Effects of Booking Horizon Reduction on Cancellation Rates: An Experimental Analysis in Pediatric Outpatient Care
Bibliographic record
Abstract
Background. The time between booking an appointment and the appointment taking place, known as lead time, has been identified as a predictor of cancellation and no-show probability in health care settings. Understanding the impact of reducing permissible lead times, that is, the booking horizon, at a policy level in an outpatient care setting is important when mitigating costly cancellation and no-show rates. Few studies have researched this in an observational or experimental setting. Methods. We leveraged longitudinal observational data from an outpatient pediatric rehabilitation organization in Ontario, Canada, consisting of 73,482 visits between June 2021 and October 2023. This organization reduced its booking horizon at the policy level from 12 to 4 wk in February 2023. Using 2 interrupted time-series approaches, we estimated the change in level, slope, and variance of the weekly combined last-minute cancellation and no-show rate associated with the policy change. Results. It is estimated that reducing the booking horizon is associated with an absolute reduction in the weekly rate of last-minute cancellations and no-shows of 1.02% to 1.85% (a relative reduction of 8.07%–15.70%). Furthermore, the variance dropped by 48.18%. Conclusion. Reducing the appointment booking horizon is associated with a significant reduction in the rate and variance of costly last-minute cancellations and no-shows. The reduced variance can also help enable effective usage of strategies such as overbooking for organizations seeking further approaches to mitigating the negative effects of no-shows. Highlights This study uses interrupted time-series approaches to assess the effects of reducing the appointment booking horizon at a policy level on last-minute cancellations and no-shows in a pediatric outpatient care setting. Reducing the permissible booking horizon from up to 3 mo to up to 4 wk is associated with a significant reduction in the rate of last-minute cancellations and no-shows. The shortened booking horizon policy is associated with a significant drop in the variance of last-minute cancellations and no-show rates, which is valuable in settings where overbooking occurs.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".