Integrating Flowsheet Data in OMOP Common Data Model for Clinical Research
This work addresses the problem of standardizing detailed clinical data for researchers, but it is incremental as it builds on existing OMOP CDM frameworks.
The paper tackled the challenge of integrating flowsheet data into the OMOP Common Data Model for clinical research, presenting two approaches: one computationally straightforward but limited in utility, and another more intensive method that mapped to standardized vocabularies like LOINC to create a higher-utility dataset for population health studies.
Flowsheet data presents unique challenges and opportunities for integration into standardized Common Data Models (CDMs) such as the Observational Medical Outcomes Partnership (OMOP) CDM from the Observational Health Data Sciences and Informatics (OHDSI) program. These data are a potentially rich source of detailed curated health outcomes data such as pain scores, vital signs, lines drains and airways (LDA) and other measurements that can be invaluable in building a robust model of patient health journey during an inpatient stay. We present two approaches to integration of flowsheet measures into the OMOP CDM. One approach was computationally straightforward but of potentially limited research utility. The second approach was far more computationally and labor intensive and involved mapping to standardized terms in controlled clinical vocabularies such as Logical Observation Identifiers Names and Codes (LOINC), resulting in a research data set of higher utility to population health studies.