Background: To end preventable maternal and newborn mortality and stillbirths, attention is shifting to improving quality of care, both provision and experience. Health information systems can improve health system performance when data are used for decision-making. High quality data are urgently needed to assess progress and accelerate efforts towards the globally agreed 2030 Sustainable Development Goals. Improving data for action is a focus of recent global movements to improve outcomes for women and newborns and reduce stillbirths. Most preventable deaths of newborns, stillbirths and women occur in settings with data gaps that impede progress for coverage, equity, and quality of care. Routine health information systems (RHIS) make use of data documented by health professionals as part of the treatment they offer in health facilities. With the proportion of births occurring in health facilities rising, there is increasing interest in using routine health facility data to track intrapartum care provided for women and newborns, especially as this is the time during pregnancy that contributes most to women and newborns surviving and thriving. The quality of data in routine labour and delivery registers, including how accurately it captures the care provided, is currently understudied. Methods: My thesis describes a completed body of work that explored the quality of routine labour and delivery register data. Analyses used the ‘Every Newborn – Birth Indicators Research Tracking in Hospitals’ (EN-BIRTH) dataset from five comprehensive Emergency Obstetric and Newborn Care hospitals in Bangladesh, Nepal and Tanzania. My focus is on the data at the foundations of the data-information pyramid, with a particular emphasis on core indicator data elements collected in routine labour and delivery registers. These core indicator data for RHIS tracking are explored for the maternal-newborn dyad, structured by two sections: Section A: Assessing labour and delivery routine register indicator data quality for hospital births. Section B: Identifying opportunities to improve labour and delivery routine register data quality for hospital births. Results: Labour and delivery register data were available, legible, and complete however data quality was very mixed, varying by indicator and hospital. Frontline health professionals in high-mortality settings face the tension of dual demands: providing high quality of care for women and newborns, while also documenting routine data on care and outcomes. Opportunities to enable a virtuous cycle of data use and data quality from labour and delivery ward registers were identified. A novel ‘Quality of Care and Quality of Data conceptual framework’ is presented, linking delivery register data quality to the WHO domains of quality of care. Central to this is a missing link expressed as the ‘Data Quality Continuum’ – that the hospital routine data culture determines quality of neonatal data used both for clinical care and to track outcomes. Conclusion: Improving the quality of care is impeded by a lack of high quality data that can be used both for clinical decisions and by policy makers for planning and investment. RHIS strengthening needs to overcome the tension for frontline health workers between care and data and create traction to enable high quality data for use to improve quality of care.