Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure

Abstract

Objective The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. Materials and Methods Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. Results We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). Conclusion Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research.

Outcomes of SARS-CoV-2 Infection in Patients With Chronic Liver Disease and Cirrhosis: A National COVID Cohort Collaborative Study

“…The use of the OMOP common data model, however, decreases such differences and enforces a degree of standardization. 15,16,44 Fourth, due to date-shifting employed in the process of de-identification in the N3C Data Enclave and differences in data harmonization times between data partner sites, there may be a delay in ascertainment of outcomes. There may be misclassification of outcomes if the date of SARS-CoV-2 testing was close to the latest known date of records ("maximum data date") for that site.…”

Outcomes of SARS-CoV-2 Infection in Patients with Chronic Liver Disease and Cirrhosis: a N3C Study

“…Moreover, all data available on the N3C Data Enclave have been synchronized into the OMOP common data model, thereby allowing reproducible queries, transformations, and analyses executed on other OMOP-based databases. 15,16,30…”

“…The use of the OMOP common data model, however, decreases such differences and enforces a degree of standardization. 15,16,30…”

Developing real‐world evidence from real‐world data: Transforming raw data into analytical datasets

“…Surrogate markers like genetics may also be used when available to assess phenotype quality. 37 National initiatives like the eMerge program have developed standards for computable phenotypes that can be applied across institutions, an example of which is shown in Figure 6. 38 With these initiatives, the informatics community has grown more adept at harmonizing EHR data.…”