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Record W4225120362 · doi:10.2196/30988

Video-Based Communication Assessment of Physician Error Disclosure Skills by Crowdsourced Laypeople and Patient Advocates Who Experienced Medical Harm: Reliability Assessment With Generalizability Theory

2022· article· en· W4225120362 on OpenAlexvenueno aff
Andrew A. White, Ann King, Angelo D’Addario, Karen Berg Brigham, Suzanne M. Dintzis, Emily Fay, Thomas H. Gallagher, Kathleen M. Mazor

Bibliographic record

VenueJMIR Medical Education · 2022
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsnot available
FundersUniversity of Washington
KeywordsGeneralizability theoryHarmReliability (semiconductor)Medical educationPsychologyScale (ratio)MedicineApplied psychologyFamily medicineSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Residents may benefit from simulated practice with personalized feedback to prepare for high-stakes disclosure conversations with patients after harmful errors and to meet American Council on Graduate Medical Education mandates. Ideally, feedback would come from patients who have experienced communication after medical harm, but medical researchers and leaders have found it difficult to reach this community, which has made this approach impractical at scale. The Video-Based Communication Assessment app is designed to engage crowdsourced laypeople to rate physician communication skills but has not been evaluated for use with medical harm scenarios. OBJECTIVE: We aimed to compare the reliability of 2 assessment groups (crowdsourced laypeople and patient advocates) in rating physician error disclosure communication skills using the Video-Based Communication Assessment app. METHODS: Internal medicine residents used the Video-Based Communication Assessment app; the case, which consisted of 3 sequential vignettes, depicted a delayed diagnosis of breast cancer. Panels of patient advocates who have experienced harmful medical error, either personally or through a family member, and crowdsourced laypeople used a 5-point scale to rate the residents' error disclosure communication skills (6 items) based on audiorecorded responses. Ratings were aggregated across items and vignettes to create a numerical communication score for each physician. We used analysis of variance, to compare stringency, and Pearson correlation between patient advocates and laypeople, to identify whether rank order would be preserved between groups. We used generalizability theory to examine the difference in assessment reliability between patient advocates and laypeople. RESULTS: Internal medicine residents (n=20) used the Video-Based Communication Assessment app. All patient advocates (n=8) and 42 of 59 crowdsourced laypeople who had been recruited provided complete, high-quality ratings. Patient advocates rated communication more stringently than crowdsourced laypeople (patient advocates: mean 3.19, SD 0.55; laypeople: mean 3.55, SD 0.40; P<.001), but patient advocates' and crowdsourced laypeople's ratings of physicians were highly correlated (r=0.82, P<.001). Reliability for 8 raters and 6 vignettes was acceptable (patient advocates: G coefficient 0.82; crowdsourced laypeople: G coefficient 0.65). Decision studies estimated that 12 crowdsourced layperson raters and 9 vignettes would yield an acceptable G coefficient of 0.75. CONCLUSIONS: Crowdsourced laypeople may represent a sustainable source of reliable assessments of physician error disclosure skills. For a simulated case involving delayed diagnosis of breast cancer, laypeople correctly identified high and low performers. However, at least 12 raters and 9 vignettes are required to ensure adequate reliability and future studies are warranted. Crowdsourced laypeople rate less stringently than raters who have experienced harm. Future research should examine the value of the Video-Based Communication Assessment app for formative assessment, summative assessment, and just-in-time coaching of error disclosure communication skills.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.013
GPT teacher head0.417
Teacher spread0.404 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2022
Admission routes1
Has abstractyes

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