We can use the context of the clinician to tailor what is presented. For example, factors such as how many years the clinician is post qualification; what they have been shown recently; whether they already overridden this type of recommendation, their speciality; and whether this recommendation is outside their specialist knowledge could all influence what is most valuable to display given the user’s limited opportunity to take in new information. Knowledge graph- based augmented clinical reasoning should mean nudge decisions where you are fitting into the moment when the clinician thinks – I might have an information need or a knowledge gap – a ‘teachable moment’. And they ask themselves, do I even bother to try to close the knowledge gap or do I just move on? If it is already halfway closed for the clinician and all they have to do is acknowledge it, then that is a win-win – for clinicians and for their patients. At a more fundamental level, even the largest guideline producers can publish a limited number of guidelines on a limited number of diseases whereas knowledge graphs can cover a far wider range of conditions and in considerably more depth. In addition, guidelines are usually updated at fixed intervals whereas knowledge graphs can be subject to continuous updating. Another problem is that evidence and subsequent guidelines don’t exist for all areas of clinical practice. This is especially true for diagnosis-focused recommendations, and those considered expert opinion best practices. A knowledge graph can scale to include this best practice-based consensus as well as the latest evidence-based recommendations. Lastly, many guidelines are quite long so it can be difficult to quickly find the exact nugget of knowledge that you need. In contrast, the knowledge graph approach enables Osheroff’s five rights of clinical decision support: the right information, to the right person, in the right format, through the right channel, at the right time in the workflow.
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Why the BMJ Knowledge Graph
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