Visualizing Empathy in Patient-Practitioner Interactions Using Eye-Tracking Technology: Proof-of-Concept Study
Bibliographic record
Abstract
Background: Communication between medical practitioners and patients in health care settings is essential for positive patient health outcomes. Nonetheless, researchers have paid scant attention to the significance of clinical empathy in these interactions as a practical skill. Objective: This study aims to understand clinical empathy during practitioner-patient encounters by examining practitioners' and patients' verbal and nonverbal behaviors. Using eye-tracking techniques, we focused on the relationship between traditionally assessed clinical empathy and practitioners' actual gaze behavior. Methods: We used mixed methods to understand clinical encounters by comparing 3 quantitative measures: eye-tracking data, scores from the Korean version of the Jefferson Scale of Empathy-Health Professional, and Consultation and Relational Empathy survey scores. We also conducted qualitative interviews with patients regarding their encounters. Results: One practitioner and 6 patients were involved in the experiment. Perceived empathy on the part of the practitioner was notably higher when the practitioner focused on a patient's mouth area during the consultation, as indicated by gaze patterns that focused on a patient's face. Furthermore, an analysis of areas of interest revealed different patterns in interactions with new as opposed to returning patients. Postconsultation interviews suggested that task-oriented and socially oriented empathy are critical in aligning with patients' expectations of empathetic communication. Conclusions: This proof-of-concept study advocates a multidimensional approach to clinical empathy, revealing that a combination of verbal and nonverbal behaviors significantly reinforces perceived empathy from health care workers. This evolved paradigm of empathy underscores the profound consequences for medical education and the quality of health care delivery.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".