Why Machine Learning Integrated Patient Flow Simulation?

TL;DR

This paper discusses the importance of integrating machine learning with patient flow simulation to better account for heterogeneity and improve personalized healthcare modeling.

Contribution

It proposes a conceptual architecture for coupling machine learning methods with traditional patient flow simulation models.

Findings

Machine learning improves prediction of patient inflow, LoS, CoT, and CP.

Traditional statistical models ignore patient heterogeneity.

The proposed architecture enhances personalized healthcare modeling.

Abstract

Patient flow analysis can be studied from a clinical and or operational perspective using simulation. Traditional statistical methods such as stochastic distribution methods have been used to construct patient flow simulation submodels such as patient inflow, Length of Stay (LoS), Cost of Treatment (CoT) and Clinical Pathway (CP) models. However, patient inflow demonstrates seasonality, trend and variation over time. LoS, CoT and CP are significantly determined by attributes of patients and clinical and laboratory test results. For this reason, patient flow simulation models constructed using traditional statistical methods are criticized for ignoring heterogeneity and their contribution to personalized and value based healthcare. On the other hand, machine learning methods have proven to be efficient to study and predict admission rate, LoS, CoT, and CP. This paper, hence, describes why coupling machine learning with patient flow simulation is important and proposes a conceptual architecture that shows how to integrate machine learning with patient flow simulation.