Altering physician referral practices is challenging, but not impossible: spine assessment clinic quality improvement study
Bibliographic record
Abstract
INTRODUCTION: Access to medical specialists is a persistent challenge, with neurosurgical spine services reporting some of the longest waits across all fields. Inappropriate and incomplete referrals contribute to delayed access to these providers. Referral guidelines and physician education have been shown to decrease such inefficiencies. Therefore, the goal of this study was to address inappropriate referrals directed to the neurosurgical spine assessment clinic via implementation of a quality improvement initiative. We hypothesised that appropriate referrals, which included patients with potential surgical pathology and fulfilled referral criteria, would increase by 25% following referral guideline distribution. METHODS: A three-phase study was implemented: (1) baseline data were collected from preintervention referrals by noting the reason for consultation and if certain information, deemed relevant for an appropriate referral, was included; (2) a referral guideline, outlining when and how to refer, was distributed to family physicians in the region; and (3) postintervention referrals were collected and analysed as in phase I. RESULTS: A total of 404 referrals were collected (161 pre-intervention and 243 post-intervention). A 36% increase in patients who were deemed appropriate surgical candidates was reported post-intervention (p=0.044), with an escalation in the proportion of patients requiring neurosurgeon assessment observed over time. Limited improvements were appreciated in the presence of the criteria indicated for inclusion in a referral document. CONCLUSION: While challenges remain when attempting to modify the referring behaviours of primary care physicians, this research has demonstrated that guidelines aimed at enhancing specialist directed referrals can lead to improvements in their performance. Nonetheless, translating guidelines into practice is a recognised issue, often requiring time and multiple exposures. Active forms of medical education and multifaceted interventions have been demonstrated to be the most effective means of implementing guidelines into practice, an approach that could further address referral inadequacies in the future.
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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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".