QCPW: a quality centric process workflow improvement approach for a legacy healthcare information system

Abstract

Workflow extraction a.k.a. Process mining (PM) is the connecting link between process modelling and data mining. Efforts led by multiple researchers and scientists to explore the opportunities of information extraction about self-loops and hidden transition of transactions or sub-processes using event log but unable to undertake any fruitful information. This study aims to display a computing approach that consolidates PM to the uncovered hidden transition of transactions from the event logs (from an extensive information system). The authors used an evolutionary algorithmic based computing approach to perform an extraction of the hidden transition of transactions from the extensive information system event logs. In this, author proposed and implement a customised version of the genetic algorithm uniquely tailored for the event logs, that used a Petri net, causal matrix, and workflow net for the intermediate processes. In this experimental study, author used different-different event logs collected from various information systems to validate the authors’ algorithm, i.e. running, repair, store, and hospital. In this experimental data-centric approach, a tailored evolutionary algorithm for the improvement of the software process as well as the software quality of legacy information systems is used and results are validated by different comparison matrices.