Personalised monitoring and enhanced self-management in mental health (CareLoop)

Serious mental illness (SMI) such as schizophrenia affects 1% of people, with an onset usually in early adult life. A lifelong, relapsing illness then occurs in 80%. Relapses often lead to urgent inpatient stays, which are expensive and unpleasant for the sufferer. Mental health services mainly focus on SMI but are seen two have two problems: the patient or service user feels unengaged with their care, and current service configuration is slow to pick up early signs of relapse and intervene promptly to avert it, using medication change or psychological support. Supported by a current MRC grant, we have developed a software application which runs on mobile smartphones (Clintouch). It prompts the user up to several times a day to rate and record their symptoms, which takes about a minute, the data then being automatically and wirelessly uploaded to a central server. We have shown that over 80% of SMI users can operate it over a week's recording, and that the data self-recorded in this way are valid (they match the same data collected using conventional gold-standard rating scales at interview). We have also developed an sms (text message) version. Thus, the technology can monitor psychiatric symptoms in real time in the community, potentially enabling prompt clinical intervention in response to early signals of relapse or low mood, and helping patients better to manage their own problems. We wish now to use detailed, in-depth feedback from SMI patients and healthcare staff through focus groups to develop the system further so that patients are keen to use it over longer periods, and build it into the way clinical teams visualise, interpret and act upon the continual stream of clinical data. The system (CareLoop) will be comprehensive and use existing, widely available technologies based on open standards. We will look at ways of personalising data collection to suit individual patients. We will then run a clinical "feasibility" trial in 80 SMI patients over 12 weeks in a clinical team in Manchester and in London to see how well it successfully picks up early warning signs of deterioration (a "proof of concept"), and how practical and acceptable the system is for patients and professionals. This will give a better idea of the potential of the system in future to reduce relapse and rehospitalsation and design large scale evaluations of its clinical effectiveness. The system has the potential to transform community care for people with serious mental illness, with personalised self-management and relapse reduction. In the context of current policy for efficiency through innovation in the NHS, the likelihood of it being used widely in practice is high if the results show that it works.

Serious mental health problems such as schizophrenia affect 2% of people. Typically the onset is in early adult life, followed by lifelong vulnerability to relapse resulting in unscheduled inpatient admission. Care is usually delivered via secondary mental health services which aim to provide community-based support. A person's symptoms, mood and functioning will fluctuate in response to a variety of factors. This can lead to adverse outcomes including relapse, self-harm, and need for unscheduled acute care. Currently, symptom monitoring relies on clinical interview at infrequent intervals, with limited ability to detect sudden change. Supported by MRC DPFS funding (G0901434), we have developed a personalised technology (ClinTouch) that enables a person's symptom data to be captured several times daily and uploaded wirelessly to a database to create a high-resolution record of symptoms and their fluctuations. We have shown that 85% of people with psychotic problems are able to use the technology appropriately over a week and that the data collected correlate well with the gold-standard clinical interview rating scales. This has the potential to enable targeted intervention and help users better to manage their own condition and received care. The current proposal, CareLoop, is to build an end-to-end system using widely available technologies based on open standards for long-term symptom monitoring, guided by qualitative input from patient and professional users, that can be personalised and linked to clinical management algorithms. We will undertake a proof of concept feasibility trial in 2 clinical sites, where it will be used by patients and carers over periods of 3 months. The results will inform a future phase 3 effectiveness trial and the shape of collaborations with commercial partners. The system has the potential to transform community care for people with serious mental illness, and can enhance the quality of clinical and personal illness management

Beneficiaries and impact from this research will include:
Patients with SMI and their caregivers, in terms of improved prospects of recovery and enhanced patient experience and opportunities for illness self-management.
NHS and non-NHS mental health service providers, in terms of opportunites for service re-engineering afforded by Clintouch; making efficiency savings from reduced hospital admissions; meeting innovation and other quality targets; (most importantly) enhancing the quality of patient care.
NHS commissioners and policy makers, including the NHS Confederation, in terms of service redesign and delivering innovations into a healthcare sector (mental health) which is expensive and traditional in its treatment models
Pharmaceutical industry in terms of a platform for running efficient multisite trials, more robust outcome measurement, new methods for stratifying samples.
Telehealth/telecare sector, in terms of the technology and IP developed as part of the project is likely to be attractive - Robert Bosch Healthcare have expressed an interest in licensing the IP
From the viewpoint of the UK growth agenda, there is great potential impact arising from ultimate vision of Clintouch/Careloop transforming mental health services (and other areas in due course) into a real-time source of continuous treatment and outcome-relevant data for the benefit of R&D by legitimate stakeholders from academia and industry.