Improving risk management for violence in mental health services: a multimethods approach

Abstract

BackgroundMental health professionals increasingly carry out risk assessments to prevent future violence by their patients. However, there are problems with accuracy and these assessments do not always translate into successful risk management.ObjectivesOur aim was to improve the accuracy of assessment and identify risk factors that are causal to be targeted by clinicians to ensure good risk management. Our objectives were to investigate key risks at the population level, construct new static and dynamic instruments, test validity and construct new models of risk management using Bayesian networks.Methods and resultsWe utilised existing data sets from two national and commissioned a survey to identify risk factors at the population level. We confirmed that certain mental health factors previously thought to convey risk were important in future assessments and excluded others from subsequent parts of the study. Using a first-episode psychosis cohort, we constructed a risk assessment instrument for men and women and showed important sex differences in pathways to violence. We included a 1-year follow-up of patients discharged from medium secure services and validated a previously developed risk assessment guide, the Medium Security Recidivism Assessment Guide (MSRAG). We found that it is essential to combine ratings from static instruments such as the MSRAG with dynamic risk factors. Static levels of risk have important modifying effects on dynamic risk factors for their effects on violence and we further demonstrated this using a sample of released prisoners to construct risk assessment instruments for violence, robbery, drugs and acquisitive convictions. We constructed a preliminary instrument including dynamic risk measures and validated this in a second large data set of released prisoners. Finally, we incorporated findings from the follow-up of psychiatric patients discharged from medium secure services and two samples of released prisoners to construct Bayesian models to guide clinicians in risk management.ConclusionsRisk factors for violence identified at the population level, including paranoid delusions and anxiety disorder, should be integrated in risk assessments together with established high-risk psychiatric morbidity such as substance misuse and antisocial personality disorder. The incorporation of dynamic factors resulted in improved accuracy, especially when combined in assessments using actuarial measures to obtain levels of risk using static factors. It is important to continue developing dynamic risk and protective measures with the aim of identifying factors that are causally related to violence. Only causal factors should be targeted in violence prevention interventions. Bayesian networks show considerable promise in developing software for clinicians to identify targets for intervention in the field. The Bayesian models developed in this programme are at the prototypical stage and require further programmer development into applications for use on tablets. These should be further tested in the field and then compared with structured professional judgement in a randomised controlled trial in terms of their effectiveness in preventing future violence.FundingThe National Institute for Health Research Programme Grants for Applied Research programme.

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

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“…Further, each unobserved variable can be ranked, in terms of sensitivity (or impact), to the specified target variable as part of a sensitivity analysis. 13 For example, in cancer care, one may wish to ask what the survival probability is, conditional on observed evidence thus far, for different treatment plans (which are unobserved). This is a hypothetical question that cannot generally be answered with discriminative machine-learning regression models that do not include causal information.…”

Risk assessment and risk management of violent reoffending among prisoners

“…The need for such a system was evident by forensic medical practitioners and scientists who work in this area of research and who, over a period of several years, remained unimpressed by the decision support offered by the classical statistical, regression and rulebased systems that still dominate this area of research (Coid et al, 2014). As a result, forensic medical practitioners have identified the need to examine new ways of modelling that include the representation of causal relationships.…”

A tool to evaluate proportionality and necessity in the use of restrictive practices in forensic mental health settings: The DRILL tool (Dundrum Restriction, Intrusion and Liberty Ladders).

“…Reliable Change Index (53) was 0.56 (range 0-7) indicating that for an individual patient, a unit change was reliable. In an explanatory system consisting of fixed risk factors, stable dynamic risk factors, acute dynamic risk factors and triggers, these imminent items are acute dynamic risk factors (54,55) though they may also be regarded as causal.…”