Machine learning integrated patient flow simulation: why and how?

Tesfamariam M Abuhaya College of Informatics, University of Gondar, Gondar, Ethiopia;b Smith School of Business, Queen’s University, Kingston, CanadaCorrespondence[email protected]

https://orcid.org/0000-0001-8436-2428

Stewart Robinsonc Business School, Newcastle University, Newcastle Upon Tyne, UK

Adane Mamuyea College of Informatics, University of Gondar, Gondar, Ethiopia

Sergey V Kovalchukd eScience Research Institute, ITMO University, Saint Petersburg, Russia

ABSTRACT

Stochastic distribution methods were used to construct patient flow simulation sub-models such as patient inflow, length of stay (LoS), cost of treatment (CoT) and clinical pathways (CPs). However, the patient inflow rate demonstrates seasonality, trend, and variation due to natural and human-made factors. LoS, CoT and CPs are determined by social-demographics factors, clinical and laboratory test results, resource availability and healthcare structure. For this reason, patient flow simulation models developed using stochastic methods have limitations including uncertainty, not recognising patient heterogeneity, and not representing personalised and value-based healthcare. This, in turn, results in a low acceptance level and implementation of solutions suggested by patient flow simulation models. On the other hand, machine learning becomes effective in predicting patient inflow, LoS, CoT, and CPs. This paper, therefore, describes why coupling machine learning with patient flow simulation is important, proposes a conceptual architecture for machine learning integrated patient flow simulation and demonstrates its implementation with examples.

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