2026-03-19: DEUSG Virtual
Meeting Details
Date & Time: 19th March 2026, 12noon UTC
Objectives
Users' discussions about the SNOMED International Drug Model and National Drug Extension Model.
Zoom Link
Link: DEUSG Zoom Meeting Link
Meeting Recording
Passcode: 2$e^^58q
Attendees:
Alejandro Lopez Osornio, Shane Byrnes, Julie James, Yongsheng Gao, Monica Harry, Ana Paredes, Emilie Nguyen, Emma Melhuish, Francois Lavoie, Frederic Doc, Guillermo Reynoso, Hanne Johansen, Ian Spiers, Jaya Sonavane, Jerry O’Sullivan, Justin S, Karen Rees, Laura Solana, Maria Gomez, Megan Berry, Michael Keary, Nicola Ingram, Noelle Horan, Nick McGraw, Julie Boutin, Wei Zhou.
Discussion items:
Description | Mins | Owner | Notes & Actions | |
|---|---|---|---|---|
| 1 | Opening | 2 min | Shane Byrnes | Welcome & Notification of Recording Update to SNOMED meeting AI/recording Policy. |
| 2 | 7Has container type | 15 min | Yongsheng Gao | Slides: https://docs.google.com/presentation/d/1MfJo_CZxwtrX0PjLXHXVGISasYhw_Aq2YIGi6xYXJxQ/edit?usp=sharing |
| 3 | CRS Requests & Updates to Implementation Demonstrator | 2 min | Alejandro / Krista | Alejandro - Updates on Addition of Medicinal Products through CRS Alejandro - Medicinal Product Class Demonstrator updated: https://ihtsdo.github.io/sct-implementation-demonstrator/#/medicinal-product-classes |
| 4 | Inert / Placebo Products | 15 min | Yongsheng Gao | Forums Link: https://forums.snomed.org/t/modelling-inert-tablets-diluents-and-solvents/772/10 Continuation of discussion from Feb 26, 2026 |
| 5 | Dose Form after transformation | 15 min | Yongsheng Gao | Slides:https://docs.google.com/presentation/d/1bCZdzzrHO_2KEloGqkQDxxSUY2A4WVQ0p8B-q05Qok4/edit?usp=sharing Continuation of discussion from Feb 26, 2026 |
| 6 | Topics for Vienna | 10 min | All | DEUSG Wed Apr 15, 2026 1.30pm-5pm (local time) Group proposals for topics |
| 7 | AOB | 2 min | All |
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Zoom AI generated summary of the meeting |
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Container Type Modeling Discussion The group discussed the domain and range considerations for modeling container types, noting that while container types can be used for various domains including devices and medicinal products, there are currently 39 devices used in international modeling compared to 29 container types. He recommended maintaining a loose range constraint for devices, allowing national extensions to determine if container constraints are suitable for their cases. The discussion also addressed attribute commonality and grouping policies, particularly regarding whether has container type represents immediate containers or alternative packaging in IDMP terms, with the recommendation that both immediate containers and alt packages may need different attribute grouping approaches. Container Modeling Implementation DiscussionThe group discussed the implementation of container modeling, agreeing that it should be optional for countries that don't need it while remaining useful for cases like super packs or dual chamber devices. Members emphasized the need to develop a container hierarchy and mentioned the potential use of the packaged clinical drug attribute for recursive packaging scenarios. It was noted this raised questions about how to represent capacity and disposition of entities, suggesting this might require further discussion in future meetings. Medicinal Product Classes Process UpdatesNew updates to the medicinal product classes tool in Implementation Demonstrator were shown to the group, noting that while clinical drug references are still required for international editions, submissions for new substance and MP concepts no longer need to include a clinical drug classes. He demonstrated a new demonstrator site showing medicinal product classes with constrained search fields and exact ECL expressions for identification. Alejandro encouraged participants to provide feedback on the expressions and suggested additions through email or forums. Dose Form Model Alignment with International StandardsUpdates were presented on Dose Form model alignment with international standards, particularly IDMP and EDQM, focusing on the distinction between manufactured and administrative dose forms. It was explained how the current model addresses six core attributes including state of matter, basic dose form, release characteristics, transformation, intended site, and administration method. The discussion centered on whether to introduce a new attribute for administrative dose form or continue with the existing model, with a question if the original request addressed medicinal product modeling requirements. Dose Form and Drug Modeling DiscussionThe importance of including administrable dose form in modeling clinical drugs was discussed, particularly for solutions and powders, to better express strength and support prescribing use cases. Some members raised concerns about transformations that can go in multiple directions, such as dissolution and dilution, which would require a zero-to-many cardinality in the model. They also noted challenges with site of administration when different transformations result in different administration methods. Dose Form Representation Challenges for Inert/Placebo ProductsThe agenda item focused on two main topics: dose form representation and modeling products with no active ingredients. Regarding dose forms, the group discussed challenges in representing strengths for different administration methods, particularly for inhalers where the delivered dose may vary from the metered dose. It was shared that a Spanish paper highlighting clinical issues around this topic, noting that while some SmPCs differentiate between metered and delivered doses, others do not consistently do so. The group also discussed representing products with no active ingredients, with a proposal to use a count of zero for active ingredients in the model. Group members expressed concerns about using the term "inert" for excipients, arguing that many components in formulations are not truly inert as they can affect drug delivery. Members noted the complexity of this issue, highlighting various use cases and the need for different approaches depending on the specific scenario. AI can make mistakes. Review for accuracy.
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