Cargando…
Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative
OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limite...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500110/ https://www.ncbi.nlm.nih.gov/pubmed/34590684 http://dx.doi.org/10.1093/jamia/ocab217 |
_version_ | 1784580392757493760 |
---|---|
author | Pfaff, Emily R Girvin, Andrew T Gabriel, Davera L Kostka, Kristin Morris, Michele Palchuk, Matvey B Lehmann, Harold P Amor, Benjamin Bissell, Mark Bradwell, Katie R Gold, Sigfried Hong, Stephanie S Loomba, Johanna Manna, Amin McMurry, Julie A Niehaus, Emily Qureshi, Nabeel Walden, Anita Zhang, Xiaohan Tanner Zhu, Richard L Moffitt, Richard A Haendel, Melissa A Chute, Christopher G Adams, William G Al-Shukri, Shaymaa Anzalone, Alfred Baghal, Ahmad Bennett, Tellen D Bernstam, Elmer V Bernstam, Elmer V Bissell, Mark M Bush, Brian Campion, Thomas R Castro, Victor Chang, Jack Chaudhari, Deepa D Chen, Wenjin Chu, San Cimino, James J Crandall, Keith A Crooks, Mark Davies, Sara J Deakyne DiPalazzo, John Dorr, David Eckrich, Dan Eltinge, Sarah E Fort, Daniel G Golovko, George Gupta, Snehil Haendel, Melissa A Hajagos, Janos G Hanauer, David A Harnett, Brett M Horswell, Ronald Huang, Nancy Johnson, Steven G Kahn, Michael Khanipov, Kamil Kieler, Curtis Luzuriaga, Katherine Ruiz De Maidlow, Sarah Martinez, Ashley Mathew, Jomol McClay, James C McMahan, Gabriel Melancon, Brian Meystre, Stephane Miele, Lucio Morizono, Hiroki Pablo, Ray Patel, Lav Phuong, Jimmy Popham, Daniel J Pulgarin, Claudia Santos, Carlos Sarkar, Indra Neil Sazo, Nancy Setoguchi, Soko Soby, Selvin Surampalli, Sirisha Suver, Christine Vangala, Uma Maheswara Reddy Visweswaran, Shyam von Oehsen, James Walters, Kellie M Wiley, Laura Williams, David A Zai, Adrian |
author_facet | Pfaff, Emily R Girvin, Andrew T Gabriel, Davera L Kostka, Kristin Morris, Michele Palchuk, Matvey B Lehmann, Harold P Amor, Benjamin Bissell, Mark Bradwell, Katie R Gold, Sigfried Hong, Stephanie S Loomba, Johanna Manna, Amin McMurry, Julie A Niehaus, Emily Qureshi, Nabeel Walden, Anita Zhang, Xiaohan Tanner Zhu, Richard L Moffitt, Richard A Haendel, Melissa A Chute, Christopher G Adams, William G Al-Shukri, Shaymaa Anzalone, Alfred Baghal, Ahmad Bennett, Tellen D Bernstam, Elmer V Bernstam, Elmer V Bissell, Mark M Bush, Brian Campion, Thomas R Castro, Victor Chang, Jack Chaudhari, Deepa D Chen, Wenjin Chu, San Cimino, James J Crandall, Keith A Crooks, Mark Davies, Sara J Deakyne DiPalazzo, John Dorr, David Eckrich, Dan Eltinge, Sarah E Fort, Daniel G Golovko, George Gupta, Snehil Haendel, Melissa A Hajagos, Janos G Hanauer, David A Harnett, Brett M Horswell, Ronald Huang, Nancy Johnson, Steven G Kahn, Michael Khanipov, Kamil Kieler, Curtis Luzuriaga, Katherine Ruiz De Maidlow, Sarah Martinez, Ashley Mathew, Jomol McClay, James C McMahan, Gabriel Melancon, Brian Meystre, Stephane Miele, Lucio Morizono, Hiroki Pablo, Ray Patel, Lav Phuong, Jimmy Popham, Daniel J Pulgarin, Claudia Santos, Carlos Sarkar, Indra Neil Sazo, Nancy Setoguchi, Soko Soby, Selvin Surampalli, Sirisha Suver, Christine Vangala, Uma Maheswara Reddy Visweswaran, Shyam von Oehsen, James Walters, Kellie M Wiley, Laura Williams, David A Zai, Adrian |
author_sort | Pfaff, Emily R |
collection | PubMed |
description | OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require. |
format | Online Article Text |
id | pubmed-8500110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-85001102021-10-08 Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative Pfaff, Emily R Girvin, Andrew T Gabriel, Davera L Kostka, Kristin Morris, Michele Palchuk, Matvey B Lehmann, Harold P Amor, Benjamin Bissell, Mark Bradwell, Katie R Gold, Sigfried Hong, Stephanie S Loomba, Johanna Manna, Amin McMurry, Julie A Niehaus, Emily Qureshi, Nabeel Walden, Anita Zhang, Xiaohan Tanner Zhu, Richard L Moffitt, Richard A Haendel, Melissa A Chute, Christopher G Adams, William G Al-Shukri, Shaymaa Anzalone, Alfred Baghal, Ahmad Bennett, Tellen D Bernstam, Elmer V Bernstam, Elmer V Bissell, Mark M Bush, Brian Campion, Thomas R Castro, Victor Chang, Jack Chaudhari, Deepa D Chen, Wenjin Chu, San Cimino, James J Crandall, Keith A Crooks, Mark Davies, Sara J Deakyne DiPalazzo, John Dorr, David Eckrich, Dan Eltinge, Sarah E Fort, Daniel G Golovko, George Gupta, Snehil Haendel, Melissa A Hajagos, Janos G Hanauer, David A Harnett, Brett M Horswell, Ronald Huang, Nancy Johnson, Steven G Kahn, Michael Khanipov, Kamil Kieler, Curtis Luzuriaga, Katherine Ruiz De Maidlow, Sarah Martinez, Ashley Mathew, Jomol McClay, James C McMahan, Gabriel Melancon, Brian Meystre, Stephane Miele, Lucio Morizono, Hiroki Pablo, Ray Patel, Lav Phuong, Jimmy Popham, Daniel J Pulgarin, Claudia Santos, Carlos Sarkar, Indra Neil Sazo, Nancy Setoguchi, Soko Soby, Selvin Surampalli, Sirisha Suver, Christine Vangala, Uma Maheswara Reddy Visweswaran, Shyam von Oehsen, James Walters, Kellie M Wiley, Laura Williams, David A Zai, Adrian J Am Med Inform Assoc Research and Applications OBJECTIVE: In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. MATERIALS AND METHODS: We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. RESULTS: Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. DISCUSSION: We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. CONCLUSION: By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require. Oxford University Press 2021-11-02 /pmc/articles/PMC8500110/ /pubmed/34590684 http://dx.doi.org/10.1093/jamia/ocab217 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Research and Applications Pfaff, Emily R Girvin, Andrew T Gabriel, Davera L Kostka, Kristin Morris, Michele Palchuk, Matvey B Lehmann, Harold P Amor, Benjamin Bissell, Mark Bradwell, Katie R Gold, Sigfried Hong, Stephanie S Loomba, Johanna Manna, Amin McMurry, Julie A Niehaus, Emily Qureshi, Nabeel Walden, Anita Zhang, Xiaohan Tanner Zhu, Richard L Moffitt, Richard A Haendel, Melissa A Chute, Christopher G Adams, William G Al-Shukri, Shaymaa Anzalone, Alfred Baghal, Ahmad Bennett, Tellen D Bernstam, Elmer V Bernstam, Elmer V Bissell, Mark M Bush, Brian Campion, Thomas R Castro, Victor Chang, Jack Chaudhari, Deepa D Chen, Wenjin Chu, San Cimino, James J Crandall, Keith A Crooks, Mark Davies, Sara J Deakyne DiPalazzo, John Dorr, David Eckrich, Dan Eltinge, Sarah E Fort, Daniel G Golovko, George Gupta, Snehil Haendel, Melissa A Hajagos, Janos G Hanauer, David A Harnett, Brett M Horswell, Ronald Huang, Nancy Johnson, Steven G Kahn, Michael Khanipov, Kamil Kieler, Curtis Luzuriaga, Katherine Ruiz De Maidlow, Sarah Martinez, Ashley Mathew, Jomol McClay, James C McMahan, Gabriel Melancon, Brian Meystre, Stephane Miele, Lucio Morizono, Hiroki Pablo, Ray Patel, Lav Phuong, Jimmy Popham, Daniel J Pulgarin, Claudia Santos, Carlos Sarkar, Indra Neil Sazo, Nancy Setoguchi, Soko Soby, Selvin Surampalli, Sirisha Suver, Christine Vangala, Uma Maheswara Reddy Visweswaran, Shyam von Oehsen, James Walters, Kellie M Wiley, Laura Williams, David A Zai, Adrian Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative |
title | Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative |
title_full | Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative |
title_fullStr | Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative |
title_full_unstemmed | Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative |
title_short | Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative |
title_sort | synergies between centralized and federated approaches to data quality: a report from the national covid cohort collaborative |
topic | Research and Applications |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8500110/ https://www.ncbi.nlm.nih.gov/pubmed/34590684 http://dx.doi.org/10.1093/jamia/ocab217 |
work_keys_str_mv | AT pfaffemilyr synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT girvinandrewt synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT gabrieldaveral synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT kostkakristin synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT morrismichele synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT palchukmatveyb synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT lehmannharoldp synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT amorbenjamin synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bissellmark synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bradwellkatier synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT goldsigfried synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT hongstephanies synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT loombajohanna synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT mannaamin synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT mcmurryjuliea synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT niehausemily synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT qureshinabeel synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT waldenanita synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT zhangxiaohantanner synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT zhurichardl synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT moffittricharda synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT haendelmelissaa synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT chutechristopherg synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT adamswilliamg synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT alshukrishaymaa synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT anzalonealfred synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT baghalahmad synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bennetttellend synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bernstamelmerv synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bernstamelmerv synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bissellmarkm synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT bushbrian synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT campionthomasr synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT castrovictor synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT changjack synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT chaudharideepad synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT chenwenjin synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT chusan synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT ciminojamesj synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT crandallkeitha synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT crooksmark synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT daviessarajdeakyne synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT dipalazzojohn synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT dorrdavid synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT eckrichdan synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT eltingesarahe synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT fortdanielg synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT golovkogeorge synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT guptasnehil synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT haendelmelissaa synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT hajagosjanosg synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT hanauerdavida synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT harnettbrettm synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT horswellronald synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT huangnancy synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT johnsonsteveng synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT kahnmichael synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT khanipovkamil synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT kielercurtis synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT luzuriagakatherineruizde synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT maidlowsarah synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT martinezashley synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT mathewjomol synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT mcclayjamesc synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT mcmahangabriel synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT melanconbrian synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT meystrestephane synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT mielelucio synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT morizonohiroki synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT pabloray synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT patellav synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT phuongjimmy synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT pophamdanielj synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT pulgarinclaudia synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT santoscarlos synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT sarkarindraneil synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT sazonancy synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT setoguchisoko synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT sobyselvin synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT surampallisirisha synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT suverchristine synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT vangalaumamaheswarareddy synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT visweswaranshyam synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT vonoehsenjames synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT walterskelliem synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT wileylaura synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT williamsdavida synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative AT zaiadrian synergiesbetweencentralizedandfederatedapproachestodataqualityareportfromthenationalcovidcohortcollaborative |