The Impact of Waiting Time on Health Gains from Surgery: Evidence from a National Patient‐reported Outcome Dataset
Bibliographic record
Abstract
Reducing waiting times has been a major focus of the English National Health Service for many years, but little is known about the impact on health outcomes. The collection of data on patient-reported outcome measures for all patients undergoing four large-volume procedures facilitates analysis of the impact of waiting times on patient outcomes. The availability of patient-reported outcome measures before and after surgery allows us to estimate the impact of waiting times on the effectiveness of treatment, controlling for pre-surgery health and the endogeneity of waiting times caused by prioritisation with respect to pre-intervention health. We find that waiting time has a negative and statistically significant impact on the health gain from hip and knee replacement surgery and no impact on the effectiveness of varicose vein and hernia surgery. The magnitude of this effect at patient level is small, 0.1% of the outcome measure range for each additional week of waiting. However, the value of this effect is substantially larger than existing estimates of the disutility experienced during the waiting period. The health losses associated with an additional week of waiting for annual populations of hip and knee replacement patients are worth £11.1m and £11.5m, respectively. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.021 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".