Summary of findings: what clinicians want
Why epistemic visualisation has the power to transform the clinical usability of EHR interfaces
Dave Pao
3 min read
November 2023
'Analytical graphics should be constructed to serve the fundamental cognitive tasks in reasoning about evidence: describing the data, making comparisons, understanding causality, assessing credibility of data and analysis. Thus the logic of design replicates the logic of analysis; design reasoning emulates evidence reasoning.'
- Edward Tufte (2002)
These research findings show that clinicians want an interface that empowers their clinical reasoning both in a fast, intuitive sense (offloading cognitive effort) and a slower, deliberate and conscious sense (supporting cognitive effort). This means an interface that supports dual process thinking, or 'think fast, then slow' (Kahneman, 2011).
But to achieve this an interface needs to acknowledge, and possibly mirror, clinical theory. This is not so easy and, arguably, only possible through visualisation.
Of all the perceptual senses, vision is arguably man’s most dominant, with a deeply embedded connection to cognition. The specific form of any visualisation is so important that it determines what information can be perceived, what processes are activated, what structures can be discovered, what errors are generated and what strategies are adopted (Zhang, 1997).
Many EHR interfaces do not yet benefit from visualisation. When they do, the design of their visual elements is dictated by the first-order characteristics inherent to the data (e.g., if it's a number, graph it!). This results in conventional table and graph formats that, while syntactically clear, lack semantic depth. In other words, they are accurate but cannot convey clinical meaning.
Clinicians still want visualisations that show characteristics inherent to the data, but more fundamentally they want an interface tailored to the way they think. But there are very few visual design approaches that incorporate this semantic dimension within their blueprint.
An epistemic (from the Greek word 'epistēmē,' meaning 'knowledge' or 'understanding') approach to visualisation does include this broader context, by regarding visualisations as vehicles for surrogative reasoning. This research identified Cheng and Barone’s (2007) Representational Epistemic (REEP) approach to visualisation, which ‘preserves the conceptual structure of the domain in the design of the structure of the representational system.’
REEP allows any and all concepts specific to clinical reasoning to be mirrored within the visual structure of an EHR interface. This enables mental computation that is easier, faster, more reliable and more insightful by harnessing the symbiosis between the visual system and cognition.
To ensure successful epistemic visualisation, Bolinska (2016) identifies three essential considerations: the user, the phenomenon of interest and the purpose for which the representation is employed. She illustrates her analysis by examining the London Underground Map designed by Harry Beck in 1933 (TfL, 2023).
In this map example (see image), the user is the journey-maker (one of millions of people a day, with a variety of backgrounds and cognitive capacities) and the phenomenon of interest is the set of possible routes connecting the stations between which the user wishes to travel. The purpose of the map is to determine the most efficient route from one station to another.
An epistemic visualisation must contain sufficiently relevant and accurate information. The user does not require details such as the distance between stations or the depth below ground. Such extraneous information does not enhance the success of the epistemic representation and may indeed detract from it. Excluding these superfluous features does not render the map less accurate, but simply less informative. Bolinska refers to this as 'accuracy‐for-a-purpose.'
An epistemic visualisation must also convey this information meaningfully (semantic salience) and, in a perceptual sense, effectively (syntactic salience). This is achieved by specifying the relative locations of stations and interchange stations along each line, and the characteristics of the visual elements used to represent them, respectively.
In essence therefore, what clinicians want from EHR visualisation is the integration of data with their cognitive workflow and mental model—an interface that not only presents data but can also transform them into relevant knowledge. The principles of epistemic visualisation, as evidenced by successful models like the London Underground map, provide a design blueprint for such transformation.
By prioritising accuracy-for-a-purpose, semantic and syntactic salience, and the user's cognitive context, epistemic EHR interfaces transcend conventional data visualisation. Even if there are limitations of an epistemic interface (i.e., simple rather than complex clinical theory), these do not become limitations of the clinician—who is able to extend their reasoning beyond the interface whilst still being supported by it.
An epistemic approach to visualisation means that EHR interfaces can become powerful, adaptable tools tailored to the nuances of clinical reasoning—serving not just as data repositories but indispensable agents in the delivery of high-quality patient care.
Bolinska, A. (2016). Successful visual epistemic representation. Studies in history and philosophy of science, 56, pp.153–160.
Cheng, P. and Barone, R. (2007). Representing complex problems: A representational epistemic approach. In: Jonassen, D. H. (Ed). Learning to Solve Complex Scientific Problems. Milton Park, UK: Routledge.
Kahneman, D., 2011. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.
Transport for London (TfL). (2023). London Underground Map.
Tufte, E. (2002). Analytical design and human factors. [Online forum]. Available at: https://www.edwardtufte.com/bboard/q-and-a-fetch-msg?msg_id=0000KI
Zhang, J. (1997). The nature of external representations in problem solving. Cognitive science, 21 (2), pp.179–217.
