Towards Effective Patient Simulators

Abstract

In this paper we give an overview of the field of patient simulators and provide qualitative and quantitative comparison of different modeling and simulation approaches. Simulators can be used to train human caregivers but also to develop and optimize algorithms for clinical decision support applications and test and validate interventions. In this paper we introduce three novel patient simulators with different levels of representational accuracy: HeartPole, a simplistic transparent rule-based system, GraphSim, a graph-based model trained on intensive care data, and Auto-ALS—an adjusted version of an educational software package used for training junior healthcare professionals. We provide a qualitative and quantitative comparison of the previously existing as well as proposed simulators.

Serious Games for Training in Patient Flow Management in Emergency Departments: State of the Art and Perspectives

“…To improve patient flow in the ED, several strategies have been proposed, such as doctor triage, rapid assessment, pointof-care testing, and effective collaboration between medical services and providers (Ortiz-Barrios and Alfaro-Saiz 2020; Jarvis 2016). Recently, strategies based on artificial intelligence (Rosemarin, Rosenfeld, and Kraus 2019;Liventsev, Härmä, and Petković 2021;Lee and Lee 2020;Arnaud et al 2022;Cho et al 2022), machine learning (Alenany and Ait El Cadi 2020;El-Bouri et al 2021;Wang et al 2021;Terning, Brun, and El-Thalji 2022), and computer modeling and simulation (Mohiuddin et al 2017;Tuominen et al 2022) have been developed.…”