Development and implementation of a digital remote symptom monitoring program (RESPONSe) to support patients undergoing IV chemotherapy at a community ambulatory cancer clinic in Richmond, BC, Canada.
Bibliographic record
Abstract
406 Background: We identified delays in accessing symptom management during treatment as an existing care gap at the Richmond Cancer Clinic. Patient-reported outcome measures (PROMs) have been demonstrated to communication, quality of life, and reduce symptom burden and acute care utilization. The RESPONSe program was initiated to address this gap by implementing a digital remote symptom monitoring (RSM) system paired with a supporting Symptom Management Nurse (SMN). Methods: We designed a digital RSM system to collect PROMs at specific timepoints during the first 6 months of IV chemotherapy initiation, with automated triaging logic to flag symptoms of concern that would result in a phone call from the SMN. Routine surveys include baseline, health-checks on days 3 and 10 post-chemotherapy, and pre-doctor’s-appointment; with optional health checks available anytime. Through competitive procurement, SeamlessMD was selected as the technology partner. From Aug 2023 to Jan 2024, multiple design cycles with dry run testing were held to incorporate branching logic, COSTARS oncology symptom specific questionnaires, automatic triaging flags, immediate self—management tips and links to educational resources. Concurrent development of SMN clinical workflows included in-person patient enrolment, digital dashboard monitoring and integration with the clinical chart. Results: From Jan 22-Apr 30, 2024, 32 patients have been enrolled with 42% over the age of 70. 90% of patients completed the baseline survey, 79% of patients completed at least 1 post-treatment health check and 45% completed at least 1 pre-doctors’-appointment survey. An average of 22 library resources were accessed per patient. Through the system, 19 severe and 34 moderate symptoms were identified. The dashboard prompted 63 telephone calls from the SMN, with a further 28 follow-up calls required. Early 3-month patient experience surveys (n=8) indicate high satisfaction, with 100% of patients saying they would recommend the program to others undergoing treatment. 87.5% of patients felt the system was very helpful in communicating their issues to the clinical team, with 100% reporting very high/extreme satisfaction with the support provided by the SMN. Additionally, 67% of patients felt very satisfied with the RESPONSe program overall. Conclusions: The RESPONSe program demonstrates the potential of digital RSM systems in improving patient-centered care and assisting with symptom reporting and management. Initial results suggest high engagement and patient satisfaction, showing this is a feasible approach in community oncology settings. We will be reviewing acute care utilization to assess the impact of the RESPONSe program on emergency room visits and hospitalization.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".