Symptom Assessment and Hospital Utilization in a Home-Based Palliative Care Program
Bibliographic record
Abstract
Palliative care delivery is shifting to the home, yet data are limited on symptom assessment tools and protocols for that setting. A quality improvement project was done in a home-based palliative care program to imbed the Edmonton Symptom Assessment System into the electronic health record. The purpose of the quality improvement project was to track symptom severity and collect utilization data. Baseline data were collected on 35 patients for symptom presence and severity as well as hospital utilization and readmission. The most common symptoms were tiredness, pain, and a lack of feeling of overall well-being. The most severe symptoms, those with a rating of 6 of 10 or higher, were pain, drowsiness, and anxiety. Seventy-seven percent of the symptoms within the Edmonton Symptom Assessment System showed an improvement over the 3-month QI project per the electronic health record data. Hospitalization rates also went from 4.2% to 2.6% and 30-day readmissions were reduced from 15% to 0%. The results suggest that the palliative care program was able to improve symptoms through the use of Edmonton Symptom Assessment System and that that may have affected hospital utilization.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".