Converting to a Common Data Model: What is Lost in Translation?

Rijnbeek, Peter R.

doi:10.1007/s40264-014-0221-4

Cited by 21 publications

(16 citation statements)

References 7 publications

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“…This allows unlimited replication studies of various data sources simultaneously, allowing more reliable and precise results [ 5 ]. However, it is still necessary to understand the characteristics of each data source and the detailed ETL process to conduct a study using a DRN [ 14 ]. Usually EHR data has a shorter observation period than that of claim data; however, EHR data has more detailed information, such as vital signs, laboratory test results, and exact times of drug administration.…”

Section: Discussionmentioning

confidence: 99%

Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research

et al. 2016

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ObjectivesA distributed research network (DRN) has the advantages of improved statistical power, and it can reveal more significant relationships by increasing sample size. However, differences in data structure constitute a major barrier to integrating data among DRN partners. We describe our experience converting Electronic Health Records (EHR) to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM).MethodsWe transformed the EHR of a hospital into Observational Medical Outcomes Partnership (OMOP) CDM ver. 4.0 used in OHDSI. All EHR codes were mapped and converted into the standard vocabulary of the CDM. All data required by the CDM were extracted, transformed, and loaded (ETL) into the CDM structure. To validate and improve the quality of the transformed dataset, the open-source data characterization program ACHILLES was run on the converted data.ResultsPatient, drug, condition, procedure, and visit data from 2.07 million patients who visited the subject hospital from July 1994 to November 2014 were transformed into the CDM. The transformed dataset was named the AUSOM. ACHILLES revealed 36 errors and 13 warnings in the AUSOM. We reviewed and corrected 28 errors. The summarized results of the AUSOM processed with ACHILLES are available at http://ami.ajou.ac.kr:8080/.ConclusionsWe successfully converted our EHRs to a CDM and were able to participate as a data partner in an international DRN. Converting local records in this manner will provide various opportunities for researchers and data holders.

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Section: Discussionmentioning

confidence: 99%

Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research

et al. 2016

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“…As a CDM from a distributed data network, data transformation is performed locally, and thus, original data are never shared, which contributes to overcoming data protection issues and adhering to local privacy laws [2,7,8,9]. Also, characteristics of each local database are described in a common format in a very comprehensive manner, thus making them more homogenous across countries or partners [2,14].…”

Section: Imecchi Cdmmentioning

confidence: 99%

“…Original databases remain under their holder's control and protection, and are locally transformed to a common data model (CDM) which harmonizes data elements, formats, and sometimes terminologies, while preserving quality [2,14]. The CDM is independent of a specific study requirement [9], but is specific to each data network [16]. Whenever a research question needs to be addressed, a common protocol is developed and embedded in a common data processing script, which is study-specific and purpose-made [2], and is run locally.…”

Section: Introductionmentioning

confidence: 99%

“…Despite their huge potential, large data networks face important challenges, such as the restraints in data sharing across jurisdictional boundaries due to local regulations about data ownership and security [ 2 , 7 - 9 ]. Additionally, dissimilar original data features pose a further difficulty for such data networks, as local databases are usually heterogeneous regarding funding (public or proprietary data source), structure (different coding systems) and content [ 7 , 10 - 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…They combine multiple disparate datasets [ 2 ], capturing varied populations, settings and time-periods. Characteristics may vary across time and formal documentation of original data might not be available or up to date [ 7 , 9 , 12 ]. Furthermore, they have different underlying data models, formats and meanings, which may affect syntactic and semantic interoperability and thereby impact on the quality of the data in the shared data network [ 9 , 11 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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Empowering knowledge generation through international data network: the IMeCCHI-DATANETWORK

Varela

Pogam

Metcalfe³

et al. 2020

IJPDS

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IntroductionThe International Methodology Consortium for Coded Health Information (IMeCCHI) is a collaboration of health services researchers who promote methodological advances in coded health information. The IMeCCHI-DATANETWORK initiative focuses on developing a multi-purpose distributed data infrastructure and common data model (CDM) to enable cross-border data sharing and international comparisons. MethodsIMeCCHI consortium partners from six different countries – Canada, Denmark, Italy, New Zealand, South Korea, and Switzerland – used a questionnaire to describe their original databases which differ in size, structure, content and coding systems. To standardize these data, they agreed on a CDM and mapped their population-based databases to meet the CDM specifications. At the end of this process, local data had a more homogenous content and structure, which made them syntactically and semantically interoperable. Data transformation was performed using a common data management software called TheMatrix. ResultsThe CDM encompasses four tables of structured data (person characteristics, hospitalizations, outpatient prescription medication and death), linked at the individual level through a person identifier. It can be used to answer research questions across countries using locally converted databases, which facilitates study replication in a distributed fashion. As a proof-of-concept study, an initial research question was addressed using an agreed protocol. Local data were transformed in csv files in the CDM structure and TheMatrix was tested to transform the standardized data from each partner into local analytical datasets. This allowed results to be shared between countries, whilst maintaining local control over each region’s data. ConclusionThe IMeCCHI-DATANETWORK, a model of a distributed data network, demonstrated that it is feasible to analyze international data using standardized analytical methods that enable independent analyses by regions, without relocating datasets thereby protecting local confidentiality obligations. The distributed data infrastructure can produce results that can be generalized to several countries, while facilitating cross-border data sharing and international comparisons.

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Choosing Among Common Data Models for Real‐World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

Schneeweiß

Brown

Bate

et al. 2019

Clin Pharma and Therapeutics

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Many real‐world data analyses use common data models (CDMs) to standardize terminologies for medication use, medical events and procedures, data structures, and interpretations of data to facilitate analyses across data sources. For decision makers, key aspects that influence the choice of a CDM may include (i) adaptability to a specific question; (ii) transparency to reproduce findings, assess validity, and instill confidence in findings; and (iii) ease and speed of use. Organizing CDMs preserve the original information from a data source and have maximum adaptability. Full mapping data models, or preconfigured rules systems, are easy to use, since all raw codes are mapped to medical constructs. Adaptive rule systems grow libraries of reusable measures that can easily adjust to preserve adaptability, expedite analyses, and ensure study‐specific transparency.

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Converting to a Common Data Model: What is Lost in Translation?

Cited by 21 publications

References 7 publications

Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research

Conversion and Data Quality Assessment of Electronic Health Record Data at a Korean Tertiary Teaching Hospital to a Common Data Model for Distributed Network Research

Empowering knowledge generation through international data network: the IMeCCHI-DATANETWORK

Choosing Among Common Data Models for Real‐World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

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