Eye Tracking Supported Human Factors Testing Improving Patient Training

Abstract

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.

Human Factors Evaluation of HeartMate 3 Left Ventricular Assist Device Peripherals: An Eye Tracking Supported Simulation Study

“…The use of ET technology, previously reported as not hindering or limiting participants in their performance [ 16 , 31 ], provided the opportunity to understand the gaze behavior of successful and unsuccessful participants when changing power supply. Based on these results training materials could be modified and the findings should be considered in the next generation of peripherals to increase patient safety and satisfaction.…”

“…Eye tracking (ET) technology systems are a valuable tool for quantitative assessment of human factors and user experience when handling and designing medical devices [ 14 , 15 ]. They have demonstrated their potential for quantitatively and objectively assessing patients’ task performance [ 16 ], understanding of participants’ gaze behavior [ 17 ], improving user-centered medical device design [ 18 , 19 ], and evaluating the effectiveness of training [ 20 , 21 ]. ET analysis allows the calculation of areas of interest (AOI) specific metrics, such as mean fixation duration or dwell time per AOI [ 22 ], which can be used to identify components of the device under test that are of particular focus.…”

Measuring teamwork for training in healthcare using eye tracking and pose estimation

“…Mobile eye tracking, an established wearable and minimally invasive technology in the field of healthcare devices and training ( Henneman et al, 2017 ; Weiss et al, 2021 ), measures what a team member wearing the glasses is looking at ( Figure 1A ). We used eye tracking and its resulting data to precisely calculate the occurrence and length of eye contact between team members.…”