🤖 AI Summary
This work proposes an open-source, web-based clinical decision support platform to address fragmented outpatient data, inefficient clinician–patient communication, and high follow-up burdens in gestational diabetes management. The platform introduces a novel dual-endpoint architecture that leverages large language models (LLMs) to intelligently aggregate and summarize patients’ extramural health data, providing clinicians with context-aware decision support. Personalized lifestyle guidance and treatment explanations are delivered directly to patients via WhatsApp. Designed with a modular architecture, the system integrates electronic health records and messaging interfaces to significantly enhance clinical oversight and patient adherence, strengthen continuity of care, and reduce the need for in-person follow-ups. Its adaptable framework also holds promise for extension to other chronic disease management contexts.
📝 Abstract
CARE-link is an open-source, web-based clinical support platform designed to improve the management of gestational diabetes by linking clinicians and patients through an LLM-mediated workflow. The system aggregates patient-generated data outside the hospital, summarizes relevant clinical information, and delivers context-aware decision support to clinicians. For patients, CARE-link provides clear explanations of management plans and delivers timely lifestyle guidance through a WhatsApp interface. The integrated dual-facing design aims to promote continuous monitoring, support individualized care, and reduce the burden of in-clinic follow-ups. Built with a modular architecture, the platform can be adapted to other chronic conditions requiring longitudinal tracking and behavioral support. CARE-link has the potential to enhance clinical oversight, promote patient compliance, and strengthen continuity of care particularly in resource-constrained settings.