Data-driven Models for Inferring the Patient Scheduling Policies via Inverse Reinforcement Learning
Bibliographic record
Abstract
In this work, we study multi-class patient scheduling with stochastic daily patient arrivals. Different classes of patients are characterized by different service times, waiting cost parameters, and rejection cost parameters. Our primary objective is to infer the policy used by the decision-makers, who schedule patients over a finite time horizon, based on their historical decisions. To achieve this, we first develop a mathematical model that captures the complexities of patient scheduling and is representative of the problem that decision-makers may consider to scheduling patients. Then, we utilize the Riccati and Hamiltonian approaches to estimate the cost parameters that have influenced the scheduling decisions made by the decision-maker. The Riccati approach begins by estimating the expert's policy, which is then used to determine the cost parameters. Conversely, the Hamiltonian approach derives the cost parameters through the optimality conditions of a path trajectory without needing to estimate the expert's policy. Using a simulation model, we demonstrate the efficiency and robustness of the proposed methods. \nFurthermore, we apply Riccati and Hamiltonian approaches to MRI data from two hospitals to estimate the cost parameters used in their scheduling decisions. \nUtilizing the estimated cost parameters, we analyze the root causes of the observed outcomes and examine the impact of these underlying factors on the scheduling process. \nFinally, through counterfactual analysis, we propose two alternative scheduling policies that reduce the total cost, even with the original cost parameters used by the decision-makers.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".