Semantic Context Consolidation and Rule Learning for Optimized Transport Assignments in Hospitals

Abstract

Abstract. The increase of ICT infrastructure in hospitals offer opportunities for cost reduction by optimizing workflows, while maintaining quality of care. This work-in-progress poster details the AORTA system, which is a semantic platform to optimize transportation task scheduling and execution in hospitals. It provides a dynamic scheduler with an upto-date view about the current context by performing semantic reasoning on the information provided by the available software tools and smart devices. Additionally, it learns semantic rules based on historical data in order to avoid future delays in transportation time.

Surgical data science – from concepts toward clinical translation

“…Such tools allow arbitrary devices to take part in communication by registering via a message broker, where messages can typically be filtered by their origin, type, destination, for instance. Examples include Apache Kafka (Kim et al, 2017;Spangenberg et al, 2018) or RabbitMQ ® (Ongenae et al, 2016;Trinkūnas et al, 2018). Such systems enable developers to create flexible data exchange architectures using technologies that are mature and usually well documented thanks to their wide application outside the field of healthcare.…”

Context-Aware and Self-learning Dynamic Transport Scheduling in Hospitals

“…The overall architecture of the simulated AORTA system is visualized in Figure 1. This work uses a Simulator to represent the information within the hospital, more information regarding the real components that are being simulated in this work can be found in Ongenae et al [5]. The simulator enables easy demonstrator purposes.…”

“…In previous work [3,5], we introduced the AORTA project 3 , where we exploit the advent of the Internet of Things and intelligent decision support systems to automatically assign the most suitable staff member to a transport based on all the available information about the context (e.g. location of staff and patients and how crowded the hospital is), the staff (e.g.…”