Barriers and Facilitators to Data Use for Decision Making: The Experience of the African Health Initiative Partnerships in Ethiopia, Ghana, and Mozambique

Early Effects of Information Revolution Interventions on Health Information System Performance in Ethiopia

Health information systems (HISs) are essential to a country's health system as they provide critical support to health policymaking, management, financing, and service delivery. A well-functioning HIS should produce timely and reliable data that are available and easily accessible to decision-makers throughout the health system. Ethiopia has transitioned from a fragmented, paper-based health management information system (HMIS) to a harmonized, digital system used at points of collection and service delivery.In 2016, the Federal Ministry of Health (MOH) launched the Information Revolution (IR), a transformative agenda aimed at enhancing the culture of data use, scaling priority HIS tools and systems, and strengthening HIS governance. Between 2016 and 2022, the MOH, Data Use Partnership, and other partners implemented a series of IR interventions that supported this agenda. These interventions included deploying and harmonizing digital HIS systems; strengthening HIS leadership, coordination, and governance; implementing the IR pathway strategy; enhancing capacity through supportive supervision, mentorship, and training; and improving performance monitoring teams. This article aims to synthesize the key HIS interventions implemented in Ethiopia as part of the IR and document the effects of these interventions on HIS performance.Early studies indicate promising improvements in HIS performance across health facilities in Ethiopia. However, challenges remain. To ensure sustainable progress, it is essential to continue addressing key challenges, such as system interoperability, HIS workforce, and capacity for data use at all levels. By building on the successes of the first Health Sector Transformation Plan and addressing these gaps, Ethiopia can advance its vision of a robust, data-driven health system capable of improving health outcomes and driving evidence-based decision-making.

Implementation status of maternal death surveillance and response system in Ethiopia: Evidence from a national-level system evaluation

BACKGROUND

In Ethiopia, Maternal Death Surveillance and Response (MDSR) was integrated into the existing Integrated Disease Surveillance and Response (IDSR) system in 2014. Despite providing valuable evidence to inform policies and actions, system implementation has not been evaluated. Thus, a national-level evaluation was conducted to assess the level and status of system implementation.

METHODS

A national cross-sectional study was conducted using a multi-stage sampling approach in 2020. A total of 629 health facilities were included in the study. A modified tool, adapted from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), was employed to assess each functional component of the system, encompassing structure, core, supportive, and system attributes. The score for each component was based on Ethiopian Public Health Institute's mid-term evaluation metrics. To objectively evaluate the implementation status, a composite score of the Maternal Death Surveillance and Response Performance Index (MDSRPI) was calculated based on five performance indicators. Descriptive statistics, independent t-tests, and one-way analysis of variance (ANOVA) with Bonferroni correction were used to examine the variations in scores among the different characteristics.

RESULTS

Of the total sample size, 82.5% (519/629) of health facilities were assessed. Among the assessed health facilities, 77.0% (400/519) fulfilled the criteria for final analysis. Accordingly, the overall readiness score was 44.9% (95% CI: 43.9% to 45.9%), which is rated as less functional. The structures of the system were rated at 51.7% (95% CI: 49.9% to 53.4%), and the system attributes were rated at 69.6% (95% CI: 68.0% to 71.2%), which were considered fairly functional. In contrast, the core functions were rated at 20.0% (95% CI: 18.9% to 21.1%), and the supportive functions were rated at 38.4% (95% CI: 36.4% to 40.4%), which were categorized as not functioning and less functional, respectively. Regionally, Tigray's overall readiness score (54.8%, 95% CI: 50.4-59.1%) was significantly higher than Oromia (41.6%, 95% CI: 40.2-43.0%, P = 0.0001), Amhara (47.7%, 95% CI: 43.9-45.9%, P = 0.05), and SNNPR (42.3%, 95% CI: 39.3-45.3, P = 0.0001). Additionally, Amhara's score was significantly higher than Oromia and SNNPR. Secondary-level healthcare facilities (49.6%, 95% CI: 45.7-53.7, P = 0.029) had a significantly higher readiness score compared to primary health facilities (44.6%, 95% CI: 43.5-45.6). The overall score for the Maternal Death Surveillance and Response Performance Index (MDSPI) was 33.9%.

CONCLUSION

Despite the noticeable regional variation, the overall system readiness and status to implement MDSR were suboptimal, characterized by low representativeness, completeness, and community engagement. Efforts should be directed toward improving community surveillance and enhancing all components of the system to address regional variations and improve overall performance through triangulation and integration with various data sources.

Onsite training-mentoring intervention improves data quality: an implementation research

BACKGROUND

The quality of health data is not satisfactory in Low and Middle Income Countries (LMICs). Haramaya University, in collaboration with Ministry of Health and Regional Health Bureau, conducted an implementation research in selected public health facilities and administrative units. This research was aimed to test the onsite training-mentoring (OTM) intervention and adaptation of the implementation strategy to improve the routine health information system (RHIS) data quality in the context of public health sector.

METHODS

An interrupted time series design with an onsite training-mentoring intervention was used to improve data quality in public health sector of Jigjiga Woreda, eastern Ethiopia from July 2021 to June 2022. Both the pre and post intervention assessments data were collected by experienced and trained public health professionals using interviewer guided self-administered interview, record review and observation data collection techniques. Data were analyzed using descriptive, bivariate, and multivariate logistic models to identify predictors of data quality.

RESULTS

The overall data accuracy was increased from 88.12% before to 95.0% after intervention; and it was above 90% in all the facilities. The overall data content completeness was increased from 75.75% to 89.9%, though it varied among the facilities. The timeliness and report completeness were 100% in all the facilities. The odds of those health workers who had poor knowledge were less likely to ensure data quality (AOR = 0.39; 95%CI: 0.19, 0.83) than their counterparts.

CONCLUSIONS

The intervention was brought substantial changes of data quality in the study setting. Knowledge of the workers towards data quality is a crucial factor to ensure data quality in the sector. Thus, collective efforts is required to continue this tested intervention to ensure the quality of the routine health information system in the lower levels of the sector.

What could improve surgical data system at health facilities with high surgical volume in Ethiopia?

BACKGROUND

The effective management of surgical and anesthesia care relies on quality data and its readily availability for both patient-centered decision-making and facility-level improvement efforts. Recognizing this critical need, the Strengthening Systems for Improved Surgical Outcomes (SSISO) project addressed surgical care data management and information use practices across 23 health facilities from October 2019 to September 2022. This study aimed to evaluate the effectiveness of SSISO interventions in enhancing practices related to surgical data capture, reporting, analysis, and visualization.

METHODS

This study employed a mixed method, pre- post intervention evaluation design to assess changes in data management and utilization practices at intervention facilities. The intervention packages included capacity building trainings, monthly mentorship visits facilitated by a hub-and-spoke approach, provision of data capture tools, and reinforcement of performance review teams. Data collection occurred at baseline (February - April 2020) and endline (April - June 2022). The evaluation focused on the availability and appropriate use of data capture tools, as well as changes in performance review practices. Appropriate use of registers was defined as filling all the necessary data onto the registers, and this was verified by completeness of selected key data elements in the registers.

RESULTS

The proportion of health facilities with Operation Room (OR) scheduling, referral, and surgical site infection registers significantly increased by 34.8%, 56.5% and 87%, respectively, at project endline compared to baseline. Availability of OR and Anesthesia registers remained high throughout the project, at 91.3% and 95.6%, respectively. Furthermore, the appropriate use of these registers improved, with statistically significant increases observed for OR scheduling registers (34.8% increase). Increases were also noted for OR register (9.5% increase) and anesthesia register (4.5% increase), although not statistically significant. Assessing the prior three months reports, the report submissions to the Ministry of Health/Regional Health Bureau (MOH/RHB) rose from 85 to 100%, reflecting complete reporting at endline period. Additionally, the proportion of surgical teams analyzing and displaying data for informed decision-making significantly increased from 30.4% at baseline to 60.8% at endline period.

CONCLUSION

The implemented interventions positively impacted surgical data management and utilization practice at intervention facilities. These positive changes were likely attributable to capacity building trainings and regular mentorship visits via hub-and-spoke approach. Hence, we recommend further investigation into the effectiveness of similar intervention packages in improving surgical data management, data analysis and visualization practices in low- and middle-income country settings.

Learning Health Systems to Bridge the Evidence-Policy-Practice Gap in Primary Health Care: Lessons From the African Health Initiative

The compilation of lessons in this supplement on the Doris Duke Charitable Foundation’s African Health Initiative’s work in the application of implementation research in primary health care in sub-Saharan Africa reflects the evolution of the discipline that is now increasingly recognized as integral to health systems strengthening.