Using routine health data to evaluate health facility upgrades and healthcare worker training for maternal care utilisation in rural Papua New Guinea

Background Papua New Guinea has among the worst maternal health indicators in the Pacific. Health facility upgrades and healthcare worker training are widely implemented to strengthen maternal health services, yet evidence of their effect on maternal healthcare utilisation in rural settings remains limited. This study assessed whether infrastructure upgrades and healthcare worker training were associated with changes in maternal care utilisation using routine data from the national health database. Methods We conducted a retrospective observational analysis of electronic National Health Information System routine service data from three rural health facilities – Kwikila Health Centre, Mumeng Health Centre, and Gaulim Community Health Post – from 2016 to 2025. We compared pre-intervention and post-intervention trends using descriptive statistics and interrupted time series methods. Interpretation relied on before–after comparisons within each health facility. Results Descriptive trends showed only modest changes in maternal service utilisation over time, with gradual increases in first antenatal care attendance and health facility deliveries but persistently low completion of fourth antenatal care visits. Coverage of birth-dose BCG and hepatitis B immunisation remained below 50% across the health facilities. Regression analysis found no significant improvement in most maternal care indicators following the interventions, except for a modest increase in monthly health facility deliveries post training (0.03, P = 0.039). Conclusion Systems are complex and ever-changing, and effective health intervention strategies must combine system-level improvements with community involvement. Additionally, data quality and use matter as much as service provision. Investments to train and incentivise staff on accurate reporting and to provide timely feedback can turn numbers into actionable insights. The study findings highlight the importance of high-quality data in program evaluation.