Increases in computing power, reductions in the cost of data storage, and the democratization of complex data analysis tools have reduced barriers to the development of clinical decision support algorithms. This fact combined with significant financial support from the federal government has, in turn, led to a substantial increase in the number of research teams that are using their clinical experience and data to develop clinical decision support algorithms. These algorithms aim to improve the standard of care in high acuity low occurrence events, reduce the cognitive burden of care provided under duress (multiple casualty or overwork), provide insights into clinical risks that are not easily ascertained from patient vital signs, or expand the clinical capabilities of the person providing care. Government/academic research teams engaged in development of algorithms that require physiological signals are generally limited to reaching a technology readiness level of 4 absent industry support, because readiness level 5 requires some integration into the host patient monitor where the algorithm will ultimately be deployed. Therefore, successful engagement with industry partners is a critical step in continuing the development of these algorithms. There are a variety of cultural, technical, and commercial challenges that impede establishing an academic-industrial partnership, and therefore translation of these algorithms to patient care. To engage in an encourage this kind of collaboration, ZOLL Acute Care Technology has recently developed a process map to facilitate discussions around potential partnerships with outside research organizations. The goal of the process map is to delineate the different challenges to commercial success and to establish the criteria that will be used to track algorithm development success and inform the decision-making process along the path to commercialization. Our presentation describes this process map.