Tactical Operating Room Planning Based on System Transient Performance Control
Bibliographic record
Abstract
Efficient management of operating room (OR) schedules is important as the OR is the largest cost and revenue center in a hospital and can substantially impact its staffing and finances. A major problem associated with developing OR schedules for elective surgeries is the schedule disruption from uncertainty inherent in the duration of surgical services. Another problem is the cascaded impact on overall system performance of facilities and resources upstream and downstream to the OR. Using a manufacturing system analytical approach, the peri-operative process is modeled as a transfer line with three machines and two buffers by a discrete time Markov chain. Uncertain surgical and recovery duration is quantified probabilistically and incorporated in the Markov chain model with multistate geometrical machines. Model predictive control (MPC) to pace patient release into the ORs is then applied to control system transient performance. With this model and empirical studies of surgery and recovery duration, guidance can be given to OR managers on how to dynamically schedule and reschedule patients throughout an OR's day that minimizes cost for a given workload. The proposed predictive control model can also control other transient performance metrics such as OR and recovery room (RR) utilization, patient flow, and cost.
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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 | 0.003 | 0.002 |
| 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".