EP53 Optimizing care through an interdisciplinary hip pain referral triage system: our one-year experience
Bibliographic record
Abstract
Abstract Purpose Hip pain is a complex condition with multiple pain generators, often leading to misdiagnosis, unnecessary investigations, and treatment delays. This burdens the healthcare system and frustrates patients. Over four years, we developed a high-volume interdisciplinary hip preservation clinic, integrating orthopedic surgeons, sports medicine physicians, physiatrists, and pain specialists. To optimize care delivery, we implemented a novel hip pain referral triage system to streamline the assessment and management of non-arthritic hip conditions. This study reviews our system’s impact after one year. Methods An interdisciplinary team was formed in 2020, supported by Zoom rounds and educational meetings. A website was launched to facilitate referrals, publicized via online events and PathwaysBC. Monthly interdisciplinary rounds involved orthopedics, sports medicine, physiatry, radiology, physiotherapy, and chiropractic care. Referrals followed a hub-and-spoke model, with centralized triaging by orthopedic surgeons based on clinical data and imaging. Patients were directed to orthopedics, sports medicine, or physiatry, depending on their condition and location. Standardized, evidence-based physiotherapy protocols were developed and shared with providers and patients. A retrospective chart review of triage referrals to our clinic from the 2023 calendar year was performed by three independent reviewers. Data points extracted included demographic information, radiologic diagnosis, initial referral date, treatment pathway (surgical or non-surgical), time to first specialist visit, time to treatment (surgery or intervention) and procedure performed. Data is presented through descriptive statistics. Results 169 patients (208 hips) were referred in the 2023 calendar year. Of these, 147 patients were accepted, with an 87% acceptance rate. The most common reason for rejection was due to advanced stage osteoarthritis requiring arthroplasty. The most common diagnoses were femoroacetabular impingement (83 hips), labral tears (56 hips) and osteoarthritis (49 hips). Mean time from referral to first specialist appointment was 104.27 days. Among patients who received injection, mean time to injection was 156.83 days. Mean time from referral to surgery date was 271.31 days, while mean time on the surgical waitlist was 177.12 days. Conclusion Our interdisciplinary triage system has improved access to care for non-arthritic hip pain patients. Future efforts should focus on educating referring practitioners to minimize inappropriate referrals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".