Factors affecting the quality and timeliness of the Health Management Information System data of Medicines and Health supplies in Mbarara District

Abstract

Background: Poor quality data leads imbalances in stocking of medicines at health facilities. In addition, if data is not submitted in time, it will affect timely planning and decision making. Quality of data and timeliness of reporting is still a challenge in Uganda. Factors that affect the quality and timeliness of HMIS data should be addressed to ensure availability of essential medicines and health supplies for patient use. Objective: To assess the quality and determine timeliness of HMIS data of medicines and health supplies as well as establish the factors affecting the timeliness and quality of the HMIS data. Methods: This was a cross sectional study that employed a triangulation mixed methods approach. This was carried out in Kashari county, Mbarara city and Rwampara county all located in Mbarara district. It was conducted in 15 government health facilities. Data was collected between November 2021 and December 2021. Both a structured questionnaire and a semi-structured interview guide were used. Document review of the HMIS forms of the tracer commodities was done. The data collected was first cleaned using EpiData then transferred to SPSS for further analysis. Cross-tabulation analysis was used at bivariate level to assess associations between factors and quality/timeliness. Multivariate linear regression analysis was further performed. Content analysis was used to analyze the qualitative data. Key findings: The overall data quality was 64.8%, and overall timeliness was 65.3%. Data availability, staff trainings, staff sufficiency, and risks encountered with p-values of (P=0.024), (p=0.001), (p=0.027%) and (p=0.036) respectively, were significantly associated with timeliness, and costs incurred in data collection (P=0.000), staff trainings (p=0.043) and use of standard operating procedures (p=0.045) were significantly associated with data quality. The common challenges faced when collecting data using the HMIS tools were the HMIS 105 being copious, poor documentation, too much time taken to review the paper work and registers as well as shortage of technical staff. Conclusion: the quality and timeliness were sub-optimal and several factors such as costs incurred, data availability, staff trainings, use of standard operating procedures should be put into consideration. Challenges such as very long HMIS tools, lack of enough tools and poor documentation should as well be considered.