Emergency Department Online Patient-Caregiver Scheduling

Abstract

Emergency Departments (EDs) provide an imperative source of medical care. Central to the ED workflow is the patientcaregiver scheduling, directed at getting the right patient to the right caregiver at the right time. Unfortunately, common ED scheduling practices are based on ad-hoc heuristics which may not be aligned with the complex and partially conflicting ED's objectives. In this paper, we propose a novel online deep-learning scheduling approach for the automatic assignment and scheduling of medical personnel to arriving patients. Our approach allows for the optimization of explicit, hospital-specific multi-variate objectives and takes advantage of available data, without altering the existing workflow of the ED. In an extensive empirical evaluation, using real-world data, we show that our approach can significantly improve an ED's performance metrics.

Machine learning in patient flow: a review

“…With this approach authors assume that there is fixed resource and how it is used can be optimised with varying patient numbers. Rosemarin et al (2019) define the ED scheduling problem as needing to satisfy the following constraints: the schedule must minimise the risk of adverse consequences, minimise patient waiting time, minimise patient length-of-stay, minimise ED crowding and minimise interruption to caregivers. They use a mixture of health record data of the patients and data on the status the ED to reconstruct the state of the ED when the patients were there.…”

AI for Explaining Decisions in Multi-Agent Environments

“…If the AI decision is made using neural networks (e.g., (Rosemarin, Rosenfeld, and Kraus 2019;Li, Qin, and others 2019)) then XAI methods can be used to identify important features that led to the decision (Shrikumar, Greenside, and Kundaje 2017;Bach, Binder, and others 2015). These methods should be adapted to xMASE-related problems (Lee 2019;Selvaraju et al 2017).…”