Digital Patient-Reported Outcome Measures for Monitoring of Patients on Cancer Treatment: Cross-sectional Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Oncology has been facing increasing outpatient activity associated with higher cancer incidence, better survival rates, and more treatment options. Innovative technological solutions could help deal with this increasing demand. Using digital patient-reported outcome measures (PROMs) to identify patients who need a face-to-face (FTF) appointment is a potential approach. OBJECTIVE: This study aims to assess the feasibility of digital PROM questionnaires to enable remote symptom monitoring for patients undergoing cancer treatment and their ability to highlight the requirement for an FTF appointment. METHODS: This study was performed at a tertiary oncology center between December 2018 and February 2019. The Common Terminology Criteria for Adverse Events were adapted into patient-friendly language to form the basis of treatment-specific digital questionnaires covering specific cancer drugs and radiotherapy treatments. These treatment-specific digital PROM questionnaires were scored by both patients and their clinicians during FTF appointments. Patients and clinicians did not see each other's scored PROMs. Agreement between patients and clinicians was assessed using descriptive statistics. Patient and staff feedback was also obtained. RESULTS: In total, 90 patients participated in the study across 10 different treatment pathways. By comparing paired patient and clinician responses, the sensitivity of the patient-completed questionnaires in correctly highlighting the need for FTF review was 94% (44/47), and all patients with severe or grade 3+ symptoms were identified (6/6, 100%). Patient-completed PROMs appropriately revealed that 29% (26/90) of the participating patients did not need FTF review based on their symptoms alone. Certain oncological treatment pathways, such as immunotherapy, were found to have a larger proportion of patients with minimal symptoms than others, such as conventional chemotherapy. Patient and staff feedback showed high approval of digital PROMs and their potential for use in remote monitoring. CONCLUSIONS: Digital PROM questionnaires can feasibly highlight the need for FTF review in oncology clinics for treatment. Their use with specific treatments could safely reduce the requirement for FTF care, and future work should evaluate their application in the remote monitoring of patients.
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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 | 0.000 |
| 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".