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Pfaff ER, Girvin AT, Gabriel DL, Kostka K, Morris M, Palchuk MB, Lehmann HP, Amor B, Bissell M, Bradwell KR, Gold S, Hong SS, Loomba J, Manna A, McMurry JA, Niehaus E, Qureshi N, Walden A, Zhang XT, Zhu RL, Moffitt RA, Haendel MA, Chute CG, Adams WG, Al-Shukri S, Anzalone A, Baghal A, Bennett TD, Bernstam EV, Bernstam EV, Bissell MM, Bush B, Campion TR, Castro V, Chang J, Chaudhari DD, Chen W, Chu S, Cimino JJ, Crandall KA, Crooks M, Davies SJD, DiPalazzo J, Dorr D, Eckrich D, Eltinge SE, Fort DG, Golovko G, Gupta S, Haendel MA, Hajagos JG, Hanauer DA, Harnett BM, Horswell R, Huang N, Johnson SG, Kahn M, Khanipov K, Kieler C, Luzuriaga KRD, Maidlow S, Martinez A, Mathew J, McClay JC, McMahan G, Melancon B, Meystre S, Miele L, Morizono H, Pablo R, Patel L, Phuong J, Popham DJ, Pulgarin C, Santos C, Sarkar IN, Sazo N, Setoguchi S, Soby S, Surampalli S, Suver C, Vangala UMR, Visweswaran S, von Oehsen J, Walters KM, Wiley L, Williams DA, Zai A. Synergies between centralized and federated approaches to data quality: a report from the national COVID cohort collaborative. J Am Med Inform Assoc 2022; 29:609-618. [PMID: 34590684 PMCID: PMC8500110 DOI: 10.1093/jamia/ocab217] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/19/2021] [Accepted: 09/23/2021] [Indexed: 02/01/2023] Open
Abstract
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.
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Affiliation(s)
- Emily R Pfaff
- Department of Medicine, UNC Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA
| | | | - Davera L Gabriel
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kristin Kostka
- The OHDSI Center at the Roux Institute, Northeastern University, Portland, Maine, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Harold P Lehmann
- Department of Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | | | | | | | - Sigfried Gold
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Stephanie S Hong
- Section of Biomedical Informatics and Data Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | - Amin Manna
- Palantir Technologies, Denver, Colorado, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | | | - Anita Walden
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Richard L Zhu
- Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, New York, USA
| | - Melissa A Haendel
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, USA
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Miller R, Coyne E, Crowgey EL, Eckrich D, Myers JC, Villanueva R, Wadman J, Jacobs-Allen S, Gresh R, Volchenboum SL, Kolb EA. Implementation of a learning healthcare system for sickle cell disease. JAMIA Open 2020; 3:349-359. [PMID: 33215070 PMCID: PMC7660956 DOI: 10.1093/jamiaopen/ooaa024] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 04/09/2020] [Accepted: 06/25/2020] [Indexed: 11/13/2022] Open
Abstract
Objective Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. Materials and Methods Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. Results The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. Conclusions Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.
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Affiliation(s)
- Robin Miller
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Nemours Center for Cancer and Blood Disorders, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Department of Pediatrics, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Erin Coyne
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Erin L Crowgey
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Nemours Center for Cancer and Blood Disorders, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Department of Pediatrics, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Dan Eckrich
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Jeffrey C Myers
- Nemours Center for Cancer and Blood Disorders, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Department of Pediatrics, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Raymond Villanueva
- Information Systems Clinical Applications, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Jean Wadman
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Sidnie Jacobs-Allen
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | - Renee Gresh
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Nemours Center for Cancer and Blood Disorders, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Department of Pediatrics, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
| | | | - E Anders Kolb
- Nemours Sickle Cell Center of Biomedical Research Excellence (COBRE), Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Nemours Center for Cancer and Blood Disorders, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA.,Department of Pediatrics, Nemours Alfred I. duPont Hospital for Children, Wilmington, Delaware, USA
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