Analysis of England's incident and mental health nursing workforce data 2015-2022.

WHAT IS KNOWN ON THE SUBJECT?: Mental health services report adverse incidents in different ways and the relationship between adverse incidents and the workforce is uncertain. In England, there are national datasets recording all incidents and workforce statistics though there is no peer-reviewed evidence examining recent trends. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Although there has been an overall increase in the number of mental health nurses, more are working in the community and the number of nurses relative to adverse incidents has decreased. There have been service-provision changes but the role of mental health nurses has not significantly changed in this period, and we can therefore assume that their current practice is saturated with risk or increased reporting. To help understand the relationship between nurses and incidents, we need to transform how incidents are recorded in England. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: English mental health services report greater levels of patient-related factors such as self-harm or aggression rather than missed or erroneous care. This makes it difficult to understand if a rise in incident frequency is linked to reporting behaviour, patient risk, unsafe/ineffective care or other reasons and therefore planning workforce deployment to improve care quality is problematic. ABSTRACT: INTRODUCTION: There is a paucity of empirical data examining incidents and mental health nurses and the relationship between the two remains uncertain. AIM: Comparison of English national data for incidents and nursing workforce to examine recent trends. METHOD: Descriptive analysis of two national datasets of incidents and workforce data for England between 2015 and 2022. RESULTS: A 46% increase in incidents was found; the leading causes are self-harm and aggressive behaviour. Despite the rise in adverse incident reporting, a 6% increase in mental health nurses was found, with more nurses in community settings than hospitals. DISCUSSION: Current services are incident reporting at greater concentrations than in previous years. Patient-related behaviour continues to be most prominently reported, rather than possible antecedent health services issues that may contribute to reporting. Whilst staffing has increased, this does not seem to have kept pace with the implied workload evident in the increase in incident reports. IMPLICATIONS FOR PRACTICE: Greater emphasis should be placed on health service behaviour in reporting mechanisms. Self-harm and aggression should continue to be considered adverse outcomes, but causal health service factors, such as missed care, should be present in pooled reporting to help reduce the occurrence of adverse outcomes.

Could artificial intelligence write mental health nursing care plans?

WHAT IS KNOWN ON THE SUBJECT?: Artificial intelligence (AI) is freely available, responds to very basic text input (such as a question) and can now create a wide range of outputs, communicating in many languages or art forms. AI platforms like OpenAI's ChatGPT can now create passages of text that could be used to create plans of care for people with mental health needs. As such, AI output can be difficult to distinguish from human-output, and there is a risk that its use could go unnoticed. WHAT THIS PAPER ADDS TO EXISTING KNOWLEDGE?: Whilst it is known that AI can produce text or pass pre-registration health-profession exams, it is not known if AI can produce meaningful results for care delivery. We asked ChatGPT basic questions about a fictitious person who presents with self-harm and then evaluated the quality of the output. We found that the output could look reasonable to laypersons but there were significant errors and ethical issues. There are potential harms to people in care if AI is used without an expert correcting or removing these errors. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: We suggest that there is a risk that AI use could cause harm if it was used in direct care delivery. There is a lack of policy and research to safeguard people receiving care - and this needs to be in place before AI should be used in this way. Key aspects of the role of a mental health nurse are relational and AI use may diminish mental health nurses' ability to provide safe care in its current form. Many aspects of mental health recovery are linked to relationships and social engagement, however AI is not able to provide this and may push the people who are in most need of help further away from services that assist recovery. ABSTRACT: Background Artificial intelligence (AI) is being increasingly used and discussed in care contexts. ChatGPT has gained significant attention in popular and scientific literature although how ChatGPT can be used in care-delivery is not yet known. Aims To use artificial intelligence (ChatGPT) to create a mental health nursing care plan and evaluate the quality of the output against the authors' clinical experience and existing guidance. Materials & Methods Basic text commands were input into ChatGPT about a fictitious person called 'Emily' who presents with self-injurious behaviour. The output from ChatGPT was then evaluated against the authors' clinical experience and current (national) care guidance. Results ChatGPT was able to provide a care plan that incorporated some principles of dialectical behaviour therapy, but the output had significant errors and limitations and thus there is a reasonable likelihood of harm if used in this way. Discussion AI use is increasing in direct-care contexts through the use of chatbots or other means. However, AI can inhibit clinician to care-recipient engagement, 'recycle' existing stigma, and introduce error, which may thus diminish the ability for care to uphold personhood and therefore lead to significant avoidable harms. Conclusion Use of AI in this context should be avoided until a point where policy and guidance can safeguard the wellbeing of care recipients and the sophistication of AI output has increased. Given ChatGPT's ability to provide superficially reasonable outputs there is a risk that errors may go unnoticed and thus increase the likelihood of patient harms. Further research evaluating AI output is needed to consider how AI may be used safely in care delivery.

Psychologically informed care of patients with anorexia nervosa on an acute medical ward.

Anorexia nervosa is a challenging and highly distressing illness associated with significant and often debilitating symptoms that affect the person's physical and mental well-being, as well as their wider social networks. Although some patients can make important steps in their recovery in the community, many will become significantly unwell and require medical stabilisation and refeeding in an acute medical ward as a result of significant weight loss. This article describes some of the challenges experienced by adult nurses when caring for patients with anorexia nervosa on acute medical wards and explores how the patient's distress may manifest and complicate the recovery process. The article also discusses the principles of psychologically informed care and therapeutic interactions that nurses can use to promote recovery and ensure optimal practice.

Evidence of effectiveness of specialist supportive clinical management for anorexia nervosa in routine clinical practice: Outcomes from a clinical case series.

OBJECTIVE: This study provides a preliminary report on the effectiveness of Specialist Supportive Clinical Management (SSCM) in a clinical case series of adults with anorexia nervosa, to supplement evidence of efficacy from controlled trials. METHOD: Body mass index (BMI), eating disorder symptoms, mood and anxiety were measured at the start and end of treatment for 42 adults who received SSCM in a community eating disorders service. RESULTS: Significant improvements were observed on all outcome measures, with larger effect sizes for symptom change than BMI. Recovery rates appear similar to those in clinical trials. DISCUSSION: The study offers preliminary support for the effectiveness of SSCM in routine settings and identifies several areas for further research.