Using tablet technology in routine patient-reported outcome measure surveys to improve cancer quality care: A patient acceptability assessment.
Bibliographic record
Abstract
155 Background: Patient-reported outcome measure (PROM) surveys are a tool used to collect information about clinically-relevant symptoms in patients. Touch-screen technology has been previously identified as a feasible and effective method of routinely capturing PROMs.Our aim was to evaluate cancer patients’ acceptance and perception of usefulness of tablet technology as a means of communicating PROMs to healthcare providers during delivery of quality care. Methods: 337 adult cancer patients across all outpatient clinics and disease sites at Toronto’s Princess Margaret Cancer Centre (PMCC) completed PROMs surveys using touch-screen technology. Acceptance and feasibility of completing the tablet-based PROMs data on a routine basis were also assessed. Results: The study population consisted of 45% males; median age 59 (19-91) years; 75% Caucasian, and 48% had a post-secondary degree.20% had lung cancer, 20% genitourinary, 16% breast, 16% lymphoma, 11% gastrointestinal, 7% gynecologic, and 7% head/neck. 88% were happy to complete the survey on a touch-screen tablet and 65% of these were willing to complete such surveys routinely. 86% did not find it time-consuming. Only 2% found that the completion of surveys made their clinic visit more difficult. Of the 72% that thought the survey was a useful means to inform the clinician of how they felt physically and emotionally, only 81% were willing to complete the survey at every visit. Conclusions: Tablet-technology was found to be an acceptable tool for survey administration, however, not on a routine basis. While the majority of patients found PROMs to be clinically important, almost 20% did not want to fill it in regularly at every visit. Current mixed-methods analysis is being utilized to help discern whether this discrepancy is related to the tablet technology or survey burden.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.062 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".