๐ค AI Summary
This study addresses the significant inefficiencies and inequities in outpatient care caused by patient no-shows, which conventional fixed double-booking strategies fail to mitigate due to their inability to adapt to dynamic environments and individual heterogeneity. To overcome these limitations, we propose an adaptive double-booking framework grounded in multi-objective reinforcement learning. The approach integrates a multi-head attention-based soft random forest to predict individual no-show risk, which is then embedded into the state representation of a Markov decision process. We further design a multi-policy proximal policy optimization algorithm augmented with a KL divergenceโbased ฯ-rule to enable selective knowledge transfer across policies, enhancing both convergence and solution diversity. Additionally, SHAP values are employed to improve the interpretability of scheduling decisions. Experimental results demonstrate that our method substantially outperforms traditional heuristic strategies in alleviating clinic congestion and mitigating the adverse effects of no-shows.
๐ Abstract
Patient no-shows disrupt outpatient clinic operations, reduce productivity, and may delay necessary care. Clinics often adopt overbooking or double-booking to mitigate these effects. However, poorly calibrated policies can increase congestion and waiting times. Most existing methods rely on fixed heuristics and fail to adapt to real-time scheduling conditions or patient-specific no-show risk. To address these limitations, we propose an adaptive outpatient double-booking framework that integrates individualized no-show prediction with multi-objective reinforcement learning. The scheduling problem is formulated as a Markov decision process, and patient-level no-show probabilities estimated by a Multi-Head Attention Soft Random Forest model are incorporated in the reinforcement learning state. We develop a Multi-Policy Proximal Policy Optimization method equipped with a Multi-Policy Co-Evolution Mechanism. Under this mechanism, we propose a novel {\tau} rule based on Kullback-Leibler divergence that enables selective knowledge transfer among behaviorally similar policies, improving convergence and expanding the diversity of trade-offs. In addition, SHapley Additive exPlanations is used to interpret both the predicted no-show risk and the agent's scheduling decisions. The proposed framework determines when to single-book, double-book, or reject appointment requests, providing a dynamic and data-driven alternative to conventional outpatient scheduling policies.