Implementation of an electronic patient reported outcome system in a prostate cancer urology clinic.
Bibliographic record
Abstract
143 Background: The Prostate Clinic at Vancouver General Hospital recently implemented electronic Patient Reported Outcomes (ePRO) collection from prostate cancer patients (pts). The ePRO system was adopted from the University Health Network, Toronto. Our objective was to review the successes and challenges related to the implementation of the system during its first 6 months of use. Methods: All newly diagnosed pts, those going for a prostate biopsy, and pts booking surgery are asked to complete ePRO on iPads at their clinic appointment. The ePRO includes the EPIC-26, EQ-5D-5L, WHODAS 2.0 questionnaires and the Distress Thermometer. It takes about 4-5 minutes to complete the questions, and consent to be contacted about future prostate cancer research is also requested. Afterwards, pts complete ePROs at each clinic appointment. They receive a report summarizing that day’s responses in the context of previous responses. Results: Between 4-1-17 and 10-16-17, 360 pts were eligible to complete the ePRO. 311/360 pts (86.4%) filled out the ePRO at least once. Of the 49 who didn’t, 24 were missed by the coordinator and 21 were not referred by clinic staff. Only 4 pts (8.2%) refused to answer the ePRO. 18 of the 311 pts only partially answered the ePRO, most often due to the length of the questionnaires. 396 instances of ePRO were completed by 311 pts. WiFi connectivity issues were encountered 30/396 times, which prolonged ePRO completion time, and on 3 occasions resulted in patients refusing to complete ePRO. Conclusions: Implementation of the ePRO system into our urology clinic is a success based on a high completion and low refusal rate, and was organized mainly by a single individual. Challenges include training clinic staff to refer pts to complete ePROs at appropriate times, technical difficulties with WiFi connectivity, and the need to interact with each pt individually, which can be time consuming and result in lost data. Eventually, all pts will be trained to pick up an iPad and complete ePROs prior to each appointment. We found the system straightforward to use, and have updated our iPads with additional questionnaires. It is clear that systems collecting ePROs will become standard practice as technology improves and costs go down.
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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 | 0.022 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".