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J Biomed Inform. 2019 Aug;96:103253. doi: 10.1016/j.jbi.2019.103253. Epub 2019 Jul 17.

Facilitating phenotype transfer using a common data model.

Author information

1
Department of Biomedical Informatics, Columbia University, New York, NY, United States; Medical Informatics Services, NewYork-Presbyterian Hospital, New York, NY, United States. Electronic address: hripcsak@columbia.edu.
2
Department of Biomedical Informatics, Columbia University, New York, NY, United States.
3
Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, WI, United States.
4
Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
5
Research Information Science and Computing, Partners Healthcare, Boston, MA, United States.
6
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.
7
Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States.
8
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
9
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States.
10
Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, United States.
11
Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
12
Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States.
13
Department of Biomedical Informatics, Columbia University, New York, NY, United States; Medical Informatics Services, NewYork-Presbyterian Hospital, New York, NY, United States.
14
National Human Genome Research Institute, NIH, Bethesda, MD, United States.

Abstract

BACKGROUND:

Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process.

METHODS:

Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network.

RESULTS:

It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode.

CONCLUSION:

Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

KEYWORDS:

Common data model; Electronic health records; Phenotyping

PMID:
31325501
PMCID:
PMC6697565
[Available on 2020-08-01]
DOI:
10.1016/j.jbi.2019.103253

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