  This work uses a data-driven approach to analyse how the resource
requirements of patients with chronic obstructive pulmonary disease (COPD) may
change, quantifying how those changes impact the hospital system with which the
patients interact. This approach is composed of a novel combination of often
distinct modes of analysis: segmentation, operational queuing theory, and the
recovery of parameters from incomplete data. By combining these methods as
presented here, this work demonstrates that potential limitations around the
availability of fine-grained data can be overcome. Thus, finding useful
operational results despite using only administrative data. The paper begins by
finding a useful clustering of the population from this granular data that
feeds into a multi-class M/M/c model, whose parameters are recovered from the
data via parameterisation and the Wasserstein distance. This model is then used
to conduct an informative analysis of the underlying queuing system and the
needs of the population under study through several what-if scenarios. The
analyses used to form and study this model consider, in effect, all types of
patient arrivals and how those types impact the system. With that, this study
finds that there are no quick solutions to reduce the impact of COPD patients
on the system, including adding capacity to the system. In this analysis, the
only effective intervention to reduce the strain caused by those presenting
with COPD is to enact external policies which directly improve the overall
health of the COPD population before they arrive at the hospital.
