Integrating Flowsheet Data in OMOP Common Data Model for Clinical Research

TL;DR

This paper explores methods to incorporate detailed inpatient flowsheet data into the OMOP CDM, enhancing its utility for clinical research by enabling more comprehensive patient health data analysis.

Contribution

It introduces two approaches for integrating flowsheet data into OMOP, including a detailed mapping to standardized vocabularies for improved research utility.

Findings

The straightforward method is easy but less useful for research.

The detailed mapping approach is labor-intensive but yields higher research value.

Enhanced data integration supports better population health studies.

Abstract

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.