A Predictive Model for Estimating Risk of Harm and Aggression in Inpatient Mental Health Clinics
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Serious mental illness is a major risk factor for aggression and violence. The present study aimed to develop and test an algorithm to predict inpatient aggressions that involve a risk of harm to self or others. This work is based on a retrospective study aimed to investigate the prediction of risk of harm and aggressions at St. Joseph's Healthcare Hamilton, between 2016 and 2017. An analysis of the risk factors most strongly associated with harmful incidents is, followed by the description of the process involved in the development of a predictive model which estimates the risk of harm. The efficiency of the model developed is finally evaluated, showing an overall accuracy of 75%: the specificity to identify episodes considered not at risk of harm is equal to 91.85%, whereas the sensitivity to identify episodes considered harmful is equal to 28.57%. The model proposed can be seen as a seminal project towards the development of a more comprehensive, precise and effective tool capable to predict the risk of harm in the inpatient setting.
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