Assessing capacity for culture change in serious illness communication across McGill University cancer centers.
Bibliographic record
Abstract
457 Background: Patients with advanced malignancy at McGill University’s Rossy Cancer Network (RCN) frequently die in the emergency room or hospital in the context of cancer directed therapy. Internal data indicate delayed referral to palliative care for high-risk patient groups and lack of training and physician discomfort with serious illness communication (SIC). To plan for a system-wide quality improvement (QI) initiative, we aimed to assess capacity for culture change around SIC across three university-affiliated cancer centers. Methods: Interdisciplinary oncology and palliative care leaders from the three RCN sites completed an institutional readiness survey to assess local data and QI infrastructure and potential strengths and challenges related to SIC. We conducted a retrospective chart review of patients who received care at RCN sites for advanced malignancy and who died over a 24-month period. We organized pilot three-hour multi-disciplinary training sessions in use of a Serious Illness Conversation Guide and targeted physicians, nurses, and social workers from each RCN site. Participants completed a survey upon training completion. We developed a quality improvement plan. Results: Institutional readiness surveys highlighted challenges in identifying patients who could benefit from serious illness conversations and lack of consistency and localization of electronic health record documentation of SIC. Preliminary analysis from a retrospective chart review (n=370) demonstrated that 98% of patients had documentation of some aspect of SIC. Only 38% of SIC documentation described patient goals and values, and only 23% referred to patient preferences or wishes. Most instances of SIC documentation (74%) focused on code status and orientation of treatment toward either comfort or cure. 28% of patients had first documentation of SIC within 30 days of death. Fifteen healthcare providers took part in the multidisciplinary training sessions. Training evaluation data suggest that trainees found the training acceptable and feasible, and appreciated the interprofessional nature of the sessions. Results include presentation of an implementation plan. Conclusions: Culture change initiatives related to SIC benefit from a robust assessment of existing resources and challenges. We used a multi-modal approach to assess and build capacity for a cross-institution QI initiative. This process and the resulting network and hospital-level QI plans can assist other academic institutions in leveraging SIC to transform and improve cancer care.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".