CAI-X

AI project

AutoDok

Full title: Automatic selection and imputation of vital signs in emergency departments

Most acute hospital settings deploy observation protocols stratified by patient severity to ensure that deterioration is identified in due time. To achieve this, the departments utilise advanced patient monitors for tracking the state of admitted patients. These monitors are typically capable of continuously tracking and reporting vital values at a much higher rate than documented in clinical knowledge management systems.

Past research has found that most patients are monitored sparsely, and a subset of patients are monitored more than required by clinical protocols. The latter situation, which also applies to patients who are continuously monitored, are poorly represented by spot-based registrations. The former situation complicates clinical assessment and forecasting.

Aim

In the AutoDok project, we extend the Patient Deterioration Warning System with functionality for handling clinical documentation for patients who are extensively monitored, and patients who lack monitoring during periods of their admission. Furthermore, the project will explore integration with the EPJ SYD system through the Medical Device Information Collection (MDIC) platform.

Participants

The Emergency Department at OUH 

The Maersk Mc-Kinney Moller Institute at SDU.

Funding

The project is funded by the Innovation Fund of the Region of Southern Denmark.

Project period

Start: January 2020

End: December 2021

Contact

Thomas Schmidt

Associate Professor

Maersk Mc-Kinney Moller Institute

(+45) 24 23 74 34
[email protected]

Turkis firkant (dekoration)

Annmarie Lassen

Professor, Chief Physician

The Emergency Department

(+45) 65 41 50 48
[email protected]