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• To recognise differences in adverse incident reporting between mental health and general healthcare settings
• To appreciate the need to alter adverse incident reporting processes in mental health to consider workforce-related issues
• To acknowledge the need for nurses to feel able to include workforce-related issues in adverse incident reports
While overall incident reporting in mental health settings has increased in recent years, so too has the frequency of self-harm and aggression towards patients and staff, which continue to be leading causes of adverse incidents. However, unlike adverse incident reporting in general hospitals, which focuses on factors such as suboptimal treatment or care, adverse incidents in mental health still focus on patients and their acts of self-harm or perceived aggression. Mental health adverse incident policy needs to change to emphasise that incidents occur when staff are unable to provide appropriate care, rather than simply when a patient becomes aggressive or engages in self-harm. However, this requires a shift in the values used to monitor adverse incidents so that patients’ self-harm or aggression is regarded partly as an outcome of inappropriate or omitted care, rather than solely a result of the patient’s actions.
Mental Health Practice. doi: 10.7748/mhp.2023.e1685Peer review
This article has been subject to external double-blind peer review and checked for plagiarism using automated software
Woodnutt S (2023) Changing the focus of adverse incident reporting in mental health nursing. Mental Health Practice. doi: 10.7748/mhp.2023.e1685
Published online: 05 December 2023
In 2022, more adverse incidents were reported in mental health services in England than in previous years (NHS England 2022). Incidents of self-harm have increased by 94%, from 12,908 incidents in 2015, to 25,037 in 2022 (Woodnutt 2022), as have service-related adverse incidents, such as patients going missing during transfer or admission (NHS England 2022).
For the first time, in 2022 mental health nurses were also more prevalent in community settings than in hospitals (NHS Digital 2023). Between 2015 and 2022, the numbers of mental health nurses working in inpatient settings decreased by 12%, from 21,575 in 2015 to 19,023 in 2022. Conversely, the numbers of community mental health nurses increased by 33%, from 14,968 in 2015 to 19,863 in 2022. This poses significant challenges for today’s health workforce and policymakers, with inpatient services managing a higher concentration of adverse incidents and risk but with fewer mental health nurses available (Brimblecombe 2023).
This article aims to begin a debate about how adverse incidents in mental health settings are recorded so that nurses and researchers can better understand the link between staffing levels and safety outcomes; in particular, by monitoring causative factors, such as a lack of resources for therapeutic engagement, rather than outcomes such as adverse incidents, including patient or staff injury.
Researchers have yet to fully understand the relationship between mental health nurse numbers and adverse incidents in mental health settings. Previous studies have noted a positive correlation between higher nurse numbers and the amount of adverse incidents and/or greater restrictive practices (Bowers et al 2009, Staggs 2016, Fukasawa et al 2018). This is less surprising than might be expected because patients with greater risk profiles can often be admitted to wards with high numbers of staff (Fukasawa et al 2018). Similarly, nurses working on wards with high staff numbers are more able to initiate procedures such as enforced administration of medicines, which could result in adverse incidents (Bowers et al 2009, Bowers and Crowder 2012).
More recent studies have started to use complex statistical models to explore the relationship between staffing levels and adverse incidents. Rather than using the numbers of staffing hours per patient day compared with the volume of adverse incidents, studies by Cook et al (2020) and Feyman et al (2023) have measured the deviation between required and actual staffing numbers, or used a statistical correction to account for the increases seen in staff numbers when responding to an adverse incident. These studies have produced marginally clearer and more reliable findings; primarily that increasing the numbers of registered nurses reduces the risk of adverse incidents involving aggression (Cook et al 2020) as well as reducing the overall likelihood of self-harm (Feyman et al 2023). For example, Feyman et al (2023) noted that an increase in mental health staff led to a reduction in suicide.
There has been much less focus on community mental health nursing and safety outcomes, a generally under-researched subject due to it being an unpopular area for study and one that lacks research funding (Baker and Pryjmachuk 2016, Thibaut et al 2019). Parity of esteem between mental health and physical healthcare services is also an issue, particularly in light of the recent coronavirus disease 2019 (COVID-19) pandemic, during which mental health generally received less attention (Hannigan et al 2021).
These mixed data concerning outcomes and staffing levels in mental health settings provide a challenge for policymakers who rely on accurate data to govern their decisions. The mixed picture also contrasts with evidence in general healthcare settings, which shows that high nurse numbers equate to fewer adverse incidents and care omissions and lower mortality (Ball et al 2016, Griffiths et al 2018, 2020, Dall’Ora et al 2022).
An exploration of the literature suggests there are two major factors that influence why data on staffing levels and outcomes differ so significantly between mental health settings:
• First, mental health services practise in different ways and adopt individual incident-recording habits. For example, nurses in one mental health unit may be more prepared to administer medicines in response to adverse incidents than staff in another unit; similarly, what is recorded as an adverse incident can also differ markedly between units (Bowers et al 2004, Staggs 2016, Doedens et al 2017, 2021).
• Second, the perception of incidents, such as their severity, can differ between individual staff and at ward, hospital, provider and national levels (Staggs 2013, Kalisova et al 2014, Staggs 2015, 2016, Schlup et al 2021, Woodnutt 2022).
These factors create challenges for researchers attempting to investigate the relationship between staffing levels and outcomes and make policy implementation problematic because of the significant amount of conflicting evidence.
Much of the research into the mental health nursing workforce in recent years has been conducted with large samples, taking into consideration multiple providers. Whole-country or service-level analysis has been undertaken in a number of high-income countries – often using routinely collected data from rostering and incident reporting systems or health insurance data (Bowers et al 2012, 2013, 2015, Staggs 2013, 2015, 2016, Bak et al 2015, Fukasawa et al 2018, Park et al 2020). In general, the larger samples used in this type of research tend to lead to more reliable results, as does retrospective analysis of routinely collected data. Bias and confounders are mitigated to some extent through routine collection and there is less risk of a Hawthorne effect, where individuals change their behaviour because they are aware of being observed, or issues with reliability between the people collecting the data. However, the aggregation of data into composite variables, where different incident types such as verbal, physical and/or sexual aggression are combined despite having different causes, may disguise underlying variance or associations, thereby creating more confusion in the data and obscuring clear findings.
In the UK, mental health services lack a unified definition of risk and adverse incidents (Baker et al 2019, Phoenix 2019, Archer et al 2020, Samartzis and Talias 2020), with adverse incident reports often reflecting staff attitudes in different settings; for example, in a secure unit a substance such as glue might be regarded as a risk, despite not being regarded as such in other settings (Janssen et al 2007, Doedens et al 2020, 2021, Schlup et al 2021). This may explain why studies with large samples show either no relationship between staffing levels and adverse incidents (Kalisova et al 2014, Staggs 2016) or, when results from multiple studies are pooled and given a weighted overall effect, no clear associations due to conflicting findings.
• The focus of adverse incident reporting in mental health should be on deficits in care rather than the frequency of patient-related factors, such as aggression
• To understand the link between staffing levels and patient safety outcomes would require monitoring of causative factors rather than outcomes
• Significant organisational support is required to enable mental health nurses to feel confident in reporting they were unable to provide sufficient care in relation to adverse incidents
It is possible that adverse incidents are recorded differently at all levels of mental healthcare and there is a lack of consensus on the leading causes of adverse incidents. As an example, in one US study with a large sample, Staggs (2015) noticed different associations between assaults on staff and assaults on patients; this suggests that the categorisation of incidents is important in understanding how to reduce them (Staggs 2015, 2016). The researchers reported that when staffing levels were higher, it was more likely for staff and less likely for patients to be assaulted; but when staffing levels were lower, it was more likely for patients to be assaulted than staff (Staggs 2015, 2016). This is important because gathering all incidents of assault in relation to staffing levels might suggest there is no relationship between them. However, in reality it may be that assault rates are declining in some contexts and increasing in others and cancel each other out when added together, thus giving the appearance that there is no relationship between them.
In the NHS, incident reports are pooled and recorded centrally to provide national statistics (NHS England 2022). The leading causes of adverse incidents in mental health services are listed as ‘self-harm’ and ‘aggression’ (these are NHS reporting categories and may include patient-to-staff and patient-to-patient aggression). However, in general settings there is a marked difference in incident reporting. Here, the leading causes of adverse incidents do not involve factors related to patients’ pathologies; rather, the most common causes are listed as related to treatment, erroneous assessment or other service-related factors (NHS England 2022). This type of reporting also suggests that patient-related factors, such as myocardial infarction or respiratory disease, are ‘to be expected’ as part of the patient’s healthcare journey and not regarded as adverse incidents.
This variance in reporting of adverse incidents between these settings suggests that different reporting paradigms are applied in each (Figures 1 and 2). In general settings, adverse incidents are reported when there is an issue with the service or care provided; procedures are then monitored and an action plan is formulated to prevent reoccurrence. Conversely, in mental health settings, patient-related factors such as aggression or self-harm are labelled as adverse incidents.
For most patients, being admitted into secondary or tertiary mental health services means that they are deemed to be at high risk either to themselves or to others through self-harm, aggression or disruptive behaviour, much in the same way that cardiology patients are admitted to acute healthcare settings as they are deemed to be at increased risk of heart issues (Glick et al 2011). Mental health nurses might ask why mental health services are reporting patient-related factors such as aggression and self-harm as adverse incidents, while ignoring factors such as low staffing levels that may contribute to such incidents.
The provision of primary care mental health services has increased in recent years and the use of cognitive behavioural therapy in the NHS Talking Therapies programme (formerly known as Improving Access to Psychological Therapies) has grown to meet the needs of people with ‘common’ or less severe mental health issues, such as anxiety or mild depression (NHS England 2023).
Part of the diagnostic criteria for what is deemed a severe mental illness includes disruptive or dangerous risk factors in that person’s presentation, directed at themselves or others (American Psychiatric Association 2022). These risk factors include perceived aggression, suicide risk and self-neglect and ostensibly drive inpatient admissions as well as drawing the attention of staff. In addition, these risk factors are ‘expected’ when managing the care of people with mental health issues, much as respiratory distress is expected when caring for people with asthma in general healthcare. However, mental health incident reporting focuses on the frequency of patients’ aggression or self-harm, but not on whether the care provided was appropriate. Conversely, nurses in asthma services monitor the frequency of asthma attacks and seek to determine whether a nebuliser or other prophylactic treatment was applied in a timely manner. Similarly, nurses on an acute general ward would not simply monitor the frequency of sepsis without checking whether the patient was administered antibiotics within a specific timeframe on admission (Manias et al 2020).
The relationship between healthcare services and the potential for a minority of adverse outcomes is well-established as causal (Peer and Shabir 2018, Varley and Varma 2021). While most healthcare service outcomes are positive and a functioning health service benefits society overall, there are unintended consequences in every healthcare decision. All medicines have side effects, all medical procedures increase other risks. However, healthcare staff rely on a risk-benefit ratio, where the benefit of the treatment outweighs the risk and staff must quantify and monitor these risks. This phenomenon is known as ‘loop feedback’. Closed-loop feedback is where staff can establish causal factors that may have led to an adverse incident; open-loop feedback is where causal factors cannot be established (Gandhi et al 2005).
Open-loop feedback is not a new phenomenon in healthcare. In his 2015 book Black Box Thinking: Why Most People Never Learn From Their Mistakes – But Some Do, Matthew Syed discussed the use of bloodletting by medieval physicians (Syed 2015). Bloodletting was a clear example of open-loop feedback whereby if the patient survived, the technique was deemed to be successful; if the patient died, it was reasoned that their illness had been so severe that bloodletting could not possibly have saved them. Therefore, bloodletting was not regarded as a practice that could conceivably be harmful, only beneficial. Those who died were unable to provide evidence of its ineffectiveness and therefore the feedback loop was open. Such thinking enabled harmful practices to proliferate for hundreds of years.
In the author’s opinion, the current reporting of adverse incidents in mental health settings represents a similar example of open-loop feedback. When potential contributory factors to patients’ self-harm or aggression, such as low staffing levels, are not included in adverse incident reporting, any follow-up interventions cannot be evidence-based or contribute to future risk reduction. For example, where a patient in an acute mental health centre has become violent but the incident report fails to mention a lack of staff engagement immediately before the incident, the current response is to control the patient’s behaviour rather than arrange for more staff to be available in the future.
To make another comparison with general healthcare, hypoglycaemia or hyperglycaemia are adverse outcomes in people with diabetes mellitus that can sometimes be attributable to medication errors on the part of healthcare staff. Therefore, medication errors are crucial to the feedback loop if staff are to understand how to reduce future incidents. Consequently, prescribing in the context of diabetes is carefully monitored to the point where algorithmic approaches are used to identify medication errors (Khan et al 2013).
Treating illness as a physical phenomenon can be traced back to Hippocrates in ancient Greece and the introduction of the ‘four humors’ (black bile, yellow bile, blood and phlegm), which provided the first-recorded western divergence from earlier theories that regarded illness as supernaturally driven (Yapijakis 2009). However, in mental health this paradigm shift from a supernatural towards a physical cause of ill-health did not occur until much later and is often credited to 19th century physicians such as German psychiatrist Emil Kraepelin, who theorised that mental health conditions originated from biological and genetic malfunctions (Bentall 2003). Similarly, negative societal judgements about people’s ‘abnormal’ behaviour and mental health issues continued until the latter half of the 20th century; for example, homosexuality was classified as a paraphilia (an abnormal sexual behaviour or impulse) in early diagnostic manuals (Drescher 2010). Stigma or stereotypes from behaviours that did not fit within ‘normal’ society also influenced policies on mental health issues such as suicide, which was unlawful in the UK until the Suicide Act 1961.
In the author’s opinion, it is possible that legacy stigma associated with mental health issues is partly driving incident reporting in the sector, where there is a ‘zero tolerance’ attitude towards behaviours such as self-harm and aggression rather than an acceptance that they may result from inappropriate or omitted care.
In the author’s opinion, many adverse incidents occur when mental health nurses, for whatever reason, are unable to provide sufficient care to patients, and this should be the focus of reporting rather than outcomes such as patients’ aggression or self-harm. However, to plan mental healthcare policy around reducing incidents of self-harm and aggression, healthcare leaders would need to know the causal factors for such adverse incidents. If, for example, it could be shown that high levels of adverse incidents involving aggression occurred because staff were unable to engage therapeutically with patients, then policies could be implemented to promote these aspects of care. However, because the emphasis in national incident reporting is on the occurrence of aggression or self-harm, rather than the omitted care that may lead to these outcomes (NHS England 2022), it is challenging to identify what difference mental health nurses could make to the collection of such data.
It may be that qualitative outcomes could be used in routine incident reporting – such as asking nurses if they felt able to provide empathic care during a shift. Alternatively, nurses could be encouraged to report when they were unable to provide care for patients due to ward-related pressures or staffing issues. Some researchers have already been undertaking work in this area. For example, McKeown et al (2019) investigated the effect of staffing levels on minimising the use of restraint and where the ‘climate’ of the ward was directed by patients and staff. However, such initiatives require a shift in the attitudes of many mental health nurses, who may be unlikely to report incidents for fear of punishment (Muir-Cochrane et al 2018). The reporting of existing adverse incident categories such as aggression is already influenced by mental health nurses’ concerns that they may inadvertently represent themselves as being unable to manage risk (Gifford and Anderson 2010). Therefore, it would require significant organisational support for mental health nurses to begin to feel comfortable in reporting that they were unable to provide sufficient care.
Algorithmic approaches or large-language models that are used in machine learning or artificial intelligence (AI) to analyse health data are now reasonably prevalent in healthcare services (Allen and Woodnutt 2023, Woodnutt et al 2023). Automation of processes using AI are crucial to future health service delivery because they will provide a deeper understanding of errors, enabling staff to reduce adverse incidents. For example, AI could combine rostering, medicines administration and patients’ physical health data to flag patterns that would enable nurses to plan support packages for patients. However, for an algorithmic process to be applied to health data there needs to be a shared understanding of what is being recorded and where care deviates from this. Due to the heterogenous factors that influence the perception of adverse incidents in mental health services, meaningful data will not be provided if staff simply continue to record the frequency of such incidents.
To improve the quality and safety of care in mental health settings requires policies that emphasise openness and transparency; for example, reporting where care does not reflect best practice principles, such as those espoused by the Star Wards initiative (Janner and Delaney 2012). The Star Wards project encourages staff to structure therapeutic daily programmes for inpatients across the full range of mental health wards (www.starwards.org.uk).
It should be acknowledged that purely transactional responses towards patients may become a ‘natural’ response for mental health nurses who can develop significant feelings of depersonalisation or burnout over time (Laker et al 2019). Within the current system for reporting adverse incidents little is known about nurses’ depersonalisation or factors that diminish their capacity to provide empathic care (Laker et al 2019). Instead, mental health services have adopted a zero-tolerance approach to patients’ self-harm and aggression by focusing on recording the frequency of such adverse incidents.
There will always be variance in the frequency and nature of adverse incidents involving self-harm and aggression in mental health settings. However, the completion or omission of caring interventions by mental health nurses could be monitored more clearly if nurses were able to work in an atmosphere where they could openly report adverse incidents (Gifford and Anderson, 2010, Muir-Cochrane et al 2018). If nurses could be encouraged to be honest about the factors that influenced their care decisions, and without being reprimanded, they could then be provided with appropriate support, which in turn would increase the safety of services.
Current reporting processes in mental health services address the risks that patients pose, rather than deficits in care that may make these risks more likely to occur. To improve the quality and safety of care and allocate appropriate staffing levels, mental health leaders need to be able to understand which factors cause patient-related adverse incidents such as aggression or self-harm rather than focusing on the frequency and severity of such incidents.
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