Patient Voices: Multimethod Study on the Feasibility of Implementing Electronic Patient-Reported Outcome Measures in a Comprehensive Cancer Center
Bibliographic record
Abstract
BACKGROUND: "Patient Voices" is a software developed to promote the systematic collection of electronic patient-reported outcome measures (ePROMs) in routine oncology clinical practice. OBJECTIVE: This study aimed to assess compliance with and feasibility of the Patient Voices ePROM system and analyze patient-related barriers in an Italian comprehensive cancer center. METHODS: Consecutive patients with cancer attending 3 outpatient clinics and 3 inpatient wards were screened for eligibility (adults, native speakers, and being able to fill in the ePROMs) and enrolled in a quantitative and qualitative multimethod study. Compliance, reasons for not administering the ePROMs, patients' interaction needs, and patient-perceived System Usability Scale (range 0-100) were collected; semistructured interviews were carried out in a subsample of patients. RESULTS: From June 2020 to September 2021, a total of 435 patients were screened, 421 (96.7%) were eligible, and 309 completed the ePROMs (309/421, 73.4%; 95% CI 69.8%-77.5%; mean age 63.3, SD 13.7 years). Organization problems and patient refusal were the main reasons for not administering the ePROMs (outpatients: 40/234, 17.1% and inpatients: 44/201, 21.9%). Help for tablet use was needed by 27.8% (47/169) of outpatients and 10.7% (15/140) of inpatients, while the support received for item interpretation was similar in the 2 groups (outpatients: 36/169, 21.3% and inpatients: 26/140, 18.6%). Average System Usability Scale scores indicated high usability in both groups (outpatients: mean 86.8, SD 15.8 and inpatients: mean 83.9, SD 18.8). Overall, repeated measurement compliance was 76.9% (173/225; outpatients only). Interviewed patients showed positive attitudes toward ePROMs. However, there are barriers to implementation related to the time and cognitive effort required to complete the questionnaires. There is also skepticism about the usefulness of ePROMs in interactions with health care professionals. CONCLUSIONS: This study provides useful information for future ePROM implementation strategies, aimed at effectively supporting the routine clinical management and care of patients with cancer. In addition, these findings may be relevant to other organizations willing to systematically collect PROMs or ePROMs in their clinical routines. TRIAL REGISTRATION: ClinicalTrials.gov NCT03968718; https://clinicaltrials.gov/study/NCT03968718.
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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) | 0.001 | 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".