User Perceptions of Visual Clot in a High-Fidelity Simulation Study: Mixed Qualitative-Quantitative Study.

BACKGROUND

Viscoelastic hemostatic assays, such as rotational thromboelastometry (ROTEM) or thromboelastography, enable prompt diagnosis and accelerate targeted treatment. However, the complex interpretation of the results remains challenging. Visual Clot-a situation awareness-based visualization technology-was developed to assist clinicians in interpreting viscoelastic tests.

OBJECTIVE

Following a previous high-fidelity simulation study, we analyzed users' perceptions of the technology, to identify its strengths and limitations from clinicians' perspectives.

METHODS

This is a mixed qualitative-quantitative study consisting of interviews and a survey. After solving coagulation scenarios using Visual Clot in high-fidelity simulations, we interviewed anesthesia personnel about the perceived advantages and disadvantages of the new tool. We used a template approach to identify dominant themes in interview responses. From these themes, we defined 5 statements, which were then rated on Likert scales in a questionnaire.

RESULTS

We interviewed 77 participants and 23 completed the survey. We identified 9 frequently mentioned topics by analyzing the interview responses. The most common themes were "positive design features," "intuitive and easy to learn," and "lack of a quantitative component." In the survey, 21 respondents agreed that Visual Clot is easy to learn and 16 respondents stated that a combination of Visual Clot and ROTEM would help them manage complex hemostatic situations.

CONCLUSIONS

A group of anesthesia care providers found Visual Clot well-designed, intuitive, and easy to learn. Participants highlighted its usefulness in emergencies, especially for clinicians inexperienced in coagulation management. However, the lack of quantitative information is an area for improvement.

Informations de copyright

©Greta Gasciauskaite, Clara Castellucci, Amos Malorgio, Alexandra D Budowski, Giovanna Schweiger, Michaela Kolbe, Daniel Fries, Bastian Grande, Christoph B Nöthiger, Donat R Spahn, Tadzio R Roche, David W Tscholl, Samira Akbas. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 11.01.2024.