Integrating Palliative Care Services in Ambulatory Oncology: An Application of the Edmonton Symptom Assessment System
Bibliographic record
Abstract
PURPOSE: Research in palliative care demonstrates improvements in overall survival, quality of life, symptom management, and reductions in the cost of care. Despite the American Society of Clinical Oncology recommendation for early concurrent palliative care in patients with advanced cancer and high symptom burden, integrating palliative services is challenging. Our aims were to quantitatively describe the palliative referral rates and symptom burden in a South Texas cancer center and establish a palliative referral system by implementing the Edmonton Symptom Assessment Scale (ESAS). METHODS: As part of our Plan-Do-Study-Act process, all staff received an educational overview of the ESAS tool and consultation ordering process. The ESAS form was then implemented across five ambulatory oncology clinics to assess symptom burden and changes therein longitudinally. Referral rates and symptom assessment scores were tracked as metrics for quality improvement. RESULTS: On average, one patient per month was referred before implementation of the intervention compared with 10 patients per month after implementation across all clinics. In five sample clinics, 607 patients completed the initial assessment, and 430 follow-up forms were collected over 5 months, resulting in a total of 1,037 scores collected in REDCap. The mean ESAS score for initial patient visits was 20.0 (standard deviation, 18.1), and referred patients had an initial mean score of 39.0 (standard deviation, 19.0). CONCLUSION: This project highlights the low palliative care consultation rate, high symptom burden of oncology patients, and underuse of services by oncologists despite improvements with the introduction of a symptom assessment form and referral system.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".