The feasibility of using AI-driven tools to support clinical workflows
Research type
Research Study
Full title
Evaluating the feasibility of using artificial intelligence-driven tools to support clinicians across the patient care pathway
IRAS ID
358425
Contact name
Adam Frampton
Contact email
Sponsor organisation
University of Surrey
Duration of Study in the UK
1 years, 3 months, 1 days
Research summary
Multidisciplinary team (MDT) meetings represent the gold standard in NHS cancer care under the 2000 National Cancer Plan. These meetings facilitate collaborative, patient-centred decision-making by bringing together specialists across disciplines, leading to improved care coordination and treatment quality. However, increasing caseloads, greater case complexity, and systemic resource pressures are challenging the sustainability of current MDT models.
Artificial intelligence (AI) has emerged as a potential solution to support clinical decision-making and alleviate administrative burdens in healthcare. AI-driven clinical decision support systems (CDSS) can enhance adherence to evidence-based guidelines and improve care consistency. Despite this potential, there remains a lack of robust real-world evidence on the feasibility, effectiveness, and integration of such tools into clinical workflows.
Within NHS cancer care, MDTs face growing operational pressures, including workforce shortages and rising demand, which impact their efficiency. AI-based CDSS may help address these challenges by providing guideline-aligned treatment recommendations. While surveys indicate increasing acceptance of AI among healthcare professionals and the public, qualitative insights are needed to understand the practical, ethical, and contextual factors influencing adoption.
This doctoral research project employs a mixed-methods approach to evaluate the feasibility of integrating the Deontics clinical decision support into surgical oncology MDTs. The Deontics clinical decision support system (https://deontics.com/) is being introduced specifically for this study as a pilot at Royal Surrey NHS Foundation Trust (RSFT). The study comprises five stages: a retrospective concordance analysis comparing historical MDT decisions with AI-generated recommendations; a prospective pilot assessing real-time AI-assisted decision-making; surveys of MDT members and broader stakeholders to evaluate perceptions of AI utility and acceptability; and semi-structured interviews to explore barriers and facilitators to implementation.
The study aims to generate practical, evidence-based insights into the role of AI in enhancing MDT decision-making and workflow efficiency. Findings will inform strategies for the ethical and sustainable integration of AI tools into routine cancer care, supporting NHS objectives for technology-enabled healthcare improvement.
REC name
London - London Bridge Research Ethics Committee
REC reference
25/PR/1010
Date of REC Opinion
29 Aug 2025
REC opinion
Further Information Favourable Opinion