Finding the Value: Identifying the Key Elements of Recorded Clinic Visits From the Perspective of Patients, Clinicians, and Caregivers
Bibliographic record
Abstract
OBJECTIVE: We aimed to understand what patients, caregivers and clinicians identified as the most important information from their audio-recorded clinic visits and why. METHODS: We recruited patients, caregivers and clinicians from primary and speciality care clinics at an academic medical centre in New Hampshire, U.S. Participants reviewed a recording or transcript of their visit, identifying meaningful moments and the reasons why. Two researchers performed a summative content analysis of the data. RESULTS: Sixteen patients, four with caregivers, from six clinicians participated. Patients, caregivers and clinicians identified a median of 7.5 (3-20), 12.5 (6-50) and 18 (4-31) meaningful visit moments, respectively. Moments identified were similar across stakeholders, including patient education, symptoms, recommendations and medications. Four themes emerged as a rationale for finding visit information meaningful: providing and receiving information, sharing the patient experience, forming a care plan, and providing emotional support. Clinicians rarely identified patient statements as important. CONCLUSION: There was considerable agreement between patients, clinicians and caregivers regarding visit information that is most valuable. Patient contributions may be undervalued by clinicians. PRACTICE IMPLICATIONS: These findings can be used to improve patient-centred visit communication by focusing visit summaries and decision support on information of the most value to participants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".