Medical Library Education: Acronyms and Initialisms
Rooting out topics worthy of mention or of special significance to medical library education can be a challenging yet at the same time rewarding pursuit, especially when a possible vein of interest seems to forever resolve itself into a funky set of initials or a catchy, snappy acronym. This can make tracking down and capturing an intriguing idea that ties in to both the health sciences and to librarianship somewhat tricky—nay, even problematic. It can be initial this, acronym that, but what does the shorthand involved actually describe, define, or mean in real terms?
Take CBK, for example. A very basic, quick search for CBK in CINAHL or in PubMed might reveal a host of suspects, or, er, possibilities. In fact, CBK can mean clean birth kits; or chemistry, biology, and keyword; or center for business knowledge; or cyberknife; or calcific band keratopathy; or core body of knowledge; or—well, you get the picture. However, the concept sought in this case is “computable biomedical knowledge.” So, what is a fast definition for this version of CBK? According to a manifesto located online on the University of Michigan’s web pages:
Computable Biomedical Knowledge is the result of an analytic and/or deliberative process about human health, or affecting human health, that is explicit, and therefore can be represented and reasoned upon using logic, formal standards, and mathematical approaches. [1]
As it turns out, the University of Michigan is a great overall resource pertaining to CBK. Another fount of information is a 2018 article, “Desiderata for Sharable Computable Biomedical Knowledge for Learning Health Systems,” by Harold P. Lehmann and Stephan M. Downs [2]. The article is littered with abbreviations and at least one acronym—not the least of which are LHSs, CDSs, and KOs. According to Lehmann and Downs, learning health systems (LHSs) and clinical decision support systems (CDSs) can lead to knowledge objects, which are also known as—you guessed it—KOs. KOs are identified as guidelines, models, or similar information structures that are often produced as byproducts of the individual systems. Much of the focus of the article, however, seems to be KOs born of LHSs.
One framework the authors point to as being commonly linked to KOs is FAIR. FAIR translates as findable, accessible, interoperable, and reusable [3]. Lehmann and Downs report that KOs generated from CDSs are often years in the making, while LHS KOs are, well, not. The reason for this phenomenon, the authors explain, is a matter of disparity between sources of data. For instance, information derived from CDSs can come from extensive and exacting clinical studies, whereas information obtained from LHSs can be based upon empiric or observational data and/or processes.
The authors contend that, due to inherent differences in the two types of systems, the resulting KOs should not necessarily be treated interchangeably or in the same fashion. In their commentary, the authors champion decision analytic modeling as a useful schema for use with LHS KOs. To illustrate and to help explain this type of model, the authors use the acronym “You SHOULDT.” They also tout the use of and the ease with which a threshold system can be employed by a decision model to enhance as well as to guide decision-making processes. They further identify two levels of decision-making surrounding LHS KOs: one being institutional and the other intrinsic to the KO itself.
Of course, this is just the tip of the proverbial iceberg of what there is to know about CBK. A set of briefing papers accessible through the University of Michigan website examine the concept in great depth and detail, including a look into the sensitive nature of, and the trust factors associated with CBK.
References
- Michigan Medicine, University of Michigan. Mobilizing Computable Biomedical Knowledge (CBK): a manifesto [Internet]. The University; 7 Oct 2018 [cited 15 Jan 2020]. <https://medicine.umich.edu/sites/default/files/content/downloads/MCBK%20Manifesto%20Ver%2010.7.18.pdf>.
- Lehmann, HP, Downs, SM. Desiderata for sharable computable biomedical knowledge for learning health systems. Learn Health Syst. 2018 Oct;2(4):e10065. DOI: http://dx.doi.org/10.1002/lrh2.10065.
- Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten JW, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJ, Groth P, Goble C, Grethe JS, Heringa J, ’t Hoen PA, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone SA, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15;3:160018. DOI: http://dx.doi.org/10.1038/sdata.2016.18.