10034136Unlocking mental health records at scale using few-shot AIClosedCollaborative R&DInnovate UK**Summary** Akrivia is using a new innovation from the field of artificial intelligence (AI) research to unlock the potential of electronic health record (EHR) data. Akrivia curates the world's largest database of psychiatric EHRs, with 4 million+ patients' deidentified data managed securely on behalf of 16 NHS healthcare organisations (HCOs). Akrivia's goal is to use this unique resource to transform mental illness and dementias research, driving treatment discovery and reducing trials costs. However, ~85% of UK psychiatric EHRs' actionable data is stored in unstructured, 'free-text' notes, which are difficult to analyse at scale and completely inaccessible to non-NHS researchers due to their level of personal information. 'Natural language processing' (NLP) can provide a solution to this inaccessibility, and Akrivia has developed an AI-based NLP system to extract new structured data on medications, symptoms, diagnoses etc. NLP models like Akrivia's can achieve high accuracy, but are traditionally limited by the need for large amounts of human-annotated training data. Creating this training data takes a long time, meaning that Akrivia's current NLP solution is not scalable enough meet the demands of their user base. In response, Akrivia has developed an alternative, prototype NLP solution using a novel 'few-shot' training method published in 2021\. This few-shot model requires very little training data to achieve high single task performance. Akrivia has used their prototype model to replace human annotators in their standard NLP development pipeline, achieving equal (or better) task accuracy with far shorter time-to-production (~4 weeks per concept versus ~6-9 months). **Vision** With this project, Akrivia will create the tools to scale their NLP development far faster than previously possible, with significantly less exposure of sensitive patient data. The toolkit will allow clinicians without technical AI expertise to develop models directly, creating a 'researcher-in-the-loop' solution to ensure Akrivia's NLP library embeds expert domain knowledge. The toolkit will also open a potential new service line in bespoke NLP solutions. Akrivia wants to provide researchers and clinicians working on mental illness and dementias with as broad and deep a dataset as possible. These diseases are complex, costly, and historically lacking in funding and treatment options. Large scale patient data with deep, broad descriptions of disease states has been critical to drug development and effective therapy provision in other areas like oncology. Through this project, Akrivia will develop the tools to make comparable data for mental illness and dementias a reality within a matter of months.