🚨 Diabetes and AI Tools 🚨

Analysis of Patient Secure Patient Message through AI

Artificial Intelligence in Diabetes Care: Unlocking Patient Insights Through NLP of Secure Messages

Introduction

When integrated into clinical practice, AI tools that promote patient-centered approaches can enhance patient autonomy and optimize care efficiency. An effective management of a disease requires a personalized patient centered care(PCC) designed according to the individual needs and preferences. It is estimated that the global burden of diabetes is increasing and will affect around 1.3 billion individuals by 2050. During the time of COVID-19 pandemic secure messaging in patient portals was increasing in trend and has become essential for communication. Though valuable source of data, patient messages were not utilized appropriately due to the difficulty encountered during the analysis of large scale text data. So, in order to address this, this study investigates how Artificial Intelligence (AI) and Natural Language Processing (NLP) can appropriately analyze the messages and help customize a design for custom AI tools to support the care.

Methods

From diabetic patient, secured messages were analyzed using NLP and AI models (e.g BERT-bidirectional encoder representations from transformer). To extract important patients concerns as well as to generate AI tool concepts, topic modelling along with prompt engineering was used which was evaluated by five endocrinologists for perceived usefulness as well as the potential risks were highlighted.

Findings

Tools involving the clinical decisions making and/or direct data interpretations were considered a risk by the involved clinicians. Medical professionals found patient education and administrative support tools such as FAQs and task organizers to be especially advantageous. The AI support which could streamline workflow and empower patients without replacing the expert judgement was highly appreciated by clinicians.

Results

A wide range of AI tool ideas were developed form low risk to more complex data integrated systems. Low risk tools (eg. Communication aids, self-care reminders etc.) were perceived positively while high risk tools (e.g., autonomous diagnosis suggestions) raised ethical and safety concerns among the users. With proper, timely and adequate use of AI tools can efficiently analyze patients messages to find out useful patterns and insights which can assist in improvement in healthcare system.

Conclusion

A secure message data form actual patients can enhance patient centered care if AI can be use to respond their issues. Educational and logistical support tools are the best for initiation of AI integration in healthcare while tools involving clinical interpretation require caution and oversight to ensure safety and trust. This study provides a framework for developing AI tools tailored to chronic disease management, starting with diabetes and extending beyond.