Improving the final year of life at the institutional level: Quality dying initiative (QDI).
Bibliographic record
Abstract
124 Background: For a tertiary academic health sciences and comprehensive cancer centre, care of the dying is a significant element of the institution’s overall patient and family care experience. The aim for this large-scale quality improvement project was to improve the quality of the experience for a patient in the final year of life and their family members. Methods: This is descriptive study involves one institution and the characterization of three distinct patient populations: A - Imminently dying patients for whom care goals have been clarified to be comfort, B - Patients for whom death “this admission” would not be a surprise and C - Among patients being discharged, death “within the next year” would not be a surprise, linking in the outpatient cancer care setting. Results: On average 19 deaths per week are in some way expected for the institution’s acute care setting. Phase 1 of the QDI included a review of evidence and best practices in care of the dying as well as comprehensive plans for both organizational engagement and communications. Phase 2 of the QDI (i.e. “Implementation Phase”) involved interventions for each patient population. A corporate-wide “Comfort Strategy” was developed to address Population A. Components include standardized order sets, standardized interprofessional “Comfort Assessment and Documentation”, the palliative care team’s “Coaching Consult”, a “Family Member Education” process and an evaluation plan that includes an experience survey routinely sent to family members following a patient’s death. The intervention was piloted on and subsequently rolled out to all inpatient oncology units. Interventions for Population B and C are the triggering of Goals of Care and Advance Care Plan discussions respectively. Key metrics have been identified for all three patient populations and are based on care elements considered important by dying patients and their family members. These now comprise a dashboard, which has been endorsed for roll out to all patient care units in the acute care setting. Conclusions: A quality framework can be effectively applied for the institutional context of developing an approach to improving the final year of life for a cancer patient.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".