Anaesthesia professionals' perspectives on ECG interpretation and arrhythmia situation awareness with Visual Patient...

The Visual Patient Avatar concept transforms conventional patient monitoring data into a dynamic, animated representation of the patient, enhancing situation awareness of vital signs and measurement status. Rapid recognition and response to cardiac pathologies are critical in anaesthesia and critical care. Visual Patient Heart (VPH) integrates cardiac data from the Philips "ST/AR algorithm", a feature in Philips patient monitors, into visual design elements inside the heart element of the Visual Patient Avatar, specifically aiming to improve situation awareness of arrhythmias and ST-segment deviations.

Participants evaluated a randomized series of 12-lead ECGs alongside corresponding VPH visualizations, each shown briefly to assess the speed of information transfer. Immediately after the exercise, they were asked two open-ended questions and encouraged to speak freely about perceived advantages and areas for improvement of the VPH concept. Their verbal responses were transcribed through field notes and analyzed using a template-guided analysis. The analysis of correct responses in the ECG-avatar comparison test is reported separately in a dedicated paper.

Analysis of 193 statements from 41 participants revealed that participants perceived VPH as intuitive, easy to learn, and cognitively less demanding than conventional ECG interpretation. Benefits reported by participants included improved situation awareness, faster rhythm recognition, and increased decision confidence. Suggestions for improvement addressed visual clarity, the need for more training, and integration with conventional ECG data. Concerns included over-reliance on the system and potential erosion of traditional ECG interpretation skills.

The VPH system was well received, with participants highlighting its simplicity, clarity, and potential to enhance situation awareness. Feedback also raised important considerations around explainability, trust, and integration of the visuals with conventional ECGs. These findings support further development and evaluation in more complex, clinically realistic settings.