AMIA 2021 Virtual Informatics Summit COVID Sessions Spotlight

The COVID-19 health crisis has highlighted the critical importance of biomedical and health informatics research, as the world attempts to manage the rippling effects of this outbreak. What has the COVID-19 pandemic taught us about the intelligent deployment of healthcare informatics? Are there lessons we can apply to our specific fields of research?

Find out by adding the following sessions and posters to your Virtual Informatics Summit calendar.

11:30 am - 1:00 pm

S05: Panel – Data Acquisition and Harmonization of COVID-19 Case Data Across Common Data Models: Early Field Reports from the National COVID Cohort Collaborative (N3C)

E. Pfaff, UNC Chapel Hill; S. Hong, D. Jiao, X. Zhang, C. Chute, Johns Hopkins University

The National COVID Cohort Collaborative (N3C), sponsored by the National Center for Advancing Translational Sciences (NCATS) is a partnership among Clinical Translational Science Awardees (CTSAs) and other academic medical centers; the National Center for Data to Health (CD2H); and members and subject matter experts from Observational Health Data Sciences and Informatics (OHDSI), PCORnet, the Accrual to Clinical Trials (ACT) network, and TriNetX. N3C’s goals are to demonstrate that a “multi-site collaborative learning health network can overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics”. This panel is composed of informaticians supporting the data acquisition, ingestion and harmonization processes of N3C, with a focus on phenotype development and implementation, building the data ingestion pipeline, improving COVID test results from early data sets and developing and implementation of a data quality framework for the project.

S06: Panel – Unlocking Clinical Concepts Embedded in Unstructured Text to Advance COVID-19 Analytics for National COVID Cohort Collaborative (N3C)

H. Liu, Mayo Clinic; R. Fuentes, National Institute of Health; J. Guinney, Sage Bionetworks; S. Liu, Mayo Clinic; P. Szolovits, Massachusetts Institute of Technology; H. Xu, The University of Texas Health Science Center at Houston

The National COVID Cohort Collaborative (N3C) aims to assemble a multi-site learning health infrastructure with electronic health record (EHR) data for a nationwide cohort made available for COVID-19 analytics. One known challenge of EHR-based observational studies is that detailed patient information required by a study often resides in clinical narratives. Various natural language processing (NLP) technologies have been investigated to accelerate the use of clinical narratives for rapid clinical research. This panel describes a collaborative effort among Clinical Data to Health (CD2H), Open Health Natural Language Processing (OHNLP), and Observational Health Data Sciences and Informatics (OHSDI) NLP towards a national clinical NLP ecosystem.

1:30 - 3:00 pm

S10 – Never Stop (Machine) Learning

Quantifying COVID-19 In-hospital Deterioration Risk Using Acuity at Admission as Measured by the Rothman Index
J. Beals, PeraHealth, Inc; J. Barnes, D. Durand, Sinai Hospital, LifeBridge Health; J. Rimar, T. Donohue, Yale New Haven Hospital; M. Hoq, Bridgeport Hospital; K. Belk, PeraHealth, Inc; A. Amin, University of California Irvine Medical Center; M. Rothman, PeraHealth, Inc.

Predictive Modeling Using Transcriptomic Signatures of COVID-19 and Other Infectious Diseases
H. Srijay, Duke University; F. Constantine, M. McClain, C. Woods, R. Henao, Duke University, Center for Applied Genomics and Precision Medicine

S11: Panel – Lessons Learned from Healthcare Organizations Contributing Clinical Data to the National COVID Cohort Collaborative (N3C)

S. Meystre, Medical University of South Carolina; R. Gouripeddi, University of Utah; J. Harper, Regenstrief Institute; J. Talbert, University of Kentucky

The COVID-19 pandemic was officially declared by the World Health Organization in March 2020, after initial cases declared in China and a worldwide expansion. The first case in the U.S. was confirmed in January and a rapid expansion to all 50 U.S. states followed. We have seen a scattered approach for data collection and public data sets not being adequately available to explain the disease progression and key insights remaining unavailable to the public. To enable data sharing and collaborative research focused on COVID-19 across healthcare organizations in the U.S., the National COVID Cohort Collaborative (N3C) was created in the Spring of 2020 with support from NCATS and a focus on CTSA program hubs. It fostered a rapidly growing collaborative network of healthcare organizations and research communities. At the end of August 2020, more than 20 healthcare organizations were already sharing clinical data with N3C regularly. In order to share clinical data with N3C, participating healthcare organizations have to go through several steps and ensure availability of clinical data in a selection of data models (OMOP CDM, PCORnet, ACT, or TriNetX). This panel features speakers from four academic healthcare organizations currently sharing clinical data with N3C. They will share their institution and practical experiences, ideas and advice for healthcare organizations already sharing or planning to share clinical data with N3C.

3:30 - 5:00 pm

S12 – Equity, Accuracy, and Reproducibility of Clinical and Translational Research

COVID-19 Diagnostic Testing Prediction Using Natural Language Processing to Power a Data-driven Symptom Checker
S. Meystre, P. Heider, Y. Kim, Medical University of South Carolina

Unmasking the Conversation on Masks: Natural Language Processing for Topical Sentiment Analysis of COVID-19 Twitter Discourse
A. Sanders, R. White, L. Severson, R. Ma, R. McQueen, H. Campos Alcântara Paulo, Y. Zhang, J. Erickson, K. Bennett, Rensselaer Polytechnic Institute

S16: Panel – Are We There Yet? Finding, Analyzing, and Using Better Information for Pandemics

K. Fultz Hollis, Oregon Health & Sciences University; N. Tatonetti, Columbia University; S. McGrath, Providence Health and Services

How do we find and analyze better and accurate information to study and report pandemics like COVID-19? This panel intends to bring together translational informaticians working on COVID data and a prominent journalist from Bloomberg News who specializes in medical science reporting. We will present both good examples of pandemic data science appearing in journals and the news as well as look at where we might not be best at explaining a pandemic to a community. For the AMIA 2021 Summits, we will attempt to bring participants what we as informaticians need: to present an integrated set of perspectives or experience on COVID-19 information and how we use this information to study the science and work to treat the disease.

11:30 am - 3:00 pm

W02 – Common Approaches to Fair Data Policy in the US and Europe: Key Issues to Accelerate Science in times of the COVID-19 Pandemic

C. Parra-Calderón, Institute of Biomedicine of Seville / Virgen del Rocío University Hospital; C. Chronaki, HL7 Foundation; U. Tachinardi, Regenstrief Institute; N. Bahroos, University of Southern California; A. Solomonides, NorthShore University HealthSystem

We are living in a historic time worldwide caused by the coronavirus pandemic. The challenge is enormous worldwide and is testing the health systems and the economy of the affected countries themselves. It is also affecting the regions globally.

It is seriously challenging the capacity of the world’s nations to collaborate in science to know and combat this enemy of Humanity. Therefore, the international dimension of knowledge and information exchange as well as the acceleration of evidence generation should be a central concern.

At this time, the FAIR principles are positioned as the pillar on which to base this necessary alignment of policies for managing data and health research results, now more essential than ever. This workshop will present an approach to the common components that should govern these FAIR policies between the US and Europe as well as the key aspects of their effective application.

11:30 am - 1:00 pm

S21: Panel – Harmonizing What to Analyze in the National COVID Cohort Collaborative (N3C): Lessons Learned

The National COVID Cohort Collaborative (N3C) was established to provide patient-level data to further COVID-19 research. While the data are made available for research in the form in which they were submitted, providing constructs of the data should make that research go faster, be more consistent across projects, and more transparent. This panel, comprising informaticians, domain experts, data scientists, and experts in common data models, will describe the process by which we selected which variables needed harmonization, how we accomplished and vetted that harmonization, and how we made public that process and their results for over hundreds of variables and code sets. Our experience has implications for others involved in large-scale projects of pooled electronic health record data.

1:30 - 3:00 pm

S26: Panel – Performance of COVID-19 Research in the CTSA ACT Network

S. Murphy, Massachusetts General Hospital, Harvard Medical School; M. Morris, University of Pittsburgh; D. Ranganathan, University of Chicago; J. Klann, Massachusetts General Hospital, Harvard Medical School; G. Weber, Harvard Medical School

The Accrual for Clinical Trials (ACT) NIH Research Network was established for CTSA-attached organizations to perform Electronic Health Record research on COVID-19 infections. The goal for performing research in the ACT network is to allow participation from all joining institutions to create hypotheses and perform analyses across the network. We began during the 2020 pandemic to assemble and work with the pieces that could enable not only general health record queries, but focused questions that were arising from the pandemic. This could be achieved by a joint effort that allowed specifically constructed COVID-19 ontologies to be applied to the site data, analytic programs to be built and run at local sites, data quality to be assessed and validated at the sites, and governance to allow results to be pooled and published. The methods, tools and data structures used are open source and can be feely learned, exchanged, and reproduced.

5:00 - 6:30 pm

Analysis of Racial and Socioeconomic Factors of COVID-19
S. Abdullah, Indiana University-Purdue University Indianapolis; C. Vieira, Indiana University; E. Mendonca, S. Hui, Regenstrief Institute, Indiana University; K. Allen, Regenstrief Institute; U. Tachinardi, Regenstrief Institute, Indiana University

Evaluation of SOFA score for Outcome Prediction in COVID-19 ICU Patients
K. Bhattarai, M. Hofford, S. Yu, S. Kim, A. Gupta, A. Lai, P. Payne, A. Michelson, Washington University in St. Louis

Extracting COVID-19 Related Symptoms from EHR Data: A Comparison of Three Methods
H. Burkhardt, N. Dobbins, B. Mollis, M. Au, K. Ma, M. Yetisgen, A. Singh, M. Thompson, K. Stephens, University of Washington

Social Determinants Associated with COVID-19 Mortality in the United States
S. Debopadhaya, A. Sprague, T. Benavides, H. Mou, S. Ahn, C. Reschke, J. Erickson, K. Bennett, Rensselaer Polytechnic Institute

Telemedicine Utilization Among Non-hospitalized Patients with COVID-19
H. Huang, E. Scheufele, I. Dankwa-Mullan, IBM; G. Jackson, IBM, Vanderbilt University Medical Center; S. Wang, IBM

Increased Utilization of Telemedicine in Cancer Patients During the COVID-19 Pandemic
H. Huang, S. Wang, I. Dankwa-Mullan, G. Jackson, Y. Arriaga, D. Weeraratne, IBM Watson Health

Syndromic surveillance for COVID19 from Reddit using multi-platform lexicons
A. Leslie, S. Lakamana, A. Sarker, Emory University

The Application of Social Network Analysis for COVID-19 Nosocomial Infection Control
M. Ostovari, C. Jurkovitz, L. Pachter, M. Drees, D. Chen, Christiana Care Health System

The COVID-19 DREAM Challenge: Enabling Continuous Benchmarking of Models on EHR Data
T. Schaffter, Sage Bionetworks; T. Bergquist, Y. Yan, University of Washington; T. Yu, Y. Chae, Sage Bionetworks; J. Prosser, Institute for Translational Health Sciences; M. Mason, Sage Bionetworks; S. Mooney, University of Washington; J. Guinney, Sage Bionetworks

Identifying Bad Actors in a Direct-to-Patient COVID-19 Registry
K. Timms, E. Brinkley, K. Hawaldar, C. Tirrell, L. Rozenblit, IQVIA

11:30 am - 1:00 pm

S30 – You had me at Ontologies: Exploring Relationships

A Knowledge Graph Strategy for Integrating Clinical and Experimental COVID-19 Data
J. Reese, D. Unni, Lawrence Berkeley National Laboratory; T. Callahan, University of Colorado; L. Cappelletti, University of Milano; V. Ravanmehr, The Jackson Laboratory for Genomic Medicine; S. Carbon, Lawrence Berkeley National Laboratory; T. Fontana, Politecnico di Milano; H. Blau, The Jackson Laboratory for Genomic Medicine; N. Matentzoglu, Independent Contractor; N. Harris, Lawrence Berkeley National Laboratory; M. Munoz-Torres, Oregon State University; M. Haendel, Oregon State University; K. Shefchek, Oregon State University; P. Robinson, The Jackson Laboratory for Genomic Medicine; M. Joachimiak, C. Mungall, Lawrence Berkeley National Laboratory

S32 – Lifelong Learning: From Undergraduate Education through Clinical Technology Adoption

Telemedicine use Among Geriatric Outpatients During the COVID Pandemic
A. Szerszen, Y. Kogan, Y. Romm, S. Mishra, E. Burns, Northwell Health

S31: Panel – Consortium for Clinical Characterization of COVID-19 by EHR (4CE)

G. Weber, Harvard Medical School; G. Brat, Beth Israel Deaconess Medical Center; S. Murphy, Massachusetts General Hospital; D. Keogh, i2b2 tranSMART Foundation

There are several large, national and international projects to build informatics infrastructure to analyze the electronic health record (EHR) data of patients with COVID-19. However, aggregating data from multiple EHRs only works if you can trust the final results. This means being able to talk to the people at each site who know the data best, to understand the local clinical guidelines, coding practices, data quality problems, and other factors that affect the data. In March 2020, we launched an international effort called the Consortium for Clinical Characterization of COVID-19 by EHR (4CE), which brings together more than 100 informatics experts, statisticians, and physicians representing 200+ hospitals around the world. We run analyses locally within sites and share aggregate results centrally, where we review the data together and iteratively fix any issues. Through this process, we have identified key laboratory tests associated with COVID-19 disease severity.

S28: Systems Demonstrations – The Digital Era of National Collaboratives for Patient Care

Genomic Sequencing: Tracking, Ordering and Collaboration with GNomEx

Demonstration of a Self-service De-identified COVID-19 Data Lake
R. Chandras, S. Johnson, C. Pulgarin, M. Winner, C. Okpara, E. Iturrate, NYU Langone Health

Synchronized Coordination of The ACT Network to Rapidly Identify COVID-19 Patients in an Evolving Global Crisis
A. Maram, G. Weber, P. Trevvett, Harvard Medical School

1:30 - 3:00 pm

S33 – COVID19 is no Match for Clinical Research Informatics

Rapid Creation of a De-identified COVID-19 Dataset for Clinical Research
T. Magoc, Y. Galvan, R. Deason, University of Florida; J. Fishe, G. Labilloy, University of Florida-Jacksonville; G. Lipori, UF Health; I. Tfirn, University of Florida-Jacksonville; C. Harle, University of Florida

Creating a National COVID-19 Limited Data Set: Regulatory and Governance Innovations within the National COVID Cohort Collaborative (N3C)
M. Haendel, Oregon Health & Sciences University; C. Suver, Sage Bionetworks; J. Solway, University of Chicago; J. Wilbanks, Sage Bionetworks; J. Rutter, National Center for Advancing Translational Science

A Specialized COVID-19 Ontology for the ACT Network
S. Visweswaran, M. Samayamuthu, M. Morris, University of Pittsburgh; G. Weber, D. MacFadden, P. Trevvett, Harvard Medical School; J. Klann, Harvard Medical School, Mass General Brigham; V. Gainer, Mass General Brigham; S. Murphy, Harvard Medical School, Mass General Brigham

Association of a History of Pneumonia with Mortality for Coronavirus Disease 2019 (COVID-2019)
Z. Strasser, Massachusetts General Hospital, Harvard Medical School; H. Estiri, S. Murphy, Massachusetts General Hospital, Harvard Medical School, Mass General Brigham

Transparency, Reproducibility, and Team Science in the National COVID Cohort Collaborative (N3C)
A. Walden, Oregon Health and Science University; D. Gabriel, John Hopkins University; J. McMurry, Oregon State University; A. Williams, Tufts University; V. Subbian, The University of Arizona; K. Gersing, National Center for Advancing Translational Sciences; N. Harris, Lawrence Berkeley National Laboratory; C. Chute, John Hopkins University; M. Haendel, Oregon Health and Science University

S34 – COVID In and Among Patients

Severity Prediction for COVID-19 Patients via Recurrent Neural Networks
J. Lee, C. Ta, J. Kim, C. Liu, C. Weng, Columbia University

A Method to Link Neighborhood-level Covariates to COVID-19 Infection Patterns in Philadelphia Using Spatial Regression
M. Boland, J. Liu, C. Balocchi, J. Meeker, University of Pennsylvania; R. Bai, University of South Carolina; I. Mellis, D. Mowery, D. Herman, University of Pennsylvania

Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions
S. Bhavnani, UTMB; E. Kummerfeld, University of Minnesota; W. Zhang, Y. Kuo, UTMB; S. Visweswaran, University of Pittsburgh; M. Raji, R. Radhakrishnan, G. Golvoko, University of Texas Medical Branch; M. Usher, G. Melton, C. Tignanelli, University of Minnesota

Discovering Changes in the Neonatal Intensive Care Unit Structures Before and During the COVID-19 Pandemic: A Network Analysis
H. Mannering, Loyola University, Vanderbilt University Medical Center; C. Yan, Vanderbilt University, Vanderbilt University Medical Center; M. Alrifai, D. France, Vanderbilt University Medical Center; Y. Chen, Vanderbilt University

Impact of COVID-19 Pandemic on the Use of Telemedicine in an Academic Medical Center in New York City
W. Cui, J. Finkelstein, Icahn School of Medicine at Mount Sinai

S37 – Too Long, Didn’t Watch: AMIA Informatics Summit in 90 Minutes

Mobilizing the Accrual to Clinical Trials (ACT) Network for Covid-19 Research (and Beyond)
S. Murphy, V. Gainer, Mass General Brigham; S. Visweswaran, M. Morris, University of Pittsburgh; J. Klann, Massachusetts General Hospital; D. MacFadden, G. Weber, Harvard Medical School

COVID-19 Data Commons: A Single Source of Truth
V. Garcia, R. Wong, S. Gayen, T. Nguyen, R. Fernandes, S. Ali, J. Saltz, J. Hajagos, R. Moffitt, T. Kurc, S. Mallipattu, R. Jawa, A. Singer, M. Saltz, Stony Brook University Hospital

Developing and Implementing a “Digital First” Patient Journey in your Organization post COVID-19
C. Kunney, R. Hammar, J. Tyler, Document Storage Systems, Inc.; D. LaBorde, Brain Trust Advisors, LLC, Document Storage Systems, Inc., Iconic Data, Inc.

How a Zoom Forum is Changing the Way Researchers Study Covid-19 at Mass General Brigham
V. Gainer, Mass General Brigham, Harvard Medical School, Massachusetts General Hospital; N. Wattanasin, V. Castro, T. Wang, H. Park, Mass General Brigham; S. Murphy, Mass General Brigham, Harvard Medical School, MGH

Implementation of a Household Level COVID-19 Symptom Screening Tool in Schools
E. Kerns, A. Honcoop, R. McCulloh, University of Nebraska Medical Center

3:30 - 5:00 pm

Characterizing Respiratory Symptoms and COVID-19 Trends from OMOP-CDM database for Public Health Reporting
M. Chang, J. Lu, B. Patel, N. Shah, J. Chen, Stanford University

Evaluation of Data Management of COVID-19 Clinical Trials Using a Cloud-Based Clinical Data-management Platform
R. Hekmat, D. Weeraratne, C. VanHouten, B. South, N. Kutub, V. Willis, W. Bradham, R. DiCicco, IBM Watson Health; G. Jackson, IBM Watson Health, Vanderbilt University Medical Center; J. Snowdon, IBM Watson Health

Using Natural Language Processing to Predict ICU Transfer in Hospitalized COVID-19 Patients
P. Kinney, Georgia Institute of Technology; A. Tariq, H. Trivedi, Emory University; J. Gichoya, Emory Winship Cancer Institute; I. Banerjee, Emory University

CoviDash-SM: A public COVID19 dashboard for social media data-based research and surveillance
S. Lakamana, M. Al-Garadi, Y. Yang, A. Sarker, Emory University

Leveraging the ACT Network for COVID-19 Research
D. MacFadden, G. Weber, Harvard Medical School

COVID-19 Research: Messy Data, Consequent Pitfalls and Lessons Learned
T. Magoc, University of Florida; G. Lipori, J. Myles, S. Sortino, UF Health; C. Harle, University of Florida

Answering Common COVID-19 Questions with Conversational Technology
M. McKillop, B. South, A. Preininger, G. Jackson, IBM Watson Health

Characterizing Effects of Air Pollution Exposure in Diabetic Patients Affected by COVID-19
N. Riches, R. Gouripeddi, W. Dere, A. Payan-Medina, J. Facelli, University of Utah

A COVID-19 Drug Repurposing Network from Integrated Text Mining and Semantic Data Mining
K. Ross, Georgetown University Medical Center; C. Chen, J. Cowart, S. Gavali, University of Delaware; C. Wu, University of Delaware, Georgetown University Medical Center

Leveraging Broadcast Text-messages to Deliver Real-time Clinical Guidance to Hospital Employees During the COVID-19 Pandemic
C. Williams, Perelman School of Medicine at the University of Pennsylvania; A. Rao, Hospital of the University of Pennsylvania, University of Pennsylvania; J. Ziemba, J. Myers, Hospital of the University of Pennsylvania, Perelman School of Medicine at the University of Pennsylvania, University of Pennsylvania Health System; N. Patel, Hospital of the University of Pennsylvania, Perelman School of Medicine at the University of Pennsylvania

10:00 am - 11:30 pm

S41 – Informatics Applications, Architecture and Methodology for Public Health and Global Health

Effect of Reopening Orders on Covid-19 Hospitalizations in the US
R. Nachum, Thomas Jefferson High School for Science and Technology; L. Pageler, Homestead High School; N. Majeti, Palo Alto Senior High School; W. Ding, San Mateo High School; J. Omiye, Stanford University

S43: Panel – Virtually Ready: Technological Tools and Adaptations in a Children’s Hospital during the COVID-19 Pandemic

T. Rungvivatjarus, B. Lee, A. Chong, M. Bialostozky, J. Huang, C. Kuelbs, University of California San Diego, Rady Children’s Hospital

In this current COVID-19 pandemic, healthcare systems across the country have undergone drastic changes in patient care and operational workflow. The distribution of up-to-date and reliable information to healthcare workers, patients, and the community is also of paramount importance. Pediatric institutions are undergoing similar changes. In this panel, we outlined the various technological tools/adaptations and organizational changes that occurred at our academic institution in the midst of the COVID-19 pandemic. With panelists from various backgrounds and administrative roles, we will share our experiences in leading organizational changes from the informatics perspective and engage the audience in the different ways to leverage telemedicine, conserve personal protective equipment (PPE), provide clinical decision support, disseminate real-time organizational data, and optimize communication and access to care for families. In a pandemic, healthcare systems need to prepare for sudden and frequent changes in patient care and disruption of usual workflows.

12:00 - 1:30 pm

S48: Panel – Challenges and Opportunities for Implementing Artificial Intelligence at the Speed of Technology Innovation During the COVID-19 Era

B. South, IBM Watson Health; W. Chapman, University of Melbourne; I. Dankwa-Mullan, IBM Watson Health; M. Matheny, Vanderbilt University; Y. Quintana, Harvard Medical School

The COVID-19 pandemic has created multiple opportunities to implement Artificial Intelligence (AI) technologies in new ways that address the initial infectious curve (e.g., triaging patients and disseminating information during disease outbreaks), as well as the subsequent curves of pandemic sequelae (managing gaps in care of chronic conditions, addressing new and exacerbated mental health needs, and rectifying worsening health disparities. However, numerous challenges limit scaling development and application of AI technologies in healthcare settings, especially in the context of a rapidly evolving public health emergency. Data representing diverse patient cohorts are necessary both to train and to test systems but often are labor intensive to create and deidentify. The need for new codes and concepts can delay data availability. Biases in data must be identified, evaluated, and managed to mitigate downstream effects. System performance must be continuously monitored and validated as clinical information, such as disease transmission characteristics, become available. This panel will discuss these challenges and propose solutions that include ensuring adequate, equitable, and unbiased data sources are used for AI development, validation of AI in clinical settings, with the context of the rapidly evolving COVID-19 public health crisis as a discussion focus.

2:00 - 3:30 pm

S53: Panel – Pandemic Informatics: Tuning Expectations of Real World Data - Lessons Learned from the National COVID Cohort Collaborative (N3C)

K. Kostka, Observational Health Data Sciences & Informatics, IQVIA; M. Morris, University of Pittsburgh; M. Palchuk, TriNetX; E. Pfaff, University of North Carolina at Chapel Hill; R. Miller, Tufts University

The first case of a novel coronavirus, subsequently named SARS-CoV-2, was detected in Wuhan, Hubei Province, China in 2019. By the end of August 2020, the coronavirus has since spread across the world, causing over 25 million cases of COVID-19 (the disease caused by SARS-CoV-2) and over 844,000 deaths. The use of real-world data is an important piece of understanding the epidemiology of COVID-19, the natural history/severity of disease and potential therapies. The National COVID Cohort Collaborative (N3C), sponsored by the National Center for Advancing Translational Sciences (NCATS), is a multi-site collaborative learning health network designed to overcome barriers to rapidly build a scalable infrastructure incorporating multi-organizational clinical data for COVID-19 analytics. This panel is composed of informaticians supporting the harmonization of COVID-19 data for downstream analytics. Here we will discuss the need to balance pragmatism versus perfectionism in informatics projects during a pandemic.

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