Background: An efficient health management information system (HMIS) improves health care delivery and outcomes. However, in most rural settings in Uganda, paper-based HMIS are widely used to monitor public health care services. Moreover, there are limited capabilities and capacity for quality HMIS in remote settings such as Kayunga district.Objectives: The quality assurance practices of HMIS in health centres (HCs) in Kayunga district were evaluated.Method: A cross-sectional descriptive study design was used to assess the quality of HMIS at 21 HCs in Kayunga district. Data were collected through in-depth interviews of HMIS focal persons as well as document analysis of HMIS records and guidelines between 15 June 2010 and 15 July 2010. The main outcomes were quality assurance practices, the HMIS programmatic challenges and opportunities. The practice of HMIS was assessed against a scale for good quality assurance practices. Qualitative data were coded and thematically analysed, whereas quantitative data were analysed by descriptive statistics using SPSS v22 software.Results: All the 21 HCs had manual paper-based HMIS. Less than 25% of HCs practised quality assurance measures during collection, compilation, analysis and dissemination of HMIS data. More than 50% of HCs were not practising any type of quality assurance during analysis and dissemination of data. The main challenges of the HMIS were the laborious and tedious manual system, the difficulty to archive and retrieve records, insufficient HMIS forms and difficulty in delivering hard copies of reports to relevant stakeholders influenced quality of data. Human resource challenges included understaffing where 43% of participating HCs did not have a designated HMIS staff.Conclusion: The HMIS quality assurance practices in Kayunga were suboptimal. Training and support supervision of HMIS focal persons is required to strengthen quality assurance of HMIS. Implementation of electronic HMIS dashboards with data quality checks should be integrated alongside the manual system.
Appreciating local variations in standardising health care data collection processes and tools: A case of health management information system in Tanzania
