Utilization of an electronic medical record trigger to promote palliative care consultation in ambulatory oncology.
Bibliographic record
Abstract
e18201 Background: Research insupportive 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 supportive care in patients with advanced cancer and high symptom burden, integrating supportive services is challenging. Our aims were to 1) implement an electronic medical record (EMR) provider alert of high symptom burden based on Edmonton Symptom Assessment Scale (ESAS) criteria and 2) determine the impact an alert has on supportive service referrals. Methods: ESAS scores were implemented in medical ambulatory oncology clinics to quantitatively assess symptom burden. An EMR alert was programmed for a total ESAS score > 30 and any single response of ≥9 to capture approximately 15% of our high symptom burden patient population. The provider could elect to accept the alert placing an order for palliative consultation or decline the prompt. Referral rates and symptom assessment scores were followed as metrics for EMR alert efficiency. Results: Over 10 months, 9,710 patient visits used the ESAS system resulting in 7,707 fully completed ESAS scores (79.4% completion rate). There were 78 total consults to palliative care, a referral rate of less than 1% of the population, which was unchanged from prior to trigger implementation. Of the total completed ESAS forms, the trigger alerted 686 times (8.9% of patient visits) with only 55 of those triggers leading to a supportive care referral (8.0%). Conclusions: This project highlights the challenges of an EMR based alert system and the need for continued efforts to improve supportive care referrals through provider education and tool implementation. [Table: see text]
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".