In Global Health, just as in many domains where large teams of stakeholders must cooperate to plan and execute a process or policy, evidence based decisions are critical to long-term success. The process of making decisions based on evidence is often hampered by obstacles to accessing the resources created to help make those decisions. In the domain of malaria for example, decades of investment in surveillance, monitoring and evaluation, and model building have resulted in an amazing suite of artifacts, and there are new contributions being made as scholarship in computational health and global development continues to be advanced. In spite of this progress, and this significant investment, we see fundamental challenges in Visibility, Trust (and Transparency), and Complexity which must be better managed to enable policy makers to truly take advantage of the resources which have been developed.

Visibility, and Trust (and Transparency) are reasonably straight forward, as decision-makers can’t use resources they don’t know exist or don’t trust. So improvements in these avenues are readily understandable. What may be a bit difficult to appreciate on the onset is the fact that complexity is also a significant barrier to the use of resources. If a model is too hard to understand, or to use, then it will not be used. Another very practical challenge is if the configuration process required to run the model, or to see the results is too complex, the same outcome will occur (resources will not be used). These obstacles must be overcome to ensure that value of the existing resources can truly be realized. We believe that technology and effective application of algorithms can help address these concerns. Further, we believe that there are new opportunities which can be realized for decision-making.