Extending the OMOP Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the SCCM C2D2

Citation

Adams MCB, Hurley RW, Bartels K, Perkins ML, Hudson C, Topaloglu U, Cobb JP, Reuter-Rice K, Stocking JC, Khanna AK. Extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model for Critical Care Medicine: A Framework for Standardizing Complex ICU Data Using the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2). Crit Care Med. 2026 Feb 1;54(2):270-279. doi: 10.1097/CCM.0000000000006969. Epub 2025 Nov 21. PMID: 41269063; PMCID: PMC12955978.

Article Type

Original Research

Topic Area

Health Services Research; Implementation Science; Policy and Health Systems

Population

ICU patients; Society of Critical Care Medicine (SCCM) Critical Care Data Dictionary (C2D2)

Summary

Critical care medicine generates vast quantities of complex, high-frequency data from intensive care unit (ICU) monitoring, yet no widely adopted framework exists to standardize these data for multicenter research. This study describes the development of a framework for extending the Observational Medical Outcomes Partnership (OMOP) common data model to accommodate the unique data requirements of critical care. The authors collaborated with the Society of Critical Care Medicine's Critical Care Data Dictionary (C2D2) initiative to map ICU-specific data elements, such as continuous physiologic monitoring, ventilator parameters, and medication infusions, to standardized OMOP concepts. The framework addresses challenges inherent to ICU data, including high-frequency time-series measurements, complex medication dosing, and device-generated signals that are not well captured by existing data models designed primarily for outpatient or administrative data. By establishing a systematic mapping process between C2D2 and OMOP, the work facilitates interoperability across critical care research networks and supports large-scale observational studies. The implications extend to pain medicine and perioperative research, where standardized ICU data could improve understanding of acute-to-chronic pain transitions and opioid use in critically ill patients.

Background

The OMOP common data model has become a widely used standard for harmonizing health data across institutions, but it was not originally designed for the complexity of ICU data. Critical care environments produce continuous streams of physiologic measurements and device data that require specialized representation. The SCCM C2D2 initiative sought to create a standardized dictionary for critical care data elements, and this study bridges C2D2 with OMOP to enable broader data sharing and analysis.

Methods

The authors developed a mapping framework that aligns C2D2 critical care data elements with OMOP concept domains. This involved identifying gaps in the existing OMOP vocabulary for ICU-specific data types, proposing extensions for high-frequency time-series data, and validating the approach across multiple institutional datasets. The framework addresses medication infusion data, ventilator settings, hemodynamic monitoring, and other ICU-specific measurements.

Key Findings

The resulting framework provides a systematic approach to incorporating critical care data into the OMOP model. The mapping identifies specific areas where OMOP extensions are needed, particularly for continuous monitoring data and complex medication administration records. The approach supports interoperability across critical care research networks while preserving the granularity needed for meaningful clinical research.

Implications for Practice and Policy

Standardized critical care data infrastructure can accelerate multicenter ICU research, improve benchmarking of clinical outcomes, and support quality improvement initiatives. For pain medicine and opioid research, this framework enables more rigorous study of analgesic and sedation practices in the ICU, including transitions from acute to chronic pain and perioperative opioid exposure.

Future Directions

Ongoing work will focus on implementing this framework across additional institutions, expanding the vocabulary mappings, and supporting the integration of ICU data into broader research networks such as the NIH HEAL Initiative.

Keywords

critical care medicine, OMOP common data model, ICU data standardization, data harmonization, health informatics, clinical data infrastructure, interoperability, pain medicine, opioid use, intensive care unit