A clinical perspective on electronically collecting patient-reported outcomes at the point-of-care for overactive bladder
Bibliographic record
Abstract
INTRODUCTION: Collecting patient-reported outcomes (PROs) can inform the treatment and management of overactive bladder (OAB). However, collecting these data at the point-of-care can be time-consuming and have a negative impact on a clinic's workflow. The purpose of this study was to pilot a digital system for collecting PROs at the point-of-care and qualitatively assess clinicians' perspectives in terms of the system's impact on the delivery of care for OAB. METHODS: Patients visiting a urology clinic for OAB completed several PRO instruments using a tablet while awaiting assessment. Clinicians reviewed their responses using a digital dashboard during clinical encounters. Qualitative interviews were conducted with the clinicians, to assess the collection system's impact in terms of: 1) logistics, 2) workflow; 3) patient communication; 4) influence on clinical decisions; 5) user experiences; and 6) the care model. RESULTS: Six interviews were conducted and thematic saturation was met, with several themes emerging. All participants were generally positive regarding the use of the digital collecting system. Participants felt that the dashboard improved workflow and enhanced communication with patients, but it was not thought to be any more influential on clinical decision-making than conventional collection methods. Several aspects of the digital PRO collection system were identified as needing improvement. CONCLUSIONS: The digital PRO collection system used at the point-of-care had a positive impact on the delivery of care for OAB. The results from this study could provide insight to other urologists who are interested in collecting PROs in their clinic.
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 | 0.067 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".