Evaluating the implementation and impact of navigator-supported remote symptom monitoring and management: a protocol for a hybrid type 2 clinical trial
Bibliographic record
Abstract
Abstract Background Symptoms in patients with advanced cancer are often inadequately captured during encounters with the healthcare team. Emerging evidence demonstrates that weekly electronic home-based patient-reported symptom monitoring with automated alerts to clinicians reduces healthcare utilization, improves health-related quality of life, and lengthens survival. However, oncology practices have lagged in adopting remote symptom monitoring into routine practice, where specific patient populations may have unique barriers. One approach to overcoming barriers is utilizing resources from value-based payment models, such as patient navigators who are ideally positioned to assume a leadership role in remote symptom monitoring implementation. This implementation approach has not been tested in standard of care, and thus optimal implementation strategies are needed for large-scale roll-out. Methods This hybrid type 2 study design evaluates the implementation and effectiveness of remote symptom monitoring for all patients and for diverse populations in two Southern academic medical centers from 2021 to 2026. This study will utilize a pragmatic approach, evaluating real-world data collected during routine care for quantitative implementation and patient outcomes. The Consolidated Framework for Implementation Research (CFIR) will be used to conduct a qualitative evaluation at key time points to assess barriers and facilitators, implementation strategies, fidelity to implementation strategies, and perceived utility of these strategies. We will use a mixed-methods approach for data interpretation to finalize a formal implementation blueprint. Discussion This pragmatic evaluation of real-world implementation of remote symptom monitoring will generate a blueprint for future efforts to scale interventions across health systems with diverse patient populations within value-based healthcare models. Trial registration NCT04809740 ; date of registration 3/22/2021.
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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) | 0.071 | 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".