A recently published special issue of eGEMs explores strategic uses of evidence to transform healthcare delivery systems. In A Framework to Support the Sharing and Re-Use of Computable Phenotype Definitions Across Health Care Delivery and Clinical Research Applications, Rachel Richesson and Michelle Smerek of the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality Core, along with coauthor C. Blake Cameron, envision an infrastructure that facilitates re-use of computable phenotypes in a learning healthcare system.
The authors elaborate on four required components of the framework:
- Searchable libraries of explicitly defined phenotype definitions
- Knowledge bases with information and methods
- Tools to identify, evaluate, and implement existing phenotype definitions
- Motivated users and stakeholders
Read the entire eGEMs open access publication here. eGEMs (Generating Evidence & Methods to improve patient outcomes), a product of AcademyHealth’s Electronic Data Methods (EDM) Forum, is a peer-reviewed, open access journal that seeks to accelerate research and quality improvement using electronic health data.
You can find extensive information on computable phenotypes in the Living Textbook chapter and in Tools for Research.
The FDA has released a Draft Guidance for Industry to facilitate the use of data from electronic health record (EHRs) in clinical investigations. The draft guidance provides recommendations on how to use EHRs as a source of data for research, ensure data quality and integrity, and satisfy the FDA’s inspection, recordkeeping, and record retention requirements. An additional goal of the draft guidance is to promote interoperability, or the ability to exchange and use information between EHR systems that capture information during patient care visits and electronic data capture (EDC) systems that support clinical investigations. Sponsors of clinical research must also consider whether there are any reasonably foreseeable risks involved in using the EHR for research—such as an increased risk of data breaches—that should be disclosed in the informed consent document.
Read the full draft guidance here.
In a recent post on the FDA’s “FDA Voice” blog, Associate Deputy Commissioner Rachel Sherman and Commissioner Robert Califf describe how to overcome barriers to data sharing and create a successful national system for medical evidence generation (or “EvGen”). To foster new approaches for creating clinical evidence the authors suggest 3 principles:
“1. There must be a common approach to how data is presented, reported and analyzed and strict methods for ensuring patient privacy and data security.
2. Rules of engagement must be transparent and developed through a process that builds consensus across the relevant ecosystem and its stakeholders.
3. To ensure support across a diverse ecosystem that often includes competing priorities and incentives, the system’s output must be intended for the public good and be readily accessible to all stakeholders.”
Drs. Sherman and Califf point to substantial pioneering work being done in secondary use of data, in which data collected for clinical care are “secondarily” used for research, including projects currently underway through the NIH Collaboratory, PCORnet, and other initiatives and networks. The experience gained from these groundbreaking efforts should provide a foundation for a national system for evidence generation.
Read the full post here.
Meredith Nahm Zozus and colleagues from the NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality 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.
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.
The NIH Collaboratory’s Phenotypes, Data Standards, and Data Quality 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).
A new Living Textbook topic chapter, “Electronic Health Records-Based Phenotyping,” has just been published. The chapter defines computable phenotypes and describes their role in data queries of electronic health records as part of pragmatic clinical trials. A main focus of the chapter is outlining mechanisms for identifying and evaluating phenotype definitions, with particular emphasis on standardization efforts of the NIH Collaboratory, including the Table 1 Project. Also included are links to recommended phenotype definitions from the Collaboratory Phenotypes, Data Standards, and Data Quality Core.
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. Details are available here. 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.