End-user evaluation of an interface for clinical decision support using predictive algorithms

• Published in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 1149 - 1151
• Main Authors: Kehoe, Iain E., Pepino, Jeremy A., Lee, Jarone, Hahn, Jin-Oh, Reisner, Andrew T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2022
• Subjects: Cultural differences; Decision support systems; Hemodynamics; Prediction algorithms; Predictive models; Safety; Software
• ISSN: 2694-0604
• DOI: 10.1109/EMBC48229.2022.9871939

There have been decades of interest in advanced computational algorithms with potential for clinical decision support systems (CDSS), yet these have not been widely implemented in clinical practice. One major barrier to dissemination may be a user-friendly interface that integrates into clinical workflows. Complicated or non-intuitive displays may confuse users and may even increase patient management errors. We recently developed a graphical user interface (GUI) intended to integrate a predictive hemodynamic model into the workflow of nurses caring for patients on vasopressors in the intensive care unit (ICU). Here, we evaluated user perceptions of the usability of this system. The software was installed in the room of an ICU patient, running for at least 4 hours with the display hidden. Afterward, we showed nurses a video recording of the session and surveyed their perceptions about the software's potential safety and usefulness. We collected data for nine patients. Overall, nurses expressed reasonable enthusiasm that the software would be useful and without serious safety concerns. However, there was a wide diversity of opinions about what specific aspects of the software would be useful and what aspects were confusing. In several instances, the same elements of the GUI were cited as most useful by some nurses and most confusing by others. Our findings validate that it is possible to develop GUIs for CDSS that are perceived as potentially useful and without substantial risk but also reinforce the diversity of user perceptions about novel CDSS technology. Clinical Relevance- This end-user evaluation of a novel CDSS highlights the importance of end-user experience in the workflow integration of advanced computational algorithms for bedside decision support during critical care.