{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sidky H"],"funding":["NCATS NIH HHS","NIEHS NIH HHS","NIGMS NIH HHS"],"pagination":["46"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9936475"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["23(1)"],"pubmed_abstract":["<h4>Background</h4>Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness.<h4>Methods</h4>Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria "],"journal":["BMC medical research methodology"],"pubmed_title":["Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C)."],"pmcid":["PMC9936475"],"funding_grant_id":["UL1 TR002373","UL1 TR002494","U54 GM115371","UL1 TR003142","UL1 TR002378","UL1 TR001442","UL1 TR002014","UL1 TR002377","UL1 TR001881","UL1 TR002537","UL1 TR002535","UL1 TR002733","UL1 TR001445","UL1 TR003107","UL1 TR001449","UL1 TR002538","UL1 TR002736","UL1 TR001409","U54 GM115458","UL1 TR001450","UL1 TR002384","UL1 TR002544","UL1 TR002389","UL1 TR002345","UL1 TR001453","UL1 TR002541","UL1 TR001855","UL1 TR001414","UL1 TR001412","P30 ES005022","U54 GM104938","UL1 TR002550","UL1 TR001422","UL1 TR001420","UL1 TR001860","UL1 TR002553","UL1 TR003167","UL1 TR001866","UL1 TR001425","UL1 TR002556","U24 TR002306","UL1 TR001863","UL1 TR002319","UL1 TR001427","U54 GM115677","UL1 TR003096","U54 GM115516","UL1 TR002243","UL1 TR002001","UL1 TR003015","UL1 TR003017","UL1 TR002240","UL1 TR001433","UL1 TR003098","UL1 TR002489","UL1 TR001873","UL1 TR001872","UL1 TR002003","UL1 TR002366","UL1 TR001430","U54 GM104941","UL1 TR001998","UL1 TR001436","U54 GM104940","UL1 TR001876","UL1 TR002645","UL1 TR002369","UL1 TR002529","UL1 TR002649","UL1 TR001439","UL1 TR001878","U54 GM104942","U54 GM115428"],"pubmed_authors":["Sidky H","Young JC","Michael S","Hotaling N","Pfaff ER","Mehta HB","Funk MJ","Setoguchi S","Chute CG","Lee E","Shao YR","Dahlin JL","Wilkins KJ","Haendel MA","Bates B","Sahner D","N3C Consortium","Girvin AT","Alexander GC","Gersing K"],"additional_accession":[]},"is_claimable":false,"name":"Data quality considerations for evaluating COVID-19 treatments using real world data: learnings from the National COVID Cohort Collaborative (N3C).","description":"<h4>Background</h4>Multi-institution electronic health records (EHR) are a rich source of real world data (RWD) for generating real world evidence (RWE) regarding the utilization, benefits and harms of medical interventions. They provide access to clinical data from large pooled patient populations in addition to laboratory measurements unavailable in insurance claims-based data. However, secondary use of these data for research requires specialized knowledge and careful evaluation of data quality and completeness. We discuss data quality assessments undertaken during the conduct of prep-to-research, focusing on the investigation of treatment safety and effectiveness.<h4>Methods</h4>Using the National COVID Cohort Collaborative (N3C) enclave, we defined a patient population using criteria ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Feb","modification":"2026-06-04T05:58:10.571Z","creation":"2025-02-19T02:56:27.444Z"},"accession":"S-EPMC9936475","cross_references":{"pubmed":["36800930"],"doi":["10.1186/s12874-023-01839-2"]}}