Level and contributing factors of health data quality and information use in two districts in Northwest Ethiopia: social-ecological perspective

Abstract

Background The health management information system has been implemented at all levels of healthcare delivery to ensure quality data production and information use in Ethiopia. Including the capacity-building activities and provision of infrastructure, various efforts have been made to improve the production and use of quality health data though the result is still unsatisfactory. This study aimed to examine the quality of health data and use in Wogera and Tach-Armacheho districts and understand its barriers and facilitators. Methods The study utilized a mixed-method; for the quantitative approach, institution-based cross-sectional study was conducted to determine the quality of health data and use by 95 departments in the two districts. The qualitative approach involved 16 in-depth interviewees from Wogera district. A descriptive Phenomenological design was used to explore factors influencing the quality and use of health data. The quantitative data were expressed descriptively with tables, graphs, and percent whereas the qualitative data were analyzed with content analysis guided by the social-ecological model framework. Result The average levels of information use for Wogera and Tach-Armacheho districts were estimated at 29 and 35.9, respectively. The overall average level of accuracy of reports for six different health services in the HCs of Wogera and Tach Armacheho districts were 0.95 and 0.86, respectively. The qualitatively identified factors that influence the production and use of quality health data include valuing data, getting staff training, being a patriotic staff, and getting supportive supervision, were identified from individual-level characteristics; similarly, coaching, supportive supervision, and peer-to-peer learning from relational/interpersonal level characteristics, and organizational culture, incentive, infrastructure establishing accountability, and staff turnover, were identified from organizational level characteristics. Conclusion The quality of data and routine information utilization was low and were influenced by a number of actors presented in and around the health system including individual, interpersonal, and organizational characteristics. Incentive affects data quality and information use directly or indirectly after modifying factors at all levels of the social-ecological model. Therefore, interventions should gear towards addressing multiple social-ecological factors of the health system concomitantly or intervene on incentive which has a multifaceted effect on the outcome.

Drivers and Barriers to Improved Data Quality and Data-Use Practices: An Interpretative Qualitative Study in Addis Ababa, Ethiopia

“…22 Another study conducted in Ethiopia stated that lack of infrastructure-such as electricity, power generators, and computers-influenced the generation and use of health data at the facility level. 11 Lack of HMIS resources were also reported to influence HMIS data quality in low-and middle-income countries. 23 Study participants recognized the value of the capacity-building work done on data quality and data use.…”

“…26 Motivational interventions and appropriate incentives that address health workers' behavior and attitudes toward HMIS can enable collection, reporting, and use of quality data from the source. 11,27 A study done in rural South Africa associated data quality problems with reduced level of health worker motivation, 28 which is indicative of the need to address motivationrelated challenges.…”

“… 4 The extent to which decision makers use data in the decision-making process can depend on various behavioral and organizational factors. 5 Some of the behavioral factors that influence data quality and use are a lack of a perceived benefit of the data to health, 6 gaps in knowledge and skill to process and analyze data, 7 – 9 and the absence of a “culture of information use.” Organizational determinants documented in the literature include lack of training, supervision, and mentorship 7 , 10 ; limited availability of resources 9 , 11 ; and inadequate leadership and coordination. 10 …”

“…5 Some of the behavioral factors that influence data quality and use are a lack of a perceived benefit of the data to health, 6 gaps in knowledge and skill to process and analyze data, [7][8][9] and the absence of a "culture of information use." Organizational determinants documented in the literature include lack of training, supervision, and mentorship 7,10 ; limited availability of resources 9,11 ; and inadequate leadership and coordination. 10 In 2008, Ethiopia launched a national health management information system (HMIS) to standardize the system across the country-including harmonizing key indicators, data collection tools, reporting systems, data quality assurance mechanisms, and data-use systems and processes.…”

National routine data for low birthweight and preterm births: Systematic data quality assessment for United Nations member states (2000–2020)

“…Insufficient access to these critical data often hampers health workers' ability to make well-informed clinical care decisions, resulting in inadequately delivered services. 22,23 Collecting data on birthweight and gestational age on every birth and generating individual level data that are inter-operable with routine health information systems such as DHIS-2 could help drive more data use at the point of care and improve data quality at the ward level. Regular data collection efforts also contribute to the enhancement of the low birthweight and preterm birth data, facilitating the flow of accurate information up the healthcare system to the district and national levels.…”