The Role of Observational Research and Common Data Model.
The article focuses on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its significance in enhancing health research through large-scale observational data. Developed by the Observational Health Data Sciences and Informatics (OHDSI) consortium, the CDM standardizes...
| Publicado en: | Journal of AHIMA |
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| Formato: | questions and answers Journal Article |
| Publicado: |
American Health Information Management Association
1/2/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190723118&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190723118 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10605487 4BH jtl: Journal of AHIMA issn: 10605487 maglogo: N pubinfo: dt: 1/2/2026 pid: 6825 pub: American Health Information Management Association place: Chicago, Illinois artinfo: ui: 190723118 190723118 190723118 190723118 ppct: 1 formats: fmt: @attributes: type: T tig: atl: The Role of Observational Research and Common Data Model. aug: sug: subj: Nonexperimental Studies Health Informatics Data Quality Models, Statistical Utilization Data Analysis, Statistical Electronic Health Records Data Management Artificial Intelligence Utilization Electronic Data Interchange Nomenclature International Classification of Diseases Snomed Privacy and Confidentiality ab: The article focuses on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) and its significance in enhancing health research through large-scale observational data. Developed by the Observational Health Data Sciences and Informatics (OHDSI) consortium, the CDM standardizes data from electronic health records (EHRs) and other sources, facilitating consistent analyses across various institutions. The article highlights the critical role of health information (HI) professionals in ensuring data quality and governance, particularly as healthcare increasingly relies on artificial intelligence and automation. It also discusses the challenges faced in implementing the CDM, including potential loss of clinical detail and the need for upskilling in data science, while presenting findings from a study that demonstrated improved patient identification through enhanced mapping techniques in the ETL process. pubtype: Periodical doctype: questions and answers Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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