This work uses a data-driven approach to analyse how the resource requirements of patients with chronic obstructive pulmonary disease (COPD) may change, and quantifies how those changes affect the strains of the hospital system the patients interact with. This is done using 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, it is demonstrated how the potential limitations around the availability of fine-grained data can be overcome in operational research. That is, despite using only administrative data, useful operational results are found. The paper begins by finding an effective 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 particular analyses used to form and study this model considers, 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 fact, the only effective intervention to reduce the strains of the COPD population is to enact some external policy that directly improves the overall health of the COPD population before arriving at the hospital.
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