Identifying patients at risk (PDWS)
Patient monitoring in emergency departments typically includes basic physiological vital signs as well as assessments of pain and consciousness.
Project period
Start: 2019
End: 2022
Automated monitoring provides a continuous stream of data that enable real-time tracking of patient state, but reliable projection of trajectory is challenged by the sparseness of data and variation in patterns. The project sought to address both the challenges of device utilisation and patient risk stratification.
Aim
The project aimed to reduce the number of unforeseen deteriorations of patients in medical emergency departments. The project’s approach was looking at how data from the information systems that hospitals already use could be utilised in novel ways through machine learning based models. Ideally, the models would be able to span the diversity of patients and medical device utilisation.
The project built on the system Patient Deterioration Warning System (PDWS), which was developed in a previous project. The PDWS is currently integrated into the emergency department’s monitoring systems.
Participants
- Emergency Department, OUH
- Department of Clinical Research, OUH
- The Maersk Mc-Kinney Moller Institute, SDU
- Department of Regional Health Research
- Centre for Innovative Medical Technology (CIMT)
Thomas Schmidt
Associate Professor
University of Southern Denmark, Maersk Mc-Kinney Moeller Institute
(+45) 2423 7434 schmidt@mmmi.sdu.dk
Amin Naemi
PhD student
University of Southern Denmark, Maersk Mc-Kinney Moller Institute
(+45) 6550 7466 amin@mmmi.sdu.dk
Kristian Kidholm
Head of Research - Professor
Centre for Innovative Medical Technology (CIMT). Odense University Hospital, Dept. of Clinical Development - Innovation, Research & HTA
(+45) 6541 7960 kristian.kidholm@rsyd.dk
Annmarie Lassen
Professor, Chief Physician
Odense University Hospital, The Emergency Department
(+45) 6541 5048 annmarie.lassen@rsyd.dk