A Process Cube based Approach of Process Mining in Analysing Frailty Progression Exploiting Electronic Frailty Index

Process mining is a data analytics technique that is used in healthcare to develop insights into care processes, care pathways and disease progression using event data extracted from Health Information Systems. The most widely used application is process discovery where models of healthcare processes are automatically inferred and visualized. These have been applied to frailty, a common geriatric condition in elderly people typically described in terms of progression through a number of stages. In this paper we use the Electronic Frailty Index which is calculated using 36 indicators of frailty deficits. We use process mining to analyse frailty progression using data from the SystmOne GP system used in UK primary care. We propose an approach for analysing frailty progression using a process cube analysis through slicing and dicing sets of attributes related to clinical frailty events. Different combinations of process cube dimensions allow us to model and analyse a comprehensible frailty progression. We illustrate the method through a case study investigating the association between frailty stages and three common issues; falls, hypertension and polypharmacy.