Elucidating Barriers to Advance Care Planning in Malignant Hematology Clinics: A Single Centre Experience in Ontario, Canada
Bibliographic record
Abstract
Introduction: Advance care planning (ACP) is a patient-centered process with clear benefits for patients. Despite the widespread recognition of ACP as an integral part of quality cancer care,compliance with ACP provision remains suboptimal especially in malignant hematology. Many barriers to end-of-life discussions among hematologists have been identified in the literature. This study aims to describe a baseline rate of ACP with hematology outpatients at the end-of-life and the local barriers to ACP, which could inform future quality improvement (QI) initiatives in this area. Setting/participants: Malignant hematologists (n = 10), hematology fellows (n=2), and hematology clinic nurses (n=4) of the Juravinski Cancer Centre (JCC) in Hamilton, Ontario, Canada participated in this study. Methods: A retrospective chart audit was undertaken to establish the baseline rate of ACP for the target population at our centre. Subsequently, key stakeholder interviews were held with our local multiple myeloma (MM) specialists to document barriers to ACP from their perspectives. The emerging themes were synthesized using a Fishbone diagram and validated by the JCC hematology clinic staff through multivoting. Results: The baseline rate of ACP with hematology outpatients at the end-of-life at our centre is 40%. The are three main local barriers to ACP with the target population: 1) lack of patient initiative; 2) time/scheduling constraints; and 3) competing priorities. Discussion: Patients who participate in ACP are much more likely to have their end-of-life wishes followed than those who do not. Malignant hematology patients are at the greatest risk of not having ACP discussions with their clinicians due to several patient, provider, and system barriers. Patient- and system-level barriers have been identified as the most prevalent at the JCC, necessitating a tailored QI initiative to achieve the standard of care. Disclosures Hillis: Roche: Honoraria.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".