Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method

Clarissa Gardner; Deborah Wake; Doogie Brodie; Alex Silverstein; Sophie Young; Scott Cunningham; Chris Sainsbury; Maria Ilia; Amanda Lucas; Tony Willis; Jack Halligan

doi:10.1177/20552076221128677

Evaluation of prototype risk prediction tools for clinicians and people living with type 2 diabetes in North West London using the think aloud method

Digit Health. 2023 Jan 8:9:20552076221128677. doi: 10.1177/20552076221128677. eCollection 2023 Jan-Dec.

Authors

Clarissa Gardner¹, Deborah Wake^{2

3}, Doogie Brodie², Alex Silverstein⁴, Sophie Young⁵, Scott Cunningham², Chris Sainsbury², Maria Ilia⁵, Amanda Lucas⁵, Tony Willis⁶, Jack Halligan¹

Affiliations

¹ Department of Surgery and Cancer, Institute of Global Health Innovation, Imperial College London, London, UK.
² MyWay Digital Health, Dundee, UK.
³ Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, UK.
⁴ Local Care Directorate, NHS North West London Integrated Care Board, London, UK.
⁵ Information Directorate, Imperial College Health Partners, London, UK.
⁶ Diabetes Transformation Team, NHS North West London Collaboration of CCGs, London, UK.

Abstract

The prevalence of type 2 diabetes in North West London (NWL) is relatively high compared to other parts of the United Kingdom with outcomes suboptimal. This presents a need for more effective strategies to identify people living with type 2 diabetes who need additional support. An emerging subset of web-based interventions for diabetes self-management and population management has used artificial intelligence and machine learning models to stratify the risk of complications from diabetes and identify patients in need of immediate support. In this study, two prototype risk prediction tools on the MyWay Diabetes and MyWay Clinical platforms were evaluated with six clinicians and six people living with type 2 diabetes in NWL using the think aloud method. The results of the sessions with people living with type 2 diabetes showed that the concept of the tool was intuitive, however, more instruction on how to correctly use the risk prediction tool would be valuable. The feedback from the sessions with clinicians was that the data presented in the tool aligned with the key diabetes targets in NWL, and that this would be useful for identifying and inviting patients to the practice who are overdue for tests and at risk of complications. The findings of the evaluation have been used to support the development of the prototype risk predictions tools. This study demonstrates the value of conducting usability testing on web-based interventions designed to support the targeted management of type 2 diabetes in local communities.

Keywords: Diabetes; artificial intelligence; behaviour change; digital health; health informatics; internet; machine learning; personalised medicine; qualitative; risk factors.