106: From Fax Machine to First Appointment: Mapping The Management of Referrals to Pediatric Sub-Specialists
Bibliographic record
Abstract
Wait time reduction for outpatient appointments has been connected to reduced healthcare costs and improved patient outcomes. At the pediatric hospital where this study was performed, one key goal of the administration was to reduce wait times for pediatric sub-specialists, particularly appointments for new referrals (also known as “Wait One”). However, attempts to measure and improve these entry-level wait times were hampered by an incomplete understanding of how referrals were handled by the sub-specialist divisions. The purpose of this ethnographic study was to describe the steps by which a new referral to each of ten of the pediatric sub-specialist divisions was processed, triaged, and booked. A qualitative study using standardized open-ended interview questions was carried out with each divisional administrator involved in referrals. They were asked to describe the process they followed from ‘when the new referral hits the fax machine’ to when the machine outpatient appointment was booked. Sub-specialist physicians in charge of triaging the referrals were also interviewed to gain greater insight into the process. In addition, employees in the central booking department, which most of the sub-specialist divisions use, were interviewed. Rough time estimates for the different steps were requested, if available. The data collected were arranged into flowcharts. The managements of referrals to sub-specialists had some similarities: all 10 had successful mechanisms for funneling urgent appointments into timely appointments if necessary. However, there was variation between divisions for less urgent new referrals: 60% of divisions utilized many triage categories based on three or more priority levels, and 20% booked based on ‘see by’ time frames, in contrast to others using only urgent and non-urgent categories; 30% had new referral declination/redirection measures to reduce backlog, while others accepted virtually all referrals; 30% pooled all referrals to be distributed to any specialist in the division, while others assigned a referral to a specific physician based on the preliminary diagnosis. These flowcharts we created highlight differences and similarities between divisions for dealing with new referrals. This information is useful in guiding future data collection on Wait One times in our hospital, and this in turn will help in the design and institution of effective and feasible measures for reducing such Wait One times.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".