SP4.1.6 Introduction of Concentric digital and remote consent into clinical practice during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Introduction Paper-based consent processes are associated with errors of omission, illegibility and unwarranted variation. During the COVID-19 pandemic the Royal College of Surgeons (England) released guidelines supporting the use of remote consent. The aim was to evaluate the introduction of Concentric, a digital consent application, into clinical practice. Method Between April 2020-Jan 2021, Concentric was used optionally for medical consent during registered service evaluations. Data was obtained from Concentric analytics. User and patient feedback was obtained via optional satisfaction surveys. Results 3417 Concentric consent episodes for 356 unique procedures were performed by 170 clinicians across 16 specialties from 13 healthcare providers. Patients were aged 7-101years, (median 58, IQR 30). Of the completed consent episodes (n = 2799), consent was given; remotely in 23% of episodes, and on the day of surgery in 67%. Consent form information was shared digitally with 82% of patients. Average patient user experience was 8.8 out of 10 (1 very poor - 10 excellent, n = 594). 546/594 (91.9%) patients agreed that Concentric provided all the information they needed to know. Clinicians (n = 23) rated the quality of the consent process with Concentric as 4.8 out of 5 with all supporting the use of Concentric across the Trust. Conclusion Concentric has been successfully introduced into clinical practice. Patients and clinicians report high satisfaction scores. Remote consent is feasible and trends in consent practice, such as day of surgery consent can be easily identified and can guide quality improvement work. The introduction of digital consent solutions should be considered for all units.
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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 | 0.048 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".