Publications
Department of Medicine faculty members published more than 3,600 peer-reviewed articles in 2024.
2019
2019
Objectives
Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement.
Materials and methods
We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format.
Results
ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept.
Conclusion
ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu).
View on PubMed2019
2019
2019
2019
2019
BACKGROUND
Electronic health record (EHR) data is increasingly used to identify patients with chronic kidney disease (CKD). EHR queries used to capture CKD status, identify comorbid conditions, measure awareness by providers, and track adherence to guideline-concordant processes of care have not been validated.
METHODS
We extracted EHR data for primary-care patients with two eGFRcreat 15-59 mL/min/1.73 m^2 at least 90 days apart. Two nephrologists manually reviewed a random sample of 50 charts to determine CKD status, associated comorbidities, and physician awareness of CKD. We also assessed the documentation of a CKD diagnosis with guideline-driven care.
RESULTS
Complete data were available on 1767 patients with query-defined CKD of whom 822 (47%) had a CKD diagnosis in their chart. Manual chart review confirmed the CKD diagnosis in 34 or 50 (68%) patients. Agreement between the reviewers and the EHR diagnoses on the presence of comorbidities was good (κ > 0.70, p < 0.05), except for congestive heart failure, (κ = 0.45, p < 0.05). Reviewers felt the providers were aware of CKD in 23 of 34 (68%) of the confirmed CKD cases. A CKD diagnosis was associated with higher odds of guideline-driven care including CKD-specific laboratory tests and prescriptions for statins. After adjustment, CKD diagnosis documentation was not significantly associated with ACE/ARB prescription.
CONCLUSIONS
Identifying CKD status by historical eGFRs overestimates disease prevalence. A CKD diagnosis in the patient chart was a reasonable surrogate for provider awareness of disease status, but CKD awareness remains relatively low. CKD in the patient chart was associated with higher rates of albuminuria testing and use of statins, but not use of ACE/ARB.
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