Regulatory spotlight
We offer selected excerpts from relevant guidances below, to help you get oriented and understand their significance.
It is your responsibility to fully examine and interrogate these guidances in detail. Click through on individual resource links to be taken to the primary source material.
Clinical trials with decentralized elements
Conducting Clinical Trials With Decentralized Elements
Coordination challenges with multiple locations in DCTs.
Variability in data collection across decentralized locations and remote tools.
Challenges in implementing certain statistical approaches in DCTs.
Need for DHTs to be accessible and suitable for all trial participants.
Ensuring compliance with local laws and regulations.
Recommendations
Develop clear protocols for integrating decentralized elements into clinical trials, specifying remote and in-person activities.
Use digital health technologies (DHTs) and electronic systems to streamline data acquisition, informed consent, and investigational product tracking.
Provide training for all stakeholders, including trial personnel, local health care providers, and participants, on decentralized processes.
Implement robust safety monitoring plans to address adverse events in decentralized settings.
Ensure compliance with local and international laws governing telehealth, data privacy, and investigational product use.
Regulatory Considerations
Maintain compliance with FDA requirements under 21 CFR parts 312 and 812 for drug and device trials, respectively.
Document all trial activities and data flows in trial protocols and data management plans, ensuring traceability and integrity.
Ensure informed consent processes meet FDA standards and provide clear communication to participants about decentralized trial activities and data handling.
Address investigational product accountability by documenting IP distribution, storage, and return or disposal.
Design electronic systems for decentralized trials to comply with 21 CFR part 11 requirements for data reliability, security, and confidentiality.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“When designing a DCT, sponsors can consider telehealth visits instead of in-person visits with trial participants if no in-person interaction is needed… The protocol should specify when a telehealth visit with a trial participant is appropriate and when a participant should be seen in person.”
– Section III.B (Remote Clinical Trial Visits and Clinical Trial-Related Activities), p. 4, Conducting Clinical Trials With Decentralized Elements, Final, 2024 (FDA)
“Sponsors should describe in the trial protocol or other trial-related documents how operational aspects of the DCT will be implemented. This description should cover, but may not be limited to, the following:
- Scheduled and unscheduled clinical trial visits
- Activities to be performed by trial personnel and those that may be performed by local HCPs
- Transmission of reports on activities performed at different locations
- Delivery of IPs to trial participants, if applicable, and accountability for IPs
- Safety monitoring and management of adverse events”
– Section III.D.1 (Roles and Responsibilities — The Sponsor), pp. 7–8, Conducting Clinical Trials With Decentralized Elements, Final, 2024 (FDA)
“Obtaining informed consent remotely may be considered as part of a DCT. Institutional review board (IRB) oversight is required to ensure the process is adequate and appropriate.”
– Section III.F (Informed Consent and Institutional Review Board Oversight), p. 12, Conducting Clinical Trials With Decentralized Elements, Final, 2024 (FDA)
“Investigators may obtain informed consent (either electronically or on paper) from trial participants at their remote locations provided that all applicable regulatory requirements are met… FDA therefore does not consider obtaining informed consent to be an appropriate activity for a local HCP to perform.”
– Section III.F (Informed Consent and Institutional Review Board Oversight), p. 13, Conducting Clinical Trials With Decentralized Elements, Final, 2024 (FDA)
“Electronic systems can be used to perform multiple functions to manage DCT operations, including:
- Managing electronic informed consent
- Capturing and storing reports from remote trial personnel, local HCPs, and local clinical laboratory facilities
- Managing electronic case report forms (eCRFs)
- Scheduling trial visits and other trial activities
- Tracking IPs that are shipped directly to trial participants
- Syncing information recorded by DHTs
- Serving as communication tools between trial personnel and trial participants”
– Section III.J (Electronic Systems Used When Conducting DCTs), p. 16, Conducting Clinical Trials With Decentralized Elements, Final, 2024 (FDA)
“Training should be provided to all parties (e.g., trial personnel, local HCPs, and trial participants) who are using electronic systems to support the conduct of DCTs.”
– Section III.J (Electronic Systems Used When Conducting DCTs), p. 17, Conducting Clinical Trials With Decentralized Elements, Final, 2024 (FDA)
Remote data acquisition
Digital Health Technologies for Remote Data Acquisition in Clinical Investigations
There is a need for comprehensive validation and verification processes for DHTs.
Ensuring data security and privacy is a significant concern.
Usability issues for diverse populations need to be addressed.
There is a lack of clarity on whether certain DHTs meet the definition of a device under the FD&C Act.
The guidance does not establish legally enforceable responsibilities.
Recommendations
Ensure DHTs are fit-for-purpose for clinical investigations.
Implement robust data security measures to protect participant information.
Conduct usability evaluations to ensure DHTs can be used by intended populations.
Engage with FDA early to discuss the use of DHTs in clinical investigations.
Develop a risk management plan to address potential issues with DHT use.
Regulatory Considerations
Verification and validation should be addressed regardless of device classification.
Sponsors should ensure compliance with data protection and privacy regulations.
FDA evaluates DHT data based on endpoints, medical products, and patient populations. Sponsors can engage with FDA’s Q-Submission Program for feedback on DHT usage in clinical trials.
Sponsors should understand the legal implications of using DHTs in clinical investigations.
The guidance provides recommendations but does not establish legally enforceable responsibilities.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Operational specifications (e.g., data storage capacity, frequency of data transmission) should be adequate to minimize missing data.
DHT alerts (e.g., low battery, poor signal, data not being recorded or transmitted to the server) are recommended to help trial participants, trial personnel, and/or sponsors prevent loss of data or missing data. The trial should include processes to ensure that trial participants understand how to respond to these alerts.”
– Section IV.A.3 (Design and Operation of DHTs and Other Technologies), p. 9, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“The sponsor should also describe the flow of data from the DHT to the first durable electronic data repository… Sponsors should describe how access to the DHT or the data collected from it is controlled to ensure privacy and security. The description should include methods for access control, when feasible, to ensure that only appropriate individuals are able to use the DHT or enter information.”
– Section IV.B (Digital Health Technology Description in a Submission), p. 11, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“Use of a DHT to remotely acquire data in a clinical investigation may impact the type and amount of missing data. Sponsors should have a plan in place to reduce the potential for missing data (e.g., sponsor and/or investigator automated data monitoring and alerts, participant reminders, ‘run-in’ period for participants, investigator outreach to participants) and to address missing data and data quality issues.”
– Section IV.E (Statistical Analysis and Trial Design Considerations), p. 17–18, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“When using DHTs to record and transmit data during a clinical investigation, the relevant data captured from the DHT, including all relevant associated metadata, should be securely transferred to and retained in a durable electronic data repository as part of the record of the clinical investigation. FDA regulations include record retention requirements for clinical investigators and sponsors and provide for FDA inspection of certain records relating to a clinical investigation.”
– Section IV.G (Record Protection and Retention), p. 21, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“The informed consent process should specify who may have access to data collected through the DHT during or after the clinical investigation (e.g., sponsors, investigators, participants, DHT manufacturers, other specified third parties) and during what time frame.
An explanation of measures to protect participant privacy and data, and limitations to those measures, when DHTs are used should be included.
If participants may incur additional expense because they are taking part in the clinical investigation, the consent process must explain the added costs, which could include costs for the participants that may result from using the DHT during the clinical investigation (e.g., data use charges).
DHTs or other technologies may be covered by end-user license agreements or terms of service as a condition of use, which may, among other things, allow DHT or other technology manufacturers and other parties to gain access to personal information and data collected by the DHT or other technology. When applicable, sponsors and investigators should ensure that the informed consent process explains to participants that their data may be shared outside of the clinical investigation, according to the end-user license agreement or terms of service. End-user license agreements and terms of service typically are lengthy and use complex terminology. Sponsors and investigators proposing use of DHTs for data collection should understand how such agreements or terms of service may affect trial participants and address this information when developing informed consent documents.”
– Section IV.F.3 (Informed Consent), p. 20, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“The sponsor should:
Develop and ensure training for trial personnel and trial participants on using DHTs according to the protocol (e.g., wearing the DHT for the specified time period). Sponsors should incorporate feedback from usability evaluations (see section IV.C.3) into the training.”
– Section IV.H.1 (Other Considerations—Sponsor’s Role), p. 22, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“Trial participants and trial personnel should be trained on the appropriate use of DHTs. In some situations, it may also be appropriate to provide training to participants’ caregivers. Trial personnel should be trained on responsibilities for data collection and maintenance of trial integrity and quality throughout the investigation. Any training materials should be included as part of the submission.”
– Section IV.H.3 (Training), p. 24, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“Investigators should:
- Ensure that trial participants understand what information will be collected by the DHT; how that information will be used, monitored, and acted upon; who will have access to the data; and how the security and privacy of data collected by the DHT will be maintained…
- Provide sponsor-developed training to participants on how to use the DHT according to the protocol (e.g., wearing the DHT for the specified time period). Training can be done remotely, as appropriate (e.g., through video conferencing).
- Review data from DHTs as specified in the safety monitoring plan (see section IV.H.1).”
– Section IV.H.2 (Other Considerations—Investigator’s Role), p. 24, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“Sponsors should consider addressing the following as part of the training for trial participants, caregivers, and/or trial personnel, as appropriate:
- Setting up, activating, and operating DHTs
- Confirming that the use of the DHT will be restricted to the trial participants and/or caregivers
- Collecting data at appropriate time intervals • Uploading or syncing data
- Ensuring the security and privacy of data collected by the DHT
- Wearing DHTs appropriately (e.g., location and duration), if applicable
- Properly cleaning the DHTs before or after use, if applicable
- Connecting to wireless or cellular networks
- Handling known adverse events associated with the DHT (e.g., rash from actigraphy bands)
- Responding to DHT signals, notifications, errors, hardware upgrades, and software updates, including procedures for troubleshooting and instructions for whom to contact for unresolved issues
- Verifying that DHTs are being used appropriately and that data are being collected, uploaded, or synchronized as planned”
– Section IV.H.3 (Training), p. 25, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“If a DHT or associated technology, such as a general computing platform, is updated during a clinical investigation (e.g., operating system update), sponsors should ensure that the DHT remains fit-for-purpose, such that the updates do not affect the measurements and that verification and validation studies (see section IV.C of this guidance) are still applicable. In situations where the measurements may be affected, it may be necessary to validate the measurements (e.g., using previously collected data or a new prospective study) after introduction of the update to ensure that no changes to the measurements occurred.
If changes to the measurement have occurred after the update, sponsors should compare data from DHTs before and after the update. Sensitivity analyses may be necessary to evaluate the impact of the update. Sponsors should take steps to mitigate any resulting differences. Sponsors should specify how these differences will be addressed in the analysis of the trial prior to unblinding (if applicable) and describe the impact differences may have on trial outcomes. Significant changes in the measurement after updates may invalidate the results from a clinical investigation.”
– Section IV.H.4 (DHT Updates and Other Changes), pp. 25-26, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
“Develop end-of-study closeout procedures (e.g., when/how data collection and/or transmission ends, revocation of system access).”
– Section IV.H.1 (Other Considerations —Sponsor’s Role), p. 24, Digital Health Technologies for Remote Data Acquisition in Clinical Investigations, Final, 2023 (FDA)
Artificial Intelligence in Software as a Medical Device (SaMD)
Artificial Intelligence and Machine Learning in Software as a Medical Device
AI/ML technologies offer dynamic learning capabilities but require careful regulation to ensure safety and effectiveness.
The FDA recognizes that traditional regulatory paradigms may not align with the adaptive nature of AI/ML and is developing frameworks to address this.
Guidance documents, such as the AI/ML SaMD Action Plan and predetermined change control plan (PCCP) recommendations, provide a structured approach for handling software updates.
Collaboration across FDA centers (CDRH, CBER, CDER) facilitates consistent regulatory practices for AI/ML across medical products.
Transparency and real-world data integration are key focuses in regulating AI/ML technologies.
Recommendations
Manufacturers should use FDA’s premarket pathways, including 510(k), De Novo, or PMA, for AI/ML-enabled SaMD.
Apply Good Machine Learning Practices (GMLP) during development to ensure algorithm reliability, transparency, and patient safety.
Include a predetermined change control plan (PCCP) in submissions to allow for iterative updates without requiring resubmissions.
Follow lifecycle management practices to maintain AI/ML system performance after deployment.
Engage with FDA early in development to align on appropriate regulatory strategies for novel AI/ML implementations.
Regulatory Considerations
AI/ML-driven SaMD updates may require premarket review, depending on the significance of changes and associated risks.
The FDA has outlined principles for transparency, including clear labeling and documentation of AI/ML system capabilities and limitations.
Guidance documents like the “Good Machine Learning Practice” and “Marketing Submission Recommendations for PCCP” should be followed for compliance.
Collaboration between FDA centers ensures alignment on the use of AI in combination products and broader healthcare applications.
Lifecycle management strategies must account for real-world data to ensure continuous learning and safe AI/ML system updates.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The complex and dynamic processes involved in the development, deployment, use, and maintenance of AI technologies benefit from careful management throughout the medical product life cycle.
– Introduction, p. 1,Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, 2021 (FDA)
“[FDA will] Support regulatory science efforts to develop methodology for the evaluation and improvement of machine learning algorithms, including for the identification and elimination of bias, and for the evaluation and promotion of algorithm robustness.”
– Section 4 (Regulatory Science Methods Related to Algorithm Bias & Robustness), p. 5, Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, 2021 (FDA)
“Because AI/ML systems are developed and trained using data from historical datasets, they are vulnerable to bias – and prone to mirroring biases present in the data.
The Agency recognizes the crucial importance for medical devices to be well suited for a racially and ethnically diverse intended patient population and the need for improved methodologies for the identification and improvement of machine learning algorithms. This includes methods for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions.”
– Section 4 (Regulatory Science Methods Related to Algorithm Bias & Robustness), p. 5, Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, 2021 (FDA)
Considerations for the use of AI in drug development
Considerations for the Use of Artificial Intelligence To Support Regulatory Decision-Making for Drug and Biological Products
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PFDD 1: Comprehensive and representative input
Patient-Focused Drug Development: Collecting Comprehensive and Representative Input
Patient experience data encompass a range of inputs, including symptom burdens, treatment impacts, patient preferences, and views on unmet medical needs.
These data inform all stages of medical product development, from discovery to post-market use.
Quantitative methods (e.g., surveys) provide numerical insights, while qualitative methods (e.g., interviews) offer in-depth understanding. Mixed methods combine both for a fuller perspective.Social media and verified patient communities present novel data collection opportunities but require consideration of verification and representativeness challenges.
Probability sampling (e.g., stratified random sampling) is emphasized for generalizability, while non-probability methods (e.g., convenience sampling) are useful for exploratory research. Representativeness ensures that patient input reflects the diversity and heterogeneity of the target population.
Data collection should adhere to good clinical practices and regulatory standards.
Research protocols should address missing data, quality assurance, and confidentiality.
Early collaboration with the FDA is recommended to align on study designs and regulatory requirements.
Recommendations
Define clear research objectives and determine specific research questions before selecting data collection methods.
Use probability sampling methods whenever feasible to ensure representativeness of the target population.
Address data quality through rigorous planning, data management, and adherence to FDA-supported standards.
Incorporate diverse perspectives by including underrepresented patient populations, tailoring methods to specific subgroups as needed.
Leverage existing data sources, such as patient registries and literature, to complement primary data collection efforts.
Regulatory Considerations
Data submitted to FDA should include clear documentation of the study protocol, intended use, and data collection methodologies.
Researchers must comply with human subject protection regulations (e.g., 21 CFR Parts 50 and 56) and good clinical practice guidelines.
For data intended to support regulatory submissions, adherence to FDA-supported data standards (e.g., CDISC) is strongly encouraged.
Missing data should be addressed through pre-planned strategies and summarized in the study report.
Patient experience data must meet methodological rigor to ensure their reliability and relevance for regulatory decision-making.
Some summaries are generated with the help of a large language model; always view the linked primary source of a resource you are interested in.
“Standardized training should be provided to the members of the research team to improve consistency of research. The roles and responsibilities of the team should be outlined in the research protocol. This will help to prevent many site issues. FDA encourages stakeholders to also have a troubleshooting guide. Researchers should anticipate and address site/field issues that might arise during data collection.”
– Section III.F (Resolving Site/Field Issues), p. 25, Patient-Focused Drug Development: Collecting Comprehensive and Representative Input, Final, 2020 (FDA)
“Data management considerations should be addressed in the early stages of a research study. Before initiating data collection, you should formulate a data management plan (DMP) — a written document that describes the data you expect to acquire or generate during your research study; how you intend to manage, describe, analyze, and store said data; and what mechanisms you will use at the end of your study to preserve and share your data (Stanford University Libraries n.d.). Creating a written DMP helps formalize the data management process, identify potential weaknesses in the DMP, and provide a record of what you intend to do.”
– Section III.J (Storing Data), p. 26, Patient-Focused Drug Development: Collecting Comprehensive and Representative Input, Final, 2020 (FDA)
Scope of PFDD guidances
The FDA’s Patient-Focused Drug Development (PFDD) Guidance Series “is intended to facilitate the advancement and use of systematic approaches to collect and use robust and meaningful patient and caregiver input that can better inform medical product development and regulatory decision making.”
While the PFDD series provides this key framework, different FDA centers emphasize distinct guidances in their assessments. For instance, the Center for Drug Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) currently utilize PFDD 1 and 2. In contrast, the Center for Devices and Radiological Health (CDRH) uses Principles for Selecting, Developing, Modifying, and Adapting Patient Reported Outcome Instruments for Use in Medical Device Evaluation, for patient-reported outcomes. All centers are also preparing for the implementation of PFDD 3 (finalized Oct 2025) and the forthcoming PFDD 4, which will together replace the older 2009 guidance on Patient-Reported Outcome Measures.
Once you’ve read the guidances, explore these best practices from the field:
Industry spotlight
Gathers real-world examples, case studies, best practices, and lessons learned from peers and leaders in the field relevant to this section. Use these insights to accelerate your work and avoid common pitfalls.