Literature DB >> 32373785

Data sharing in the era of COVID-19.

Christopher V Cosgriff1,2, Daniel K Ebner1,3, Leo Anthony Celi1,4.   

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Year:  2020        PMID: 32373785      PMCID: PMC7194831          DOI: 10.1016/S2589-7500(20)30082-0

Source DB:  PubMed          Journal:  Lancet Digit Health        ISSN: 2589-7500


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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to test the capacity of world health systems. Since the outbreak started, the global community has learned about coronavirus disease 2019 (COVID-19), the disease resulting from SARS-CoV-2. In the first few weeks of the pandemic, knowledge about the disease and its treatment was generated from sharing of anecdotal observations and small case series. Although health-care professionals use modern technology to communicate, never before has the failure to build robust data-sharing systems for large-scale near real-time analysis in health care been more obvious. In the era of electronic health records, physiologic, laboratory, imaging, decision-making, and treatment data are continuously recorded. Inferences drawn from these data can inform epidemiological inquiries and guide treatment protocols when clinical trial data do not exist or might be too slow to inform a rapidly evolving situation. While the number of trials increases, real-time treatment data accumulates, siloed within hospital systems. When considering COVID-19, the insight we could gain from a pooled, publicly available dataset analysed by researchers in academic institutes and industry is invaluable and necessary. Unfortunately, patient-level COVID-19 data is not publicly available. These data also lack comprehensive information beyond typical registry resolution. In this interconnected world, we can imagine a unifying multinational COVID-19 electronic health record waiting for global researchers to apply their methodological and domain expertise. No such database exists, and this failing is not rooted in an absence of technology or precedent. Within intensive care, for instance, the Medical Information Mart for Intensive Care (MIMIC) has been a model of publicly-available, deidentified, electronic health record data sharing since 1996.1, 2 Containing approximately 50 000 patient admissions to the Beth Israel Deaconess Medical Center (BIDMC), MIMIC represents the most studied critical care cohort in the world, allowing clinicians and computer scientists to address research questions and build predictive models. MIMIC is evidence of the possibility of data sharing beyond BIDMC's critical care department and hospital. Although the academic community has embraced data monetisation, regulatory hurdles, funding apparatuses, and a publish-or-perish academia at the expense of open data sharing, this short-sightedness does not need to be an undoing. A dreadful unprecedented worldwide event deserves an appropriate response, and this response begins with an extraordinary joining of forces—and data—to best understand the event, and the successes and failures of different treatments. The clinical and academic community will learn many lessons during these turbulent times, and crucially, needs to learn that data on health and disease should be shared universally so that everyone can benefit.
  2 in total

Review 1.  Critical Care, Critical Data.

Authors:  Christopher V Cosgriff; Leo Anthony Celi; David J Stone
Journal:  Biomed Eng Comput Biol       Date:  2019-06-12

2.  MIMIC-III, a freely accessible critical care database.

Authors:  Alistair E W Johnson; Tom J Pollard; Lu Shen; Li-Wei H Lehman; Mengling Feng; Mohammad Ghassemi; Benjamin Moody; Peter Szolovits; Leo Anthony Celi; Roger G Mark
Journal:  Sci Data       Date:  2016-05-24       Impact factor: 6.444

  2 in total
  25 in total

1.  Clinical Data Extraction During Public Health Emergencies: A Blockchain Technology Assessment.

Authors:  Joan Brown; Manas Bhatnagar; Hugh Gordon; Karen Lutrick; Jared Goodner; James Blum; Raquel Bartz; Daniel Uslan; Ernesto David-DiMarino; Alfred Sorbello; Gregory Jackson; Jeremy Walsh; Lauren Neal; Marek Cyran; Henry Francis; J Perren Cobb
Journal:  Biomed Instrum Technol       Date:  2021-07-01

2.  Measuring re-identification risk using a synthetic estimator to enable data sharing.

Authors:  Yangdi Jiang; Lucy Mosquera; Bei Jiang; Linglong Kong; Khaled El Emam
Journal:  PLoS One       Date:  2022-06-17       Impact factor: 3.752

3.  An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study.

Authors:  Joan Brown; Manas Bhatnagar; Hugh Gordon; Jared Goodner; J Perren Cobb; Karen Lutrick
Journal:  JMIR Hum Factors       Date:  2022-06-09

4.  Patient Experiences, Trust, and Preferences for Health Data Sharing.

Authors:  Rochelle D Jones; Chris Krenz; Kent A Griffith; Rebecca Spence; Angela R Bradbury; Raymond De Vries; Sarah T Hawley; Robin Zon; Sage Bolte; Navid Sadeghi; Richard L Schilsky; Reshma Jagsi
Journal:  JCO Oncol Pract       Date:  2021-12-02

Review 5.  ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences.

Authors:  Marc Jacobs; Aline Remus; Charlotte Gaillard; Hector M Menendez; Luis O Tedeschi; Suresh Neethirajan; Jennifer L Ellis
Journal:  J Anim Sci       Date:  2022-06-01       Impact factor: 3.338

6.  The RSNA International COVID-19 Open Radiology Database (RICORD).

Authors:  Emily B Tsai; Scott Simpson; Matthew P Lungren; Michelle Hershman; Leonid Roshkovan; Errol Colak; Bradley J Erickson; George Shih; Anouk Stein; Jayashree Kalpathy-Cramer; Jody Shen; Mona Hafez; Susan John; Prabhakar Rajiah; Brian P Pogatchnik; John Mongan; Emre Altinmakas; Erik R Ranschaert; Felipe C Kitamura; Laurens Topff; Linda Moy; Jeffrey P Kanne; Carol C Wu
Journal:  Radiology       Date:  2021-01-05       Impact factor: 11.105

7.  Data Sharing in Southeast Asia During the First Wave of the COVID-19 Pandemic.

Authors:  Arianna Maever L Amit; Veincent Christian F Pepito; Bernardo Gutierrez; Thomas Rawson
Journal:  Front Public Health       Date:  2021-06-16

8.  Linguistic methods in healthcare application and COVID-19 variants classification.

Authors:  Marek R Ogiela; Urszula Ogiela
Journal:  Neural Comput Appl       Date:  2021-07-06       Impact factor: 5.606

9.  Tracing open data in emergencies: The case of the COVID-19 pandemic.

Authors:  Konstantinos Gkiouras; Meletios P Nigdelis; Maria G Grammatikopoulou; Dimitrios G Goulis
Journal:  Eur J Clin Invest       Date:  2020-07-22       Impact factor: 5.722

10.  FedDPGAN: Federated Differentially Private Generative Adversarial Networks Framework for the Detection of COVID-19 Pneumonia.

Authors:  Longling Zhang; Bochen Shen; Ahmed Barnawi; Shan Xi; Neeraj Kumar; Yi Wu
Journal:  Inf Syst Front       Date:  2021-06-15       Impact factor: 6.191

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