Unknown Mental Health NIHR-supported project Psychology & Behaviour

Exploring functional outcomes, mental health severity and risk of physical illness among patients with psychosis

In plain English

AI plain-English summary

A patient’s NHS health records could reveal which combination of treatments actually helps them recover from psychosis, rather than leaving clinicians to guess. Psychosis distorts a person’s grip on reality—they may hear voices or hold beliefs that others do not share. The condition makes everyday life profoundly difficult, yet doctors lack clear evidence about which treatments or treatment combinations work best for which patients. This project mines real-world health records to find those answers, filling a gap between clinical guidelines and the messy reality of individual patient outcomes. If successful, the research could transform how mental health services personalise care. Instead of a one-size-fits-all approach, clinicians might draw on data-driven evidence to match therapies—medication, talking therapies, social support—to a patient’s specific symptom profile and risk of physical illness. That could reduce hospital admissions, improve daily functioning, and lower the long-term burden of chronic disease that often accompanies severe mental illness. The change would be invisible to most people, but for the hundreds of thousands living with psychosis in the UK, it could mean a treatment plan that actually fits their life.

View original technical description
Psychosis is a condition where a person has trouble knowing what is real, often experiencing unusual thoughts, visions, or sounds. People living with psychosis face many difficulties in everyday life. This project aims to better understand the issues people face when they have this mental health condition, this will help us come up with better ways to help them by finding evidence in health records for which treatments or combinations of different treatments work best.

Researchers

Ricardo Twumasi (Principal Investigator)

Related Research

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Original classification

Informatics

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.