• C L Downey, J M Brown, D G Jayne, and R Randell.
• Leeds Institute of Biomedical & Clinical Sciences, Clinical Sciences Building, St. James's University Hospital, University of Leeds, Leeds, LS9 7TF, United Kingdom. Electronic address: c.l.downey@leeds.ac.uk.
• Int J Med Inform. 2018 Jun 1; 114: 52-56.

BackgroundVital signs monitoring is used to identify deteriorating patients in hospital. The most common tool for vital signs monitoring is an early warning score, although emerging technologies allow for remote, continuous patient monitoring. A number of reviews have examined the impact of continuous monitoring on patient outcomes, but little is known about the patient experience. This study aims to discover what patients think of monitoring in hospital, with a particular emphasis on intermittent early warning scores versus remote continuous monitoring, in order to inform future implementations of continuous monitoring technology.MethodsSemi-structured interviews were undertaken with 12 surgical inpatients as part of a study testing a remote continuous monitoring device. All patients were monitored with both an early warning score and the new device. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis.FindingsPatients can see the value in remote, continuous monitoring, particularly overnight. However, patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care.ConclusionEarly warning score systems are widely used to facilitate detection of the deteriorating patient. Continuous monitoring technologies may provide added reassurance. However, patients value personal contact with their healthcare professionals and remote monitoring should not replace this. We suggest that remote monitoring is best introduced in a phased manner, and initially as an adjunct to usual care, with careful consideration of the patient experience throughout.Copyright © 2018 Elsevier B.V. All rights reserved.

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