Effect of Collaborative Review of Electronic Patient-Reported Outcomes for Shared Reporting in Breast Cancer Patients: Descriptive Comparative Study
Bibliographic record
Abstract
BACKGROUND: Digital monitoring of treatment-related symptoms and self-reported patient outcomes is important for the quality of care among cancer patients. As mobile devices are ubiquitous nowadays, the collection of electronic patient-reported outcomes (ePROs) is gaining momentum. So far, data are lacking on the modalities that contribute to the quantity and quality of ePROs. OBJECTIVE: The objective of our study was to compare the utilization of two versions of a subsequently employed mobile app for electronic monitoring of PROs and to test our hypothesis that a shared review of symptoms in patient-physician collaboration has an impact on the number of data entries. METHODS: The Consilium Care app engages cancer patients to standardize reporting of well-being and treatment-related symptoms in outpatient settings. For descriptive comparison of the utilization of two slightly different app versions, data were obtained from an early breast cancer trial (version 1 of the app, n=86) and an ongoing study including patients with advanced disease (version 2 of the app, n=106). In both app versions, patients and doctors were allowed to share the information from data entries during consultations. Version 2 of the app, however, randomly selected symptoms that required a detailed and shared regular patient-doctor review in order to focus on the collection and appropriate interpretation regarding awareness and guidance for severity grading. The numbers and types of symptom entries, satisfaction with both app versions, and patients' perceived effects during consultations were included for analysis. RESULTS: Symptom severity grading was performed according to the Common Terminology Criteria for Adverse Events (CTCAE) using a horizontal slider and was indicated in descriptive terminology in both apps, while a graphical display facilitated the illustration of symptom history charts. In total, 192 patients electronically reported 11,437 data entries on well-being and 33,380 data entries on individual symptoms. Overall, 628 (of 872 intended) requested patient-doctor symptom reviews were performed in version 2 of the app. Both the amount of data entries per patient and day for well-being (version 1 vs version 2: 0.3 vs 1.0; P<.001) and symptoms (version 1 vs version 2: 1.3 vs 1.9; P=.04) appeared significantly increased in version 2 of the app. Overall satisfaction with both app versions was high, although version 2 of the app was perceived to be more helpful in general. CONCLUSIONS: Version 2 of the app showed much better results than version 1 of the app. A request for collaborative patient-doctor symptom review is likely to affect the number of digital symptom data entries. This app shows high potential to improve the patient-doctor experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT02004496; https://clinicaltrials.gov/ct2/show/NCT02004496 and ClinicalTrials.gov NCT03578731; https://clinicaltrials.gov/ct2/show/NCT03578731.
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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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".