Research Informatics.org

It seems that in the biomedical informatics community, we have a tendency to focus more on the technology that can be employed to improve a particular domain, rather than the “process” that we are trying to address. If we look to the publications and best practices that are available in the business and social science communities, there is a plethora of methods and approaches that can be used to support the study and optimization of such “processes” - often referred to as “workflow.” So the question is - why don’t we see this intersection being leveraged more frequently? Based upon some recent pilot projects that I have been involved with here at Ohio State University, I would suggest there are a couple of major reasons: The basic meaning of what constitutes “workflow” is not consistently understood or shared throughout the informatics domain. For those of us with more computationally-centric backgrounds, the term is used to refer to the orchestration of multiple computational processes, agents, or components. In contrast, for those of us with social sciences-centric backgrounds, the term is used to refer to a holistic understanding of the major players, artifacts, and activities that exist in a domain, and their relationships to one-another in a particular problem area. The tools available to support the observation, modeling, and analysis/optimization of workflow are diverse, and not always easy to evaluate or adopt. As with many “standards”, the real benefit of most workflow analysis approaches is that if you don’t like one of them, there are always numerous others to chose from. The literature is rich with descriptions of ethnographic studies, time-motion studies, key participant interviews, UML activity diagrams, “swim-lane” diagrams, and workflow simulation studies. Unfortunately, most of these reports assume a relative familiarity with their methodological approaches, making it difficult to quickly ascertain their applicability to ones own projects without extensive correlative research on those methods (if you can first find the correct resources from which that research can be extracted). One of the biggest issues with this lack of consistency and explanation is that methods and results from potentially complimentary studies are rarely compatible or interchangeable with each other. Imagine if everyone used a different way of calculating and presenting statistical significance in their publications (well, in all honesty, this is sort of the case, but I am not a statistician, so I will hold my comments on that topic for a later post)? That is sort of what reading the literature in the workflow analysis domain is like. Combinations of qualitative and quantitative approaches to workflow analysis seem to be rare, and in many cases, discouraged. The communities who frequently engage in qualitative workflow analyses often argue that quantitating such models will only lead to erroneous assumptions and conclusions. Similarly, the communities who frequently engage in quantitative workflow analyses seem to be solely focused on quantitating a domain, with little regard to the complex interplay between actors, artifacts, and activities. It seems to me that we need to find a middle-ground, such that these approaches can be combined and systematized. In all honesty, a fully qualitative workflow model is of little assistance on its own if we wish to instrument, optimized, or further evaluate a domain. Developing a better understanding of how qualitative workflow models can be linked in a efficacious manner to quantitative measurements of the domain under study should be a high priority goal for our field. And finally - how does this even relate to the domain of translational informatics? Well, from my perspective, it seems unlikely that we can be effective or successful in developing and deploying informatics platforms intended to address the needs of the translational research community if we do not first understand: 1) what the current workflow of our end users is; 2) where there are opportunities to optimize that workflow using informatics interventions; and 3) how we can design such interventions to achieve the targeted workflow optimization without disrupting other areas of the contributing workflow. Such an approach is consistent with about every well validated software engineering approach known - all of which aim to ensure that new software platforms are accepted and well utilized. The real challenge is not deciding to use such workflow or participant-centric approaches, but rather, the actual “nuts and bolts” of doing so in the context of complex biomedical workflows. The alternative approach is the “if we build it, they will come” model, which I think goes without saying and being less than ideal!

For those of you involved in the biomedical informatics community, you have probably noticed in the past few years that it is becoming increasingly common for both basic science and clinical investigators to invite informaticians to work with them on their projects. These requests can include assisting teams with the preparation of grant or contract proposals, to engaging in data management and analysis services that support the scientific aims of an ongoing project. One constant that I Ã¥have experienced in this regard, especially in the context of translational research projects (which tend to focus on linking bio-markers or biological models with phenotypic data in order to diagnose a disease or to understand disease progression / treatment outcome), is that the investigator community is often unsure what types of services or support they need from informaticians, other than that they need some sort of “informatics.” In a similar manner, I have found it to be a frequent occurrence that informaticians often have a hard time translating the novel theories, methods, and technologies they have developed or evaluated in order to address the specific problems of an applied research project. I often refer to this as the “then what” problem, as in, now that we have these technologies and methods, and a motivating use case, then what do we do? The reality is that this problem really points out a number of challenges that both the translational research and informatics communities are not doing a particularly good job of addressing, namely: Educating clinical and translational researchers as to the practical capabilities of modern informatics methods and techniques, Educating informaticians as to the realities of the “real translational research world”, Placing and emphasis on (and funding) applied informatics projects that can couple rigorous science, technology development, and real-world use cases, and Ensuring that informatics is understood by funding agencies, organizations, and funding agencies as being both an applied and basic science (usually in a simultaneous fashion), thus requiring it to be more than just a service, and more than just a theoretical exercise. Just some food for thought (which will probably be of particular interest to our colleagues, myself included, who are currently working on the ubiquitous CTSA application). I would be interested in anyones comments on how to address these areas (if you happen to be reading this).

The domain of translational research informatics is by most accounts in its early and formative stages - which presents a unique opportunity to academics and professional in the informatics community to define this timely and very critical field. However, that same opportunity also presents a challenge, due to the fact that a common perspective concerning what precisely constitutes the practice and definition of translational research informatics, or as I often refer to it in a simpler form, translational informatics, is generally lacking. Therefore, since this is the first of what I hope will be many posts to come on this new blog, entitled “Translational Research Informatics”, I thought I would take this opportunity to present one potential definition of the field that I, and a number of colleagues from AMIA (including my close collaborator, Dr. Peter Embi from the University of Cincinnati) have discussed over the past several months, and that we have posted to that ultimate of Internet knowledge repositories, Wikipedia (Translational Research Informatics). Our current working definition of translational informatics as found on Wikipedia, reads as follows: Translational Research Informatics (TRI) is the sub-domain of Biomedical informatics or Medical Informatics concerned with the application of informatics theory and methods to translational research. It overlaps considerably with the related rapidly developing domain of Clinical Research Informatics. Translational research as defined by the National Institutes of Health includes two areas of translation. One is the process of applying discoveries generated during research in the laboratory, and in preclinical studies, to the development of trials and studies in humans. The second area of translation concerns research aimed at enhancing the adoption of best practices in the community. Cost-effectiveness of prevention and treatment strategies is also an important part of translational science. Another way to look at the discipline of translational informatics is to visualize the major areas that must contribute to our collective ability to generate translational biomedical knowledge (which is concerned with understanding and acting upon the meaningful relationships between biological knowledge, clinical research/practice, and the population sciences). I have developed one such visualization, which I refer to as the “Translational Informatics Triad.” I have included the preceding definition as static-content on this blog to help guide readers who are new to the field. However, I fully anticipate that the definition will evolve over time, given our collective increased understanding of the field, as well as the specific comments I hope readers of this blog will contribute.