Video-Based Communication Assessment: Development of an Innovative System for Assessing Clinician-Patient Communication
Bibliographic record
Abstract
Good clinician-patient communication is essential to provide quality health care and is key to patient-centered care. However, individuals and organizations seeking to improve in this area face significant challenges. A major barrier is the absence of an efficient system for assessing clinicians' communication skills and providing meaningful, individual-level feedback. The purpose of this paper is to describe the design and creation of the Video-Based Communication Assessment (VCA), an innovative, flexible system for assessing and ultimately enhancing clinicians' communication skills. We began by developing the VCA concept. Specifically, we determined that it should be convenient and efficient, accessible via computer, tablet, or smartphone; be case based, using video patient vignettes to which users respond as if speaking to the patient in the vignette; be flexible, allowing content to be tailored to the purpose of the assessment; allow incorporation of the patient's voice by crowdsourcing ratings from analog patients; provide robust feedback including ratings, links to highly rated responses as examples, and learning points; and ultimately, have strong psychometric properties. We collected feedback on the concept and then proceeded to create the system. We identified several important research questions, which will be answered in subsequent studies. The VCA is a flexible, innovative system for assessing clinician-patient communication. It enables efficient sampling of clinicians' communication skills, supports crowdsourced ratings of these spoken samples using analog patients, and offers multifaceted feedback reports.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".