Ongoing CRIS projects: psychosis

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.

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.

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.

Organic Psychosis

Psychosis refers to a set of symptoms that may include hearing voices (auditory hallucinations) or believing things that are not true (delusions), which can be highly distressing and impairing for a patient. Psychosis usually occurs due to an underlying mental health condition, but in around one in seven cases, it is the result of a secondary physical cause, such as a neurological condition. Checking whether patients have a secondary cause as early as possible is very important as they may require different treatments to make a recovery.

If it were possible to identify patients who are likely to have a secondary cause based on their psychiatric symptoms, this could help doctors make sure these patients receive the necessary investigations quickly to confirm the diagnosis and offer the right treatment. Research suggests there is no single symptom that can always identify if a patient has a secondary cause. However, it may be possible to combine multiple symptoms to identify patients likely to have a secondary cause with a high degree of accuracy. This could eventually be used to create a tool that helps doctors make decisions around diagnosis.

Using a very large group of patients who have received a diagnosis of psychosis, I will identify what psychiatric symptoms each patient had based on their anonymized electronic medical records and use artificial intelligence (AI) to develop a tool to predict whether their psychosis was likely due to a mental health condition, or a physical cause.

I will then seek to validate this prediction model on an independent dataset.

Risk Calculator Replication

Psychosis is a difficult mental health disorder to experience and it is preventable. Prevention of psychosis requires us to detect people at risk before they develop the disorder but currently the majority of people go undetected. To improve this, we developed a simple risk calculator for psychosis using electronic health record data in South London & Maudsley NHS Foundation Trust. This model performs well in South London & Maudsley NHS Foundation Trust, Camden & Islington NHS Foundation Trust and in Oxford Health NHS Foundation Trust and has shown to be feasible to be used in real-world clinical practice to improve care.
This risk calculator has since been improved by adding in 14 different symptom and substance use measures that can be automatically extracted from patient notes and letters. However, the performance of this new risk calculator has yet to be tested in Oxford Health NHS Foundation Trust, which we want to do in this study.