Diabetology · Published 2026-05-20 · DOI 10.3390/diabetology7050099
Mandy M. Shao, Agatha F. Scheideman, Casey Rand, David Klonoff, Juan Espinoza
The adoption of artificial intelligence (AI) tools for hospital insulin management is currently limited by data fragmentation and difficult integration into clinical workflows. This commentary examines the data infrastructure requirements for safe AI deployment in clinical settings. Device-mediated and clinician-administered dosing are the two methods by which insulin is managed in hospitals. In device-mediated dosing, glucose and insulin data often remain siloed within proprietary device ecosystems outside the electronic health record (EHR). In clinician-administered dosing, relevant data elements typically exist within the EHR but are distributed across workflows in ways that limit their usefulness for decision support. The Integration of Connected Diabetes Device Data into the Electronic Health Record (iCoDE) initiative is a standard for integrating device-generated diabetes data into clinical systems, which can lay the foundation for organizing hospital data in support of the development of trustworthy AI. A staged roadmap for hospitals building towards AI-ready insulin management infrastructure is presented along with governance requirements for trustworthy deployment. The value of iCoDE is that it helps define the conditions under which such AI can become clinically meaningful, trustworthy, and scalable.
Abstract from DOAJ. Public domain (CC0 1.0).
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Shao, M., Scheideman, A., Rand, C., et al. (2026). From Device Data to Trusted Decision Support: Building the Foundation for AI in Hospital Insulin Management. Diabetology. https://doi.org/10.3390/diabetology7050099