Data standards

March 1, 2019: Approaches to Patient Follow-Up for Clinical Trials: What’s the Right Choice for Your Study? (Keith Marsolo, PhD)

Posted on by Gina Uhlenbrauck

Speaker

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

Topic

Approaches to Patient Follow-Up for Clinical Trials: What’s the Right Choice for Your Study?

Keywords

Pragmatic clinical trial; Real-world data; Distributed research network; Electronic health records; EHR; Health data sources; Data standardization; Common data model; Fast Healthcare Interoperability Resources (FHIR); Application programming interface (API)

Key Points

  • Different sites have different capabilities and levels of sophistication around data. Clinical trial investigators should think from the beginning about the questions they want to answer and how much data is needed.
  • From different sources, such as the EHR, claims, or participant, data can be procured and provided in different ways, either by the patient, staff or clinician, or through IT and data experts.
  • PCTs with many sites may require a “patchwork quilt” of approaches for patient follow-up depending on the needs of the trial. Clinician-generated reports, direct from patients, and solutions involving application programming interfaces (APIs) are all good options for data exchange.

Discussion Themes

How do we think through the options for getting patient data where some sites may not be in the distributed research network or use a common data model?

Fast Healthcare Interoperability Resources (FHIR) is a draft standard describing data formats and elements and an application programming interface (API) for exchanging electronic health records. The FHIR interface requests data as an object, and for each defined domain it specifies allowable values and variables and predefines the information that you get out of the system.

Until data are collected/generated using the same standards/formats as the API, there will still be a need to understand the EHR-to-interface mapping.

For more information on using health data in embedded pragmatic clinical trials, visit the NIH Collaboratory’s EHR Core webpage.

Tags

#CommonDataModel, #RealWorldData, #FHIR, #pctGR, @Collaboratory1

February 22, 2019: Proposed Rule to Implement Provisions of the 21st Century Cures Act

Posted on by Karen Staman

The Office of the National Coordinator for Health Information Technology (ONC) and the Centers for Medicare and Medicaid (CMS) have announced a proposed rule intended to advance interoperability and support the access, exchange, and use of electronic health information. Notably, the rule would require that patients have the ability to electronically access their health information at no cost.

The rule also proposes a United States Core Data for Interoperability (USCDI) standard, which, if adopted, would add data beyond those included in the current common clinical data model to support nationwide interoperability of CMS data. Specifically, clinical notes, data provenance, pediatric vital signs, patient address and phone number (to support data matching) will be added if the measures are adopted.

“Today’s announcement builds on CMS’ efforts to create a more interoperable healthcare system, which improves patient access, seamless data exchange, and enhanced care coordination,” — CMS Administrator Seema Verma, from the NPRM Press Release

There are nine fact sheets on other important aspects of the rule, including sheets on interoperability, the Cures Act, and electronic health information export for patient and provider access.

New Living Textbook Chapter on Acquiring and Using Electronic Health Record Data for Research

Posted on by Karen Staman

Topic ChaptersMeredith Nahm Zozus and colleagues from the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core (now the Electronic Health Records Core) have published a new Living Textbook chapter about key considerations for secondary use of electronic health record (EHR) data for clinical research.

In contrast to traditional randomized controlled clinical trials where data are prospectively collected, many pragmatic clinical trials use data that were primarily collected for clinical purposes and are secondarily used for research. The chapter describes the steps a prospective researcher will take to acquire and use EHR data:

  • Gain permission to use the data. When a prospective researcher wishes to use data, a data use agreement (DUA) is usually required that describes the purpose of the research and the proposed use of the data. This section also describes use of de-identified data and limited data sets.
  • Understand fundamental differences in context. Data collected in routine care settings reflect standard procedures at an individual’s healthcare facility, and are not collected in a standard, structured manner.
  • Assess the availability of health record data. Few assumptions can be made about what is available from an organization’s healthcare records; up-front, detailed discussions about data element collection over time at each facility is required.
  • Understand the available data. A secondary data user must understand both the data meaning and the data quality; both can vary greatly across organizations and affect a study’s ability to support research conclusions.
  • Identify populations and outcomes of interest. Because healthcare facilities are obligated to provide only the minimum necessary data to answer a research question, investigators must identify the needed patients and data elements with specificity and sensitivity to answer the research question given the available data.
  • Consider record linkage. Studies using data from multiple records and sources will require matching data to ensure they refer to the correct patient.
  • Manage the data. The investigator is responsible for receiving, managing, and processing data and must demonstrate that the data are reproducible and support research conclusions.
  • Archive and share the data after the study. Data may be archived and shared to ensure reproducibility, enable auditing for quality assurance and regulatory compliance, or to answer other questions about the research.

FDA Releases Action Plan to Encourage Greater Patient Diversification in Trials

Posted on by Gina Uhlenbrauck

In August 2014, the Food and Drug Administration (FDA) released an action plan (link opens as a PDF) aimed at encouraging more diverse patient participation in drug and medical device clinical trials. The Action Plan to Enhance the Collection and Availability of Demographic Subgroup Data includes 27 responsive and pragmatic actions, divided into 3 overarching priorities:

  • Data quality: improving the completeness and quality of demographic subgroup data collection, reporting, and analysis
  • Participation: identifying barriers to subgroup enrollment in clinical trials and employing strategies to encourage greater participation
  • Transparency: making demographic subgroup data more available and transparent

The plan follows an August 2013 report to Congress on these concerns and reflects the agency’s commitment to encouraging the inclusion of a diverse patient population (with reference to sex, age, race, and ethnicity) in biomedical research that supports applications for FDA-regulated medical products. Increasing representation is a multifaceted challenge that requires a multifaceted approach and collaboration of federal partners, industry, healthcare providers, patients and patient advocacy groups, academicians, and community groups.

A message from the Commissioner of the FDA contains background and details.

Collaboratory Phenotypes, Data Standards, and Data Quality Core Releases Data Quality Assessment White Paper

Posted on by Gina Uhlenbrauck

The NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core (now the Electronic Health Records Core) has released a new white paper on data quality assessment in the setting of pragmatic research. The white paper, titled Assessing Data Quality for Healthcare Systems Data Used in Clinical Research (V1.0) provides guidance, based on the best available evidence and practice, for assessing data quality in pragmatic clinical trials (PCTs) conducted through the Collaboratory. Topics covered include an overview of data quality issues in clinical research settings, data quality assessment dimensions (completeness, accuracy, and consistency), and a series of recommendations for assessing data quality. Also included as appendices are a set of data quality definitions and review criteria, as well as a data quality assessment plan inventory.

The full text of the document can be accessed through the “Tools for Research” tab on the Living Textbook or can be downloaded directly here (PDF).

Grand Rounds (4-18-2014): Data Quality and Transparency Standards

Posted on by Gina Uhlenbrauck

Update:

Archived video and slides from the April 18 Grand Rounds are now available on the NIH Collaboratory Grand Rounds webpage.

This Friday’s NIH Collaboratory and PCORnet Grand Rounds (“Building PCOR Value and Integrity With Data Quality and Transparency Standards: An Introduction and Request for Input”) will be presented by Michael G. Kahn, MD, PhD, professor of epidemiology in the Department of Pediatrics at the University of Colorado Denver. Dr. Kahn is co-director of the Colorado Clinical and Translational Sciences Institute (CCTSI), Translational Informatics Core director for the CCTSI, and director of clinical informatics in the Department of Quality & Patient Safety at The Children’s Hospital.

The Grand Rounds presentation will take place from 1:00-2:00 PM Eastern time on Friday, April 18. Archived video and slides from the presentation will be available early the following week; links to archived material will be provided in an update to this post.