EP01.012 What conversation content do physicians document after implementation of serious illness conversations and what do they find useful?
Bibliographic record
Abstract
Background The Serious Illness Care Program (SICP) increases documentation about patients’ values and priorities. We explored, (1) associations between the quantity/type of elements documented after SICP conversations with patient characteristics and ‘Goals of Care’ orders and (2) aspects of documentation that different specialties find useful. Methods (1) Retrospective chart review analysed conversations documented on a standardized ‘Tracking Record’ (TR) after SICP implementation in an internal medicine teaching unit of a tertiary hospital in Calgary, Alberta, Canada. Alberta’s ‘Goals of Care Designations’ (GCD) physician orders communicate the general focus of a patient’s care, specific interventions, and preferred care locations. Univariate and multivariate generalized linear models were used to analyze associations between frequency of elements/domains documented (using a validated SICP codebook) and patient characteristics (age, gender, frailty, language spoken) and their GCD. (2) A qualitative, Interpretive Description study used clinical vignettes and TRs with varying amounts of SICP conversation detail documented. Individual interviews explored physician perceptions of documentation utility, with sampling stratified by physicians in emergency, internal medicine, hospital, and critical care. Transcripts were analyzed line-by-line and grouped by specialty. Results Of 175 documented SICP conversations, more elements were recorded for patients with a non-resuscitative GCD (‘Medical’: 2.42; 0.47–1.51; ‘Comfort’: 1.06; 0.42–0.24), except in the Goals/Values domain and fewer goals/values were documented for patients who did not understand/speak English (0.89; IQR: 0.14–1.63). In emerging qualitative themes physicians find useful details of the medical context, patients’ own values, and families’ understanding and dynamics and identified a ‘sweet spot’ for length of content. Conclusion The type and amount of content documented after SICP conversations is associated with a patient’s GCD. Physicians value conversation documentation as a starting point for new clinical encounters. The study yielded recommendations about the TR template revisions and raises questions about the equity of SICP conversations with non-English speakers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.513 | 0.120 |
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".