Predictive and Prescriptive Analytics for Multi-Site Modeling of Frail
  and Elderly Patient Services
Elizabeth Williams Daniel Gartner Paul Harper
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.