Appointment system design with interruptions and physician lateness
Bibliographic record
Abstract
Purpose Physician lateness and service interruptions are a significant problem in many health care environments but have received little attention in the literature. The purpose of this paper is to design appointment systems that reduce waiting times of the patient while maintaining utilization of the physician at a high level. Design/methodology/approach Empirical data from time studies and surveys of medical professionals from multiple outpatient clinics are used to motivate the study. Simulation optimization is used to simultaneously account for uncertainty and to determine (near) optimal scheduling solutions. Findings As lateness increases, it is shown that, in general, appointment slots should be shorter and pushed later in the session. Conversely, as interruptions rise, appointments in the middle of the session should be longer. These findings are fairly consistent over a variety of environmental conditions, including clinic sizes, service time variance, and costs of physician time compared to patients' time. Practical implications This paper demonstrates that the dome/plateau‐dome scheduling patterns that have been found in prior studies work well under many of the new factors modeled here. This is encouraging because it suggests that a generalizable pattern is emerging in the literature for the range of environments studied in these papers and this research provides guidance as to how to adjust the pattern to account for the factors studied here. In addition, it is shown that some environments will perform better with a different pattern, which the authors denote a “descending step” pattern. Originality/value This paper differs from most prior studies in that the complexity of environmental variables and stochastic elements of the model are simultaneously accounted for by the simulation optimization algorithm. The (very few) prior papers that have used simulation optimization have not addressed the factors studied here.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".