OpenRBF is a solution used in many countries to track results and payments in Results Based Funding programs in developing countries. In most programs using OpenRBF, facilities submit monthly reports to the financing entity. These reports are then verified by on site supervisions, and payments are made on the verified data. This full verification ensures the accuracy of the payments made, but is costly to implement. This project aimed at defining and testing algorithms to orient program managers willing to only verify subsets of the data reported each month.
I developped a generic framework to define and test algorithms to orient the on-site validation of reports from health facilities. I then implemented this framework in a Python module that allows an easy implementation of the algorithms and allows easy simulations to test and validate the algorithms. I implemented an algorithm empirically developed by Mathieu Anthony from AEDES, and compared it to two simple alternatives. Finally, I offered an applied decision framework for decision makers to help chosing the preferred trade-off between operational costs and overpayment.