Factors affecting data quality of health management information system at township level, Bago region, Myanmar - Hlaing, Thein; Myint, Zaw M. (International Journal Of Community Medicine And Public Health, 2022)
Background: Health information from Health management information system (HMIS) is the core essential operator for strengthening the health system. The effectiveness of the Myanmar health system is challenged by the poor-quality assurance of healthcare data. Methods: The aim of the quantitative study was to evaluate the township HMIS to assess the factors affecting the data quality assurance through three main aspects; organizational, technical, and behavioral. In this cross-sectional study, eight townships from four districts in Bago Region were randomly picked. Under these townships, from a random sample of 117 public health facilities altogether, 273 public health professionals (PHPs) were culled and 291 HMIS registers and 1270 HMIS monthly reports were reviewed. The researchers applied the PRISM tools developed for assessing district and facility HMIS. SPSS assisted the researchers in computing the frequencies and percentages, practicing cross-tabulation, and analyzing bivariate statistics using the Cox proportional hazards model. Results: Out of 281 PHPs invited, 273 were likely to participate in this study. The overall prevalence of the HMIS data quality was 30.4%. Poor data quality assurance was associated with the burden of workload (95%CI-1.16-2.91), poor management ability of the supervisors (95%CI-1.22-2.54), weak handover practice of the HMIS document (95%CI-1.65-2.22), and unavailability of HMIS resources (95%CI-1.12-2.45). The statistically significant relationships were found between low-quality data and some technical factors such as inexpertness for data analysis (95%CI-1.14-2.19), over-workload of paper-based HMIS (95%CI-1.21-2.44), differences between information systems (95%CI-1.22-2.81), and multiple reporting (95%CI-1.64-2.36). There were significant associations between the unacceptable data quality and the human factors such as lower scores of perceived confidences (95%CI-1.18-2.29), competence (95%CI-1.17-2.77), and promotion of the culture of information (95%CI-1.09-2.33). Conclusions: Current township HMIS data quality is unacceptable. It is necessary to strengthen several factors relating to organization, technology and behaviors of HMIS and to develop the effective township-level strategic plan for improving data quality.