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Get To The Point! Problem-Based Curated Data Views To Augment Care For Critically Ill Patients

Minfan Zhang, Daniel Ehrmann, Mjaye Mazwi, Danny Eytan, Marzyeh Ghassemi, Fanny Chevalier

2022Year
17Citations
5Top-tier citations

Abstract

Electronic health records in critical care medicine offer unprecedented opportunities for clinical reasoning and decision making. Paradoxically, these data-rich environments have also resulted in clinical decision support systems (CDSSs) that fit poorly into clinical contexts, and increase health workers cognitive load. In this paper, we introduce a novel approach to designing CDSSs that are embedded in clinical workflows, by presenting problem-based curated data views tailored for problem-driven discovery, team communication, and situational awareness. We describe the design and evaluation of one such CDSS, In-Sight, that embodies our approach and addresses the clinical problem of monitoring critically ill pediatric patients. Our work is the result of a co-design process, further informed by empirical data collected through formal usability testing, focus groups, and a simulation study with domain experts. We discuss the potential and limitations of our approach, and share lessons learned in our iterative co-design process.

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