Idea Description
Supplementary Information
Innovation 'Elevator Pitch':
GASP online is a fully moderated, collaborative online learning community for school children. It supports the mental health prevention agenda through access to information, signposting services and encouraging engagement.

Overview of Innovation:
GASP online provides a safe online space allowing children, teachers and organisations to learn together. By setting challenges and introducing new topics, it connects children, schools and experts from all over the UK. It takes down the walls of the classroom to brings a whole new dimension to learning.

All the content is moderated by a team of educational experts, ensuring that the space is entirely safe for users. As a digital innovation, this offers the opportunity to support to schools to scale their ‘prevention agenda for mental ill health’ enabling earlier intervention(s) at relatively low cost.

The child-centred communities enable professionals to host preventative information, deliver solutions, gather opinion and hold conversations in a safe, moderated environment. This integrated, approach provides a one-stop community where any issue which may impact upon a child's mental health can be discussed and advised upon such as housing and finances.

The communities give children help and information and signpost to services. Importantly they also give children of all ages a voice and empowerment in an environment that informs, inspires and encourages aspiration to drive confidence and resilience in future generations.

It can significantly widen the reach of NHS services impacting on cost-effectiveness. GASP online (5-11) allows multiple stakeholders involved in the health and education of a child to work together in a variety of ways. The community offers opportunities to educate and inform users and provide long term preventative solutions.

They also provide healthcare professionals a permanent place to offer support and advice to teachers and support staff who often don’t know where best to turn.

As a STEM (Science, Technology, Engineering and Mathematics) learning platform focussed on aspiration GASP can also help to tackle the upcoming skills gap in the health sector.

Taking a long-term approach, the NHS can attract the talent they will require to address the future clinical skills gap by developing a careers platform, engaging with children from when they start school to when they leave.

Developing a NHS skills pathway can create real social change and empower an effective future workforce.
Stage of Development:
Market ready and adopted - Fully proven, commercially deployable, market ready and already adopted in some areas (in a different region or sector)
Similar Content1
Innovation 'Elevator Pitch':
Early identification lies at the heart of the Mental Illness Prevention strategy. PredictX’s proven machine learning model for identification of not school ready children forms the basis for wider application.
Overview of Innovation:
The “Get It Right, First Time” program is an ambitious effort to transform the mental health treatment to focus more on early diagnosis and intervention. The benefits of early intervention are clear and well documented. PredictX (PX) believe that we can the application of Machine Learning (ML) based systems improves the identification of at-risk individuals, especially children.

At Essex CC, we developed and deployed a working ML system to identify children and households where the children had a higher probability of not being school ready. As the report states, “ the most productive and cost-effective element of any prevention of mental ill-health strategy will be during a child’s early years.”  The PX school readiness model has proven to be effective in the early identification of not school ready children - beyond previous methods. The system not only identifies at-risk children for early intervention but also indicates the most impactful intervention for a locality.

The PX model not only applies to school readiness but is a flexible framework that adapts to new use cases and available data to solve other similar challenging issues. The model uses data from housing, education, social care, police, health, benefits, and population can use this data for applications beyond school readiness. For instance, early work suggests that the model will be effective predicting domestic abuse - another causal precursor of childhood mental health issues. Because such a wide range of data is accessed, the model is resilient to sparse or missing data from any particular data source.

We believe that the school readiness model is one branch among several that leads to a much more holistic and accurate identification of individuals, families, and locations at risk of mental health issues. Moreover, the model can be tuned to further identify those who would be positively impacted by intervention thus aiding the most effective resource allocation of the intervention team.

The model has the further advantage of having real-world analogues and explainability.  This means that the model will aid the experts by identifying the key causal factors most prevalent in a particular locality, thereby systematically aiding the work with Schools and Education to identify the most critical vulnerabilities and risk markers for mental illness and aiding building the most impactful preventative resilience and wellbeing practices.

Please see:
Stage of Development:
Close to market - Prototype near completion and final form may require additional validation/evaluation and all CE marking and regulatory requirements are in place
WMAHSN priorities and themes addressed: 
Mental Health: recovery, crisis and prevention / Advanced diagnostics, genomics and precision medicine / Wellness and prevention of illness / Digital health / Innovation and adoption / Person centred care
Benefit to NHS:
The Five Year Forward View for Mental Health (2016) states that “the future health of millions of children, the sustainability of the NHS, and the economic prosperity of Britain all now depend on a radical upgrade in prevention and public health.” Further it warns that the circumstances since the Wanless review published 12 years ago has become worse. “Mental health problems account for a quarter of all ill health in the UK. Despite important new developments in mental health research it receives less than 5.5 per cent of all health research funding. Latest figures suggest that £115 million is spent on mental health research each year compared with £970 million on physical health research.”  The paper also clearly articulates the importance of Innovation to tackle current and future challenges. In particular , “We see a pivotal role for digital technology in driving major changes to mental health services over the next five years”. Better prevention tools based on AI and Machine Learning is inline with this thinking.

Twelve years ago Derek Wanless’ health review warned that unless the country took prevention seriously we would be faced with a sharply rising burden of avoidable illness. That warning has not been heeded -and the NHS is on the hook for the consequences.”

One of the few silver linings to commencing this work today rather than a decade ago is that the AI technologies to deliver advanced risk identification capability is fully proven now whereas twelve years ago, the technology was nascent if it existed at all.  Therefore the ability to execute the goal of the “Get it Right, First Time” agenda is now feasible with excellent ROI.
Initial Review Rating
4.40 (2 ratings)
Benefit to WM population:
According to Mental Health In The West Midlands Combined Authority, a report commissioned by the  West Midlands Mental Health Commission,  Nearly a quarter of adults living in the WMCA are experiencing a mental health problem at any one time. The report also documents that the cost of mental health problems in the WMCA is estimated at around £12.6 billion in 2014–15, equivalent to a cost of about £3,100 per head of population.

Clearly these challenges and the significant benefits for addressing these challenges are key drivers behind the initiatives already in place in the region.

Identification of at risk individuals, especially children must go hand in hand with the infrastructure to intervene for maximum impact. The work by the West Midlands Academic Health Science Network (WMAHSN) supported by Forward Thinking Birmingham to construct the Proof of Concept program that incorporates partners across health and social care, education and policing, as well as community resources suggests that the program will have the resources and focus to execute the intervention strategies.

The effectiveness of the ML model depends on the access to data. Part of the success of the Essex School Readiness project is the buy-in of the different service areas to share their data to achieve the overall aims of enabling a prevention-based service culture.  West Midlands is ahead with forward thinking localities already sharing data across social care and health. This is a promising position from which extension of data sharing to include education, environment and other key information can develop.

From a PredictX perspective, there is already good working relationships with many of the local authorities in the region including integrated social care data - one of the key elements of the ML model.

This work has been performed in collaboration with the Midlands and Lancashire CSU. The close and strategic working relationship established over many years between PredictX and the Midlands and Lancashire CSU will help to ensure that projects are executed per agreed scope and delivered on time and on budget.
Current and planned activity: 
PredictX works with the NHS at several levels currently. We work directly with Provider Trusts such as UCLH supplying them with advanced analytic solutions. We also work with CCGs and STPs focusing on advanced analytics that need to integrate social care and health information. We also work with Local Authorities and regions such as the School Readiness system with Essex CC.

We also have a strategic partnership with the West Midlands CSU working to serve their CCG members as well as investing in research and development efforts to apply Artificial Intelligence and Machine Learning techniques to address challenges the sector faces. This partnership has recently been codified with the launch of the Midlands and Lancashire Innovation Partnership - a partnership of the CSU, private sector and academia working together to deliver advanced, AI-enabled solutions.
What is the intellectual property status of your innovation?:
IPR is 100% the property of PredictX with no conflicts. We are happy to co-create new IPR with the sector and share the innovation
Return on Investment (£ Value): 
Very high
Return on Investment (Timescale): 
1 year
Ease of scalability: 
Read more
Hide details

Created by

Share and Follow