Calendar design for assignment of ongoing appointments: Modeling, analysis and application
Bibliographic record
Abstract
Appointment booking (AB) is a widely used technique for managing elective services like hospital surgical units, law courtrooms, and other services with high demand and limited resources. AB typically assigns services to specific time slots using an appointment calendar. However, devising an effective AB policy is challenging due to varying service types, durations, and demand variability. In this study, we present a preplanned appointment calendar (PAC), designed using a two-stage stochastic programming model to tackle service scheduling challenges based on demand and historical data. The design of the PAC is generated offline, prior to the arrival of any customers requesting an appointment, and aims to minimize the time from the initial appointment request to service completion, namely, the patient’s sojourn time. Despite the substantial public expenses incurred when appointments are scheduled far in the future, encompassing both indirect costs (e.g., those related to the development of chronic diseases) and direct costs (e.g., those arising from employee absence), the sojourn-time measure has not received sufficient attention in the existing literature. Our method minimizes patients’ sojourn time while considering operational constraints and quality of service (QoS) considerations, resulting in a practical and user-friendly appointment booking system. Our approach is adjustable and easy to apply in real time. We introduce the chained-PAC (CPAC) mechanism, in which multiple, smaller PACs are joined together, and demonstrate the applicability of this approach by implementing it in a cardiac surgical operating room at a major hospital in Toronto, Canada. Results show the PAC approach reduces wait times and improves resource utilization in the surgical unit compared to the existing AB system. Our approach benefits healthcare providers and patients and can extend to other similar service systems. • Practical method for designing an adjustable preplanned appointment calendar. • The designed calendar ensures long-term system queueing stability. • Illustrates ongoing appointment adjustments addressing assignment complexity. • Demonstrates the practicality of the method using real-life operating room data. • The resulting calendar design appeals to the typical decision-maker.
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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) | 0.000 | 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".