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


METHODS
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).


RESULTS
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 Petersburg, Russia.


CONCLUSION
The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others.
['Kovalchuk|Sergey V|SV|', 'Funkner|Anastasia A|AA|', 'Metsker|Oleg G|OG|', 'Yakovlev|Aleksey N|AN|']
D054058:Acute Coronary Syndrome D000067917:Cloud Computing D016000:Cluster Analysis D003198:Computer Simulation D019091:Critical Pathways D057225:Data Mining D057286:Electronic Health Records D006801:Humans D000069550:Machine Learning D008490:Medical Informatics D012426:Russia D057188:Workflow
Data mining Acute coronary syndrome Process mining Electronic health records Discrete-event simulation Clinical pathways Classification