In real-world applications, evidence detection involves evaluating a body of existing information from time-evolving multi-modal data sources. It seems obvious that approaches to evidence detection should consider the relative quality of data sources with respect to the value of information being produced over time. For instance, considering all data sources equally reliable can yield undesirable results. We highlight the distinction between the traditional value of information problem where information is pulled from sources and the value of information for evidence detection problem where information is pushed from the sources. We further comment on how this distinction enables new qualities of information to be measured and characterized. In this paper, we address the following questions: What should value of information mean for evidence detection? What are the components needed to characterize value of information? How should these components be measured and combined to compute a value for information? Finally, how should value of information be used in evidence detection? We develop a framework for implementing value of information for evidence detection and present the results of a preliminary feasibility study.