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We describe the development and pilot testing of a novel, web-based, violence risk monitoring instrument for use in community patients with psychoses. We describe the development of the tool, including drawing on systematic reviews of the field, how item content was operationalized, the development of a user interface, and its subsequent piloting....
Context in source publication
Citations
... However, one risk factor that has not been identified in qualitative work but reported in the current study is 'recency of violence post-sentence'. Some of these clinical factors are contained in other risk assessment tools, such as FoxWeb [26] which is based around 10 modifiable factors, and has been recently validated [27]. As with FoVOx, FoxWeb is quick to complete, includes predictors that are reliably coded, and requires little training. ...
Introduction:
Risk assessment is integral to forensic psychiatry. Previous work has highlighted the benefits of using scalable and evidence-based actuarial risk tools developed within forensic populations, such as the online Forensic Psychiatry and Violence Oxford (FoVOx) violence risk assessment tool. We examined the feasibility of using FoVOx in a Swedish forensic cohort and tested whether adding modifiable (dynamic) factors would increase its useability to clinicians.
Methods:
We completed FoVOx assessments on all patients discharged from forensic psychiatric hospitals in Stockholm County, Sweden, between 2012 and 2017 and investigated recidivism rates. In addition, interviews were conducted with the clinicians responsible for each patient on the perceived accuracy, usefulness, and impact of FoVOx, which was examined using thematic analysis.
Results:
Ninety-five discharges from forensic psychiatric hospitals were followed up. The median FoVOx score was a 7% likelihood of violent reoffending in two years after discharge. Six discharged patients (6%) were confirmed as violent recidivists using official records with a similar distribution of FoVOx risk categories as the rest of the sample. FoVOx was considered accurate by clinicians in more than half of cases, who suggested that modifiable risk factors could be added to increase acceptability. All clinicians thought that FoVOx was useful, and in 20% of discharges, it would have materially altered patient care. Overall, FoVOx was thought to impact decision-making and risk management, was practical to use, and could be completed without reference to written case material.
Conclusion:
Completing FoVOx in forensic psychiatric hospitals can complement current approaches to clinical decision-making on violence risk assessment and management.
... A total dynamic score was calculated as the sum of the unweighted individual item scores. This platform had previously been shown to be easy-to-use and acceptable to clinicians in a pilot study of forensic psychiatric outpatients (Gulati et al., 2016). It provided a web-based interface for data collection and real-time graphical output in terms of bubblegrams (see online Supplementary Figs A1 and A2). ...
... Most inpatient units in high-income countries are familiar with electronic data entry and have the required resources to implement a tool such as FOxWeb, which requires internet access. The interface remained the same from a previous pilot in outpatients where it was found to be user-friendly by clinicians with minimal training (Gulati et al., 2016). These results indicate FOxWeb and similar electronic tools to be acceptable and scalable in a clinical setting. ...
Background
Violence perpetrated by psychiatric inpatients is associated with modifiable factors. Current structured approaches to assess inpatient violence risk lack predictive validity and linkage to interventions.
Methods
Adult psychiatric inpatients on forensic and general wards in three psychiatric hospitals were recruited and followed up prospectively for 6 months. Information on modifiable (dynamic) risk factors were collected every 1–4 weeks, and baseline background factors. Data were transferred to a web-based monitoring system (FOxWeb) to calculate a total dynamic risk score. Outcomes were extracted from an incident-reporting system recording aggression and interpersonal violence. The association between total dynamic score and violent incidents was assessed by multilevel logistic regression and compared with dynamic score excluded.
Results
We recruited 89 patients and conducted 624 separate assessments (median 5/patient). Mean age was 39 ( s.d. 12.5) years with 20% ( n = 18) female. Common diagnoses were schizophrenia-spectrum disorders (70%, n = 62) and personality disorders (20%, n = 18). There were 93 violent incidents. Factors contributing to violence risk were a total dynamic score of ⩾1 (OR 3.39, 95% CI 1.25–9.20), 10-year increase in age (OR 0.67, 0.47–0.96), and female sex (OR 2.78, 1.04–7.40). Non-significant associations with schizophrenia-spectrum disorder were found (OR 0.50, 0.20–1.21). In a fixed-effect model using all covariates, AUC was 0.77 (0.72–0.82) and 0.75 (0.70–0.80) when the dynamic score was excluded.
Conclusions
In predicting violence risk in individuals with psychiatric disorders, modifiable factors added little incremental value beyond static ones in a psychiatric inpatient setting. Future work should make a clear distinction between risk factors that assist in prediction and those linked to needs.
... In several articles, we observed that patients with schizophrenia are peer trained for apps when they are still in the hospital (Bucci et al., 2018;Forchuk et al., 2015;Verhagen et al., 2017). In contrast, one study recruited participants via the Internet without providing any assistance (Gulati et al., 2016). These approaches are hardly comparable since if people are contacted remotely, they may be less likely to report adverse effects. ...
While the implementation of digital technology in psychiatry appears promising, there is an urgent need to address the implications of the absence of ethical design in the early development of such technologies. Some authors have noted the gap between technology development and ethical analysis and have called for an upstream examination of the ethical issues raised by digital technologies. In this paper, we address this suggestion, particularly in relation to digital healthcare technologies for patients with schizophrenia spectrum disorders. The introduction of digital technologies in psychiatry offers a broad spectrum of diagnostic and treatment options tailored to the health needs and goals of patients’ care. These technologies include wearable devices, smartphone applications for high-immersive virtual realities, smart homes, telepsychiatry and messaging systems for patients in rural areas. The availability of these technologies could increase access to mental health services and improve the diagnostics of mental disorders.
Additional Instruction Abstract
In this descriptive review, we systematize ethical concerns about digital technologies for mental health with a particular focus on individuals suffering from schizophrenia. There are many unsolved dilemmas and conflicts of interest in the implementation of these technologies, such as (1) the lack of evidence on efficacy and impact on self-perception; (2) the lack of clear standards for the safety of their daily implementation; (3) unclear roles of technology and a shift in the responsibilities of all parties; (4) no guarantee of data confidentiality; and (5) the lack of a user-centered design that meets the particular needs of patients with schizophrenia. mHealth can improve care in psychiatry and make mental healthcare services more efficient and personalized while destigmatizing mental health disorders. To ensure that these technologies will benefit people with mental health disorders, we need to heighten sensitivity to ethical issues among mental healthcare specialists, health policy makers, software developers, patients themselves and their proxies. Additionally, we need to develop frameworks for furthering sustainable development in the digital technologies industry and for the responsible usage of such technologies for patients with schizophrenia in the clinical setting. We suggest that digital technology in psychiatry, particularly for schizophrenia and other serious mental health disorders, should be integrated into treatment with professional supervision rather than as a self-treatment tool.
... Greer et al. (2019) formed focus groups with staff members and demonstrated that there was interest in the use of remote technology for monitoring aggression (Greer et al., 2019). Gulati et al. (2016) created a web based risk monitoring app for use in a community forensic setting (Gulati et al., 2016). They developed a novel risk assessment tool and collected qualitative feedback from the professionals who were involved: they reported 'preliminary evidence that it is feasible and user-friendly' and recommended that future studies investigate predictive validity. ...
... Greer et al. (2019) formed focus groups with staff members and demonstrated that there was interest in the use of remote technology for monitoring aggression (Greer et al., 2019). Gulati et al. (2016) created a web based risk monitoring app for use in a community forensic setting (Gulati et al., 2016). They developed a novel risk assessment tool and collected qualitative feedback from the professionals who were involved: they reported 'preliminary evidence that it is feasible and user-friendly' and recommended that future studies investigate predictive validity. ...
Consumer-focused healthcare mobile applications have seen widespread adoption in recent years. Enterprise mobile applications in hospital settings have been slower to gain traction. In this study we examine the Dynamic Appraisal of Situational Aggression: Inpatient version (DASA), a short-term risk assessment tool which is well validated and widely used in the prediction of violent incidents, within an inpatient forensic setting. The application was piloted over a period of three months, collecting 847 total DASA scores on 21 different patients. Time stamping allowed for accurate correlation between risk assessment scoring and the violent risk incidents. The internal validity of the app was measured using Cronbach’s alpha and was calculated at 0.798 indicating good internal validity. Using violent incidents as the dependent factor and the total DASA score as the independent factor, predictive validity of the app was calculated at 0.85, p=0.007. The use of this application in a forensic setting was successful with good internal and predictive validity. A major benefit of this form of data collection was the electronic time stamping so that the correlation between risk estimation and events could be more closely correlated. Deployment of such an application in a general hospital setting would bring its own challenges but would be useful in other types of risk assessment and screening tools.
... Such factors will also change dynamically over time and depend on the other matters, such as level of insight and the level of treatment compulsion based on their legal status. One solution may be to combine FoVOx scoring with other dynamic risk assessment tools [23] that provide serial monitoring of risk. ...
Background
Violence risk assessment is a routine part of clinical services in mental health, and in particular secure psychiatric hospitals. The use of prediction models and risk tools can assist clinical decision-making on risk management, including decisions about further assessments, referral, hospitalization and treatment. In recent years, scalable evidence-based tools, such as Forensic Psychiatry and Violent Oxford (FoVOx), have been developed and validated for patients with mental illness. However, their acceptability and utility in clinical settings is not known. Therefore, we conducted a clinical impact study in multiple institutions that provided specialist mental health service.
Methods
We followed a two-step mixed-methods design. In phase one, we examined baseline risk factors on 330 psychiatric patients from seven forensic psychiatric institutes in China. In phase two, we conducted semi-structured interviews with 11 clinicians regarding violence risk assessment from ten mental health centres. We compared the FoVOx score on each admission (n = 110) to unstructured clinical risk assessment and used a thematic analysis to assess clinician views on the accuracy and utility of this tool.
Results
The median estimated probability of violent reoffending (FoVOx score) within 1 year was 7% (range 1–40%). There was fair agreement (72/99, 73% agreement) on the risk categories between FoVOx and clinicians’ assessment on risk categories, and moderate agreement (10/12, 83% agreement) when examining low and high risk categories. In a majority of cases (56/101, 55%), clinicians thought the FoVOx score was an accurate representation of the violent risk of an individual patient. Clinicians suggested some additional clinical, social and criminal risk factors should be considered during any comprehensive assessment. In addition, FoVOx was considered to be helpful in assisting clinical decision-making and individual risk assessment. Ten out of 11 clinicians reported that FoVOx was easy to use, eight out of 11 was practical, and all clinicians would consider using it in the future.
Conclusions
Clinicians found that violence risk assessment could be improved by using a simple, scalable tool, and that FoVOx was feasible and practical to use.
... While passive remote monitoring technology has the potential to support risk assessments for aggression [10][11][12][13], to our knowledge, the views of frontline staff have not been investigated. Numerous implementation barriers for novel digital health care systems often result in low rates of adoption and adherence [14]. ...
Background:
Monitoring risk of imminent aggression in inpatient forensic mental health services could be supported by passive remote monitoring technology, but staff attitudes toward the relevance and likelihood of engagement with this technology are unknown.
Objective:
This study aimed to explore staff views, specifically potential benefits and implementation barriers, on using this technology for monitoring risk of inpatient aggression.
Methods:
We conducted semistructured focus groups with nurses in an inpatient forensic mental health service. We used thematic analysis with two independent raters to identify themes and subthemes related to staff attitudes toward passive remote monitoring. We subsequently checked with members to ensure the validity of the themes identified by the raters.
Results:
From January to March 2019, a total of 25 nurses took part in five focus groups. We identified five main themes, one of which concerned the potential benefits that passive remote monitoring could provide for monitoring risk of aggression. Staff suggested it could provide an early warning of impending aggression and enable support to be provided earlier. The remaining themes concerned implementation barriers, including risks to the users' physical and mental well-being; data security concerns and potential access by third parties; the negative impact of a constant stream of real-time data on staff workload; and design characteristics and user awareness of the benefits of passive remote monitoring.
Conclusions:
Passive remote monitoring technology could support existing methods of monitoring inpatient aggression risk, but multiple barriers to implementation exist. Empirical research is required to investigate whether these potential benefits can be realized, and to identify ways of addressing these barriers to ensure acceptability and user engagement.
... The True Colours system has also been modified for community outpatients, with a diagnosis of psychosis using forensic psychiatric services (FOXWEB risk violence tool). This research application involved the development of a Web-based violence risk monitoring tool for psychosis, which provides visual feedback of patient scores to clinicians to guide risk assessment [41], and this is being further piloted in inpatients. ...
The True Colours remote mood monitoring system was developed over a decade ago by researchers, psychiatrists, and software engineers at the University of Oxford to allow patients to report on a range of symptoms via text messages, Web interfaces, or mobile phone apps. The system has evolved to encompass a wide range of measures, including psychiatric symptoms, quality of life, and medication. Patients are prompted to provide data according to an agreed personal schedule: weekly, daily, or at specific times during the day. The system has been applied across a number of different populations, for the reporting of mood, anxiety, substance use, eating and personality disorders, psychosis, self-harm, and inflammatory bowel disease, and it has shown good compliance. Over the past decade, there have been over 36,000 registered True Colours patients and participants in the United Kingdom, with more than 20 deployments of the system supporting clinical service and research delivery. The system has been adopted for routine clinical care in mental health services, supporting more than 3000 adult patients in secondary care, and 27,263 adolescent patients are currently registered within Oxfordshire and Buckinghamshire. The system has also proven to be an invaluable scientific resource as a platform for research into mood instability and as an electronic outcome measure in randomized controlled trials. This paper aimed to report on the existing applications of the system, setting out lessons learned, and to discuss the implications for tailored symptom monitoring, as well as the barriers to implementation at a larger scale.
... Another limitation is that the tool provides a cross-sectional assessment at one time point (on release from prison), and therefore cannot be used to monitor risk in the community. Tools with more dynamic factors, where changes in risk scores can improve prediction, should be considered for risk monitoring 23 . The risk categories used in this study may not be suitable in other countries or criminal justice populations, and each new validation should consider using categories aligned to expected reoffending rates. ...
Scalable and transparent methods for risk assessment are increasingly required in criminal justice to inform decisions about sentencing, release, parole, and probation. However, few such approaches exist and their validation in external settings is typically lacking. A total national sample of all offenders (9072 released from prisoners and 6329 individuals on probation) from 2011–2012 in the Netherlands were followed up for violent and any reoffending over 2 years. The sample was mostly male (n = 574 [6%] were female prisoners and n = 784 [12%] were female probationers), and median ages were 30 in the prison sample and 34 in those on probation. Predictors for a scalable risk assessment tool (OxRec) were extracted from a routinely collected dataset used by criminal justice agencies, and outcomes from official criminal registers. OxRec’s predictive performance in terms of discrimination and calibration was tested. Reoffending rates in the Dutch prisoner cohort were 16% for 2-year violent reoffending and 44% for 2-year any reoffending, with lower rates in the probation sample. Discrimination as measured by the c-index was moderate, at 0.68 (95% CI: 0.66–0.70) for 2-year violent reoffending in prisoners and between 0.65 and 0.68 for other outcomes and the probation sample. The model required recalibration, after which calibration performance was adequate (e.g. calibration in the large was 1.0 for all scenarios). A recalibrated model for OxRec can be used in the Netherlands for individuals released from prison and individuals on probation to stratify their risk of future violent and any reoffending. The approach that we outline can be considered for external validations of criminal justice and clinical risk models.
... As the two imminent tools in this study rely predominantly on dynamic variables, research could investigate the role of novel dynamic variables to improve risk prediction, and whether adding static variables can add incremental performance. Further to this, new technologies that have been developed for the use of risk prediction and monitoring should be examined [49]. From a methodological perspective, future work in this area should report multiple estimates of predictive accuracy in order to provide a more complete picture of an instrument's performance, including measures of calibration. ...
Background and aims:
Violent behaviour by forensic psychiatric inpatients is common. We aimed to systematically review the performance of structured risk assessment tools for violence in these settings.
Methods:
The nine most commonly used violence risk assessment instruments used in psychiatric hospitals were examined. A systematic search of five databases (CINAHL, Embase, Global Health, PsycINFO and PubMed) was conducted to identify studies examining the predictive accuracy of these tools in forensic psychiatric inpatient settings. Risk assessment instruments were separated into those designed for imminent (within 24 hours) violence prediction and those designed for longer-term prediction. A range of accuracy measures and descriptive variables were extracted. A quality assessment was performed for each eligible study using the QUADAS-2. Summary performance measures (sensitivity, specificity, positive and negative predictive values, diagnostic odds ratio, and area under the curve value) and HSROC curves were produced. In addition, meta-regression analyses investigated study and sample effects on tool performance.
Results:
Fifty-two eligible publications were identified, of which 43 provided information on tool accuracy in the form of AUC statistics. These provided data on 78 individual samples, with information on 6,840 patients. Of these, 35 samples (3,306 patients from 19 publications) provided data on all performance measures. The median AUC value for the wider group of 78 samples was higher for imminent tools (AUC 0.83; IQR: 0.71-0.85) compared with longer-term tools (AUC 0.68; IQR: 0.62-0.75). Other performance measures indicated variable accuracy for imminent and longer-term tools. Meta-regression indicated that no study or sample-related characteristics were associated with between-study differences in AUCs.
Interpretation:
The performance of current tools in predicting risk of violence beyond the first few days is variable, and the selection of which tool to use in clinical practice should consider accuracy estimates. For more imminent violence, however, there is evidence in support of brief scalable assessment tools.
... Relative risks of violence in psychiatric patients are high compared to the general population, with odds ratios of around seven in patients with schizophrenia-spectrum disorders (Fazel, Wolf, Palm, & Lichtenstein, 2014), and five in patients with bipolar disorder (Webb, Lichtenstein, Larsson, Geddes, & Fazel, 2014). Although rates of reoffending following discharge from secure hospitals may compare favourably to certain comparative groups including prisoners of similar age and gender, they remain high and range from 273 to 8403 per 100 000 person-years (Gulati et al., 2016). In Sweden, 40% of forensic psychiatric patients violently offend after their first discharge over a mean follow-up of 9.4 years (Fazel, Wolf, Fimińska, & Larsson, 2016). ...
... Research in general psychiatric populations requires larger sample sizes and longer follow-ups to detect differences in police or hospital recorded violence compared to aggression scales as it is a rarer outcome. To overcome this, future research should consider novel designs involving linkage of randomized intervention data and register-based data (Lauer & D'Agostino, 2013), and improve identification of high-risk groups through validated risk tools and monitoring of dynamic risk (Gulati et al., 2016). Furthermore, risk tools can be used to create enriched samples for intervention research by including only high risk groups. ...
Relative risks of violence in psychiatric patients are high compared to the general population and existing evidence in non-psychiatric populations may not translate to reductions in violence in psychiatric populations. We searched 10 databases including Medline, EMBASE, CINAHL and Scopus, from inception until August 2015 for systematic reviews and meta-analyses of violence prevention interventions in psychiatry. Reviews were included if they used a hard outcome measure (i.e. police or hospital recorded violence, or reincarceration) and contained randomized or non-randomized controlled studies. Five reviews met our inclusion criteria (n = 8876 patients in total), of which four received a GRADE rating of ‘low’ or ‘very low’. Three randomized studies (n = 636) reported that therapeutic community interventions may reduce reincarceration in drug-using offenders with co-occurring mental illness (‘moderate’ GRADE rating). The lack of intervention research in violence prevention in general and forensic psychiatry suggests that interventions from non-psychiatric populations may need to be relied upon.
