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Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations

Authors :
Andrew W. Wood
Carol Gee
John R. Geddes
Daniel Bayley
Oliver Gibson
Alvaro Barrera
Source :
Evidence Based Mental Health. 23:34-38
Publication Year :
2020
Publisher :
BMJ, 2020.

Abstract

BackgroundAll patients admitted to an acute inpatient mental health unit must have nursing observations carried out at night either hourly or every 15 minutes, to ascertain that they are safe and breathing. However, while this practice ensures patient safety, it can also disturb patients’ sleep, which in turn can impact negatively on their recovery.ObjectiveThis article describes the process of introducing artificial intelligence (‘digitally assisted nursing observations’) in an acute mental health inpatient ward, to enable staff to carry out the hourly and the 15 minutes observations, minimising disruption of patients’ sleep while maintaining their safety.FindingsThe preliminary data obtained indicate that the digitally assisted nursing observations agreed with the observations without sensors when both were carried out in parallel and that over an estimated 755 patient nights, the new system has not been associated with any untoward incidents. Preliminary qualitative data suggest that the new technology improves patients’ and staff’s experience at night.DiscussionThis project suggests that the digitally assisted nursing observations could maintain patients’ safety while potentially improving patients’ and staff’s experience in an acute psychiatric ward. The limitations of this study, namely, its narrative character and the fact that patients were not randomised to the new technology, suggest taking the reported findings as qualitative and preliminary.Clinical implicationsThese results suggest that the care provided at night in acute inpatient psychiatric units could be substantially improved with this technology. This warrants a more thorough and stringent evaluation.

Details

ISSN :
1468960X and 13620347
Volume :
23
Database :
OpenAIRE
Journal :
Evidence Based Mental Health
Accession number :
edsair.doi...........e639088ff8451e2a429654d3f4b666fe
Full Text :
https://doi.org/10.1136/ebmental-2019-300136