Making clinic related data from satellites accessible to national level stakeholders

BACKGROUND: Kenya Pharma is a USAID funded project that works with the government of Kenya to manage the supply of HIV commodities to about 1200 health facilities in Kenya. To supply commodities, the project requires monthly reports of patient numbers, consumption of HIV commodities, and orders for the supplies required. Of the 1200 facilities, 167 are ordering sites which report and order commodities directly from the project. In addition, each ordering site is responsible for several satellite sites and submits an aggregated report and order on the satellites behalf. There are more than 1100 satellites. The project has a web-based electronic supply chain management system (e-SCM) through which it receives the reports and orders. Data on the e-SCM are used by the project and the National AIDS and STI Control Program (NASCOP) to forecast and quantify the quantities of HIV commodities needed by the sites. The data are also used by other stakeholders at national level to make decisions about treatment and care of HIV patients. OBJECTIVE: Initially, data from individual satellites were manually recorded and sent on paper forms to the responsible ordering site. The ordering site would then manually aggregate the satellites' data in order to submit a single report to the project. Individual satellite data was therefore not accessible to the project, NASCOP and other national stakeholders. Furthermore, because the aggregation was calculated manually, the process was slow, time consuming and errors could easily be made during manual calculation. METHOD: To overcome these obstacles, the project developed an aggregation tool on the e-SCM. Satellites with access to the internet can now submit their data directly onto the eSCM. An ordering site accesses its satellites' data on the e-SCM and uses the aggregation tool to automatically aggregate the data and generate the report and order required by the project. For satellites that do not have internet access, the ordering site enters the paper data into the e-SCM and then aggregates it along with the other satellites' data. RESULT: *Individual satellite data is available on the e-SCM. *Aggregation is automated making it easier, faster, and reducing the chances of introducing errors through manual calculation. CONCLUSION: The aggregation tool allows archival and retrieval of ordering site data and corresponding individual satellites' data by national level stakeholders. Automated aggregation reduces the margin of error and improves the accuracy of forecasting and quantifying commodities for health facilities.