Choose and Book: An Audit of the Appropriateness of Referrals and Their Effect on Patients' Attendance to An Inner City Hospital Secondary Care Provider
Bibliographic record
Abstract
Conventional, paper-based, urological referral has been challenged by the computer-based, choose and book (C&B) system. To determine the efficiency of this new system, we audited the appropriateness of these bookings, the percentage that required re-direction, the reasons for doing so and the ‘did not attend’ (DNA) rate. 1147 electronic bookings were made to different urological clinics between June 2006 and August 2007. The patient's age, date and type of clinic originally booked to, via C&B, and finally re-directed to was collected from our C&B record, PAS and Medisec. 1952 referrals via all modes were identified between April and November, 2006 and data on patient demographics, type of referral and whether attended or DNA was recorded. Nearly a quarter of C&B appointments were re-directed, due to referrals being made to an inappropriate clinic, inappropriate consultant, inappropriate speciality, to the wrong hospital. Additionally, 32.3% were inappropriately prioritised, 7% being given inappropriate urgency and 25.3% not enough priority. DNA rate (18.9%) was higher for bookings made via C&B when compared to bookings made via standard paper-based GP referrals (15.3%). Although C&B facilitates patients to make their choice of appointments, nearly a quarter of our patients had arrangements made inappropriate to their needs. This meant consultants still had to screen referrals and increased workload on ancillary staff. Despite being offered a choice, DNA rate was high in referrals via C&B. Refinement of C&B pathways may reduce this inefficiency but the inflexibility of this system makes it an inefficient way of referring urological cases.
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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 | 0.000 |
| 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".