NHS mental health Trusts that have augmented their clinical practice with Oxevision have reported:

Reduction in bedroom self-harm incidents and use of ligatures

12-month pre-post study (with control group) in acute mental health care1,2
Relative reduction in bedroom self-harm3
(p < 0.002)
Relative reduction in bedroom ligatures3
(p < 0.001)
Relative reduction in bathroom ligatures
(p < 0.001)
1 Two intervention wards (22-bed female acute and 20-bed male acute) and two control wards (20-bed female acute and 20-bed male acute).
2 Reductions denote the relative percentage change, which was calculated to compare the rate of change from the pre to the post go-live period on intervention and control wards.
3 Bedroom incidents include those occurring in ensuite bathrooms.
Ndebele, F., et al. (2023). Non-contact health monitoring to support acute care in mental health. Journal of Mental Health. Advance online publication.

Reductions in falls, transfers to A&E and 1:1 observations

22-month pre-post study in dementia inpatient care4
48% reduction in bedroom falls at night p < 0.01
195 falls were recorded in the pre go-live period. 101 falls were were recorded in the post go-live period.
Accumulated projected falls pre go-live were calculated using the monthly average bedroom falls at night during this period.
Reduction in transfers to A&E
(p = 0.02)
Reduction in moderately severe falls in bedrooms at night5
Reduction in 1:1 observations6
4 Two intervention wards (12-bed male dementia and 12-bed female dementia). Oxevision was installed in 6 bedrooms per ward. i.e. half of all bedrooms across the two wards.
5 Moderate severity falls are those that resulted in a moderate increase in treatment, possible surgical intervention, cancelling of treatment, or transfer to another area, and which caused significant but not permanent harm to one or more patients. p value could not be calculated due to low sample size (7 falls pre go-live; 1 fall post go-live).
6 This reduction is equivalent to saving 7,810 clinical hours per year for the two wards. p value could not be calculated because patient identifiers were not available.
Wright, K., & Singh, S. (2022). Reducing falls in dementia inpatients using vision-based technology. Journal of Patient Safety, 18(3), 177-181.

Reduction in assaults and rapid tranquillisation related to assaults

12-month pre-post study in psychiatric intensive care7,8
Reduction in assaults
(p = 0.004)
Reduction in rapid tranquilisation related to assaults
(p = 0.002)
7 One intervention ward (11-bed male PICU).
8 Reductions denote the percentage change in incident rate from the pre to the post go-live period.
Ndebele, F., et al. (2022). Non-contact health monitoring to support care in a psychiatric intensive care unit. Journal of Psychiatric Intensive Care, 18(2), 95-100.

Economic savings associated with reductions in 1:1 observations and incidents, and with more efficient night-time observation rounds9,10

Cash-releasing savings: annual cost savings on a typical ward11

Older adult
Reduction in bank and agency spend related to 1:1 observations*
Associated cost savings*
Return on investment*
Reduction in bank and agency spend related to 1:1 observations* in acute 27%, older adult 34% and PICU 15%. Associated cost savings* in Acute £54,324, older adult £63,980 and PICU £, Return on investment* in acute 105%, older adult 141% and PICU 202%

Non cash-releasing savings: annual time savings (hours) on a typical ward

Time savings due to
Older adult
More efficient night-time observation rounds*,12
Reductions in incidents (incident type)†,‡,13
(bedroom self-harm)
(bedroom falls at night)
(rapid tranquillisation related to assaults)
Reductions in 1:1 observations†,‡,13
Total hours saved
Approximately 3 hours per 12-hour shift14
Time saving data table. Total hours saved: 7,762 (equating to approximately 3 hours per 12-hour shift)
9 Acute and older adult results are standardised for a 16-bed ward with 90% occupancy; PICU results are standardised for a 12-bed ward with 90% occupancy.
10 Cost savings are associated with staff working beyond planned staffing levels. Time savings are for staff working within planned staffing levels.
11 Acute and PICU data comes from seven wards; older adult data comes from five wards.
Data comes from nine wards (three PICU, three acute, three older adult).
Data comes from five wards (one PICU, two acute, two older adult).
Weighted average for a typical ward, calculated by estimating the proportion of wards that are acute, older adult and PICU in NHS England (70%, 20%, 10%, respectively). Average hours saved per 12-hour shift = 2.82.
* York Health Economics Consortium. (2023). NHS Innovation Accelerator economic impact case study: Oxevision.Malcolm, R., et al. (2022a). Economic evaluation of a vision-based patient monitoring and management system in an acute adult and an older adult mental health hospital in England. Journal of Medical Economics, 25(1), 1207-1217.Malcolm, R., et al. (2022b). Economic evaluation of a vision-based patient monitoring and management system for working-age people in psychiatric intensive care units in England. Journal of Medical Economics, 25(1), 1101-1109.

Increase in the rate of obtaining physical health observations during seclusion sessions

6-month pre-post study in mental health seclusion15
Graphic showing 12.3x increase in the rate of obtaining clinically accurate vital signs post go-live with OXevision
Physical health monitoring rate (physical healthcare checks per hour)
95% CI: 8.4, 17.8

Using digitally assisted nursing observations to keep patients safe and improve patient and staff experience

4-month qualitative study in acute mental health care16

All 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.

Objective This 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.

The 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.

Discussion This 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 implications These 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.
16 One intervention ward (18-bed male acute).
Barrera, A., et al. (2020). Introducing artificial intelligence in acute psychiatric inpatient care: qualitative study of its use to conduct nursing observations. Evidence-Based Mental Health, 23(1), 34-38.

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