Innovation with Artificial Intelligence and Real-world NHS Data: Opportunity for SMEs
KTN is once again offering innovative SMEs the opportunity to work with real NHS data and help solve challenges in healthcare systems.
KTN is working with partners from NHS, Academia and Industry to develop and enhance the innovation landscape around the use of Artificial Intelligence (AI) and related technologies. This type of technology has already demonstrated benefits to how both patient and clinician experience healthcare and its provision. For example, chat bots saved 1 hour of clinician’s time per service user (for emotional and suicidal ideation applications). Predictive analytics have shown 35% improvement in prediction accuracy compared to existing statistical methods or clinical scores for conditions such as cystic fibrosis and cardiovascular risks.
A typical patient in our ageing population may well present with more than one condition or disease, and many will pick new ones up over the course of their lives. A typical example could be a patient managing diabetes and mild or more severe depression. This individual may also be receiving social support either through a Local Authority and may interface with a range of professionals between Primary Care, Social Care, Community and Secondary Care settings. This obviously results in increasingly complex health and care pathways and the need for a multi-disciplinary approach to the support, care and management of patients.
The pressures on the healthcare systems mean that during clinician patient interactions these complexities need to be considered, new information transcribed to enable decision making and for that to be communicated to a patient in a short appointment time of between 5 and 10 minutes. As disease comorbidity, treatment complexity and patient expectation continue to rise, the current model is unsustainable.
The current patient journey and clinic appointment model requires disruption to embed digital technologies as experienced in almost every other aspect of our lives.
The healthcare ecosystem is evolving to electronic workflow with many areas currently using a mixed model of structured and unstructured electronic data and some information still captured on paper. Much of the latter is scanned to an electronic storage solution. These developments have improved availability of data but in some circumstance the clinicians are hampered by difficulties surfacing the most relevant clinical information from the electronic systems for a particular patient or consultation setting. Likewise, patients themselves typically have little sight of, or input to their own care plans meaning consultations are low value-add and heavy on repetitive administrative activity.
How to get involved
KTN is offering innovative SMEs with ideas in the area of Artificial Intelligence and other digital tools and solutions that might tackle this situation, the chance to get involved with and work on ‘real’ NHS data.
The information provided below will take potential SME partners to a ‘use case’ which describes the challenge and to a data description of the types and quality of data that can be provided.
Please read this USE CASE and look at the data description that is also included. Your technology/innovation might be able to support some, or all, of the processes that are required to revolutionise the experience patients and clinicians as described there.
If you feel your skills and experience fit, you will be required to apply by completing our online survey.
This short questionnaire has been designed to help us decide which SMEs/organisations can be introduced to the NHS Mersey Care and AIMES teams by way of a workshop to be hosted near Liverpool on 7th November 2019.
If you have questions about this or anything is unclear please contact David Calder.
They must have received your questionnaires by close of business 23rd September 2019. They will then be in touch to let you know the outcome and the next stages for each of you in good time to plan attendance for successful applicants in November.