Enhancing data accuracy and reliability in maternal and child health: MCGL success story

Abstract

Background: Accurate maternal and child health data are essential for informed decision-making; however, maintaining consistent data quality in postnatal care and live birth records remains a persistent challenge. Since the inception of MCGL, discrepancies within these datasets have compromised the data's quality for decision making. Recognizing this, the need for an effective solution to rectify data integrity issues and enhance reliability became evident. Methods: Mentorship at the facility level was conducted for healthcare staff to ensure a clear understanding of indicators for accurate reporting, focusing on new data elements introduced in the KHIS 2020 revision. In collaboration with health information officers, the project monitoring, evaluation, and learning officer identified sets of indicators, along with closely related proxy indicators, for monitoring. A systematic approach was employed, involving a comparative analysis of primary indicators and corresponding proxy indicators. For instance, if 100 live births were recorded, an expectation of 100 infants receiving postnatal care within 48 hours was anticipated. Regular monthly communication was established with HRIOs to identify and validate discrepancies that emerged during the comparison. Results: Significant improvement in data quality was observed. From October 2020 to September 2021, 6,337 infants received PNC within 48 hours, accounting for 56% of the 11,309 live births. In the subsequent years (October 2021 to September 2022 and October 2022 to September 2023), this p increased to 89% and 92%, respectively. Conclusion: The combined impact of mentorship and regular monthly communication can enhance data quality, instilling increased confidence in data use for informed decision making.