A Common Data Model for the standardization of intensive care unit (ICU) medication features in artificial intelligence (AI) applications


Objective: Common Data Models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a Common Data Model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to two key feature domains: drug product-related (n=43) and clinical practice-related (n=63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Discussion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This was work was supported by Agency of Healthcare Research and Quality (AHRQ) grant number R21HS028485 and R01HS029009.

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The University of Georgia Institutional Review Board (IRB) deemed this project to be exempt from IRB review (PROJECT00006204).

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Data Availability

The data underlying this article are available in GitHub, under ICURx at: https://www.icurxforai.com/.

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