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Record W2791664647 · doi:10.1093/jcag/gwy009.210

A210 THE IMPACT OF IBD REFERRAL QUALITY ON WAIT TIMES

2018· article· en· W2791664647 on OpenAlexaffabout
Holly Mathias, Courtney Heisler, J B Morrison, Jennifer Jones

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsMedicineReferralTriageFamily medicineCohortHealth careEmergency medicineMedical emergencyPediatricsInternal medicine

Abstract

fetched live from OpenAlex

Due to health and socioeconomic burdens associated with Inflammatory Bowel Disease (IBD), timely access to specialist care is important. In Canada, speciality gastroenterology (GI) care is accessed only by referral. To receive timely care, referrals must include a high quantity/quality of information. In 2012, 2 in 3 Canadian specialist physicians surveyed reported a lack of basic information on referrals. Referrals are often returned to referring physicians for more information, which is costly to patients and physicians. Some studies have examined referral quality, but not how the quality of referrals influences patient outcomes. The objectives of this study are to determine if referrals to the Nova Scotia Collaborative IBD (NSCIBD) program contain enough information to allow accurate triage for timely access to care, and how the quality of initial referrals to the NSCIBD program inform patient outcomes (e.g. disease flare, hospitalization) while waiting for specialist consultation. This is an ongoing retrospective cohort study of patients referred for appointments in the NSCIBD program between August 2016–2017. A sample size of 200 was required to have a power of 0.80 (p=0.50). Patients were included if they were referred for a first visit to the NSCIBD program for confirmed or suspected IBD. Referrals were excluded if they were for a non-IBD-related concern, an endoscopic test, or a follow up. Referrals were evaluated using a data abstraction form developed with an IBD specialist and two GI nurse practitioners. Based on the information included, referrals were classified as either low, moderate, or high quality. Descriptive statistics were used for a preliminary analysis of the baseline, cross-sectional data. To date, 150 records have been reviewed. There were 9 high quality referrals (6.0%), 32 moderate (21.0%), and 109 low quality referrals (72.7%). The majority of referrals were from family doctors (49.3%) with 81.1% of those being low quality. On average, patients with low quality referrals had a mean wait time of 48.0 days (SD=173.3, range=0–873 days) until triage and a mean wait time of 29.9 weeks (SD=37.3, range=0–160 weeks) to be seen by a GI. Patients with moderate-high quality referrals had a mean wait time of 16.6 days (SD=21.9, range=0–81 days) for triage and a mean of 16.7 weeks (SD=14.9, range=1–65 weeks) to be seen by a GI. The majority of referrals analyzed to date are low quality and have longer average wait times. Prolonged wait time is concerning given its documented impact on patient satisfaction, quality of life and administrative resources. Further analysis will focus on whether there are significant differences in patient outcomes between the qualities of referrals, and factors informing referral quality. Moving forward, higher levels of referrer education, as well as patient awareness and advocacy are needed. CIHRNova Scotia Health Authority Fund

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.309

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.032
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.025
GPT teacher head0.290
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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