Digital Tools for Decision Making and Diagnostics in Women's Health
Research type
Research Study
Full title
Investigating the use of Interactive Digital and Automated Intelligence (AI) Tools for decision-making and diagnostics in Women's Health
IRAS ID
338417
Contact name
Sara Hillman
Contact email
Sponsor organisation
University College London
Clinicaltrials.gov Identifier
UCL , Data protection reference
Duration of Study in the UK
3 years, 0 months, 0 days
Research summary
Informed decision-making is critical to ensure that patients feel empowered and educated about interventions, procedures, and potential treatments they are considering especially in the context of new tests and technologies that may be offered to investigate and treat conditions. For example, genetic testing of an unborn child has become more complex due to the increasing number of tests that can be offered. Face-to-face counselling, on the day, by a clinician is the most commonly used method, designed to help patients make informed choices and decisions, but there are limitations to this method. These include, but are not limited to, time pressures, patients feeling overwhelmed with medical information and language and cultural barriers.
Providing interactive, digital information in varied formats, including the evolving use of artificial intelligence (AI) tools, may provide a novel and improved way of delivering patients more personalised and relevant information, empowering them to make truly informed decisions and consent.
We want to collect data to support this idea (through short questionnaires, focus groups and interviews). This will cover gathering information about what exists currently and how people (both patients and healthcare practitioners) feel about this information in decision-making scenarios about interventions, treatments and diagnoses. We will initially focus on maternity and fetal medicine issues but hope to cover a range of Women's Health topics. This information will be used to revise and test currently existing interactive digital (internet) tools to ensure they provide relevant information in a different more ‘user-friendly’ format that is truly inclusive. Through a current collaboration, we will also ask participants to test out a platform that delivers AI-generated predictions of the functional impact of genetic tests to see if it can help with conveying/ describing these difficult results and incorporate a value based approach for cosent by asking a series of specfic questions.REC name
North of Scotland Research Ethics Committee 2
REC reference
24/NS/0106
Date of REC Opinion
7 Oct 2024
REC opinion
Further Information Favourable Opinion