Predictive and Prescriptive Analytics for Multi-Site Modeling of Frail and Elderly Patient Services

• URL: http://arxiv.org/abs/2311.07283v2
• Date: Fri, 31 Jan 2025 21:27:28 GMT
• Title: Predictive and Prescriptive Analytics for Multi-Site Modeling of Frail and Elderly Patient Services
• Authors: Elizabeth Williams, Daniel Gartner, Paul Harper,
• Abstract summary: The aim of this research is to assess how various predictive and prescriptive analytical methods contribute to addressing the operational challenges within an area of healthcare that is growing in demand. On the prescriptive side, deterministic and two-stage programs are developed to determine how to optimally plan for beds and ward staff. Our research reveals that healthcare managers should consider using predictive and prescriptive models to make more informed decisions.
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• Abstract: 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.

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