Abstract
INTRODUCTION: Access to essential medicines is limited in developing countries mainly due to inefficiencies in health supply chain management, such as the absence of standard monitoring frameworks and poorly designed Logistics Management Information Systems (LMIS). Health supply chain managers need accurate and timely data for decision-making. However, routine health information systems suffer from poor data quality, reliance on paper-based reports, insufficient logistic formats, inadequate infrastructure, and limited human resources. OBJECTIVE: This study evaluates the data quality of LMIS for health commodities in public health facilities in the Amhara National Regional State of Ethiopia. METHODS: The study was conducted in Ethiopia's Amhara National Regional State. The study employed an institution-based concurrent mixed-methods design. Data collection involved 102 facilities selected through multi-stage stratified random sampling, adhering to sampling criteria set by USAID's Logistics Indicators Assessment Tool (LIAT). Data abstraction checklists were used to collect data. RESULTS: Of the seven tracer medicines selected to evaluate data quality, there was substantial variability in inventory accuracy rates. Inventory discrepancies were significant, highlighting potential issues with manual and digital record-keeping systems, with overall mean physical and electronic inventory accuracy rates of 74.7% and 70.6%, respectively. Additionally, the Report and Requisition Form (RRF) showed trends of timely submission, with the overall mean percentage completeness for the seven tracer medicines at 90.2%. However, the data quality experienced fluctuations, with the overall average percentage of legality (authorization of LMIS reports) and the accuracy of the RRF at 77.2% and 76%, respectively. CONCLUSION AND RECOMMENDATION: The evaluation of data quality revealed significant discrepancies in physical and electronic records, with notable fluctuations in completeness, legality, legibility, and accuracy within the health LMIS. To rectify these issues, robust data quality verification processes, clear guidelines, targeted interventions, strengthened monitoring systems, regular audits, and comprehensive training for health supply chain staff are needed.