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 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 | 0.018 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".