Monday, April 8, 2013

Biomedical and Health Informatics vs. Data Science, mHealth, etc. - New Disciplines or New Terminology?

When I entered the field of informatics in the 1980s, a great deal of the research was driven by "artificial intelligence" (AI). Many people were trying to build "rule-based expert systems," while those interested in knowledge representation were constructing "semantic networks." We rarely hear these terms in quotes these days, perhaps with the exception of AI that one hears occasionally. It is not, however, that no one is trying to build systems that guide decision-making and represent knowledge in complex ways, but we just different terminology now, such as clinical decision support and ontologies.

Fast forward to the present, and we see the introduction of new terms, most prominently right now data science [1] and mHealth [2]. Many who are doing work in these areas talk of them as the primary focus of their work. I question, however, whether these are truly new disciplines, or just concentrations (at least for those working in health-related areas) within biomedical and health informatics [3]?

I am most concerned about mHealth, when I see new people coming forward with brilliant ideas and truly innovative technologies, yet not incorporating the experiences from decades of work in informatics. I do not deny that some aspects of using mobile connected devices for health are truly novel, yet what I consider to be the basic principles of informatics still apply, namely things like scalability, interoperability, usability, and so forth. I just see nothing novel enough about mHealth to not call it part of informatics.

The same holds, in my opinion, for data science. There are certainly "computationalist" techniques of which many who work in informatics are not skilled. "Big data" applications will require specialized knowledge. But informatics is a broad field, and no one can master everything. There are other aspects of informatics, such as (I am repeating myself from the previous paragraph here) scalability, interoperability, usability, and so forth that must be married from the results of data science to make the latter's output truly usable. One case in point is the growing number of analyses that predict undesired outcomes, such as hospital readmissions [4]. I am as intellectually interested in these applications as much as anyone, but until it is shown these analyses can be actionable, they will mostly remain interesting theoretical exercises.

I am excited for mobile health applications and advanced uses of data techniques to improve health, healthcare, and research. I hope that those pursuing them do not lose sight of the larger picture of providing end-to-end value for the use of data, information, and knowledge in health-related endeavors, i.e., the goal of biomedical and health informatics [3].

References

1. Davenport, TH and Patil, DJ (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, October, 2012. http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/.
2. Krohn, R and Metcalf, D (2012). mHealth: From Smartphones to Smart Systems. Chicago, IL, Healthcare Information Management Systems Society.
3. Hersh, W (2009). A stimulus to define informatics and health information technology. BMC Medical Informatics & Decision Making. 9: 24. http://www.biomedcentral.com/1472-6947/9/24/.
4. Gildersleeve, R and Cooper, P (2013). Development of an automated, real time surveillance tool for predicting readmissions at a community hospital. Applied Clinical Informatics. 4: 153-169.

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