RELIEF: A Digital Health Tool for the Remote Self-Reporting of Symptoms in Patients with Cancer to Address Palliative Care Needs and Minimize Emergency Department Visits
Bibliographic record
Abstract
The lack of timely symptom reporting remains a barrier to effective symptom management and comfort for patients with cancer-related palliative care needs. Poor symptom management at home can lead to unwanted outcomes, such as emergency department visits and death in hospital. We developed and evaluated RELIEF, a remote symptom self-reporting app for community patients with palliative care needs. A pilot feasibility study was conducted at a large, community hospital in Ontario, Canada. Patients self-reported their symptoms each morning using validated clinical symptom measures and RELIEF would alert for worsening or severe symptoms. RELIEF alerts were monitored by palliative care nurses who would then contact patients to determine if appropriate clinical intervention could be initiated to avoid unnecessary emergency department visits. A total of 20 patients were recruited to use RELIEF for two months. Patients completed 80% of daily self-report assessments; 133 alerts were trigged, half of which required clinical intervention. No patient visited the emergency department for symptom management during the study. Clinical staff estimated five emergency department visits were avoided because of RELIEF-saving an estimated cost of over CAD 60,000. RELIEF is a feasible and acceptable method for the remote monitoring of patients with palliative care needs through regular symptom self-reporting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".