Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data Model: A Multidimensional Quality Assurance Approach.
Kristine E. Lynch,Stephen A. Deppen,Scott L. DuVall,Benjamin Viernes,Aize Cao,Daniel Park,Elizabeth Hanchrow,Kushan Hewa,Peter Greaves,Michael E. Matheny +9 more
TLDR
A quality assurance (QA) process and code base is developed to accompany the incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse health care database into the Observational Medical Outcomes Partnership (OMOP) CDM to prevent incremental load errors.Abstract:
Background The development and adoption of health care common data models (CDMs) has addressed some of the logistical challenges of performing research on data generated from disparate health care systems by standardizing data representations and leveraging standardized terminology to express clinical information consistently. However, transforming a data system into a CDM is not a trivial task, and maintaining an operational, enterprise capable CDM that is incrementally updated within a data warehouse is challenging. Objectives To develop a quality assurance (QA) process and code base to accompany our incremental transformation of the Department of Veterans Affairs Corporate Data Warehouse health care database into the Observational Medical Outcomes Partnership (OMOP) CDM to prevent incremental load errors. Methods We designed and implemented a multistage QA) approach centered on completeness, value conformance, and relational conformance data-quality elements. For each element we describe key incremental load challenges, our extract, transform, and load (ETL) solution of data to overcome those challenges, and potential impacts of incremental load failure. Results Completeness and value conformance data-quality elements are most affected by incremental changes to the CDW, while updates to source identifiers impact relational conformance. ETL failures surrounding these elements lead to incomplete and inaccurate capture of clinical concepts as well as data fragmentation across patients, providers, and locations. Conclusion Development of robust QA processes supporting accurate transformation of OMOP and other CDMs from source data is still in evolution, and opportunities exist to extend the existing QA framework and tools used for incremental ETL QA processes.read more
Citations
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Common Problems, Common Data Model Solutions: Evidence Generation for Health Technology Assessment.
Seamus Kent,Edward Burn,Dalia Dawoud,Pall Jonsson,Jens Torup Østby,Nigel Hughes,Peter R. Rijnbeek,Jacoline C. Bouvy +7 more
TL;DR: It is shown that the common data model has the potential to facilitate access to relevant data, enable multidatabase studies to enhance statistical power and transfer results across populations and settings to meet the needs of local HTA decision makers, and validate findings.
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APOL1 Risk Variants, Acute Kidney Injury, and Death in Participants With African Ancestry Hospitalized With COVID-19 From the Million Veteran Program.
Adriana M. Hung,Alexander G. Bick,Zhi Yu,Hua Chang Chen,Christine M. Hunt,Frank R. Wendt,Otis D. Wilson,Robert A. Greevy,Cecilia P. Chung,A. Suzuki,Yuk-Lam Ho,Elvis A. Akwo,Renato Polimanti,Jin Zhou,Peter D. Reaven,Philip S. Tsao,J. Michael Gaziano,Jennifer E. Huffman,Jacob Joseph,Shiuh Wen Luoh,Sudha K. Iyengar,Kyong-Mi Chang,Juan P. Casas,Michael E. Matheny,Christopher J. O'Donnell,Kelly Cho,Ran Tao,Katalin Susztak,Cassianne Robinson-Cohen,Sony Tuteja,Edward D. Siew +30 more
TL;DR: In this cohort study of veterans with African ancestry hospitalized with COVID-19 infection, APOL1 kidney risk variants were associated with higher odds of AKI, AKI severity, and death, even among individuals with prior normal kidney function.
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Comparison of COVID-19 versus influenza on the incidence, features, and recovery from acute kidney injury in hospitalized United States Veterans.
Bethany C. Birkelo,Sharidan K. Parr,Sharidan K. Parr,Amy M. Perkins,Amy M. Perkins,Robert A. Greevy,Adriana M. Hung,Adriana M. Hung,Shailja C. Shah,Juan Pablo Arroyo,Jason Denton,Andrew J. Vincz,Andrew J. Vincz,Michael E. Matheny,Edward D. Siew,Edward D. Siew +15 more
TL;DR: In this article, the authors compared the incidence, features, and outcomes of acute kidney injury among Veterans hospitalized with COVID-19 or influenza and adjusted for baseline conditions using weighted comparisons.
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EHR-Independent Predictive Decision Support Architecture Based on OMOP.
Philipp Unberath,Hans-Ulrich Prokosch,Julian Gründner,Marcel Erpenbeck,Christian Maier,Jan Christoph +5 more
TL;DR: An EHR-independent means of integrating prediction models for deployment in clinical settings, utilizing the widely used Observational Medical Outcomes Partnership (OMOP) common data model rather than on a proprietary EHR data structure is proposed.
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Baseline phenotype and 30-day outcomes of people tested for COVID-19: an international network cohort including >3.32 million people tested with real-time PCR and >219,000 tested positive for SARS-CoV-2 in South Korea, Spain and the United States
Asieh Golozar,Lana Yh Lai,Anthony G. Sena,Anthony G. Sena,David Vizcaya,Lisa M. Schilling,Vojtech Huser,Fredrik Nyberg,Scott L. DuVall,Daniel R. Morales,Thamir M. Alshammari,Hamed Abedtash,Waheed-Ul-Rahman Ahmed,Waheed-Ul-Rahman Ahmed,Osaid Alser,Heba Alghoul,Ying Zhang,Mengchun Gong,Yin Guan,Carlos Areia,Jitendra Jonnagaddala,Karishma Shah,Jennifer C E Lane,Albert Prats-Uribe,Jose D. Posada,Nigam H. Shah,Vignesh Subbian,Lin Zhang,Lin Zhang,Maria Tereza Fernandes Abrahão,Peter R. Rijnbeek,Seng Chan You,Paula Casajust,Elena Roel,Martina Recalde,Sergio Fernandez-Bertolin,Alan Andryc,Jason Thomas,Adam B. Wilcox,Stephen Fortin,Clair Blacketer,Clair Blacketer,Frank J. DeFalco,Karthik Natarajan,Karthik Natarajan,Thomas Falconer,Matthew E. Spotnitz,Anna Ostropolets,George Hripcsak,George Hripcsak,Marc A. Suchard,Kristine E. Lynch,Michael E. Matheny,Andrew E. Williams,Christian G. Reich,Talita Duarte-Salles,Kristin Kostka,Patrick B. Ryan,Patrick B. Ryan,Daniel Prieto-Alhambra +59 more
TL;DR: The findings highlight the importance of large scale characterization of COVID-19 international cohorts to inform planning and resource allocation including testing as countries face a second wave of SARS-CoV-2.
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