Factors affecting data quality of health management information system at township level, Bago region, Myanmar
Keywords:HMIS data quality, Determinant factors, Township level
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.
WHO. Monitoring the building blocks of health systems: A handbook of indicators and their measurement strategies. Introduction and Objectives of the Handbook. Switzerland: WHO Press; 2010: 5-8.
Health Systems Global. A new era for the WHO health system building blocks?, 2014. Available at: https://healthsystemsglobal.org/news/a-new-era-for-the-who-health-system-building-blocks/. Accessed on 23 December 2021.
Measure Evaluation. Health Management Information System (HMIS) Facilitator's Guide for Training of Trainers. HMIS Reform in Ethiopia. SNNP Regional Health Bureau; 2013: 10-13.
Hlaing T, Zin T. Organizational factors in determining data quality produced from health management information systems in low- and middle-income countries: A systematic review. Health Informatics. Int J. 2020;9(4): 1-17.
Department of Public Health. Data Dictionary for Health Services Indicators. Myanmar: Ministry of Health and Sports; 2018: 3-6.
Department of Medical Research (Lower Myanmar). Report on assessment of routine public health information system by basic health staff at the township level, Myanmar with special reference to data reporting and data quality. Myanmar: Department of Health Planning, MOHS, WHO; 2011: 19-34.
Department of Public Health. Public Health Statistics (2014‐2016). Myanmar: Ministry of Health and Sports; 2017: 1-10.
Htun ML. Assessment on completeness of medical record documentation and quality of disease coding in West Yangon general hospital. Yangon: University of Public Health; 2015: 21-44.
Naing W. Assessment of completeness of record documentation and quality of coding of modified trauma register of form in Naypyitaw general hospital. Yangon: University of Public Health; 2016: 23-48.
Harikumar S. Evaluation of Health Management Information Systems- A study of HMIS in Kerala. Thiruvananthapuram: Sree Chitra Tirunal Institute for Medical Sciences and Technology; 2012: 20-54.
Hotchkiss DR, Aqil A, Lippeveld T, Mukooyo E. Evaluation of the Performance of Routine Information System Management (PRISM) framework: evidence from Uganda. BMC Health Serv Res. 2010;10:188.
Saw KK. Assessment on data quality of routine public health management information using District Health Information Software Version 2 in East District. Yangon Region: University of Public Health; 2016: 20-37.
Simba DO, Mwangu MA. Factors influencing quality of health management information system (HMIS) data: The case of Kinondoni District in Dar ES Salaam Region, Tanzania. East African J Public Health. 2006;3(1):28-31.
14. Hazerijian J. Measure Evaluation: Facts about Lot Quality Assurance Sampling. In: Lot Quality Assurance Sampling. Chapel Hill: Measure evaluation, Carolina Population Center; 2021: 5-9.
Ahanhanzo Y, Ouedraogo LT, Kpozèhouen A, Coppieters Y, Makoutodé M, Dramaix M. Factors associated with data quality in the routine health information system of Benin. Arch Public Health. 2014;72(1):25.
Shrestha LB, Bodart C. Data transmission, data processing and data quality. In: Lippeveld T, Sauerborn R, Bodart C, eds. Design and implementation of health systems. Geneva: WHO; 2000: 24-37.
Teklegiorgis K, Tadesse K, Mirutse G, Terefe W. Level of data quality from health management information systems in a resource-limited setting and its associated factors, eastern Ethiopia. South African J Inf Manag. 2016;17(1):1-8.
Ouedraogo M, Kurji J, Abebe L, Labonté R, Morankar S, Bedru KH, et al. A quality assessment of Health Management Information System (HMIS) data for maternal and child health in Jimma Zone, Ethiopia. PLoS One. 2019;14(3):0213600.
Rumisha SF, Lyimo EP, Mremi IR, Tungu PK, Mwingira VS, Mbata D, et al. Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania. BMC Med Inform Decis Mak. 2020;20(1):340.
Nshimyiryo A, Kirk CM, Sauer SM, Ntawuyirusha E, Muhire A, Sayinzoga F, et al. Health management information system (HMIS) data verification: A case study in four districts in Rwanda. PLoS One. 2020;15(7):0235823.
Kebede M, Adeba E, Chego M. Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia. BMC Med Inform Decis Mak. 2020;20(1):107.
Aqil A, Lippeveld T, Hozumi D. PRISM framework: a paradigm shift for designing, strengthening and evaluating routine health information systems. Health Policy Plan. 2009;24(3):217-28.
Mboera LEG, Rumisha SF, Mbata D, Mremi IR, Lyimo EP, Joachim C. Data utilisation and factors influencing the performance of the health management information system in Tanzania. BMC Health Serv Res. 2021;21(1):498.
Seid MA, Bayou NB, Ayele FY, Zerga AA. Utilization of Routine Health Information from Health Management Information System and Associated Factors Among Health Workers at Health Centers in Oromia Special Zone, Ethiopia: A Multilevel Analysis. Risk Manag Healthc Policy. 2021;14:1189-98.
Haftu B, Taye G, Ayele W, Habtamu T, Biruk E. A mixed-methods assessment of Routine Health Information System (RHIS) data quality and factors affecting it, Addis Ababa City Administration, Ethiopia, 2020. Ethiop J Health Dev. 2021;35(1):15-24.
Ngusie HS, Shiferaw AM, Bogale AD, Ahmed MH. Health Data Management Practice and Associated Factors Among Health Professionals Working at Public Health Facilities in Resource Limited Settings. Adv Med Educ Pract. 2021;12:855-62.