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Dyna AI to integrate medication dosing and safety data into DynaMedex workflow -

EBSCO Clinical Decisions and Micromedex have announced an expansion of Dyna AI for DynaMedex® that will integrate medication dosing and safety information directly into the clinical decision support platform, creating a unified workflow for disease and drug-related queries at the point of care.

Targeted for availability in early 2026, the enhancement will modernize drug reference delivery within DynaMedex by embedding Micromedex medication intelligence into Dyna AI–generated responses. The integration is designed to provide clinicians with immediate access to structured, concise summaries that include pediatric and adult dosing guidance, medication precautions, contraindications, adverse effects, black box warnings, and pregnancy and lactation considerations.

By uniting DynaMed’s clinician-curated disease evidence with Micromedex’s authoritative drug data, the expanded partnership aims to position DynaMedex as a single destination for clinical and medication decision-making. The integrated workflow is intended to reduce the need for clinicians to consult multiple tools, supporting faster and more informed decisions at the point of care.

The AI-generated summaries will be delivered through Dyna AI’s governed platform, which incorporates expert clinician review and embeds trusted content directly into the system’s answer logic. Unlike many AI tools that rely on probabilistic outputs or external, unvetted datasets, Dyna AI is designed to draw exclusively from the curated content of DynaMed and Micromedex.

Medication safety content from Micromedex will be presented in a streamlined format within DynaMedex, allowing clinicians to quickly interpret critical information without leaving the platform. The addition of integrated dosing information is expected to support clinical efficiency by reducing search time and simplifying access to medication-specific guidance.

EBSCO Clinical Decisions stated that the development reflects an ongoing commitment to responsible AI, with an emphasis on transparency, traceability, and regulatory compliance. The tool is designed to avoid pulling from external sources and to meet expectations for trustworthy clinical decision support in healthcare settings.

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