Waiting times and decision-making behind acute plastic surgery referrals in the UK
Bibliographic record
Abstract
Over the past decade, Accident and Emergency (A&E) departments in the United Kingdom have struggled to cope with a doubling in patient attendances and the combined impacts of the ‘four-hour rule’, the European Working Time Directive and Modernising Medical Careers. Our previous prospective study suggested that A&E referrals to Plastic Surgery may not always be clinically motivated, but could have been affected by lack of blinding to the Plastic Surgery department participating in the study. The current study aims to corroborate those findings retrospectively, thereby overcoming this bias, and to conduct a detailed analysis of the reasons for observed referral behaviours. Medical notes of one hundred consecutive A&E referrals to a single Plastic Surgery Unit were retrospectively reviewed, documenting patient demographics, referral reason, time taken for referral and review, patient transfer time and referral appropriateness, defined by the need for hospital admission, surgery or senior review. ‘Appropriate’ and ‘inappropriate’ referrals were compared statistically for Referral Time, Transfer Time and Review Time using the independent samples t-test. To explore the reasons for observed referral patterns, six semi-structured interviews were carried out with all grades of A&E staff members using the previously-documented ‘framework analysis method’ of deductive analysis to investigate emerging and assumed themes systematically. 73 referrals from A&E to Plastic Surgery were included in the study, most commonly for adult males with upper limb injuries. The most frequent treatment outcomes were conservative management or wound exploration with no identifiable deep injury. Mean Referral Time and Review Time was 1:53 hours and 0:58 hours respectively. 69% of referrals were considered Appropriate according to the study definition. As in the previous study, inappropriate referrals were referred significantly more quickly than appropriate ones, whereas review time was similar irrespective of referral appropriateness. Interview themes analaysis revealed junior doctor inexperience, resource constraints and unintended consequences of targets and protocols to be major non-clinical determinants of whether a referral was made. This study confirms that priorities other than clinical need may influence A&E referral behavior and provides valuable insights into the challenges and drivers determining it. A significant proportion of referrals from A&E to Plastic Surgery are inappropriate and quicker referrals are more likely to be inappropriate. Many junior doctors currently lack the confidence and competence to manage routine cases that fall under the remit of Plastic Surgery. Significant change is needed to address the current situation. The insights of this study may help to inform such change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".