SMU Data Science Review
Abstract
Violence and overdose events in Las Vegas occur at rates above the national average, with fewer than half of violent injuries reported to law enforcement [2,7]. The Cardiff Model offers a proven framework for standardized data collection and sharing between hospitals and public safety partners, yet many implementations still rely on manual entry. We propose an ambient triage pipeline integrated with Oracle-Cerner electronic health record systems to listen to nurse–patient dialogue, convert speech to text, extract Cardiff fields, and write standards-based FHIR Bundles for analytics. Using SMART on FHIR standards and Cerner Millennium APIs, the study evaluates whether ambient capture can improve completeness, accuracy, and timeliness of Cardiff data while reducing clinical burden. The research design uses a synthetic dataset of 400 encounters balanced across injury mechanisms, with statistical evaluation of data quality, geospatial assessment of location fidelity, and a feasibility plan for future hospital pilots. We seek an Oracle Health engineering contact and a hospital IT partner for schema validation and a limited test endpoint. Anticipated outcomes include higher data fidelity, reduced reporting latency, and stronger foundations for violence and overdose prevention.
Recommended Citation
Augustine, Simi; Lopez, Marco A.; Cheun, Jacquelyn; and Papesh, Chris
(2025)
"Automating Cardiff Model Data Capture in Emergency Departments: Ambient NLP Integration with Oracle-Cerner FHIR Systems,"
SMU Data Science Review: Vol. 9:
No.
3, Article 8.
Available at:
https://scholar.smu.edu/datasciencereview/vol9/iss3/8
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