Electronic Patient-Reported Outcome Measures in Radiation Oncology: Initial Experience After Workflow Implementation
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) technologies are increasingly used in various medical fields. However, the potential of mHealth to improve patient care in radiotherapy by acquiring electronic patient reported outcome measures (ePROMs) during treatment has been poorly studied so far. OBJECTIVE: The aim of this study was to develop and implement a novel Web app (PROMetheus) for patients undergoing radiotherapy. Herein, we have reported our experience with a focus on feasibility, patient acceptance, and a correlation of ePROMs with the clinical course of the patients. METHODS: In the period between January and June 2018, 21 patients used PROMetheus to score side effects, symptoms, and quality of life-related parameters during and after their treatment. Items of the Patient Reported Outcome version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) were chosen based on the primary site of disease, 27 items for head and neck tumors, 21 items for thoracic tumors, and 24 items for pelvic tumors. RESULTS: In total, 17 out of the 21 patients (81%) regularly submitted ePROMs and more than 2500 data points were acquired. An average of 5.2, 3.5, and 3.3 min was required to complete the head and neck, thorax, and pelvis questionnaires, respectively. ePROMS were able to detect the occurrence of both expected and unexpected side effects during the treatment. In addition, a gradual increase in the severity of side effects over the course the treatment and their remission afterward could be observed with ePROMs. In total, 9 out of the 17 patients (53%), mostly those with head and neck and thoracic cancers, reported PRO-CTCAE grade III or IV fatigue with severe impairments of activities of daily life. CONCLUSIONS: This study shows the successful implementation of an ePROM system and a high patient acceptance. ePROMs have a great potential to improve patient care in radiotherapy by providing a comprehensive documentation of symptoms and side effects, especially of ones that are otherwise underreported.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".