Patient Wait Time Recall Accuracy for Gastroenterology Specialty Consultation in Nova Scotia
Bibliographic record
Abstract
BACKGROUND: Inflammatory Bowel Disease (IBD) is a chronic disease with lifelong health, social, and economic burden. The province of Nova Scotia (NS) has the highest age-adjusted incidence and prevalence rates of IBD in Canada, with Canada having the highest prevalence rates of IBD in the world. The Canadian Association of Gastroenterology guidelines suggest wait times between two and 16 weeks for those with active IBD symptoms. Despite these guidelines, a 2015 audit of the NS Health Authority revealed that 50% of IBD referrals were seen within 12 weeks and 90% of referrals waiting longer (up to two years). Long wait times can lead to increased anxiety, decreased quality of life, and reduction in patient satisfaction and overall health. Estimating wait times is complex but essential in order to evaluate access to specialty care. Aims: 1) To determine whether patients referred to GI specialty services in NS can accurately estimate the length of time between GP referral and first GI specialty appointment; 2) To examine demographic, disease-related, and system factors which may influence the accuracy of patient wait time estimates. METHODS: Questionnaires were distributed to patients following their appointment with a luminal GI or IBD nurse practitioner. Patients were asked to estimate their wait time for seeing a GI specialist. They were also asked to report on factors that could influence their wait time recall (geographic locale, age, employment status, completed education, disease severity, and relevant comorbidities). Completed questionnaires were returned and retrospective chart reviews were performed to validate the patients' responses. Descriptive analyses (means, standard deviations) on disease phenotype, complications and surgeries, current and past medication use, distance travelled to appointment, and perceived acceptability of wait times were completed. Spearman's correlations were run on wait time estimates and actual wait times to determine level of estimate accuracy. RESULTS: A total of 70 patients were prospectively enrolled as of July 2017. Forty-three (61.4%) patients were female, with an average age of 45.3 years (SD=20.3 years). When patients were asked to estimate their wait time between referral and seeing a GI specialist, they reported an average of 35.7 weeks. Following retrospective chart reviews, the patient estimates were shown to be conservative. In reality, records showed the average patient waited 39.8 weeks from the time the referral was sent to seeing a GI specialist. Spearman's correlation was used to determine the relationship between patient estimates and referral dates. There was a strong positive correlation between patient estimates and referral dates (rs=0.8, N=67, P<0.0001). CONCLUSION(S): This study is the first of its kind to look at the accuracy of patient recall of wait times for gastroenterology. The initial pilot data highlights the reality of excessive wait times in NS for patients seeking GI speciality care and suggests that patient wait time recall may be used to provide estimates of specialist wait times for patients with IBD. Future research will look at access to GI care using a healthcare systems mapping approach using patient recall to estimate perceived wait times to better inform clinical care pathways in the province.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".