“My Quality of Life is Not There. I’m Dying Here. I Cannot Take This Anymore.” Exploring Patient Experiences With Surgical Wait Times in Otolaryngology: A Mixed Methods Study
Bibliographic record
Abstract
BackgroundNew patient referral models are needed to reduce long wait times for otolaryngology surgical procedures, such as a Single-Entry Model (SEM). However, patient perspectives about SEM in otolaryngology remain unexplored.MethodsIn this mixed methods study, a retrospective chart review was conducted to examine the times from referral to consultation (T1) and from consent to surgery (T2) for all elective otolaryngology surgical procedures at a large community hospital between 2020 and 2023. We then conducted journey mapping interviews with 10 patients and parents of pediatric patients who underwent otolaryngologic surgeries to understand their experiences of waiting for their own or their child's procedure, and perspectives on how an SEM might impact patient experiences. Data were analyzed using descriptive statistics and thematic analysis.ResultsWe identified that average wait times among 2414 elective (oncologic and non-oncologic) otolaryngology procedures often exceeded provincial target wait times. On average, oncology procedures had the shortest wait times (T1: 34 ± 47; T2: 101 ± 161 days), and otologic procedures had the longest (T1: 67 + 72; T2: 355 ± 285 days). While patients often did not wait as long to have a consultation with their surgeon, the time between consenting to and receiving surgery tended to drive wait time duration. Patients who had endured extended wait times experienced poor quality of life, worsening symptoms, and often worried about how long they would need to wait. Systems such as an SEM that could shorten wait times were generally well-perceived. However, patients emphasized the importance of trusting relationships with referring physicians and surgeons, which could be an enabling factor for implementing an SEM.ConclusionLong surgical wait times in otolaryngology are negatively impacting patients. A SEM could offer a way to improve patient experiences and outcomes.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".