Value-of-information (VOI) analysis can establish the expected benefits from health research. This typically involves the modeling of a disease and its treatment to fully characterize the uncertainty in outcomes of the interventions under study, which is generally complex and costly. As such, full modeling VOI often limits the practical use of VOI in prioritizing and designing research studies, particularly low-cost studies. This study 1) identifies approaches that can minimize the costs of performing VOI analysis; and 2) describes an algorithm for selecting the best VOI approach for a given clinical question. As alternatives to full modeling VOI, we identified conceptual VOI, minimal modeling and maximal modeling to be lower-cost approaches to VOI. In conceptual VOI, information is used about each of the multiplicative elements of VOI, including the durability of research evidence, to provide informative bounds on VOI without formally quantifying this through modeling. When data on comprehensive outcome measures, like QALYs or net benefit, are readily available from existing research, it may be possible to perform VOI with only minimal modeling. Instead of constructing separate models, a maximal modeling VOI uses a single comprehensive model to simultaneously inform multiple clinical questions. To select the best approach to VOI, our algorithm begins with conceptual VOI, followed by the clustering of clinical questions and maximal modeling VOI, and then minimal modeling using comprehensive outcomes. In applying the algorithm to inform priority-setting for systematic reviews within a U.S.-based agency, we found the algorithm useful and found practical applications for each of the lower-cost VOI approaches. Although full modeling VOI may aid in the planning and design of research, we find limited conditions for its use in prioritizing low-cost studies.  We conclude that VOI may be useful in research priorization and design, especially because methodology exists that can minimize the costs of analyzing the value of health research.