Objective This study investigated how effectively simplified cognitive walkthroughs, performed independently by four nonclinical researchers, can be used to assess the usability of clinical decision support software. It also helped illuminate the types of usability issues in clinical decision support software tools that cognitive walkthroughs can identify. Method A human factors professor and three research assistants each conducted an independent cognitive walkthrough of a web-based demonstration version of T3, a physiologic monitoring system featuring a new clinical decision support software tool called MAnagement Application (MAP). They accessed the demo on personal computers in their homes and used it to walk through several pre-specified tasks, answering three standard questions at each step. Then they met to review and prioritize the findings. Results Evaluators acknowledged several positive features including concise, helpful tooltips and an informative column in the patient overview which allows users direct (one-click) access to protocol eligibility and compliance criteria. Recommendations to improve usability include: modify the language to clarify what user actions are possible; visually indicate when eligibility flags are snoozed; and specify which protocol's data is currently being shown. Conclusion Independent, simplified cognitive walkthroughs can help ensure that clinical decision support software tools will appropriately support clinicians. Four researchers used this technique to quickly, inexpensively, and effectively assess T3's new MAP tool, which suggests positive actions, such as removing a patient from a ventilator. Results indicate that, while there is room for usability improvements, the MAP tool may help reduce clinician's cognitive load, facilitating improved care. The study also confirmed that cognitive walkthroughs identify issues that make clinical decision support software hard to learn or remember to use.
Tremoulet PD. Clinical decision support for intervention reduction in neonatal patients: A usability assessment. DIGITAL HEALTH. 2022;8. doi:10.1177/20552076221113696
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