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10.1093/jamia/ocab108 http://scihub22266oqcxt.onion/10.1093/jamia/ocab108 33993254!8243354!33993254     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33993254&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 261.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (8): 1605-1611 Nephropedia Template TP {{tp|p=33993254|t=2021. Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research |pdf=|usr=}}{{33993254}} gab.com Text Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33993254 #FOAvip OBJECTIVE: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. METHODS: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. RESULTS: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. CONCLUSIONS: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research. Twit Text FOAVip Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33993254 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33993254 #FOAvip English Wikipedia <ref>{{Cite pmid|33993254}}</ref> Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research #MMPMID33993254 Lenert LA; Ilatovskiy AV; Agnew J; Rudisill P; Jacobs J; Weatherston D; Deans KR Jr J Am Med Inform Assoc 2021[Jul]; 28 (8): 1605-1611 PMID33993254show ga OBJECTIVE: The rapidly evolving COVID-19 pandemic has created a need for timely data from the healthcare systems for research. To meet this need, several large new data consortia have been developed that require frequent updating and sharing of electronic health record (EHR) data in different common data models (CDMs) to create multi-institutional databases for research. Traditionally, each CDM has had a custom pipeline for extract, transform, and load operations for production and incremental updates of data feeds to the networks from raw EHR data. However, the demands of COVID-19 research for timely data are far higher, and the requirements for updating faster than previous collaborative research using national data networks have increased. New approaches need to be developed to address these demands. METHODS: In this article, we describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical data model and the automated transformation of clinical data to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs for data sharing and research collaboration on COVID-19. RESULTS: FHIR data resources could be transformed to operational PCORnet and OMOP CDMs with minimal production delays through a combination of real-time and postprocessing steps, leveraging the FHIR data subscription feature. CONCLUSIONS: The approach leverages evolving standards for the availability of EHR data developed to facilitate data exchange under the 21st Century Cures Act and could greatly enhance the availability of standardized datasets for research. |*COVID-19[MESH] |*Data Warehousing[MESH] |*Electronic Health Records[MESH] |*Health Information Interoperability[MESH] |*Information Dissemination[MESH] |Biomedical Research/*organization & administration[MESH] |Common Data Elements[MESH] |Data Management/organization & administration[MESH]Automated production of research data marts from a canonical fast heal(epmc).pdf DeepDyve Pubget Overpricing