Ongoing CRIS projects: psychosis

COVIDSMI

The COVID-19 pandemic is having a profound effect on people’s lives. Those with a diagnosis of severe mental health problems may be being disproportionately affected. There are many reports of a lack of access to mental health services and resources for people with a diagnosis of severe mental illness at this time Many of the mental health services that continue to operate during the pandemic have had to make considerable changes to the way they are able to provide mental health care, through limiting contact or reducing the amount of activity and treatment they provide. There are also greater social and economic risks for people with a diagnosis of severe mental illness, many of whom are already at high risk of social isolation and exclusion due to their mental illness. Despite this, most of the attention on the impact of COVID-19 on mental health has been on anxiety and depression in the general population.

This study aims to better understand how the COVID pandemic has changed the treatment that people with a diagnosis of severe mental illness receive, and how this might influence their health outcomes including incidents of self-harm, relapse of their illness, and death.

We aim to do this by comparing access to care and health outcomes of people with a diagnosis of severe mental illness during the pandemic in comparison to previous years. We will also look at whether factors such as age, ethnicity, socioeconomic status, the type of illness and any mental or physical comorbidity, or the type of treatment people are receiving influence these differences in access to care and health outcomes during the pandemic.

Our study aims to provide evidence that could support better application of treatments during pandemics in order to protect the health of those with a diagnosis of severe mental illness in order to reduce further health inequalities and associated economic crisis that pandemics may cause in this group of people.

A study estimating long-term rates of relapse, patterns of health outcomes, mental health service use, and moderating factors in patients with psychosis (Psychosis1).

In this study, we aim to estimate long term health outcomes & healthcare costs for psychosis patients in CRIS. Using patient characteristics, symptoms and history we will identify the key predictors of health outcomes, such as risk of psychosis relapse. We will also examine the use of mental health services by psychosis patients. The use of services will be costed and we will identify the main predictors of costs. This data will be used to build an economic model to estimate the value for money of new psychosis treatments. The resulting model will help policy makers determine which treatments to implement in the NHS. As a case study, we will apply the model to examine the long-term value of a new virtual reality therapy. The effectiveness of this therapy is currently being evaluated in a 6-month clinical trial.

End-to-end NLP and supervised learning for patient triage (Chronosig P2 Patient Triage NLP).

People needing treatment for mental health problems are often first seen by their general practitioners, in hospital Emergency Departments or in educational settings and staff then refer them to mental health services. This network of referral routes converges on community mental health teams who make decisions about suitability for treatment, how urgently to see people and which specialist teams need to be involved. In routine care, all referrals are assessed or ‘triaged’ by a multi-disciplinary team and often require the person is clinically reviewed over one or more contacts to eventually decide on treatment pathways. In 2019 this process cost the NHS £326 million with the referral and triage system often introducing delays to care and this is a common source of frustration for patients and carers.

Electronic health records contain data from clinical encounters between patients and clinicians, over multiple visits and create a trajectory of the patient’s health and outcomes over time.  However, this information ranges from voluminous or very sparse and triage is time consuming even by expert clinicians. Recent advances in artificial intelligence and natural language processing can identity patterns in a patient’s history and their referral documentation which could help make triage more efficient and expedite access to the “right team”.

Aims: We will build clinical-decision tools to support clinicians in making decisions and triaging referrals in routine clinical practice. The tools will identify and improve access to care for people who have traditionally been excluded from, under represented or poorly served by mental health services ensuring they receive equitable care.

OxMIV

Community mental health teams called ‘Early Intervention in Psychosis’ (EIP) services assess and support people with the first signs of psychosis, a group of symptoms that can affect how you feel, think or act. The significant majority of people that suffer with this type of illness never act violently. However, there are certain ‘risk factors’ that might increase the chances of this happening. One thing EIP services have to routinely assess therefore is whether a patient might, due to having some of these risk factors, have any clinical needs related to violence. Importantly this can guide what help someone might need to reduce the risk.

A simple tool called ‘OxMIV’ could support clinicians in EIP services to do this routine assessment of risk in a more structured and consistent manner. OxMIV uses information that is already routinely collected during full clinical assessments, such as about an individual’s past history including of any violent convictions, family history, what treatment they have received and whether there are factors present such as difficulties with drug or alcohol use. Although clinicians already typically gather this information when they get to know someone, having a structured tool can help them be more consistent and confident that they are covering the important information every time. Some of these factors increase risk more than others, so OxMIV also then combines the information in a similar way to how a GP might use a tool to support them thinking about somebody’s risk of having a heart attack or stroke, and whether for example someone might need some support with diet and exercise to reduce this risk. So in a similar way OxMIV can support clinicians to more consistently think about whether there are any clinical needs around reducing violence risk that they can support with. The tool is very quick and simple, and is designed to support the full clinical assessments that clinicians are already doing, without replacing this full assessment or adding to the overall workload. This project is to help look at whether some preliminary local work with clinicians using the tool (which is available alongside the usual risk assessment forms on the standard electronic health record as a form with drop-down boxes to complete the assessment) has helped with how risk is considered and documented.

NICEPsychinterventions 

A study investigating the implementation of NICE recommended psychological interventions for people with severe mental illness following a psychiatric inpatient admission

The National Institute for Health and Care Excellence (NICE) produces guidelines which outline recommendations for the treatment of different disorders, including mental illness. Severe mental illnesses include psychosis, bipolar disorder and personality disorders. Previous research has shown that people who have a diagnosis of psychosis rarely receive the psychological treatments which are recommended in the NICE guidelines. Research has also shown that you could be more likely to receive these treatments if you have particular characteristics, for example if you are from a white ethnic background. Most of the people who are admitted into psychiatric hospitals have a diagnosis which can be described as a severe mental illness. The period of time following a hospital admission can be very stressful and NICE recommend that psychological therapy is discussed as part of the discharge plan. This study aims to see how many people with a severe mental illness receive a NICE recommended therapy in the year after they have been discharged from hospital and whether this is more likely if they have a particular diagnosis or if they have certain characteristics.

Psychosis & Housing

Many people who live without a fixed address or in temporary housing have mental health difficulties. Being homeless or having unstable housing can create additional problems for people who need mental health care, as it can be harder for these people to get the treatment they need. Currently, there has not been much research on the links between specific mental health difficulties and housing problems.

As psychosis is common in people who are homeless or in temporary housing, this is an important group to research.

One factor that might contribute to housing difficulties is symptoms of psychosis, which commonly include distressing beliefs and/or hearing voices. People with these symptoms often experience feelings of being unsafe and/or distressed. The beliefs can also impact a person’s behaviour. For example, beliefs might lead to avoiding and escaping feared situations. The content of symptoms and related behaviours could be factors that contribute to people losing or leaving permanent housing.

This study therefore aims to look at whether symptoms of psychosis relate to living without a fixed address. It is hoped that this will improve understanding of people with psychosis living without a fixed address, so we can know how best to support them.

EIS Relapse

Preventing and minimising the impact of relapse is an important component of early intervention in psychosis. Up to 80% of people with first episode psychosis (FEP) will experience a psychotic relapse in the first five years following remission of their initial episode. Relapse has a negative impact on the individual’s social functioning, education, and employment opportunities. It increases the need for more intensive care in the community or hospitalisation, and is linked to poorer long-term outcomes.

Early identification of individuals who are likely to relapse would allow a more personalised approach to care, enabling early intervention services to match resources and interventions to those who would most benefit from them. However, at present, it is not possible to predict which individuals are more likely to experience a psychotic relapse following their initial episode.

The aim of this study is to develop a statistical model which estimates the risk of relapse for individual patients within two years of their initial episode of psychosis. To produce these estimates, the model will use information about routinely collected sociodemographic and clinical factors that are known to affect relapse risk from previous research. We will then test the model’s predictions on data from another early intervention service to assess how accurate they are. The model developed in this study will be translated into a prediction tool which can be used to estimate the likelihood of experiencing a relapse for patients following a first episode of psychosis.

EIS Cardio

People with psychotic disorders like schizophrenia are known to die earlier than the general population as they have an overall higher rate of cardiometabolic disease. We also know that this shortened life expectancy is down to physical health conditions (such as heart disease including heart attacks, strokes, diabetes). Early signs of a developing physical health condition are present even in young adult presenting with a first onset of psychosis. A recent study showed that young patients with a first episode of psychosis showed a death rate of 14.1% compared to 3.8% for same-aged individuals in the background population.

Long term outcomes after a first psychosis episode can vary, ranging from complete recovery, to a life-long condition including schizophrenia, bipolar disorder and schizoaffective disorder. Yet, at present it is not possible to predict who will do well or less well with any certainty. This can lead to delays in patients accessing the right care. We have shown that physical health markers – including bloods – taken at the onset of psychosis can be used to help predict a specific long term mental health condition; however, in this work we want to use extend this work to other mental health outcomes.

Further, we don’t know whether specific types of psychotic disorders show different patterns of physical health conditions and death. Knowing who is at highest risk can help us to prevent and give more care to physical health disease in people with mental disorders. For this reason in this work we will examine how quickly and how often these physical health conditions present in each category of mental disease after a first episode of psychosis.