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Record W4377224515 · doi:10.1136/spcare-2023-acp.139

EP01.012 What conversation content do physicians document after implementation of serious illness conversations and what do they find useful?

2023· article· en· W4377224515 on OpenAlexaffabout
Alessandra Paolucci, Seema King, Xun Hu, Sidra Javed, Selena Au, Pavan Ahluwalia, Jennifer R. Hughes, Jessica Simon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAlberta Health ServicesUniversity of Calgary
Fundersnot available
KeywordsConversationDocumentationPsychological interventionSpecialtyFamily medicineMedicineHospital medicineExplanatory modelPsychologyNursingComputer science

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.513
Threshold uncertainty score0.694

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.5130.120

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.

Opus teacher head0.072
GPT teacher head0.383
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations0
Published2023
Admission routes2
Has abstractyes

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