Strengthening Malaria Data Quality in Ghana

Access to accurate health data is foundational to inform decision making for effective malaria prevention and control. However, health staff in Ghana often face many challenges in submitting high quality data that can be trusted in decision-making. Staff may lack data recording skills, creating challenges in accurately transferring that data across registries for different health areas. In other cases, limited experience with malaria case management, and a lack of active data validation teams who can ensure that the reported data is accurate may limit data quality.

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Recognizing these challenges, the Upper East and Savannah Regional Health Directorates in Ghana have committed to enhancing health information systems with better data quality to support a more responsive and data-driven health system at all levels.

As part of these efforts, CHISU supported these regional health directorates and the National Malaria Elimination Programme (NMEP) to address the data quality challenges health staff face through supportive supervision and coaching visits.

Supportive supervision is a process of helping health workers improve their skills through supervisory visits and mentorship to improve knowledge and skills. Using this process, the supervisory team was able to support the improvement of malaria data quality and use in 155 health facilities across the two regions with a focus on data capture and reporting principles.

In the Upper East Region, the supervising team visited 109 health facilities across the region’s 15 districts, engaging a total of 337 staff. In the Savannah Region, the team visited 67 health facilities across each of the region’s 7 districts and provided coaching for 278 health staff.

During the visits, the teams prioritized routine data validation and verification practices, ensuring data quality for informed decision-making. The team worked with staff to develop action plans to address gaps identified during the visit, leading the way for lasting improvements in data skills and usage that will translate to better malaria data quality for better programming decisions.