Discrete Conditional Phase‐type models utilising classification trees: Application to modelling health service capacities

Discrete Conditional Phase‐type models (DC‐Ph) consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC‐Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian phase‐type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case‐study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three‐phase Coxian distribution. The resulting DC‐Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.