2021
DOI: 10.1055/s-0041-1726481
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Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI

Abstract: Summary Objective: The current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science. Methods: OHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world’s population. Using a common data model with a practical schema and extensive vocab… Show more

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Cited by 47 publications
(38 citation statements)
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“…All datasets were previously mapped to the Observational Medical Outcomes Partnership common data model, which is maintained by the Observational Health Data Sciences and Informatics network, an international open science initiative to generate reproducible evidence from observational data. 14 This initiative brings together hundreds of researchers from 30 countries, working with health records from around 600 million unique patients in its distributed database. The analysis code was distributed across all centres contributing to Observational Health Data Sciences and Informatics without sharing patient level data.…”
Section: Methodsmentioning
confidence: 99%
“…All datasets were previously mapped to the Observational Medical Outcomes Partnership common data model, which is maintained by the Observational Health Data Sciences and Informatics network, an international open science initiative to generate reproducible evidence from observational data. 14 This initiative brings together hundreds of researchers from 30 countries, working with health records from around 600 million unique patients in its distributed database. The analysis code was distributed across all centres contributing to Observational Health Data Sciences and Informatics without sharing patient level data.…”
Section: Methodsmentioning
confidence: 99%
“…These hospital data included information from all public and private hospitals in Catalonia (Conjunt Mínim Bàsic de Dades d’Alta Hospitalària, CMBD-AH). 21 Both databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) common data model, 22 which allowed the same analytical code to be run against both datasets without the need to share patient level data.…”
Section: Methodsmentioning
confidence: 99%
“…To support health professionals in sharing EHRs, integrated communication by means of vocabularies such as SNOMED CT or LOINC is crucial [4]. Therefore, in the future, mappings to other terminologies, especially to SNOMED CT, will enable the international exchange of ICF data in standardized data models [74][75][76] and facilitate big data analysis [77]. To this end, linkage to other established vocabularies is an important goal in addition to establishing a uniform framework and language in dietetics.…”
Section: Integration Of the Icf Cataloguementioning
confidence: 99%