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At ieso, we aim to make high-quality, evidence-based mental healthcare accessible to all. Around the world, individuals in need often have to wait too long for help, and even when it does come it may be poor quality or not be right for them. While we’ve seen tremendous progress in other areas of healthcare over the past 50 years, mental health has fallen behind. We’re determined to change that.
Just like for physical healthcare, we’ve shown learning from data can transform best practice in care. We’re using the latest scientific methods to help crack the challenge, producing digital products that are derived from scientific data analysis and clinically validated.
ieso Therapy Service captures, with permission, the exchange between a therapist and patient. Coupled with session-by-session progress measures, ieso has a globally unique, de-identified dataset that we analyse to increase the quality and effectiveness of therapy.
But historically it has been impossible to predict which elements of therapy provide specific benefits to patients. The result? A relatively inefficient, one-size-fits-all approach to mental healthcare where only approximately 50% of patients recover.
ieso’s approach allows us to overcome the traditional barrier: how can we possibly improve therapy when past conversations are lost - captured only by imperfect human memory?
ieso's Therapy Service allows patients to access treatment faster and makes therapy delivery more efficient (6.1 ieso sessions vs. the average 7.5 sessions). Accessing psychological therapy sooner is associated with better mental health outcomes) and lower healthcare costs.
We have developed AI-based tools to monitor 100% of therapy delivered. By measuring session-by-session patient progress and analysing precise elements of psychological therapy, we are pinpointing the ‘active ingredients’ of therapy to help ensure that each patient receives exactly the right care.
Often people suffering from very different things are grouped together under a broad diagnosis like “depression”. Our research has uncovered different sub-types of mental health conditions and shown how people with different types follow specific trajectories on their path to recovery. This offers new ways to personalise treatments that provide therapies that are most likely to help everyone.
We publish our research findings in peer-reviewed academic journals (e.g., The Lancet; JAMA), as well as through easier to read blogs and newsletters. We want our findings and insights to be shared to help drive progress across the whole field of mental health science research. For updates on when we publish new research papers, blogs and other resources sign up to our newsletter.
ieso commissioned this research study, using a deep learning approach, to understand the relationship between patient language and outcomes in internet-enabled cognitive behavioural therapy.
ieso uses therapy transcripts derived from internet enabled cognitive behavioural therapy (CBT) treatment sessions to understand how therapist variables are associated with clinical outcomes in IAPT
ieso uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).
ieso introduces and demonstrates the usefulness of a tool that automatically annotates therapist utterances in real-time according to the therapeutic role
In this study, ieso adopts a deep learning model to automatically categorize therapist utterances from approximately 90 000 hours of internet-enabled cognitive behavior therapy (CBT).
ieso piloted a comparative study of therapy sessions following the interaction of 10 participants with human therapists versus a chatbot to recommend ways of overcoming the drawbacks of chatbots
This study, commissioned by ieso, investigates the demographic and clinical predictors of response to one-to-one CBT delivered via the internet
This article reports on the experience of internet-enabled cognitive behavioural therapy (IECBT) for older people diagnosed with depression and anxiety.
In this multicentre, randomised controlled trial, ieso investigates the effectiveness of CBT delivered online in real time by a therapist for patients with depression in primary care
Director of Evidence Generation
Director of Clinical Science
Director of Mental Health Sciences
Director of AI Research
Principal Scientist
Group Chief Science & Strategy Officer
Chief Artificial Intelligence Officer
EVP Impact
We are now accepting a select number of customers to partner on our journey to scale clinically effective tools to support access to mental healthcare.
UK innovator closes Series B financing to develop AI-enabled, evidence-based digital therapeutics built on the world's largest mental health treatment data set.