Examining the Association Between Referral Quality, Wait Time and Patient Outcomes for Patients Referred to an IBD Specialty Program
Bibliographic record
Abstract
BACKGROUND: 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. METHODS: Two hundred referrals to a collaborative IBD program in Canada for patients with confirmed or suspected IBD were reviewed. Referral quality was evaluated using an evidence- and consensus-based metric. The association between referral quality and patient outcomes (wait time, hospitalizations, disease flares and additional referrals) for semi-urgent referrals was assessed through multivariate analysis. RESULTS: The majority of referrals for IBD speciality care were categorized as being low quality. Referral quality was not significantly associated with any of the patient outcomes; however, longer wait times significantly increased the occurrence of disease flares, hospitalizations and additional referrals while waiting for an IBD specialist appointment. CONCLUSION: Prolonged wait times for IBD patients are significantly associated with poor patient outcomes and increased costs for the health care system. Although there is literature that suggests that referral quality may be associated with wait time, it is still unclear how it relates to wait time and patient outcomes. Moving forward, the current referral process needs to be critically addressed in order to improve wait times and patient outcomes.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".