Research Informatics.org

Translational researchers (e.g., basic scientists who move their discoveries into clinical trials, or clinical investigators who work to disseminate the results of their work as community-accepted guidelines or best practices) often look at the area of operations research as being a completely unrelated field to their research efforts. Instead, it is often seen as the purview of “business types”, such as organizational management or financial analysts. However, I would pose the question of whether this is in fact an appropriate division of focus, effort, and expertise. In this particular context, when I talk about operations research, I am referring to: “an interdisciplinary branch of applied mathematics which uses methods like mathematical modeling, statistics, and algorithms to arrive at optimal or good decisions in complex problems which are concerned with optimizing the maxima (profit, faster assembly line, greater crop yield, higher bandwidth, etc) or minimal (cost loss, lowering of risk, etc) of some objective function. The eventual intention behind using operations research is to elicit a best possible solution to a problem mathematically, which improves or optimizes the performance of the system.” (a definition as found on Wikipedia) When you connect the dots from last weeks post, there is a clear connection between analyzing and understanding workflow in the biomedical domain, and the optimization of that workflow, as can be achieved through the theories and methods associated with such operational research. While workflow analysis is a much more “fuzzy”, social-sciences anchored discipline, you can see how, given the preceding definition, operations research is where the scientific “rubber meets the road” in terms of acting upon the data generated by workflow modeling studies. Now - why is this important to Translational Research, and in particular informatics? Well, I would argue that our collective understanding of how new biomedical innovations or methods diffuse from basic science discovery to clinical research, and again from clinical research to clinical practice, is at the heart of enabling truly translational work. Basic scientists or clinical investigators, and their informatics collaborators, could generate the most amazing findings in the history of biology or medicine, but if we do not understand how to opitmally disseminate those findings, and maximize their uptake and application in the “real world” of clinical care or population-based sciences/services (such as public health), then those finding may end of becoming stale or forgotten in the academic literature or isolated islands of knowledge and practice. There are many reasons to believe that informatics, as a discipline, can serve as the mechanism by which operations research and translational science can be combined. As informaticians, we know how to instrument complex systems and collect high throughput data from a targeted environment, and furthermore, how to use that data in conjunction with other sources of knowledge (e.g., ontologies, terminologies, the published literature) and analytical pathways (e.g., hypothesis discovery, data mining, biostatistics, computational simulation). Such methods are critical to feeding the algorithms and models associated with operations research, such that we can understand system-based factors that affect knowledge or practice dissemination and uptake. However, to create such a research “pipeline” requires us (e.g., informaticians AND translational researchers) to recognize that to truly be successful in translational endeavors, we must recognize the need to engage in basic, clinical, AND operations research within the scope of a given project. Needless to say, this is a tall order, but one that I feel we need to embrace and act upon if we truly intend to change the face of biomedicine over the coming decades to focus upon data-driven, personalize health care delivery.