Optimizing outpatient appointment scheduling: Innovative strategies for enhanced efficiency in psychiatric clinics
Bibliographic record
Abstract
Patient punctuality significantly impacts resource utilization and patient waiting times, among other quality indicators, within psychiatry clinics. In pursuit of service improvement, this study endeavors to develop effective appointment scheduling systems that optimally distribute patients' needs during clinical sessions, thereby enhancing resource utilization and patient satisfaction. In developing these scheduling rules, three patient-related uncertainties are considered: preference, availability, and punctuality. Various scheduling rules are evaluated based on their average total cost under different scenarios. The HSBGDM rules have emerged as a balanced approach for clinic operations, effectively managing physician time but occasionally leading to overtime variations. Increased patient delays often exacerbate physician idle times, particularly under IBVST and VBVST rules. Hybrid rules, such as the HSBGDM series, adapt well, improving patient wait times and managing unscheduled patients. However, scheduling systems like REPDM may prolong waits, potentially impacting patient satisfaction. Systems prioritizing new appointments can increase physician idle times due to unpredictability. While accommodating unscheduled patients enhances service quality, it may also cause disruptions. This study provides valuable insights into scheduling dynamics, assisting administrators in balancing efficiency, cost, and patient satisfaction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".