Baby, let's look at the data!

NEST360 developed and implemented a dashboard to support data-driven decision-making for small and sick newborn care in 68 hospitals across Kenya, Malawi, Nigeria, and Tanzania. The dashboard integrates three data sources: clinical care, facility context, and device functionality. It displays metrics on admissions, outcomes, quality and coverage of interventions, devices, data quality, and facility readiness. Between May 2023 and June 2025, user logged in over 65,000 times, with 93% of hospitals using it monthly. The dashboard supports hospital QI, sub-national learning, national advocacy, and program planning to expand and improve care. It has helped secure backup power and additional staff. This tool demonstrates that timely, quality data can be collected, visualized, and used effectively in low-resource settings for improving care. Integration into national health information systems is essential for sustainability, scale-up, and strengthening health systems to improve newborn outcomes.

High-quality data is essential for improving neonatal outcomes in low- and middle-income countries. The NEST Query Tracker (NEST-QT) was developed to address persistent data quality challenges across 65+ health facilities in five NEST360 program countries. This automated system retrieves data weekly, identifies issues such as missing or inconsistent values, and generates structured queries for resolution. It replaces a manual, Excel-based process with a secure, role-based web platform that supports real-time feedback and accountability. Since deployment, NEST-QT has processed over 400,000 queries, enabling 2,000-3,000 resolutions per week and reducing average resolution time from three weeks to one. Over 150 active users across country and facility teams engage with the system, contributing to a 30% improvement in query resolution rates. By streamlining data quality workflows and supporting continuous monitoring, NEST-QT strengthens the use of routine neonatal data for timely, evidence-based decision-making.

Malawi's aggregate newborn care data in DHIS2 lacks granularity for quality improvement. To address this, the NEST Alliance developed an interoperable, individual-level Neonatal Inpatient Dataset (NID) within DHIS2, enabling detailed tracking of care processes and outcomes. The system was developed through evidence-based variable selection, iterative REDCap validation across 37 facilities, and offline-capable DHIS2 Tracker implementation following WHO's digital health framework. Healthcare workers (n=214) were trained, and between 2021-2024, 100,640 admissions were recorded. Reporting completeness rose by 56%, data entry delays fell from 49 to 6 days, and discharge documentation reached 99.5%. Facility dashboards were accessed 24 times/month on average, demonstrating active data use for decision making. The NID system proves that individual-level data enhances newborn care monitoring in resource-limited settings. Currently piloted in two districts for real-world validation, this DHIS2-based approach offers a model for other countries to standardise indicators, improve interoperability, and