Data quality and associated factors in the health management information system at health centers in Shashogo district, Hadiya zone, southern Ethiopia, 2021

Abstract

Background Poor quality routine data contributes to poor decision-making, inefficient resource allocation, loss of confidence in the health system, and may threaten the validity of impact evaluations. For several reasons in most developing countries, the routine health information systems in those countries are described as ineffective. Hence, the aim of this study is to determine the quality of data and associated factors in the routine health management information system in health centers of Shashogo district, Hadiya Zone. Methods A facility-based cross-sectional study was conducted from June 1, 2021, to July 1, 2021, and 300 participants were involved in the study through simple random sampling. The data was collected with a self-administered questionnaire by trained data collectors. After checking its completeness, the data was entered into EPI data version 3.1 and exported to SPSS version 25 for statistical analysis. Finally, variables with p < 0.05 during multivariable analysis were considered significant variables. Result A total of 300(100%) participant were included in the interview and HMIS data quality was 83% in Shashogo district health centers. The data quality in terms of accuracy, completeness, and timeliness was 79%, 86%, and 84%, respectively. Conducting supportive supervision [AOR 3.5 (1.4, 8.9)], checking accuracy [AOR 1.3 (1.5, 3.5)], filling registrations [AOR 2.7 (1.44, 7.7)], and confidence level [AOR 1.9 (1.55, 3.35)] were all rated positively found to be factors associated with data quality. Conclusion The overall level of data quality in Shashogo district health centers was found to be below the national expectation level. All dimensions of data quality in the district were below 90% in data accuracy, content completeness, and timeliness of data. Conducting supportive supervision, checking accuracy, filling registrations and confidence level were found to be factors associated with data quality. Hence, all stakeholders should give all necessary support to improve data quality in routine health information systems to truly attain the goal of providing good quality data for the decision-making process by considering the identified factors.

Data completeness and consistency in individual medical records of institutional births: retrospective crossectional study from Northwest Ethiopia, 2022

“…Our study revealed that supportive supervision by higher-level officials is another significant factor linked to the good completeness of data in institutional birth. This finding is consistent with prior studies conducted in Ethiopia and Indonesia [ 65 , 66 ], which highlighted the effectiveness of supportive supervision in improving health care data quality. Therefore, Policymakers, program managers, and officials at the district and higher administrative levels need to prioritize strengthening regular supervision and directives for healthcare facilities, enabling healthcare providers to promptly identify and correct data quality gaps in the services they deliver [ 67 ].…”

“…In this study, conducting RCA to address identified data quality gaps was found to have a positive association with the good consistency of institutional birth data.Prior studies have reported the effectiveness of routine data quality evaluation methods like LQAS in ensuring the quality of RHIS data [ 62 , 65 ]. In our study, although LQAS performance was uniform across health facilities, differences in RCA practice demonstrated its importance in improving the data quality across IMRs in institutional birth.…”

Identifying Strengths and Gaps in Data Management and Reporting through Malaria Routine Data Quality Assessment: Results from Two Health Regions in Madagascar

“…Similar findings have been reported in previous studies conducted in Senegal, 6 Rwanda, 7 Mozambique, 8 and Ethiopia, 9 suggesting that data quality issues are prevalent in malaria reporting systems across different countries. These studies highlight discrepancies in data accuracy, completeness, and timeliness, as well as the availability of written guidelines, standardized registers, and reporting forms.…”

Level of implementation of district health information system 2 at public health facilities in Eastern Ethiopia

“…Even though Ethiopia has made impressive progress in the utilization of HIS in recent years, a growing body of evidence indicates that the level of data quality and the use of information for decision-making is still not adequate, which in turn affects the level of HMIS implementation. 12 , 16 , 17 In addition, to the best of the author’s knowledge, no research has been conducted on the level of DHIS2 implementation in the public HFs in the study setting. As a result, this study aimed to assess the level of DHSI2 implementation among public HFs in Dire Dawa City Administration.…”