Oxevision Evidence

Behavioral health providers, who have augmented their clinical practice with Oxevision, have reported:

Reduction in bedroom self-harm incidents and in use of ligatures

12-month pre-post study (with control group) in acute psychiatric 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 units (22-bed female and 20-bed male) and two control units (20-bed female and 20-bed male).
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 units.
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 1:1 observations, time taken to complete night-time safety checks and incidents, and associated cost savings

12-month economic evaluation across three behavioral health care settings4,5
Geriatric unit
Acute psychiatric unit
High intensity unit
Reduction in 1:1 observations
Associated annual savings
(based on a 16-bed unit)
(based on a 16-bed unit)
(based on a 12-bed unit)
Improvement in night-time rounding efficiency
Associated annual savings
Dependent on facility size and observation protocol
Reduction in incidents (type of incident)
bedroom falls
bedroom self-harm
emergency treatment orders related to assaults
Associated annual savings
(based on a 16-bed unit)
(based on a 16-bed unit)
(based on a 12-bed unit)

Reduction in assaults and emergency treatment orders related to assaults

12-month pre-post study in high intensity psychiatric care6,7
Reduction in patient assaults
(p = 0.004)
Reduction in emergency treatment orders
(p = 0.002)
6 One intervention unit (11-bed male).
7 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. Advance online publication.

Reductions in falls, transfers to acute general care and 1:1 observations

22-month pre-post study in geriatric care8
Oxehealth Evidence
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 ER
(p = 0.002)
Reduction in moderately severe falls in bedrooms at night9
Reduction in 1:1 observations10
8 Two intervention units (12-bed male and 12-bed female). Oxevision was installed in 6 bedrooms per unit. i.e. half of all bedrooms across the two units.
9 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).
10 This reduction is equivalent to saving 7,810 clinical hours per year for the two units. 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.

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

6-month pre-post study in behavioral health seclusion11
Oxehealth Evidence
95% CI: 8.4, 17.8
Physical health monitoring rate (physical healthcare checks per hour)

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

4-month qualitative study in acute psychiatric care12

All patients admitted to an acute inpatient mental health unit must have nursing observations carried out at night, typically every 15-60 minutes, to ascertain that they are safe and breathing. 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 unit, to enable staff to carry out the hourly and the 15 minutes observations, minimizing 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 unit. The limitations of this study, namely, its narrative character and the fact that patients were not randomized 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.
12 One intervention unit (18-bed male).
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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