Key to these recommendations, according to an agency press release, is FDA’s removal of previous requirements that real-world evidence submitted to the agency include “private, confidential information at the individual patient level,” which the agency said made large databases with valuable macro-level data impractical to maintain. 

On December 18, 2025, the Food and Drug Administration (FDA) issued guidance clarifying how the agency evaluates real-world data to determine whether data are capable of generating real-world evidence that can be used in regulatory decision-making for medical devices. The new guidance expands on recommendations in the 2017 guidance, “Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices.”

Real-world data, according to FDA, are data “relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.” These include electronic health records, medical claims data, data from product and disease registries, and data gathered from other sources, such as digital health technologies. Real-world evidence is the clinical evidence regarding the “usage, and potential benefits or risks, of a medical product derived from analysis” of real-world data. FDA uses real-world data and evidence collected during the treatment and management of patients under certain circumstances to inform FDA’s understanding of the benefit-risk profiles of medical devices.

In December 2022, Congress signed the Food and Drug Omnibus Reform Act of 2022 (FDORA) into law as part of the Consolidated Appropriations Act of 2023, directing FDA to issue and update guidance on the use of real-world data and real-world evidence to support regulatory decision-making.

Pursuant to that directive, FDA’s new guidance, “Use of Real-World Evidence to Support Regulatory Decision-Making for Medical Devices; Guidance for Industry and Food and Drug Administration Staff,” updates FDA’s recommendations and considerations on the criteria that sponsors should evaluate when determining whether real-world data are relevant and reliable for a particular regulatory decision relating to medical devices. The recommendations apply regardless of the real-world data source and include studies to generate real-world evidence.

Key to these recommendations, according to an agency press release, is FDA’s removal of previous requirements that real-world evidence submitted to the agency include “private, confidential information at the individual patient level,” which the agency said made large databases with valuable macro-level data impractical to maintain. This, FDA Commissioner Marty Makary said, will remove unnecessary barriers that have prevented sponsors from deploying real-world evidence and changing treatments to patients faster.

Appropriate Uses of Data

According to FDA, real-world data are appropriate to generate real-world evidence when the data are “relevant to and reliable for informing or supporting a particular regulatory decision.”

FDA lists data sources that may be appropriate, depending on the context:

• Registries;
• Electronic health records;
• Administrative claims data;
• Chargemaster and/or billing data;
• Patient-generated data that are created, reported or gathered by patients, including in-home use, potentially using wearables and other digital health technologies;
• Device-generated data;
• Public health surveillance data;
• Clinically annotated biobanks; and
• Medical device data repositories

Although FDA’s guidance provides examples of appropriate data sources, the agency clarifies that it does not endorse one type of real-world data over another and encourages study sponsors to select data sources that are most suitable under the circumstances to address appropriate study questions. Further, FDA recognizes that real-world evidence can be generated from sources that are primarily intended for purposes other than research, such as administrative claims data.

Those data sources are appropriate in a number of situations relating to medical devices and FDA enumerates various nonexhaustive purposes for which the use of real-world data may be appliable in a regulatory submission including to generate a hypothesis to be tested in a clinical study, to use as or support controls in a study, as well as additional scenarios relating to the generation of clinical evidence in pre- and post-market activities.

Real-world data may also be used in connection with devices used under specifically, statutorily defined circumstances. Investigational device exemptions (IDEs) under 21 C.F.R. 812 permit devices to be shipped lawfully for the purpose of conducting investigations of the device without complying with requirements in the Food, Drug and Cosmetics Act (FDCA). FDA will assess whether collections of real-world data for a legally marketed device require an IDE on a case-by-case basis, explaining that an IDE is not likely required if the device is used in the normal course of medical practice. FDA explains that routine clinical use of a device used pursuant to emergency use authorization under Section 564 of the FDCA is “not considered to be a clinical investigation” and that data gathered under those circumstances may be used to support regulatory decision-making if deemed relevant and reliable for the study in question.

Relevance and Reliability of Real-World Data

FDA directs sponsors of studies to conduct and submit a relevance and reliability assessment for FDA to review and evaluate, addressing the relevance and reliability of the real-world data sources, design study and analytic components of a study. If multiple real-world data sources are used, the submission should address how each source contributes to the relevance and reliability of the final dataset. Further, FDA explains that the data should be accurate and complete and that clinical investigations should comply with good clinical practices. The studies, the agency says, should also be designed to mitigate potential bias and use data related to the appropriate demographic characteristics of the intended user population.

To evaluate the relevance of data, FDA considers the availability, timeliness and generalizability of the real-world data, assessing: 

Data Availability

Whether the data contains sufficient detail to capture the information needed to evaluate the question, including whether the data source captures the device identifier for device identification in a study, outcomes of interest in the study, covariates that impact the exposure of outcomes of interest, and the length of time that data is captured within the real-world data source.

Linkages

Whether and how data from different sources can be acquired and integrated “given the potential for heterogeneity in target population characteristics, clinical practices, and coding across data sources.” Sponsors should use a predefined linkage methodology that assesses in their submissions the adequacy of line-level linkages and applications of strategies to correct for redundant data.

Timeliness

Whether the time between data collection and release for research is reasonable and reflects the current clinical environment, characteristics of a condition, and health status of a population.

Generalizability

Whether, once a study is defined, the sample that is selected adequately represents the patients in the real-world data source that are reflective of the proposed intended use population. 

Similarly, to evaluate the reliability of data, FDA considers the accrual, quality and integrity of real-world data:

Data Accrual

Whether data should be collected and processed in a consistent in methodical manner—which will differ depending on whether the real-world data sources are actively collecting data, using nationally or internationally recognized coding systems, or using unstructured data capture. FDA will assess whether sponsors have submitted sufficient information and descriptors about data sources and the methods of data accrual to assess whether a sponsor’s approach demonstrates reliability.

Data Quality and Integrity

Whether the methods and systems used to help ensure sufficient data quality—including data quality assurance plans and procedures—include (1) quality control processes, (2) assessment of completeness, accuracy and consistency across sites and over time, (3) adherence to data collection, recording and source verification procedures, (4) adequate patient protections and (5) prior demonstration of real-world evidence from the data source.

FDA advises that other approaches to address the considerations identified in the guidance for relevance and reliability may suffice and recommends that sponsors discuss their specific approaches with FDA—particularly if those approaches diverge from the recommendations in FDA’s guidance. While the guidance is helpful in determining the use of relevant and reliable data to generate clinical evidence, the guidance does not mandate the use of real-world data or evidence nor restrict other means of providing evidence to support regulatory decision-making.

Methodologies for Collection and Analysis of Real-World Data to Generate Real-World Evidence

FDA explains that studies using real-world data should undergo careful assessment before the development of a study and that sponsors consider methodologies to address factors that can impact the interpretability of a study using real-world data. To ensure sound clinical study planning, FDA recommends that sponsors document their decisions and rationale for the following:

• Whether to include randomization, concurrent or historical controls;
• The choice of performance goals and objective performance criteria;
• Type I and type II error control;
• Data gathering or dependence on extant data;
• Bias mitigation strategies;
• Precision of outcome measures and other data elements, as applicable; and
• All other known factors pertinent to the interpretation of the study results (e.g., generalizability of the real-world evidence findings to the intended use population).

Although FDA does not endorse specific study designs, it suggests that sponsors developing methods for study using real-world data consider the regulatory purpose of the generated clinical evidence and, as with all clinical evidence generation, choose the appropriate design depending on the study question, device, outcome, key covariates, and the specific study objectives or hypotheses. Study designs following those guidelines could include single-arm studies, non-interventional studies, or randomized controls, for example.

For studies using real-world data, FDA recommends that sponsors have a study design that describes the study time frame, the predefined set of data elements, and a systematic consideration that the proposed data elements are all necessary for inclusion and represent all the key data elements.

What This Means for Study Sponsors

Sponsors seeking to use real-world data to generate real-world evidence for regulatory purposes should identify such data and evidence as part of their regulatory submission cover letters to help facilitate review, including: the purpose of using real-world evidence to support the submission; study design; the real-world data sources used to generate evidence; and specific information (e.g., data source name, data source provider) of the real-world data sources. As with other clinical studies, sponsors should include necessary information in their regulatory submissions to FDA, including the protocol(s) for real-world data-based studies, incorporating the relevance and reliability concepts in the guidance, as well as a study report with their assessment of relevance and reliability, and any additional information necessary for FDA to conduct its review.

For More Information

If you have any questions about this Alert, please contact Frederick R. Ball, Annie Blackman, any of the attorneys in our Life Sciences and Medical Technologies Industry Group or the attorney in the firm with whom you are regularly in contact.

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