Coaching visits and supportive supervision for primary care facilities to improve malaria service data quality in Ghana: an intervention case study

Abstract

Effective decision-making for malaria prevention and control depends on timely, accurate, and appropriately analyzed and interpreted data. Poor quality data reported into national health management information systems (HMIS) prevent managers at the district level from planning effectively for malaria in Ghana. We analyzed reports from data coaching visits and follow-up supervision conducted to 231 health facilities in six of Ghana’s 16 regions between February and November 2021. The visits targeted health workers’ knowledge and skills in malaria data recording, HMIS reporting, and how managers visualized and used HMIS data for planning and decision making. A before-after design was used to assess how data coaching visits affected data documentation practices and compliance with standards of practice, quality and completeness of national HMIS data, and use of facility-based malaria indicator wall charts for decision-making at health facilities. The percentage of health workers demonstrating good understanding of standards of practice in documentation, reporting and data use increased from 72 to 83% (p<0.05). At first follow-up, reliability of HMIS data entry increased from 29 to 65% (p<0.001); precision increased from 48 to 78% (p<0.001); and timeliness of reporting increased from 67 to 88% (p<0.001). HMIS data showed statistically significant improvement in data completeness (from 62 to 87% (p<0.001)) and decreased error rate (from 37 to 18% (p<0.001)) from baseline to post-intervention. By the second follow-up visit, 98% of facilities had a functional data management system (a 26-percentage-point increase from the first follow-up visit, p<0.0001), 77% of facilities displayed wall charts, and 63% reported using data for decision-making and local planning. There are few documented examples of data coaching to improve malaria surveillance and service data quality. Data coaching provides support and mentorship to improve data quality, visualization, and use, modeling how other malaria programs can use HMIS data effectively at the local level.