People with psychosis often wait weeks for appointments, then struggle to recall how they felt during that time, leading to imprecise treatment. This matters because the NHS is overwhelmed by demand for mental health services, and current care is reactive rather than proactive. Patients report feeling left out of their own care planning, and the one-size-fits-all approach fails to deliver the right treatment at the right moment. Digital tools could solve this, but most promising interventions never make it from research into routine clinical practice. If this research succeeds, it will produce a personalised digital system that uses machine learning to analyse patient data in real time, then adjusts treatment recommendations accordingly. Instead of relying on memory at a monthly appointment, a patient might receive prompts or support when they actually need it. The team will also redesign care pathways and train staff to use these tools confidently, so the innovation does not stall at the clinic door. The ultimate change is a mental health service that responds to people’s fluctuating needs as they happen, rather than weeks later.
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Background: Worldwide, mental health is in crisis: we cannot accommodate the majority of those in need. Psychosis is a severe mental health problem (SMI) and a huge public health challenge with a high burden of disease. Local service structures are under immense pressure to keep up with demand. Management of people with SMI is the focus of NHS mental health providers, yet the Care Quality Commission reports patients feeling uninvolved in their care planning and a one-size-fits-all approach. Long waiting times and reactive care results in delayed, inefficient and imprecise treatment. Conventional methods of treating psychosis involve assessments which rely on patients recalling emotional states over a preceding time period at a scheduled appointment time. This results in reduced precision in treatment planning, which is problematic in a condition that requires precise, time-sensitive treatment. Digital technology offers a potential solution: we have an unprecedented opportunity to drive improvements in quality, efficiency, cost and access to care. The NHS has a clear digital agenda for addressing mental health challenges, and self-management in long-term conditions is a cornerstone of NHS policy. Many digital health interventions are being developed and show benefit, but there are few examples of successful implementation and many challenges in closing the innovation-translation gap. Aim: To improve health outcomes in psychosis by delivering the right treatment, at the right time, in the right context. We will co-produce a contextually responsive digital health intervention within re-designed digital care pathways to improve quality and efficiency of care in psychosis, delivered by a digitally confident and capable workforce. Methods: This programme will be delivered over five years with four interlinked workstreams: 1) synthesising the evidence and identifying, through qualitative methods, barriers to digital uptake; 2) building a theory that describes the underpinning mechanisms of digital health interventions, translated into good practice guidelines; 3) advancing current digital health interventions through the application of advanced statistics and machine learning to make them more personalised and tailored, and testing this in a feasibility study; 4) implementation research on translating digital health interventions into clinical practice with a focus on redesigning digital care pathways. Patient and public involvement: Ideas for this research programme were identified by patients and clinicians who have already used some of our digital interventions. People said they would like to see a more intelligent and interactive system that is tailored and personalised to patient and service needs. With support from the McPin Foundation, consultations with our advisory group will ensure our co-produced work is relevant and meaningful. Impact and dissemination: We will be recognised as a world-leading digital mental health research group with a successful programme of funded research, high-quality outputs and translational impact. Findings from this work will be shared with clinicians, patient groups, professional societies, media channels, at academic and industry conferences and clinical services. The promise of this programme of work is a critical mass of interdisciplinary digital mental health researchers developing co-produced tools that are implemented into routine practice, delivered by a confident and digitally-enabled mental health workforce.
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