Background Cardiopulmonary resuscitation (CPR) training improves CPR skills while heavily relying on feedback. The quality of feedback can vary between experts, indicating a need for data-driven feedback to support experts. The goal of this study was to investigate pose estimation, a motion detection technology, to assess individual and team CPR quality with the arm angle and chest-to-chest distance metrics. Methods After mandatory basic life support training, 91 healthcare providers performed a simulated CPR scenario in teams. Their behaviour was simultaneously rated based on pose estimation and by experts. It was assessed if the arm was straight at the elbow, by calculating the mean arm angle, and how close the distance between the team members was during chest compressions, by calculating the chest-to-chest distance. Both pose estimation metrics were compared with the expert ratings. Results The data-driven and expert-based ratings for the arm angle differed by 77.3%, and based on pose estimation, 13.2% of participants kept the arm straight. The chest-to-chest distance ratings by expert and by pose estimation differed by 20.7% and based on pose estimation 63.2% of participants were closer than 1 m to the team member performing compressions. Conclusions Pose estimation-based metrics assessed learners’ arm angles in more detail and their chest-to-chest distance comparably to expert ratings. Pose estimation metrics can complement educators with additional objective detail and allow them to focus on other aspects of the simulated CPR training, increasing the training’s success and the participants’ CPR quality. Trial registration Not applicable.
The handling of left ventricular assist devices (LVADs) can be challenging for patients and requires appropriate training. The devices’ usability impacts patients’ safety and quality of life. In this study, an eye tracking supported human factors testing was performed to reveal problems during use and test the trainings’ effectiveness. In total 32 HeartWare HVAD patients (including 6 pre-VAD patients) and 3 technical experts as control group performed a battery change (BC) and a controller change (CC) as an everyday and emergency scenario on a training device. By tracking the patients’ gaze point, task duration and pump-off time were evaluated. Patients with LVAD support ≥1 year showed significantly shorter BC task duration than patients with LVAD support <1 year (p = 0.008). In contrast their CC task duration (p = 0.002) and pump-off times (median = 12.35 s) were higher than for LVAD support patients <1 year (median = 5.3 s) with p = 0.001. The shorter BC task duration for patients with LVAD support ≥1 year indicate that with time patients establish routines and gain confidence using their device. The opposite effect was found for CC task duration and pump-off times. This implies the need for intermittent re-training of less frequent tasks to increase patients’ safety.
Teamwork is critical for safe patient care. Healthcare teams typically train teamwork in simulated clinical situations, which require the ability to measure teamwork via behavior observation. However, the required observations are prone to human biases and include significant cognitive load even for trained instructors. In this observational study we explored how eye tracking and pose estimation as two minimal invasive video-based technologies may measure teamwork during simulation-based teamwork training in healthcare. Mobile eye tracking, measuring where participants look, and multi-person pose estimation, measuring 3D human body and joint position, were used to record 64 third-year medical students who completed a simulated handover case in teams of four. On one hand, we processed the recorded data into the eye contact metric, based on eye tracking and relevant for situational awareness and communication patterns. On the other hand, the distance to patient metric was processed, based on multi-person pose estimation and relevant for team positioning and coordination. After successful data recording, we successfully processed the raw videos to specific teamwork metrics. The average eye contact time was 6.46 s [min 0 s – max 28.01 s], while the average distance to the patient resulted in 1.01 m [min 0.32 m – max 1.6 m]. Both metrics varied significantly between teams and simulated roles of participants (p < 0.001). With the objective, continuous, and reliable metrics we created visualizations illustrating the teams’ interactions. Future research is necessary to generalize our findings and how they may complement existing methods, support instructors, and contribute to the quality of teamwork training in healthcare.
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