data standards

September 2, 2020: Chapter on Assessing Fitness for Use of Real-World Data Sources Added to the Living Textbook

Posted on by Karen Staman

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 new chapter includes the following sections:

The new chapter updates a previous resource based on work by experts in the NIH Collaboratory’s Electronic Health Records Core Working Group.

August 27, 2020: Chapter on Acquiring Real-World Data Added to the Living Textbook

Posted on by Karen Staman

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 includes the following sections:

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.

July 31, 2020: Using Real-World Data to Plan Eligibility Criteria and Enhance Recruitment: Actionable Recommendations and Resources from the Clinical Trials Transformation Initiative (Sudha Raman, PhD, MA; John Sheehan, PhD, MBA, RPh)

Posted on by Gina Uhlenbrauck

Speakers

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?

Read and download CTTI’s recommendations for using RWD. Learn more about FDA’s guidance for real-world data and real-world evidence. A publication is available about the health plan recruitment method used in the ADAPTABLE aspirin study.

Tags

#pctGR, @Collaboratory1

August 4, 2020: New Living Textbook Sections Describe Interoperability, Patient Access to Data

Posted on by Karen Staman

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:

  • Interoperability
    • 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.

July 14, 2020: Grand Rounds Webinar Presents the New N3C Analytics Platform for COVID-19 Research

Posted on by Gina Uhlenbrauck

Watch the recent Grand Rounds webinar presented by Dr. Ken Gersing of the National Center for Advancing Translational Sciences and Dr. Robert Star of the National Institute of Diabetes and Digestive and Kidney Diseases to learn more about the COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C).

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

Watch the Grand Rounds webinar or download the slides. For more details, visit the NCATS N3C website.

July 10, 2020: COVID Open Science Collaborative Analytics Platform: National COVID Cohort Collaborative (N3C) (Ken Gersing, MD; Robert Star, MD)

Posted on by Gina Uhlenbrauck

Speakers

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.

  • 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

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.

Read more on the NCATS N3C website as well as view a short video demonstration.

Tags

#pctGR, @Collaboratory1, @ncats_nih_gov

July 9, 2020: New Chapter of Living Textbook Addresses EHR-Based Phenotyping

Posted on by Damon Seils

The NIH Collaboratory this week published a new chapter of its Living Textbook of Pragmatic Clinical Trials. The chapter, “Electronic Health Records-Based Phenotyping,” provides an overview of considerations for identifying, defining, and evaluating computable phenotypes for use with electronic health records (EHRs).

EHR-based phenotyping is an important strategy in large-scale pragmatic clinical trials, because these studies typically rely on standard phenotype definitions for EHR-based inclusion and exclusion of participants and consistent data analysis and reporting across data sources. Standardized queries of EHR data can be replicated at multiple sites, enabling efficiencies and ensuring that populations identified from different healthcare systems have similar features or were identified in the same way.

The new chapter includes the following sections:

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 (formerly the Phenotypes, Data Standards, and Data Quality Core Working Group).

June 5, 2020: PCORnet COVID-19 Common Data Model Design and Results (Thomas Carton, PhD, MS; Keith Marsolo, PhD; Jason Perry Block, MD, MPH)

Posted on by Gina Uhlenbrauck

Speakers

Thomas W. Carton, PhD, MS
Chief Data Officer
Louisiana Public Health Institute

Keith Marsolo, PhD
Associate Professor
Department of Population Health Sciences
Duke Clinical Research Institute
Duke University School of Medicine

Jason Perry Block, MD, MPH
Associate Professor of Population Medicine
Department of Population Medicine Harvard
Pilgrim Health Care Institute
Harvard Medical School

Topic

PCORnet COVID-19 Common Data Model Design and Results

Keywords

COVID-19; PCORnet; Common Data Model; CDM; Data query; Health disparities; Distributed data network

Key Points

  • For data to be useful in research, they have to be standardized across systems. The PCORnet Common Data Model standardizes data into a single language, enabling fast insights.
  • All the core data elements needed to support COVID-19 research and surveillance have a home in the PCORnet CDM. The goal for PCORnet is to characterize the cohort of COVID-19 patients and provide detailed information on demographics and pre-existing conditions.

Discussion Themes

Can PCORnet partners stand up a version of the CDM with more up-to-date information to allow for a faster characterization of the PCORnet COVID-19 population?

Is there a query to discover and address COVID-19 health disparities and social determinants of health?

Can PCORnet and NCATS’ National COVID Cohort Collaborative (N3C) work together?

Read more about PCORnet’s code lists and case definitions on GitHub.

Tags

#COVID19, #pctGR, @Collaboratory1

May 26, 2019: Final Rule to Implement Provisions of the 21st Century Cures Act

Posted on by Karen Staman

On May 1, 2020, the Office of the National Coordinator for Health Information Technology (ONC) and the Centers for Medicare and Medicaid Services (CMS) announced a final rule to implement provisions of the 21st Century Cures Act. The final rule is intended to advance interoperability and support the access, exchange, and use of electronic health information by patients and their caregivers.

“Patients should be able to access their electronic medical record at no extra cost. Providers should be able to choose their IT tools that allow them to provide the best care for patients, without excessive costs or technical barriers.” —ONC Cures Act Final Rule Fact Sheet

To enable the use of smartphone applications (apps) for secure access to healthcare data, the rule requires standardized, open application programming interfaces (APIs) to be built using HL7’s FHIR (Fast Health Interoperability Standard). Part of the intention of the rule is to promote competition and support provider and patient independence in choosing which certified apps to acquire and use for healthcare purposes.

The rule establishes the United States Core Data for Interoperability (USCDI) standard, which sets forth data classes and elements that support nationwide interoperability; it also includes a broad range of data elements, such as clinical notes, test results, and medications. The final rule includes a prohibition on “information blocking” to restrict practices that are likely to interfere with access to or exchange of health information.

The rule is effective on June 30, 2020, and compliance is required by November 2, 2020.

May 8, 2020: Advances at the Intersection of Digital Health, Electronic Health Records, and Pragmatic Clinical Trials: Real World Evidence: Contemporary Experience and Future Directions (Patrick Heagerty, PhD, Jacqueline Corrigan-Curay, JD, MD, Joshua C. Denny, MD, MS)

Posted on by Gina Uhlenbrauck

Speakers

Guest Moderator:
Patrick J. Heagerty, PhD
Professor, Department of Biostatistics, University of Washington

Panel:
Jacqueline Corrigan-Curay, JD, MD
Director of CDER’s Office of Medical Policy (OMP)
U.S. Food and Drug Administration (FDA)

Joshua C. Denny, MD, MS, FACMI
Chief Executive Officer, All of Us Research Program, NIH

Topic

Advances at the Intersection of Digital Health, Electronic Health Records, and Pragmatic Clinical Trials: Real World Evidence: Contemporary Experience and Future Directions

Keywords

Electronic health records; Real-world evidence; RWE; Real-world data; RWD; FDA; All of Us; Phenotypes; Regulatory; Fit-for-use data; Digital heath

Key Points

  • To create quality clinical/research records, we must design for multiuse by integrating standards-based tools in the EHR to bring together health care and research. 
  • Quality real-world evidence cannot be built without quality real-world data. With greater efficiencies in data capture, randomization with real-world data provides a pathway for reliable—and persuasive—real-world evidence.

Discussion Themes

Patient-generated health data is part of FDA’s MyStudies Application, designed to facilitate the input of real-world data directly by patients, which can be linked to electronic health data supporting traditional clinical trials, pragmatic trials, observational studies, and registries.

In assessing data quality we can ask, How does a data element travel from clinical care to a research data set?

The NIH’s All of Us program is building a diverse database that can inform thousands of studies on a variety of health conditions.

The All of Us study is tracking COVID-19 in its patients. Sites have identified their COVID-19 participants and relevant labs. Consent is obtained for future sharing of data.

Tags

#pctGR, @Collaboratory1