Obtaining Patient-Reported Outcome Data via a Home Patient Monitoring App: Development, Implementation, and Validation of Novel Interface Terminology
Bibliographic record
Abstract
BACKGROUND: Adverse events (AEs) related to cancer treatment represent a valuable source of information that can be used to adjust therapy for individual patients. The NIH developed the Common Terminology Criteria for Adverse Events (CTCAE), a comprehensive standardized terminology for healthcare providers to consistently report AEs during patient visits. mHealth technologies, in principle, also allow AEs to be self-reported by patients in-between visits; however, the terminology poses challenges for them, both in selecting the correct symptom to report and in rating its severity. NIH developed the Patient-Reported Outcomes (PRO)-CTACE as the patient-oriented companion of the CTCAE. However, it shows some weaknesses in completeness and precision when used for continuous home patient monitoring and for decision support. OBJECTIVE: The aim of this work is to propose a new terminology for reporting AEs, which is easy for patients to use while also being clinically meaningful for healthcare providers, and easily exploitable by decision support systems. Moreover, we aim to demonstrate its implementation and validation within the CAPABLE EU project. METHODS: The development of the new terminology starts from the CTCAE, which includes a comprehensive list of signs and symptoms along with guidance for accurately grading their severity. Through a multi-step, participatory approach involving both patients and healthcare providers, we reduced and adapted the AE list for patient-oriented applications. During the CAPABLE project, the proposed terminology was integrated in a mobile app and evaluated within a clinical pilot study involving 86 patients who were monitored through the app for at least 6 months, and a control cohort of 133 patients monitored using standard care practices. RESULTS: The final terminology includes 124 AEs, 49 expressed as "present/absent", and 77 associated with four description levels. A mapping between the description levels and the original CTCAE grades enables running the decision support system embedded in the CAPABLE app. The pilot study demonstrated that the majority of the patients used the symptoms reporting functionality, sharing also 24 unique AEs that are not present in the PRO-CTCAE. Symptoms reported using the proposed terminology allowed the enactment of the clinical practice guidelines included in the CAPABLE decision support tool, triggering 11 distinct recommendations. CONCLUSIONS: The results obtained from the clinical study support our claim regarding the need for a novel terminology for the self-reporting of AEs, characterized by ease of use, completeness, and clinical meaningfulness. Finally, by mapping our terminology to the CTCAE, we demonstrated that it is possible to exploit self-reported data to trigger decision support rules consistent with clinical practice guidelines.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".