Recent research has highlighted the potential of linking predictive and prescriptive analytics. However, it remains widely unexplored how both paradigms could benefit from one another to address today's major challenges in healthcare. One of these is smarter planning of resource capacities for frail and elderly inpatient wards, addressing the societal challenge of an aging population. Frail and elderly patients typically suffer from multimorbidity and require more care while receiving medical treatment. The aim of this research is to assess how various predictive and prescriptive analytical methods, both individually and in tandem, contribute to addressing the operational challenges within an area of healthcare that is growing in demand. Clinical and demographic patient attributes are gathered from more than 165,000 patient records and used to explain and predict length of stay. To that extent, we employ Classification and Regression Trees (CART) analysis to establish this relationship. On the prescriptive side, deterministic and two-stage stochastic programs are developed to determine how to optimally plan for beds and ward staff with the objective to minimize cost. Furthermore, the two analytical methodologies are linked by generating demand for the prescriptive models using the CART groupings. The results show the linked methodologies provided different but similar results compared to using averages and in doing so, captured a more realistic real-world variation in the patient length of stay. Our research reveals that healthcare managers should consider using predictive and prescriptive models to make more informed decisions. By combining predictive and prescriptive analytics, healthcare managers can move away from relying on averages and incorporate the unique characteristics of their patients to create more robust planning decisions, mitigating risks caused by variations in demand.

翻译：近期研究已揭示预测性分析与规定性分析相结合的潜力，但这两种方法如何相互促进以应对当今医疗领域重大挑战的问题仍鲜有探索。其中一大挑战在于针对体弱与老年住院病房的智能资源容量规划，以应对人口老龄化带来的社会问题。体弱与老年患者通常患有多种共病，在治疗过程中需要更多照护。本研究旨在评估不同预测性与规定性分析方法（单独及联合应用）如何助力应对需求日益增长的医疗领域运营挑战。研究收集了超过16.5万份患者记录中的临床与人口学特征属性，用于解释和预测住院时长。为此，我们采用分类与回归树（CART）分析建立相关关系。在规定性分析方面，构建了确定性模型与两阶段随机规划模型，以成本最小化为目标优化床位与病房人员配置。进一步，通过将CART分类结果作为规定性模型的需求输入，实现两种分析方法的联动。结果表明，与单纯使用平均值相比，联动方法提供了异同并存的结果，并更真实地捕捉了患者住院时长的现实变异。本研究表明，医疗管理者应考虑结合预测性与规定性模型制定更明智的决策。通过整合两类分析方法，管理者可摆脱对平均值的依赖，将患者个体特征纳入考量，制定更稳健的规划方案，从而缓解需求波动带来的风险。