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Real World Data (RWD) has various types of data sources, but each source has a different format and terminology code, which makes analysis process cumbersome and repetitive. The OMOP Common Data Model (CDM) is an open standard for analysis of RWD on a global scale, and the OHDSI community is responsible for its maintenance and development. What sets the OMOP CDM apart from other data standards is the way in which it has created a structure for integrating and handling terminology globally, and the way in which analysis is conducted without exposing individual patient information outside. Such features facilitate international collaboration. The method of not releasing patient data outside is expected to be widely utilized in future because it is highly compatible with Japan's pseudonymously processed information (PPI) based on the personal information protection act, in which PPI data cannot be provided to any third party but the purpose of use can be easily changed. There are many advantages not only for international collaboration, but also for domestic collaboration or in-house use. Epidemiologists and data scientists will be able to handle data in the same model they are accustomed to both domestically and internationally. That will be of great benefit to students, personnel, and their organizations especially when they study abroad, return home, or transfer internationally. Globally, collaborators from more than 70 countries are working on this project. Data on more than 800 million people after eliminating estimated duplicates, or 10％ of the world's population, has been converted to the OMOP CDM. More than 250 related published articles have been registered with PubMed. On the other hand in Japan, there are many issues to be solved, such as support system and terminology mapping. To catch up with international levels, strong cooperation from a wide range of fields is needed.
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The usage of controlled biomedical vocabularies is the cornerstone that enables seamless interoperability when using a common data model across multiple data sites. The Observational Health Data Science and Informatics (OHDSI) initiative combines over 100 controlled vocabularies into its own. However, the OHDSI vocabulary is limited in the sense that it combines multiple terminologies and does not provide a direct way to link them outside of their own self-contained scope. This issue makes the tasks of enriching feature sets by using external resources extremely difficult. In order to address these shortcomings, we have created a linked data version of the OHDSI vocabulary, connecting it with already established linked resources like bioportal, bio2rdf, etc. with the ultimate purpose of enabling the interoperability of resources previously foreign to the OHDSI universe.
Assuntos
Informática , Informática Médica , Vocabulário , Vocabulário ControladoRESUMO
Objective Chronic obstructive pulmonary disease,asthma,interstitial lung disease and pulmonary thromboembolism are the most common and severe respiratory diseases,which seriously jeopardizing the health of the Chinese citizens.Large-scale prospective cohort studies are needed to explore the relationships between potential risk factors and respiratory disease outcomes and to observe disease prognoses through long-term follow-ups.We aimed to develop a common data model (CDM) for cohort studies on respiratory diseases,in order to harmonize and facilitate the exchange,pooling,sharing,and storing of data from multiple sources to serve the purpose of reusing or uniforming those follow-up data appeared in the cohorts.Methods The process of developing this CDM of respiratory diseases would follow the steps as:①Reviewing the international standards,including the Clinical Data Interchange Standards Consortium (CDISC),Clinical Data Acquisition Standards Harmonization (CDASH) and the Observational Medical Outcomes Partnership (OMOP) CDM;②Summarizing four cohort studies of respiratory diseases recruited in this research and assessing the data availability;③Developing a CDM related to respiratory diseases.Results Data on recruited cohorts shared a few similar domains but with various schema.The cohorts also shared homogeneous data collection purposes for future follow-up studies,making the harmonization of current and future data feasible.The derived CDM would include two parts:①thirteen common domains for all the four cohorts and derived variables from disparate questions with a common schema,②additional domains designed upon disease-specific research needs,as well as additional variables that were disease-specific but not initially included in the common domains.Conclusion Data harmonization appeared essential for sharing,comparing and pooled analyses,both retrospectively and prospectively.CDM was needed to convert heterogeneous data from multiple studies into one harmonized dataset.The use of a CDM in multicenter respiratory cohort studies would make the constant collection of uniformed data possible,so to guarantee the data exchange and sharing in the future.
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Objective Chronic obstructive pulmonary disease,asthma,interstitial lung disease and pulmonary thromboembolism are the most common and severe respiratory diseases,which seriously jeopardizing the health of the Chinese citizens.Large-scale prospective cohort studies are needed to explore the relationships between potential risk factors and respiratory disease outcomes and to observe disease prognoses through long-term follow-ups.We aimed to develop a common data model (CDM) for cohort studies on respiratory diseases,in order to harmonize and facilitate the exchange,pooling,sharing,and storing of data from multiple sources to serve the purpose of reusing or uniforming those follow-up data appeared in the cohorts.Methods The process of developing this CDM of respiratory diseases would follow the steps as:①Reviewing the international standards,including the Clinical Data Interchange Standards Consortium (CDISC),Clinical Data Acquisition Standards Harmonization (CDASH) and the Observational Medical Outcomes Partnership (OMOP) CDM;②Summarizing four cohort studies of respiratory diseases recruited in this research and assessing the data availability;③Developing a CDM related to respiratory diseases.Results Data on recruited cohorts shared a few similar domains but with various schema.The cohorts also shared homogeneous data collection purposes for future follow-up studies,making the harmonization of current and future data feasible.The derived CDM would include two parts:①thirteen common domains for all the four cohorts and derived variables from disparate questions with a common schema,②additional domains designed upon disease-specific research needs,as well as additional variables that were disease-specific but not initially included in the common domains.Conclusion Data harmonization appeared essential for sharing,comparing and pooled analyses,both retrospectively and prospectively.CDM was needed to convert heterogeneous data from multiple studies into one harmonized dataset.The use of a CDM in multicenter respiratory cohort studies would make the constant collection of uniformed data possible,so to guarantee the data exchange and sharing in the future.
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OBJECTIVES: A 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). METHODS: We 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. RESULTS: Patient, 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/. CONCLUSIONS: We 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.