHO Labor Care Guide customization, AI-driven labor monitoring, IoT-enabled maternal and fetal health, Digital obstetric decision support systems, Blockchain for maternal health records, Smart maternal healthcare in low-resource settings, Predictive analytics for childbirth complications, Context-aware clinical workflow optimization