A97 QUALITY OVER QUANTITY: THE ASSOCIATION BETWEEN QUALITY OF REFERRALS RECEIVED BY IBD SPECIALISTS AND PATIENT OUTCOMES
Bibliographic record
Abstract
Most speciality inflammatory bowel disease (IBD) care can only be accessed through a referral. Timely access to specialty care has been associated with improved disease-related outcomes. To receive appropriate care, the referral needs to include high quality information. To date, no research has explored the association between referral quality and IBD patient outcomes. The study objectives were to determine if the quality of referrals to a collaborative IBD program influenced triage accuracy, wait times, and patient outcomes. 200 referrals to a collaborative IBD program in Nova Scotia, Canada for patients with confirmed or suspected IBD were reviewed. Referral quality was evaluated as low, moderate or high quality using an evidence and consensus-based metric. The association between referral quality and patient outcomes (wait time, hospitalizations, disease flares and additional referrals) was assessed using multivariate regression analysis. The majority of referrals for IBD speciality care received by the program were categorized as being low quality. The findings of this study also suggest that quality of referral influences wait times and patient outcomes including disease flares and IBD-related hospitalizations. In particular, we noted that moderate-high quality referrals that included a diagnosis, were legible, were sent by GIs, nurse practitioners or emergency room physicians, had shorter wait times. Low quality referrals were associated with longer wait times. Additionally, we noted that patients who had referrals that included a diagnosis and were legible had fewer disease flares and IBD-related hospitalizations than referrals that did not included this information, presumably due to shorter wait time. Patients with a low quality referral to IBD speciality care may experience longer wait times and increased healthcare resource utilization than patients with higher quality referrals. Improvements in referral-based communication and content quality are needed. Defining minimum referral quality expectations and facilitation of high quality referrals through the development of standardized referral forms could be a solution to this problem. Specialist recommendations for first-line investigations and treatments for IBD patients waiting to be seen in post-triage communication to referring physicians could reduce healthcare resource utilization. None
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".