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. Lay Summary Medication data pose a unique challenge for interpretation by artificial intelligence (AI) because of the alphanumerical combinations (e.g., ibuprofen 200mg every 4 hours) and because of the technical detail associated with drug prescriptions (e.g., ibuprofen 200mg and acetaminophen 325mg are both starting doses and round tablet sizes, so it would be incorrect for the machine to view 325mg as ‘more’ than 200mg). Because AI has great potential to improve the safety and efficacy of medication use, a common data model for ICU medications (ICURx) is proposed here to overcome these challenges and support AI efforts in medication analysis. ### 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. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The University of Georgia Institutional Review Board (IRB) deemed this project to be exempt from IRB review (PROJECT00006204). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data underlying this article are available in GitHub, under ICURx at: .