Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification

Journal of Biomedical Informatics
Sergey V KovalchukAleksey N Yakovlev

Abstract

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study. A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others). The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint P...Continue Reading

Citations

Jan 5, 2019·Health Care Management Science·M A MirandaV Rodríguez
Dec 29, 2020·Journal of Biomedical Informatics·Emma AsplandPeter Barrett-Lee

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