Booking patients for hospital admissions: evaluation of a pilot programme for day cases
Bibliographic record
Abstract
PROBLEM: NHS patients requiring elective surgery usually have to wait before being treated and are usually told when a date becomes available. DESIGN: 18 month pilot programme to enable day case patients to book date of hospital admission at time of decision to operate. BACKGROUND AND SETTING: 24 pilot sites in England with relatively short waiting times and some experience of booking appointments. KEY MEASURES FOR IMPROVEMENT: Proportion of patients with booked or "to come in" date during and after pilot programme, proportion not attending for admission, and proportion waiting > or = 6 months. Comparison of pilot sites with non-pilot sites. STRATEGIES FOR CHANGE: National Patients' Access Team established to help pilot sites enable patients to book admission dates. Provision of 9.9m pounds sterling to pilot sites to employ project managers, purchase equipment, buy extra time from clinical and other staff, and invest in information and communications technology. EFFECTS OF CHANGE: Proportion of patients with booked or "to come in" date increased from 51.1% to 72.7% between end of March 1999 and end of March 2000, and then fell to 66.2% by end of March 2001. Over the same periods, the proportion of patients waiting > or = 6 months fell from 10.9% to 10.5% and then increased to 11.9%. The proportion of patients failing to attend fell from 5.7% to 3.1% between the first quarter of 1999 and the first quarter of 2000, and then increased to 4.0% in the first quarter of 2001. Pilot sites varied widely in performance during and after the pilot phase. Pilot sites had higher proportions of patients with booked or "to come in" date than non-pilot sites at end of each period. LESSONS LEARNT: Increasing the proportion of patients who book their date of hospital admission is possible, but there are difficulties in sustaining this. Several factors facilitated or hindered the implementation of booking, and the roll out of the programme across the NHS is seeking to incorporate these factors.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".