Physicians’ Perceptions of a Situation Awareness–Oriented Visualization Technology for Viscoelastic Blood Coagulation Management (Visual Clot): Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Viscoelastic tests enable a time-efficient analysis of coagulation properties. An important limitation of viscoelastic tests is the complicated presentation of their results in the form of abstract graphs with a multitude of numbers. We developed Visual Clot to simplify the interpretation of presented clotting information. This visualization technology applies user-centered design principles to create an animated model of a blood clot during the hemostatic cascade. In a previous simulation study, we found Visual Clot to double diagnostic accuracy, reduce time to decision making and perceived workload, and improve care providers' confidence. OBJECTIVE: This study aimed to investigate the opinions of physicians on Visual Clot technology. It further aimed to assess its strengths, limitations, and clinical applicability as a support tool for coagulation management. METHODS: This was a researcher-initiated, international, double-center, mixed qualitative-quantitative study that included the anesthesiologists and intensive care physicians who participated in the previous Visual Clot study. After the participants solved six coagulation scenarios using Visual Clot, we questioned them about the perceived pros and cons of this new tool. Employing qualitative research methods, we identified recurring answer patterns, and derived major topics and subthemes through inductive coding. Based on them, we defined six statements. The study participants later rated their agreement to these statements on five-point Likert scales in an online survey, which represented the quantitative part of this study. RESULTS: A total of 60 physicians participated in the primary Visual Clot study. Among these, 36 gave an interview and 42 completed the online survey. In total, eight different major topics were derived from the interview field note responses. The three most common topics were "positive design features" (29/36, 81%), "facilitates decision making" (17/36, 47%), and "quantification not made" (17/36, 47%). In the online survey, 93% (39/42) agreed to the statement that Visual Clot is intuitive and easy to learn. Moreover, 90% (38/42) of the participants agreed that they would like the standard result and Visual Clot displayed on the screen side by side. Furthermore, 86% (36/42) indicated that Visual Clot allows them to deal with complex coagulation situations more quickly. CONCLUSIONS: A group of anesthesia and intensive care physicians from two university hospitals in central Europe considered Visual Clot technology to be intuitive, easy to learn, and useful for decision making in situations of active bleeding. From the responses of these possible future users, Visual Clot appears to constitute an efficient and well-accepted way to streamline the decision-making process in viscoelastic test-based coagulation management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".