Skip to content Skip to navigation

New tool predicts patient aggression in healthcare settings

October 20, 2011
by root
| Reprints

Healthcare providers may have a new way to identify violent patients by utilizing a specially designed risk assessment tool created by American researchers.

The 10-point Aggressive Behaviour Risk Assessment Tool (ABRAT) was completed within 24 hours of admission for patients in medical-surgical settings and appears to “provide a promising tool for predicting which patients will become violent during their hospital stay,” researchers said. Findings of a study on ABRAT appear in the November issue of the Journal of Advanced Nursing.

“The results from this study indicate that the 10-item ABRAT could be useful in identifying potentially violent patients in medical-surgical units, with acceptable accuracy and agreement between users,” researchers said. “Further studies are now needed to see whether the use of the ABRAT can actually reduce violence in clinical settings.”

Researchers studied more than 2,000 patients admitted to an American acute care hospital over a five-month period. Three percent of those patients had been involved in one or more violent incidents, including verbal abuse, physical attacks, threats of violence, sexual harassment and incidents where an emergency call had to be placed with security personnel.

On the 10-point ABRAT scale, 41 percent of the patients with a score of two or more ended up becoming violent, compared to less than 1 percent of patients with a rating of zero becoming violent.

Half of the violent incidents involved patients aged over 70, despite the fact that they only made up 40 percent of the patients studied. Males, who made up 48 percent of the patients studied, were almost twice as likely to become violent as females (64 percent versus 34 percent).

The five most common predictors of violence were: confusion/cognitive impairment, anxiety, agitation, shouting/demanding and a history of physical aggression.

Nurses who had undergone a training course in use of the tool collected the data from patients admitted to six different medical-surgical units.

Topics