Developing an AI-Based clinical decision support system for basal insulin titration in type 2 diabetes in primary Care: A Mixed-Methods evaluation using heuristic Analysis, user Feedback, and eye tracking.
Journal:
International journal of medical informatics
PMID:
39787659
Abstract
BACKGROUND AND AIM: The progressive nature of type 2 diabetes often, in time, necessitates basal insulin therapy to achieve glycemic targets. However, despite standardized titration algorithms, many people remain poorly controlled after initiating insulin therapy, leading to suboptimal glycemic control and complications. Both healthcare professionals and people with type 2 diabetes have expressed the need for novel tools to aid in this process. Traditional titration methods often lack the precision needed to address individual differences in glycemic response. Recent studies have highlighted the potential of AI-driven solutions, which can leverage large datasets to model patient-specific characteristics. Therefore, this study aims to develop a digital platform for an AI-based clinical decision support system to assist healthcare professionals in primary care with personalized and optimal basal insulin titration for people with type 2 diabetes.