While working for the RTI StopPalu Project in Conakry, Guinea I have been lucky enough to have access to a considerable amount of health data. The project has expended significant time and resources facilitating data quality standards at the health facility and prefectural government levels. As such, their data is widely regarded as the best in the country. Although it isn’t perfect, as with any database that derives information from a variety of sources, I have found that other parts of the country do not come close in terms of data integrity.

Accurate data deficits are not simply a Guinean problem. Poor data quality inhibits information-based decision making across the region. Progress in this area is limited by budgetary constraints, lack of political will, poor or non-existent data infrastructure systems, and technical capacity. Although donors are becoming more interested in data collection and analysis within projects, it is difficult to convince local governments to make the necessary investments and take data seriously. However, there is some progress in Guinea’s Health Ministry as the National Malaria Control Program has begun to implement a web-based data collection database, DHIS2, populated by health-facility level data.

The institution of a national DHIS2 system is the first step in developing an effective data collection and analysis system. However, this information is limited as it reveals information about discrete visits to health facilities and very little about long term care seeking trends. This weakness can be largely attributed to the absence of robust case management systems. Information can be supplemented with large scale surveys. However, due their expense and resource requirements, surveys such as the DHS occur infrequently.

Alternatively, SMS-based interventions can be a powerful approach to data collection due to the ubiquity of mobile phones and advances in SMS data collection with platforms such as UNICEF’s RapidPro. This platform has been used in clinics to record patient information that is exported to a central database and has the potential to record information about individual health practices and needs.

Analyzing maternal health data in Conakry.

Analyzing maternal health data in Conakry.

While effective data collection and management does require a resource investment, it doesn’t have to break the bank. Using existing social and technology structures can help NGOs and governments alike leverage powerful data sources. Large scale SMS health messaging and reminder systems can facilitate positive programming outcomes while archiving the relevant information for later analysis.

However, SMS-based ICT is not a panacea. As with any other development intervention it must be adjusted to fit the needs of the context. Effective, adaptive, and thoughtful planning and implementation is critical to the success of this approach. Without a clear vision of the data goals, complexities, and processes an organization can be left with terabytes of information that reveal little insight.

Programs across the region are beginning to harness the potential of ICT for better programming. However, work must continue on the governmental level to improve national capacities and standards. In order to have meaningful data that can explain trends and help target programs and policy the government must set the tone and take responsibility for a national system. Guinea and its neighbors have a long way to go to fill the data gap, but they are slowly on their way to building these critical systems.

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