The goal of this observational study is to explore the value of eye-tracking technology in perioperative management and teaching. The study aims to understand how eye-tracking can help analyze the attention distribution of clinicians during perioperative procedures and optimize workflows for improved safety and teaching outcomes. The main questions it seeks to answer are: How does eye-tracking technology reveal the focus distribution patterns of experienced and novice clinicians in perioperative scenarios? Can visualizing expert clinicians' eye movement patterns improve the learning outcomes and operational skills of medical students? Participants include anesthesiologists and medical students involved in perioperative management training. Eye-tracking data, such as fixation duration, fixation count, and heatmaps, will be collected during surgical and training scenarios. The study will analyze correlations between attention distribution and operational performance. This research will provide insights into optimizing perioperative safety and revolutionizing medical education using objective attention data.
Study Type
OBSERVATIONAL
Enrollment
300
2nd Affiliated Hospital, School of Medicine, Zhejiang University, China
Hangzhou, Zhejiang, China
RECRUITINGIdentify the "hotspots" where anesthesiologists focus their attention within different areas of the anesthesia machines.
Every anesthesiologist will record data from three cases. Data collection will commence as the general anesthesia procedure begins when the patient moves onto the surgical table and will complete when the patient reaches the states of sedation and unconsciousness in preparation for the operation. This period, during which the anesthesiologist plays a leading role, typically lasts less than 10 minutes. No data will be recorded once the surgical operation begins. The Tobii eye-tracker will generate a diverse dataset, encompassing eye trajectory, fixation (a gaze resting on a spot for more than 150 ms), saccades (rapid eye movements between two distinct points), and pupil size. This comprehensive dataset enables us to identify the "hotspots" where anesthesiologists focus their attention within different areas of the anesthesia machines.
Time frame: 1 year
The percentage of time anesthesiologists' eyes attention.
Moreover, we will calculate the percentage of time anesthesiologists' eyes spend on team members. We will also quantify the duration of eye contact and communication content between the anesthesiologist and each team member.
Time frame: 1 year
Attention of the clinician
Finally, we will examine the correlation between these behavioral measures (e.g., fixation on hotspots, percentage of time focused on team members, communication episodes) and the duration of the anesthesia procedure.
Time frame: 1 year
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.