Innovation in Managing the Referral Process at a Canadian Pediatric Hospital
Bibliographic record
Abstract
The provision of timely and optimal patient care is a priority in pediatric academic health science centres. Timely access to care is optimized when there is an efficient and consistent referral system in place. In order to improve the patient referral process and, therefore, access to care, an innovative web-based system was developed and implemented. The Ambulatory Referral Management System enables the electronic routing for submission, review, triage and management of all outpatient referrals. The implementation of this system has provided significant metrics that have informed how processes can be improved to increase access to care. Use of the system has improved efficiency in the referral process and has reduced the work associated with the previous paper-based referral system. It has also enhanced communication between the healthcare provider and the patient and family and has improved the security and confidentiality of patient information management. Referral guidelines embedded within the system have helped to ensure that referrals are more complete and that the patient being referred meets the criteria for assessment and treatment in an ambulatory setting. The system calculates and reports on wait times, as well as other measures.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".