COVID-19 / Emergency Preparedness

Pandemic Response and Emergency Preparedness


Health information technology is at the center of the fight against COVID-19 with the ability to harness critical data extraction and analytics to drive successful treatments and enable a safe return to economic stability. Testing, personal protective equipment, ICU equipment and other life sustaining devices are critical to saving lives and enabling people to return to normal activities. Specifically, there is a need to inform health technology innovation gaps and resource planning to advance and scale COVID–19 treatment and preparedness efforts. Research and surveillance will help secure, scale up of testing, supply chains, ensure the quality of products, and develop disruptive real-world evidence innovations with generation of faster, less expensive, and higher-quality data. There is also a critical need to understand the impact of COVID-19 mitigation on other health conditions; resulting: the morbidity and mortality of COVID-19 on the vulnerable populations cared for by specialty societies and networks of clinical providers; and in critical delays in care of urgent exacerbations of chronic health conditions. The unpredictable and urgent nature of pandemics and similar public health challenges requires an anticipatory, flexible expert framework to guide an efficient and effective strategic response.

MDEpiNet is a global public private partnership that brings together leadership, expertise, and resources from health care professions, industry, patient groups, payers, academia, and government to advance a national system for patient-centered medical device evaluation and surveillance. Support of Coordinated Registry Network (CRN) development is a key MDEpiNet strategy to bring together real world evidence from a variety of sources to address device evaluation needs for multiple stakeholders. Since 2017, MDEpiNet has been developing Collaborative Learning Communities (CLC) for CRNs to speed the development and maturity of the networks. CLC’s main goal is to promote CRN development as a robust source of evidence for device evaluation in multiple areas.

MDEpiNet is now launching the Pandemic Response and Emergency Preparedness Taskforce (PREPT) to bring together interested experts and data owners to advance the evidence and to guide CRN Learning Communities toward strategic solutions for the most pressing needs in the areas of health technologies and public health emergencies. The MDEpiNet PREPT will coordinate activities with the MDEpiNet Coordinating Center and MDEpiNet Blockchain and Artificial Intelligence Taskforce (BAIT). Coordination will help advance use cases and apply innovative methods in research and surveillance.

Three such cross-cutting frameworks outlined here support multiple use cases for health information technology innovation through development of infrastructure and methods for management of the current pandemic and preparation for future adversities.

The Taskforce has three focus areas:
• Scale up testing addressing the unique needs of vulnerable populations and evaluate the

impact of COVID-19 on delivery of health care and patient outcomes
• Promoting real world evidence for more efficient evidence generation to evaluate critical technology essential

to response to the pandemic (testing, ICU equipment, PPE).
• Improving supply chain management through modeling of national use and performance of

essential medical devices.

PREPT will leverage the US health technology ecosystem, which includes medical device manufacturers, health professionals, health systems, regulators and patients that have been working together for over a decade. Evidence can be efficiently and quickly generated through use of innovations including: real world evidence (bringing together data collected as part of routine care from multiple health systems) and advances in methods (e.g., AI, blockchain, modeling and data analytics.)

Areas of Focus

MDEpiNet proposes the Taskforce focus its work across the following three areas:


1. Scale up testing addressing the unique needs of vulnerable populations and evaluate the impact of COVID-19 on delivery of health care and patient outcomes

Resuming normal activity and protecting vulnerable populations require massive testing for COVID-19.. Rapid scale up to testing, and study of morbidity/mortality can be accomplished through the large number of established professional organizations and their data activities (e.g., registries, data hubs), along with large scale health systems, and data aggregators.

The MDEpiNet Collaborative Learning Community (CLC) is working to track the effect of the pandemic across major surgical and interventional subspecialties working with a series of emerging Coordinated Registry Networks (CRN) representing large populations of medically vulnerable patients (cardiac, vascular, cancer, and others). Member specialty societies and their registries include: Robotic-Assisted Surgical Devices (RASD), Abdominal Core Health, Women’s Health Technologies (WHT), Vascular Implant Surveillance and Interventional Outcomes Network (VISION), Orthopedic Devices, Study of Prostate Ablation Related Energy Devices (SPARED), National Breast Implant Registry (NBIR), Devices used for Acute Ischemic Stroke Intervention (DAISI), Temporomandibular Joint (TMJ), Venous Access: National Guideline & Registry Development (VANGUARD), End Stage Renal Disease (ESRD), and evolving Cardiac CRN through collaboration with the National Cardiovascular Data Registry (NCDR). These platforms include a large portion of medically vulnerable populations in the US today.

CRNs are a key MDEpiNet strategy to bring together real world evidence from a variety of sources provides an infrastructure to scale up testing and to address the needs of device evaluation for multiple stakeholders (including testing technology). The CRN approach circumvents the limitations of traditional registries and data repositories by building linked data systems from multiple sources.

Taskforce partners bring together additional patients who are medically and socially vulnerable to COVD-19. PREPT builds on MDEpiNet’s CLC by partnering with major health systems with sophisticated health information systems including other health systems working with MDEpiNet such as large network of providers organized as a platform trial in California, Rockefeller Neuroscience Institute with its pilot network of West Virginia hospitals, CRISP (regional four state health exchange system), VA, Louisiana Public Health Department, National Association of Community Health Centers, and American Society for Hematology. The Taskforce also includes large data aggregation activities such as All Claims Data Bases of California and New York through MDEpiNet, and multiple large data sources through IQVIA. Aggregation of data from those partners will create a large scale capacity to understand trends and issues in laboratory testing and other health technology efficiently and rapidly. Modeling and AI tools might be used to help understand potential shortages, quality, and effective use of the technology. It is also important to conduct continuous horizon scanning and to obtain near-real- time access to admission and hospitalization data in areas affected by COVID-19.


2. Promoting real world evidence for more efficient evidence generation to evaluate critical technology essential to response to the pandemic (testing, ICU equipment, PPE).

The new challenges created by SARS-CoV-2 has created a demand for rapid solutions based on real world evidence . We need to quickly understand the nature of the illness, the behavior and meaning of laboratory tests, and uses of tests and key participant characteristics to guide decisions regarding a safe return to work and other activities of daily living. Principles like data harmonization, innovative data architecture, automation of quality data collection and storage from disparate sources, agile data analytics and modeling to support critical decision-making, and conformance with security and privacy requirements will be essential to success.

Emergency authorization also provides an excellent opportunity for “pre-post” market shift, making products available very quickly while ensuring that real world evidence solutions for product evaluation are also in place. For example, Systemic Harmonization and Interoperability Enhancement for Laboratory Data (SHIELD) is a multi-stakeholder, public-private partnership to improve the quality, utility and portability of electronic laboratory data (i.e., in vitro diagnostic [IVD] data) through the harmonized implementation of semantic data standards that have been appropriately qualified by a sole authoritative source. Codes for laboratory data should be interoperable: “Describe the same test the same way, every time.” By improving the semantic interoperability of laboratory data within and between institutions, diagnostic information can be used to better support clinical decisions and enable real world evidence relevance and reliability. SHIELD is current pushing out harmonized standards for use by laboratories and health systems for COVID-19 and SARS-CoV-2. SHIELD supports the provision of vetted and harmonized codes from manufacturers/industry to laboratories. SHIELD is testing the quality of data as it flows

from laboratories, through hospital health information systems into CRISP, a regional four state health information exchange (HIE).

3. Improved supply chain management through modeling of national use and performance of essential medical devices.

The COVID-19 pandemic has highlighted the need for rapid, accurate information on the supply chain of essential medical devices (e.g., personal protecting equipment, swabs, and ventilator capacity). Differences between models have led to major disagreements about needs for medical equipment and devices critical to the response to the pandemic. MDEpiNet will partner with relevant groups or develop/refine its own model for epidemic spread and impact on device/technology resource use, building on our existing algorithms in the context of specific pandemic events developed before this pandemic. The model maps projected patients through the national hospital system from admission to discharge (or death) using the following specifications: transition probabilities, transferring them from one hospital unit (i.e., the ED, the ICU, or the floor) to another according to knowledge of medical care and evidence-based or approximated unit-specific lengths of stay, with consideration of unit-specific mortality rates. Device needs and consumption are estimated by multiplying scenario-, unit-, and patient severity-specific patient counts by resource requirements per patient type. The model needs updating to make it more applicable in the current environment. Machine learning and deep machine learning algorithms will be considered in this process.

Task Force Charge

  1. Propose a PREPT mission charter, operating procedures and a structure of the task-force;

  2. Engage major stakeholders within industry, academia, patient organizations, clinical systems and

    regulatory bodies;

  3. Define areas of focus, gaps and technological solution pilots;

  4. Propose pilots to advance the MDEpiNet CRN framework in the areas of health technology and

    public health emergencies and identify sources of support. 


  • July 20, 2020 - Virtual kick off call

  • August 7th – RADx - UP submission

  • August – September, 2020 – Identification of additional strategic and funding opportunities

  • October 31, 2020 – White Paper


Gregory Pappas, MD, PhD

Kristi Mitchell, MPH

Nick Tsinoremas, PhD


Ali H. Mokdad, Ph.D.

Andrew Auerbach, MD, MPH

Bruce J. Tromberg, PhD

Elise Berliner, PhD

Jack Lewin, MD

Jeff Dunkel

Jens Jorgensen, MD, FACS

Julia Skapik

Kevin Baskin, MD

Laura Esserman, MD, MBA

Liz Paxton, PhD

Marni Hall, PhD, MPH

Meena Vythilingam

Michael Waters, PhD

Nancy A Dreyer, PhD, MPH, FISPE, Fellow DIA

Ross D. Martin, MD, MHA, FAMIA

Scott Smith, PhD

Sharon-Lise Normand, PhD

Vahan Simonyan, MS, PhD

William Wood

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