The Need for AI in Mental Health
Mental health conditions, such as anxiety and depression, affect one in six people in the UK at any given time, costing the economy between £70 billion and £100 billion per year. Despite the scale of the problem, many individuals face long delays in receiving a diagnosis and effective treatment. Recent research by the British Medical Association (BMA) has highlighted critical challenges in the mental health system, including staff shortages, lack of resources, and gaps in training. AI has the potential to address these issues by making sense of vast amounts of data, improving efficiency, and identifying individuals at risk before a crisis occurs.
How AVERT Works
Electronic Health Records (EHRs) contain a wealth of patient information but are often underutilised due to their complexity and inconsistency. AVERT applies machine learning algorithms to process and analyse EHR data, uncovering patterns that can help clinicians prioritise caseloads and intervene earlier, enabling more proactive and personalised mental healthcare.
Key Outcomes and Next Steps
Beyond proving the technical feasibility of AI in mental health prediction, AVERT has successfully demonstrated how Liverpool’s healthcare resources can be integrated to tackle mental health challenges. Looking ahead, the team plans to:
- Scale the system by incorporating a larger and more up-to-date dataset.
- Engage with healthcare professionals at Merseycare to refine and enhance the AI model.
- Improve usability, performance, and reliability to ensure effective real-world application.
AVERT represents a significant step forward in leveraging AI for mental health support—paving the way for faster diagnoses, better interventions, and improved patient outcomes.
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