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

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.

Methods

An iterative mixed-method approach was used for system development, incorporating usability engineering principles. Initial requirements were gathered from domain experts and followed by heuristic evaluation and user-based evaluation. Data from these evaluations guided successive iterations of the prototype.

Results

The initial prototype featured a retrospective graph of insulin doses and fasting glucose levels and a dose adjustment simulation environment. Heuristic evaluation identified 92 issues, primarily related to minimalistic and aesthetic design. The second prototype addressed these concerns, but user-based evaluation found 66 additional usability problems, notably with HbA1c presentation and the need for more glucose measures. The final prototype showed high usability, with a median System Usability Scale score of 93.8. Task completion rates were high (task 1: 87.5%, task 2: 75.0%, and task 3: 100%). Eye-tracking data showed minimal distractions.

Conclusions

The AI-based Clinical Decision Support System shows promise in managing basal insulin titration for people with type 2 diabetes, addressing clinical inertia, and providing a user-friendly, efficient tool to improve glycemic control during insulin titration.