Hannah Ajayi and Anwuli Nwankwo (Lead writers)
Malawi’s maternal mortality ratio stands at an estimated 381 deaths per 100,000 live births, while neonatal mortality stands at roughly 24 deaths per 1,000 live births in the first 28 days of life. Birth asphyxia, complications of prematurity, and neonatal infections continue to account for a significant share of newborn deaths, most of which are strongly linked to the quality and timeliness of care around labour and delivery. The data points to a persistent gap in providing higher-quality care due to shortages of trained staff, essential medicines, reliable utilities, and clinical protocols, contributing to preventable newborn deaths.
Even though 9 out of 10 women in Malawi attend antenatal care and many deliver in health facilities, the quality of care often falls short due to staff shortages, limited in-service training and supervision, gaps in essential equipment and medicines, high patient volumes, weak referral and escalation pathways, and inconsistent management support. The World Health Organization (WHO) defines quality care across dimensions, including effectiveness, safety, people-centeredness, timeliness, equity, integration, and efficiency, areas that labour-monitoring tools aim to strengthen, particularly in improving timely decision-making and clinical safety.
As more women deliver in facilities, labour wards may become overcrowded, particularly where staffing and equipment have not expanded in line with demand. Health worker density remains below recommended thresholds, and persistent gaps in reliable foetal monitoring, oxygen, blood supply, theatre capacity, and timely referrals undermine outcomes for high-risk mothers and newborns. Delays in detecting clinical deterioration and escalating care in a timely manner continue to contribute to preventable mortality.
A digital companion for caregivers
In response to persistent gaps in labour monitoring, especially in busy wards where midwives may be stretched caring for several women, digital tools are being tested to detect complications earlier. PeriWatch Vigilance aims to reduce missed signs of deterioration by providing continuous, interpretable, and actionable foetal monitoring. It does not replace clinicians; it supports them by analysing foetal heart rate and contraction patterns and flagging signs of foetal distress, so staff can review cases sooner.
Introduced in 2020 in partnership with the Malawi Ministry of Health, PeriWatch Vigilance is an AI-supported foetal surveillance and documentation tool that helps staff interpret foetal heart-rate patterns and escalate concerns earlier. In settings where continuous monitoring is inconsistent, this can enhance intrapartum decision-making by helping teams:
- identify concerning foetal-heart-rate patterns earlier
- prioritise women who need urgent review
- document observations more consistently and support escalation decisions
In Lilongwe, Malawi’s capital, an evaluation at Area 25 health centre reported a significant reduction in stillbirths and early neonatal deaths after PeriWatch Vigilance was introduced. When Excyna went into labour with signs of a high-risk complication, clinicians used PeriGen’s PeriWatch Vigilance labour-monitoring system to track foetal heart rate and contractions in real time. The system flagged patterns consistent with foetal distress, prompting earlier review and escalation of care. Afterwards, Excyna described the tool as helping staff “act faster” during labour.
The maternal health context in Nigeria and lessons from Malawi
Similar pressures are evident in Nigeria. Labour wards often operate with too few skilled staff to meet the volume of women in labour, particularly in busy public facilities. In this context, tools that improve the consistency of foetal monitoring and support timely escalation can help teams identify deterioration earlier without increasing workload.
Nigeria also bears a significant burden of stillbirths and newborn deaths. Yet, in many facilities, continuous foetal monitoring is inconsistent, partographs are underutilised, and documentation gaps persist. Delays between recognising danger signs and taking action, such as reviewing, clinical review, referring, gaining theatre access, administering blood transfusions, or initiating newborn resuscitation, remain common.
Malawi’s experience suggests that strengthening labour monitoring can improve newborn outcomes even in constrained settings. At the Area 25 health centre, an evaluation reported a reduction in stillbirths and early neonatal deaths by 82% after PeriWatch Vigilance was introduced, alongside improved staff confidence and earlier escalation for high-risk labours. The Malawi experience offers several practical lessons for Nigeria’s policymakers and health leaders:
- Co-design with users; build the workflow with midwives, doctors, and records staff, so alerts match how decisions are made on shift.
- Design for constraints; tools must work with intermittent power, low bandwidth, and basic hardware, with clear offline and maintenance plans.
- Augment clinical judgement; alerts should prompt review, not override clinicians; escalation protocols and accountability must be explicit.
- Measure what matters; track outcomes (fresh stillbirths, early neonatal deaths), process indicators (monitoring frequency, escalation time, decision-to-incision time), and safety signals (false alarms, alert fatigue).
- Plan sustainability early; include procurement, licensing, device replacement, training, supervision, and data governance from the start.
In Nigeria, AI adoption is most likely to succeed when aligned with federal and state priorities for Emergency Obstetric and Newborn Care and broader quality improvement efforts. Scaling tools like PeriWatch Vigilance would require reliable electricity, basic connectivity where necessary, integration with existing labour ward protocols and documentation systems, and a clearly funded plan for training, supportive supervision, and ongoing maintenance. A realistic pathway would be to begin in high-volume referral facilities, assess clinical outcomes and workflow implications, and then expand based on evidence rather than enthusiasm.
Nigeria would also require a clear regulatory framework covering data governance and the ethical use of AI, including guidance on informed consent where appropriate, secure data storage, access controls, and clarity on who owns and is permitted to use the data. Safeguards would be needed to assess and mitigate potential algorithmic bias, particularly if systems were developed or trained outside Nigeria. Procurement processes should minimise the risk of vendor lock-in, and the rollout must be equity-focused to ensure digital tools do not widen the gap between well-resourced urban facilities and underserved rural settings.
Scaling what works
Once an intervention shows impact in routine care, the harder question is scale, financing, maintenance, supervision, and integration into national systems. As Nigeria advances the Maternal and Newborn Mortality Reduction Innovation and Initiative (MAMII), the focus is shifting from pilot projects to system-wide quality improvement. Digital tools that support monitoring during labour would need to align with MAMII’s priorities in Emergency Obstetric and Newborn Care. Some countries are exploring similar approaches, but results are context-dependent and should be judged on transparent data, not headlines. Experience from Malawi suggests that AI in maternal and newborn care is most useful when it augments overstretched teams and strengthens timely escalation during labour.
For Nigeria, the next step is therefore not a national rollout on paper, but a disciplined pilot-to-scale pathway embedded within MAMII implementation. This would mean selecting high-volume facilities, defining success metrics aligned with national maternal and newborn targets, investing in reliable power and essential infrastructure, training and supervising staff, and building robust data governance systems. With those foundations in place, AI-supported monitoring could contribute to MAMII’s objective of reducing preventable stillbirths and early neonatal deaths by making danger signs harder to miss and clinical response faster and more consistent.