Dataset Information

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

ABSTRACT: We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

SUBMITTER: Brat GA

PROVIDER: S-EPMC7438496 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

Brat Gabriel A GA Weber Griffin M GM Gehlenborg Nils N Avillach Paul P Palmer Nathan P NP Chiovato Luca L Cimino James J Waitman Lemuel R LR Omenn Gilbert S GS Malovini Alberto A Moore Jason H JH Beaulieu-Jones Brett K BK Tibollo Valentina V Murphy Shawn N SN Yi Sehi L' SL Keller Mark S MS Bellazzi Riccardo R Hanauer David A DA Serret-Larmande Arnaud A Gutierrez-Sacristan Alba A Holmes John J JJ Bell Douglas S DS Mandl Kenneth D KD Follett Robert W RW Klann Jeffrey G JG Murad Douglas A DA Scudeller Luigia L Bucalo Mauro M Kirchoff Katie K Craig Jean J Obeid Jihad J Jouhet Vianney V Griffier Romain R Cossin Sebastien S Moal Bertrand B Patel Lav P LP Bellasi Antonio A Prokosch Hans U HU Kraska Detlef D Sliz Piotr P Tan Amelia L M ALM Ngiam Kee Yuan KY Zambelli Alberto A Mowery Danielle L DL Schiver Emily E Devkota Batsal B Bradford Robert L RL Daniar Mohamad M Daniel Christel C Benoit Vincent V Bey Romain R Paris Nicolas N Serre Patricia P Orlova Nina N Dubiel Julien J Hilka Martin M Jannot Anne Sophie AS Breant Stephane S Leblanc Judith J Griffon Nicolas N Burgun Anita A Bernaux Melodie M Sandrin Arnaud A Salamanca Elisa E Cormont Sylvie S Ganslandt Thomas T Gradinger Tobias T Champ Julien J Boeker Martin M Martel Patricia P Esteve Loic L Gramfort Alexandre A Grisel Olivier O Leprovost Damien D Moreau Thomas T Varoquaux Gael G Vie Jill-Jênn JJ Wassermann Demian D Mensch Arthur A Caucheteux Charlotte C Haverkamp Christian C Lemaitre Guillaume G Bosari Silvano S Krantz Ian D ID South Andrew A Cai Tianxi T Kohane Isaac S IS

NPJ digital medicine 20200819

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on tem ...[more]

PMID: 32864472

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Project description:BackgroundThere is increasing recognition that health care providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error-prone. However, there is uncertainty about whether routinely collected clinical data within electronic health record (EHR) systems includes the data most relevant to measuring and comparing outcomes and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality.ObjectiveIn this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multistakeholder consultation. Adopting this approach, we performed a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure.MethodsAll available registries compatible with the diagnosis of heart failure within an EHR data repository of a general hospital (142,345 visits and 12,503 patients) were extracted and mapped to the ICHOM format. We focused our pilot assessment on 5 commonly used data quality dimensions: completeness, correctness, consistency, uniqueness, and temporal stability.ResultsWe found high scores (>95%) for the consistency, completeness, and uniqueness dimensions. Temporal stability analyses showed some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, the investigation of data correctness suggested several issues concerning the characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail that is not explicitly available in the coded data of an EHR.ConclusionsOverall, results of this pilot study revealed good data quality for the subset of heart failure outcomes collected at the Hospital del Mar. Nevertheless, some important data errors were identified that were caused by fundamentally different data collection practices in routine clinical care versus research, for which the ICHOM standard set was originally developed. To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM standards set and across multiple hospitals.

Dataset Information

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

Publications

International electronic health record-derived COVID-19 clinical course profiles: the 4CE consortium.

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