The NIH Collaboratory is pleased to share a new resource to help clinical investigators successfully partner with healthcare system leaders. The Quick Start Guide for Researcher and Healthcare Systems Leader Partnerships provides advice from NIH Collaboratory and healthcare system leadership and serves as an annotated table of contents for the Living Textbook, pointing readers to essential content.
The Quick Start Guide is part of a series of tools intended to support the successful conduct of ePCTs within healthcare systems. The first guide in the series, the Quick Start Guide for Investigators, is designed for clinical investigators interested in learning how to conduct an ePCT. The NIH Collaboratory Coordinating Center is developing more Quick Start Guides for different audiences and use cases.
In an article published today in the New England Journal of Medicine, Drs. Richard Platt, Adrian Hernandez, and Greg Simon of the NIH Collaboratory discuss barriers to healthcare system participation in embedded research and strategies for improvement.
“We advocate creating a robust national [embedded pragmatic clinical trial] capability to generate evidence to guide decisions by patients, clinicians, health systems, and regulators and respond to urgent national health crises, like COVID-19 or the opioid crises,” the authors wrote.
The article recommends a 4-pronged strategy that researchers and funders should consider to increase healthcare system participation in pragmatic clinical trials:
Reimburse for the additional costs of trial participation.
In some highly engaged systems, support permanent, reusable infrastructure.
Offload research-specific tasks to minimize burden on sites (such as IRB oversight, obtaining informed consent, and mailing medications to participants).
Assign and promote reputational benefit for these activities.
Patrick O’Connor, MD, MA, MPH Senior Clinical Investigator HealthPartners Institute
JoAnn Sperl-Hillen, MD Senior Clinical Investigator HealthPartners Institute
Topic
Outpatient Clinical Decision Support – An Evidence-Based Implementation Framework
Keywords
Clinical decision support; Electronic health record (EHR); Automated tools; Web applications; Clinical informatics
Key Points
A well-designed clinical decision support (CDS) system should fire only when there is a potential large benefit, such as a cardiovascular benefit for patients with a reversible risk. The CDS trigger should be patient-centric, and the system should save clinician time and improve the quality of care.
The CDS in question was designed for use in cardiovascular (CV) disease to:
Identify and target individuals with the greatest potential for a CV benefit and prioritize CV risk factors based on potential benefit.
Gordon R. Bernard, MD CONNECTS ACC Science Unit P Professor of Medicine Executive Vice President for Research Senior Associate Dean for Clinical Science Vanderbilt University Medical Center
Sonia Thomas, DrPH CONNECTS ACC Principal Investigator Senior Research Statistician RTI International
Topic
Launching CONNECTS: Collaborating Network of Networks for Evaluating COVID-19 and Therapeutic Strategies
Keywords
COVID-19; CONNECTS; NHLBI; Collaborative research; Data sharing; Adaptive trials; Data standardization; ACTIV; Therapeutic agent prioritization
Key Points
The Collaborating Network of Networks for Evaluating COVID-19 and Therapeutic Strategies (CONNECTS) is a research partnership coordinated by the Research Triangle Institute, Vanderbilt University Medical Center, and the National Heart, Lung, and Blood Institute (NHLBI) of the NIH.
CONNECTS aims to build on existing clinical research networks to better understand the risk of severe illness from COVID-19 and to identify therapies that will slow or halt the disease progression and speed recovery. Studies will enroll participants with health conditions that are known to increase their risk for severe complications from COVID-19.
The immediate goal is to design and implement master protocol-driven adaptive clinical trials, including outpatient, inpatient, and recovering master protocols.
CONNECTS is part of a larger ecosystem in the Department of Health and Human Services that includes the FDA, CDC, BARDA, Operation Warp Speed, and NIH. More than 34 trial networks and 1,000 sites are participating in CONNECTS.
Discussion Themes
Are the CONNECTS resources, such as the common data elements manual, draft protocols, and case report forms, publicly available?
In your effort to reach underrepresented communities, have you considered collaborating with Historically Black Colleges and Universities (HBCUs), particularly those that conduct health research?
While COVID-19 is providing you with plenty to focus on, do you see the potential for sustainability of CONNECTS beyond this pandemic?
The NIH Collaboratory published a new chapter of its Living Textbook of Pragmatic Clinical Trials. The chapter, “Assessing Fitness-for-Use of Real World Data Sources,” describes several approaches for determining whether real-world data are fit for their intended purpose in pragmatic clinical trials.
“Real-world data” are collected for clinical care, insurance claims, administrative purposes, registries, or are generated directly by the patient. Because these data are collected for a purpose other than a specific research project, an investigator must understand the characteristics and limitations of the data to determine whether they can be used in a pragmatic trial.
The NIH Collaboratory this week published a new chapter of its Living Textbook of Pragmatic Clinical Trials. The chapter, “Acquiring Real-World Data,” outlines strategies for obtaining real-world data for use in research.
“Real-world data” include data relating to the health status of a patient or the delivery of healthcare services. Common sources include electronic health records (EHRs), administrative claims, patient-reported outcomes, patient-generated health data, medical product and device registries, and databases relating to environmental factors or social determinants of health. Real-world data can support a number of activities in pragmatic clinical trials, such as patient identification and recruitment, monitoring of outcomes, and ascertainment of endpoints.
The new chapter updates a previous resource, one of the most popular on the Living Textbook, based on work by experts in the NIH Collaboratory’s Electronic Health Records Core Working Group.
Sudha Raman, PhD, MA Assistant Professor Department of Population Health Sciences Duke University
John Sheehan, PhD, MBA, RPh Senior Director, Value and Evidence (HEOR) Neuroscience Janssen Scientific Affairs, LLC
Topic
Using Real-World Data to Plan Eligibility Criteria and Enhance Recruitment: Actionable Recommendations and Resources from the Clinical Trials Transformation Initiative
Keywords
Clinical Trials Transformation Initiative (CTTI); Real-world data (RWD); Recruitment planning; EHR; Eligibility criteria; Fit-for-purpose data
Key Points
Real-world data (RWD) are data relating to patient health status and/or the delivery of health care routinely collected by a variety of sources.
CTTI provides recent recommendations, resources, and case studies that highlight actionable tools and best practices for evaluating and using real-world data (RWD) in clinical trial recruitment activities:
General principles for using RWD
Using RWD to plan eligibility criteria
Using RWD to support recruitment
Enhancing RWD capabilities for the research enterprise
Using RWD from data sources such as electronic health records and claims data brings challenges for completeness, accuracy, and generalizability of the data.
RWD holds the potential to increase patient eligibility and enrollment as well as reduce recruitment timelines.
Discussion Themes
Insights from RWD should be sought early in the product lifecycle and include context from patients and sites.
One challenge of RWD data sources is finding appropriate databases for the disease area of interest, especially for trials of rare diseases.
Are there lessons learned about when using RWD becomes prohibitive or too expensive?
Members of the NIH Collaboratory Electronic Health Records Core have authored 2 new sections of the Living Textbook chapter Using Electronic Health Record Data in Pragmatic Clinical Trials:
This section describes efforts to support interoperability and the sharing of patient data across care teams and organizations, including the creation the United States Core Data for Interoperability (USCDI) standard.
Patient Access to Data
This section describes provisions of the 21st Century Cures Act intended to support the access, exchange, and use of electronic health information by patients and their caregivers.
Many of the NIH Collaboratory Trials use electronic health record and claims data for pragmatic research, and these data are from a fundamentally different context than data prospectively collected for more traditional, explanatory research. The Living Textbook chapter, Using Electronic Health Record Data in Pragmatic Clinical Trials, describes how data from real-world sources can be used in pragmatic clinical trials to develop and refine research questions, identify the study population and assess baseline prognostic characteristics, implement and monitor the delivery of the intervention, and assess outcomes.
The 2 new sections describe the latest developments and considerations for use of electronic health data in pragmatic clinical trials.
The N3C initiative aims to build a centralized national data resource that researchers can use to study COVID-19 and identify potential treatments as the pandemic continues to evolve. N3C is a partnership among the Clinical and Translational Science Awards Program hubs and the National Center for Data to Health, with overall stewardship by the National Center for Advancing Translational Sciences (NCATS).
The goals of N3C are to:
Rapidly collect and aggregate clinical, lab, and imaging data from hospitals, health plans, and CMS at the peak of the COVID-19 pandemic and as it evolves
Provide a longitudinal dataset to understand acute hospital and recovery phases
Understand pathophysiology of disease
Support clinical trials by identifying patients who might wish to participate in trials
Ken Gersing, MD Director of Informatics, NCATS National Institutes of Health
Robert A. Star, MD Director, Division of Kidney, Urologic, and Hematologic Disorders, NIDDK Chief, Renal Diagnostics and Therapeutics Unit, NIDDK National Institutes of Health
Topic
COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C)
Keywords
COVID-19; Coronavirus; Pandemic; Data exchange; Data use agreement; Phenotypes; Data harmonization; Common data model; Fast Healthcare Interoperability Resources (FHIR); Synthetic data
Key Points
The National COVID Cohort Collaborative (N3C) initiative aims to build a centralized national data resource that the research community can use to study COVID-19 and identify potential treatments as the pandemic continues to evolve.
Rapidly collect and aggregate clinical, lab, and imaging data from hospitals, health plans, and CMS at the peak of the COVID-19 pandemic and as it evolves
Provide a longitudinal dataset to understand acute hospital and recovery phases
Understand pathophysiology of disease
Support clinical trials by identifying patients who might wish to participate in trials
Discussion Themes
The N3C analytics platform is cloud-based and provides a secure data enclave. Data can be received via multiple data models and transformed into a common analytic model for research.
As a centralized data model, N3C complements existing federated data models like PCORnet and OMOP. The tool does not replace the need for randomized controlled trials.
NCATS, FDA, and NCI are working together on common data model (CDM) harmonization so that data will be publicly available and reusable in human and machine-readable formats.
