Predictors in use of mental health resources: The role of behaviour problems in patients with severe mental illness

Survival of patients with severe mental disorders: Influence of social functioning

BACKGROUND

Patients with severe mental disorders have a high risk of premature death due to the interaction of various factors. Social functioning is a strategic functional factor in understanding the course of psychotic disorders.

AIM

Analyze the relationship between social functioning and its various dimensions and survival during a 10-year follow-up.

METHOD

The Social Functioning Scale (SFS) was administered to 163 close relatives of patients under treatment at a Community Mental Health Unit. Survival was described by Kaplan-Meier analysis and any differences in survival by level of social functioning were found by long-rank analysis. Finally, Cox regression was used to predict premature mortality.

RESULTS

Significant differences in mortality were identified in the interpersonal behavior dimension of social functioning, while there were no significant gender or diagnostic differences in the rest of the dimensions. The interpersonal behavior dimension and age were found to be factors predicting premature death.

CONCLUSION

These findings show the protective effect of social functioning retained by patients with psychotic disorders on their survival, and the need to apply evidence-based psychotherapy focused on recovery of social functioning in the early stages of the disorder.

Ten-year follow-up of social functioning and behaviour problems in people with schizophrenia and related disorders

BACKGROUND

In recent years, several variables in the course of schizophrenia and related psychotic disorders have been studied. However, an instrumental analysis of the evolution of social functioning and behaviour problems has scarcely been explored.

AIM

To analyse the evolution of social functioning and behaviour problems and find any diagnosis or gender differences.

METHOD

The Social Functioning Scale (SFS) and the Behaviour Problems Inventory (BPI) were administered in Stages I (2003-2007) and II (2014-2017) to 100 close relatives of patients under treatment at a Community Mental Health Unit. A related samples t-test, analysis of variance and multivariate analysis of variance were performed to study the evolution and differences in social functioning and behaviour problems. Then a stepwise multiple linear regression analysis was done to predict the evolution of social functioning.

RESULTS

No deterioration in the evolution of social functioning or behaviour problems was observed, and schizophrenia patient scores were lower. Women scored higher in withdrawal/social engagement, interpersonal behaviour, independence-performance, independence-competence and total social functioning, with no significant differences in behaviour problems. Previous social functioning, underactivity/social withdrawal and education are predictive factors in the evolution of social functioning. Conclusion: The results show the need for implementing psychosocial intervention programs that promote functional recovery and keep problems from becoming chronic.

AI in patient flow: applications of artificial intelligence to improve patient flow in NHS acute mental health inpatient units

Introduction

Growing demand for mental health services, coupled with funding and resource limitations, creates an opportunity for novel technological solutions including artificial intelligence (AI). This study aims to identify issues in patient flow on mental health units and align them with potential AI solutions, ultimately devising a model for their integration at service level.

Method

Following a narrative literature review and pilot interview, 20 semi-structured interviews were conducted with AI and mental health experts. Thematic analysis was then used to analyse and synthesise gathered data and construct an enhanced model.

Results

Predictive variables for length-of-stay and readmission rate are not consistent in the literature. There are, however, common themes in patient flow issues. An analysis identified several potential areas for AI-enhanced patient flow. Firstly, AI could improve patient flow by streamlining administrative tasks and optimising allocation of resources. Secondly, real-time data analytics systems could support clinician decision-making in triage, discharge, diagnosis and treatment stages. Finally, longer-term, development of solutions such as digital phenotyping could help transform mental health care to a more preventative, personalised model.

Conclusions

Recommendations were formulated for NHS trusts open to adopting AI patient flow enhancements. Although AI offers many promising use-cases, greater collaborative investment and infrastructure are needed to deliver clinically validated improvements. Concerns around data-use, regulation and transparency remain, and hospitals must continue to balance guidelines with stakeholder priorities. Further research is needed to connect existing case studies and develop a framework for their evaluation.

Understanding social withdrawal in euthymic bipolar patients: The role of stigma

Social withdrawal acts as a risk factor in mental health, disturbing clinical management and quality of life in euthymic bipolar patients. However, no previous study has analyzed what variables might predict it. We conducted a cross-sectional study in which 49 euthymic bipolar patients were assessed. The analysis showed that taken together, stereotype endorsement, discrimination experience and control over illness as measured by the ISMI, together explained 80.4% of the variability in social withdrawal. In conclusion, an early assessment of self-stigma and perception of control over illness would help euthymic patients to improve their social situation, reducing social withdrawal.

Behavior Problems Inventory (BPI): Psychometric characteristics of an instrument for routine assessment of persons with psychoses and related disorders

This study explores the psychometric characteristics of the Behavior Problems Inventory (BPI), an instrument for routine clinical assessment of behavior problems in outpatients with psychosis based on information provided by key family informants. Six hundred and twenty-one patients diagnosed with psychosis and bipolar affective disorder (ICD-10 F20-F31) attended at Community Mental Health Units were evaluated in routine reviews using the BPI and the Social Functioning Scale (SFS). Twenty-five subjects were simultaneously administered the Social Behavior Schedule (SBS) and 28 were again administered the BPI eight weeks later. The instrument shows adequate psychometric characteristics with high internal consistency and robust temporal reliability, as well as satisfactory concurrent and construct validity. Factor analysis identified three factors: Underactivity/Social Withdrawal, Active Problems and Lack of Impulse Control, with adequate saturation of the items on each of the factors. The BPI is easy to apply, reliable and valid, takes up little of valuable clinical time, allowing routine assessment in public service contexts for persons diagnosed with psychosis and bipolar affective disorder for whom key family informants are available.