AdSoLve Mental Health Project

Short Title: AdSoLve

Full Title: Addressing Socio-technical Limitations ov LLMs for Medical and Social Computing.

Project summary

AdSoLve aims to explore the feasibility and usability of an AI-assisted self-monitoring and self-management tool for people with severe mental illness receiving care within the NHS. The project seeks to co-produce and test this tool in collaboration with patients and health and social care professionals. The goal is to assess how AI can support mental health monitoring and self-management while addressing challenges such as potential inaccuracies (hallucinations), ethical concerns, and integration within existing NHS practices and NICE guidelines.

 Through a participatory action research approach, the AdSoLve project will involve workshops and feasibility studies to refine the AI-assisted tool based on stakeholder feedback. The project will investigate how AI can augment clinician capacity, enhance self-management interventions, and improve mental health outcomes.

Background

People with severe mental illness often face challenges in monitoring their mental health due to limitations in traditional subjective measures, such as questionnaire fatigue, lack of real-time insights, and limited choice of responses. AI-assisted monitoring tools have the potential to offer more comprehensive and personalised insights by analysing various data sources and providing real-time feedback. However, concerns such as privacy, accuracy, and integration into clinical workflows remain significant hurdles. 

The AdSoLve project seeks to address these challenges by developing an AI-assisted tool that aligns with NHS care guidelines, ensuring it meets the needs of both patients and healthcare professionals. This project will provide insights into the feasibility of AI-driven solutions in mental health care and inform future research and policy recommendations.

Aims

 To co-produce an AI-assisted self-monitoring and self-management tool for individuals with severe mental illness.

 To assess the feasibility and acceptability of the tool within NHS mental health services.

 To identify implementation challenges and opportunities for improving AI-assisted mental health interventions.

 Methodology: The study follows a participatory action research cycle with workshops and feasibility testing. Data collection will include:

• Quantitative measures: Pre-post assessments on quality of life, symptoms, and social networks.
• Qualitative analysis: Thematic analysis of participant feedback on usability and implementation challenges.

 

Research Team

Chief Investigator: Professor Domenico Giacco

Giacco is a professor at Warwick Medical School with expertise in mental health interventions and service user involvement. He oversees the project and ensures alignment with NHS and NICE standards.

Co-Lead: Mrs. Emily Thelwell

Thelwell is a postdoctoral research assistant at Warwick Medical School with experience in mixed-methods research. She is responsible for day-to-day project management.