ArticleApplied clinical informatics2021
A Perioperative Care Display for Understanding High Acuity Patients.
Article in Applied clinical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
backgroundThe data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types.
objectivesIn this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms.
methodsWe used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions.
resultsThrough a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries.
conclusionThis study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.
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