标题： 基于大语言模型增强的数据驱动的个性化和公平的临床医生排班：一种预测后优化的方法 显示英文标题

标题： LLM-Enhanced, Data-Driven Personalized and Equitable Clinician Scheduling: A Predict-then-Optimize Approach

摘要： 临床人员排班仍然是一项持续的挑战，这是由于临床资源有限和需求波动所致。 这种复杂性在大型学术麻醉学部门尤为突出，因为医生需要在多个具有冲突优先级的临床场所之间平衡职责。 此外，排班必须考虑个人的临床和生活方式偏好，以确保工作满意度和健康。 传统的做法通常基于统计或基于规则的优化模型，依赖于结构化数据和明确的领域知识。 然而，这些方法常常忽略了非结构化信息，例如 routinely administered clinician well-being surveys（定期进行的临床人员健康调查）和排班平台中的自由文本注释。 这些注释可能揭示隐含且未被充分利用的临床资源。 忽视此类信息可能导致排班不一致、增加职业倦怠、忽视人员配置的灵活性以及可用资源的非最优利用。 为解决这一差距，我们提出了一种预测-优化框架，将基于分类的临床人员可用性预测与混合整数规划排班优化模型相结合。 大型语言模型（LLMs）被用于从非结构化排班注释中提取可操作的偏好和隐含约束，从而提高可用性预测的可靠性。 然后，这些预测会考虑四个目标来指导排班优化：首先，确保临床全职等效合规性；其次，通过强制执行轮班类型的公平比例来减少工作量不平衡；第三，最大化分配轮班的临床人员可用性；第四，排班一致性。 通过将大型语言模型的解释能力与数学优化的严谨性相结合，我们的框架提供了一个稳健的数据驱动解决方案，在提高运营效率的同时支持公平性和临床人员的健康。 显示英文摘要

摘要： Clinician scheduling remains a persistent challenge due to limited clinical resources and fluctuating demands. This complexity is especially acute in large academic anesthesiology departments as physicians balance responsibilities across multiple clinical sites with conflicting priorities. Further, scheduling must account for individual clinical and lifestyle preferences to ensure job satisfaction and well-being. Traditional approaches, often based on statistical or rule-based optimization models, rely on structured data and explicit domain knowledge. However, these methods often overlook unstructured information, e.g., free-text notes from routinely administered clinician well-being surveys and scheduling platforms. These notes may reveal implicit and underutilized clinical resources. Neglecting such information can lead to misaligned schedules, increased burnout, overlooked staffing flexibility, and suboptimal utilization of available resources. To address this gap, we propose a predict-then-optimize framework that integrates classification-based clinician availability predictions with a mixed-integer programming schedule optimization model. Large language models (LLMs) are employed to extract actionable preferences and implicit constraints from unstructured schedule notes, enhancing the reliability of availability predictions. These predictions then inform the schedule optimization considering four objectives: first, ensuring clinical full-time equivalent compliance, second, reducing workload imbalances by enforcing equitable proportions of shift types, third, maximizing clinician availability for assigned shifts, and fourth, schedule consistency. By combining the interpretive power of LLMs with the rigor of mathematical optimization, our framework provides a robust, data-driven solution that enhances operational efficiency while supporting equity and clinician well-being.