Industry spotlight
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Sensor Data Integrations
CHECKLIST BY DIME
Final Data Integrity Checklist, Sensor Data Integrations (SDI) Project: Provides a comprehensive, post-trial checklist for data managers to verify immutability, audit trail completeness, and final data provenance before archival.
Developing Novel Endpoints Generated by Digital Health Technology for Use in Clinical Trials
BEST PRACTICES BY CTTI
“Stakeholders, including digital health technology companies and sponsors who may produce competing tools or develop competing therapies, should share knowledge and lessons learned regarding the development of novel endpoints. Early successes could be made public as use cases to inform future efforts and allow collaboration among stakeholders. The exchange of information may provide the scientific basis for developing or using a technology, allow investigators and technology manufactures to invest time and money with an assurance that the results with be universally useful, and increase the end user’s confidence in the output of the technology.”
Patient Technology Implementation Framework
FRAMEWORK BY TRANSCELERATE
The framework emphasizes an iterative, rapid-learning approach to patient technology (PT) implementation, which inherently includes reflection throughout the process, and particularly at the conclusion of a pilot or study. Key elements suggesting reflection include:
- Capturing Insights: It explicitly recommends capturing both positive and negative insights to refine, improve, or adapt future technology experimentation.
- User Feedback: The Patient Technology Toolkit, which supports the framework, includes the Patient Technology Site Feedback Questionnaire (PTSFQ). This tool is designed to gather insights from participating patients and site staff, which informs future study design based on user feedback.
- Mitigating Risks: The framework aims to help teams identify and mitigate potential issues as a study progresses and inform future study design based on the learnings.
Sensor Data Integrations
2022
Digital Medicine Society
Recommendations / best practices
Authors
Sensor Data Integrations Project Team
The Sensor Data Integration guides provide a structured approach to integrating sensor-generated data into digital health technologies, emphasizing best practices for data collection, transmission, processing, quality assurance, privacy, and security. Each guide outlines key principles and operational considerations for ensuring that sensor-generated health data are accurate, secure, and useful for clinical decision-making. The guides align with regulatory and industry standards, supporting interoperability and ethical handling of digital health data while optimizing usability for diverse patient populations.
Findings
Sensor-generated health data must be collected in a way that ensures completeness, contextual metadata, and fit-for-purpose accuracy to support clinical applications.
Data security and privacy regulations vary globally, necessitating the implementation of adaptable frameworks such as the FAIR data principles and cybersecurity best practices.
Standardized data transmission and processing protocols are required to ensure interoperability across digital health platforms and prevent data loss or corruption.
Validation frameworks, such as DiMe’s V3 (Verification, Analytical Validation, and Clinical Validation), are essential to confirm the reliability of digital clinical measures.
Equity and accessibility considerations must be prioritized, ensuring that digital health solutions work across diverse populations and healthcare settings.
Recommendations
Digital health developers should follow standardized methodologies for data collection, leveraging frameworks such as the EVIDENCE checklist and DiMe’s V3 validation process.
Privacy-by-design principles should be embedded into sensor-based data systems to comply with HIPAA, GDPR, and emerging digital health privacy regulations.
Data processing workflows must be transparent, well-documented, and validated to ensure consistent, unbiased, and reproducible results in clinical applications.
Organizations should adopt cybersecurity best practices, including end-to-end encryption, authentication protocols, and risk mitigation strategies, to protect sensor data.
Sensor data integration strategies should be aligned with industry standards and open-source protocols to promote interoperability and scalability in healthcare ecosystems.
Regulatory Considerations
Regulatory agencies such as the FDA encourage the use of validated digital biomarkers and structured sensor data processing methodologies to support regulatory submissions.
Sensor data privacy policies must comply with local and international regulations, requiring clear user agreements, informed consent, and transparent data governance.
Secure data transmission protocols must be implemented to prevent unauthorized access, aligning with industry standards for encryption, authentication, and network security.
Organizations deploying sensor-based health technologies should conduct risk assessments and audits to ensure compliance with evolving regulatory requirements for AI and digital health.
Global harmonization of data security and transmission standards is necessary to support cross-border data exchange, facilitating regulatory approval and market access for digital health innovations.
Sensor-generated health data must be collected in a way that ensures completeness, contextual metadata, and fit-for-purpose accuracy to support clinical applications.
Data security and privacy regulations vary globally, necessitating the implementation of adaptable frameworks such as the FAIR data principles and cybersecurity best practices.
Standardized data transmission and processing protocols are required to ensure interoperability across digital health platforms and prevent data loss or corruption.
Validation frameworks, such as DiMe’s V3 (Verification, Analytical Validation, and Clinical Validation), are essential to confirm the reliability of digital clinical measures.
Equity and accessibility considerations must be prioritized, ensuring that digital health solutions work across diverse populations and healthcare settings.
Recommendations
Digital health developers should follow standardized methodologies for data collection, leveraging frameworks such as the EVIDENCE checklist and DiMe’s V3 validation process.
Privacy-by-design principles should be embedded into sensor-based data systems to comply with HIPAA, GDPR, and emerging digital health privacy regulations.
Data processing workflows must be transparent, well-documented, and validated to ensure consistent, unbiased, and reproducible results in clinical applications.
Organizations should adopt cybersecurity best practices, including end-to-end encryption, authentication protocols, and risk mitigation strategies, to protect sensor data.
Sensor data integration strategies should be aligned with industry standards and open-source protocols to promote interoperability and scalability in healthcare ecosystems.
Regulatory Considerations
Regulatory agencies such as the FDA encourage the use of validated digital biomarkers and structured sensor data processing methodologies to support regulatory submissions.
Sensor data privacy policies must comply with local and international regulations, requiring clear user agreements, informed consent, and transparent data governance.
Secure data transmission protocols must be implemented to prevent unauthorized access, aligning with industry standards for encryption, authentication, and network security.
Organizations deploying sensor-based health technologies should conduct risk assessments and audits to ensure compliance with evolving regulatory requirements for AI and digital health.
Global harmonization of data security and transmission standards is necessary to support cross-border data exchange, facilitating regulatory approval and market access for digital health innovations.
Keywords
Technologies
Open Resource
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.
Developing Novel Endpoints Generated by Digital Health Technology for Use in Clinical Trials
2022
CTTI
Recommendations / best practices
Authors
Clinical Trials Transformation Initiative
This document provides a set of recommendations for developing novel endpoints generated by digital health technologies (DHTs) for use in clinical trials. It emphasizes patient-centric, clinically relevant measures, stakeholder engagement, early and frequent regulatory interactions, and the inclusion of digitally-derived endpoints in early-phase clinical trials. Additionally, it outlines the benefits, challenges, and best practices for technology selection, validation, and knowledge sharing in the context of DHTs.
Findings
Novel digitally-derived endpoints can provide more reliable data, increase trial efficiency, and enhance patient centricity.
Selecting appropriate outcome measures that are meaningful to patients and clinicians is critical to success.
Developing these endpoints requires a resource-intensive, systematic approach to meet stakeholder needs.
Demonstrating validity and utility of novel endpoints poses unique challenges, especially for new measures without established validation standards.
Sharing lessons learned and promoting transparency can advance the field by enabling collaboration and establishing standards.
Recommendations
Focus on measures that are meaningful to patients and clinically relevant by incorporating both patient and clinician perspectives.
Select technology after identifying the appropriate outcome to ensure alignment between the technology and trial objectives.
Engage with regulators early and often to ensure endpoint acceptance and alignment with regulatory requirements.
Include digitally-derived endpoints in early-phase trials and observational studies to validate their fit-for-purpose status.
Encourage knowledge sharing and collaboration among stakeholders to establish shared standards and accelerate adoption.
Regulatory Considerations
Engage with FDA, EMA, or other regulatory bodies during early stages of endpoint development to gather critical input.
Use established regulatory frameworks, such as Investigational New Drug (IND) or Investigational Device Exemption (IDE), for guidance on endpoint use in pivotal trials.
Validate technologies to meet performance characteristics, ensuring outputs correspond to clinical concepts of interest.
Include digitally-derived endpoints in exploratory studies to build evidence for their regulatory approval.
Reference resources such as the FDA and EMA guides for navigating endpoint-related regulatory interactions.
Novel digitally-derived endpoints can provide more reliable data, increase trial efficiency, and enhance patient centricity.
Selecting appropriate outcome measures that are meaningful to patients and clinicians is critical to success.
Developing these endpoints requires a resource-intensive, systematic approach to meet stakeholder needs.
Demonstrating validity and utility of novel endpoints poses unique challenges, especially for new measures without established validation standards.
Sharing lessons learned and promoting transparency can advance the field by enabling collaboration and establishing standards.
Recommendations
Focus on measures that are meaningful to patients and clinically relevant by incorporating both patient and clinician perspectives.
Select technology after identifying the appropriate outcome to ensure alignment between the technology and trial objectives.
Engage with regulators early and often to ensure endpoint acceptance and alignment with regulatory requirements.
Include digitally-derived endpoints in early-phase trials and observational studies to validate their fit-for-purpose status.
Encourage knowledge sharing and collaboration among stakeholders to establish shared standards and accelerate adoption.
Regulatory Considerations
Engage with FDA, EMA, or other regulatory bodies during early stages of endpoint development to gather critical input.
Use established regulatory frameworks, such as Investigational New Drug (IND) or Investigational Device Exemption (IDE), for guidance on endpoint use in pivotal trials.
Validate technologies to meet performance characteristics, ensuring outputs correspond to clinical concepts of interest.
Include digitally-derived endpoints in exploratory studies to build evidence for their regulatory approval.
Reference resources such as the FDA and EMA guides for navigating endpoint-related regulatory interactions.
Keywords
Clinical trial endpoints
Clinical trials
Device selection
Digital endpoints
Digital health technologies (DHTs)
Drug development
Endpoint validation
Exploratory studies
FDA
Meaningful outcome measures
Neurodegenerative diseases
Novel endpoints
Patient centricity
Regulatory guidance
Sponsor experiences
Stakeholder collaboration
Validation
Technologies
Open Resource
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.
Patient Technology Implementation Framework
2018
TransCelerate
Framework or roadmap
,
Recommendations / best practices
Authors
TransCelerate
TransCelerate provides a structured framework to guide the implementation of patient technologies (PT) in clinical trials. The framework consists of six stages: strategy development, use case ideation, use case development, proof of concept, technology pilot, and expansion. It emphasizes iterative and holistic planning, addressing organizational challenges, and aligning PT implementation with long-term strategic goals. The framework also includes tools and resources to support patient and site engagement, technology evaluation, and data-driven decision-making.
Findings
Successful PT implementation requires iterative planning across six stages to adapt to changing study goals and patient needs.
Early engagement with patients, caregivers, sites, and regulatory bodies is critical to align PT goals with stakeholder priorities.
Identifying technical, operational, and compliance risks early on is essential for smooth implementation and scalability.
Rapid, small-scale tests of technologies can help address unknowns, refine user experience, and mitigate technical risks before full-scale pilots.
Scaling PT across geographies and populations requires addressing regional variations in infrastructure, regulations, and cultural acceptance.
Recommendations
Start with a clear, organization-wide PT strategy to align with clinical trial goals and define success metrics.
Involve patients, sites, and regulatory authorities early to gather insights and ensure alignment with their needs and priorities.
Test technologies in controlled environments to validate functionality, usability, and integration before pilot studies.
Use technology pilots to collect data on feasibility, usability, and implementation challenges to inform broader rollouts.
Leverage learnings from pilots to develop a scalable strategy that addresses technical, regulatory, and cultural barriers.
Regulatory Considerations
Determine whether the technology is classified as a medical device and ensure compliance with relevant regulations (e.g., FDA, EMA, MDR).
Address data protection regulations, such as GDPR, by implementing robust privacy measures and secure data storage practices.
Account for region-specific regulatory requirements and infrastructure challenges during scaling.
Proactively consult regulatory agencies during pilots to align on technology use, endpoints, and data validity.
Successful PT implementation requires iterative planning across six stages to adapt to changing study goals and patient needs.
Early engagement with patients, caregivers, sites, and regulatory bodies is critical to align PT goals with stakeholder priorities.
Identifying technical, operational, and compliance risks early on is essential for smooth implementation and scalability.
Rapid, small-scale tests of technologies can help address unknowns, refine user experience, and mitigate technical risks before full-scale pilots.
Scaling PT across geographies and populations requires addressing regional variations in infrastructure, regulations, and cultural acceptance.
Recommendations
Start with a clear, organization-wide PT strategy to align with clinical trial goals and define success metrics.
Involve patients, sites, and regulatory authorities early to gather insights and ensure alignment with their needs and priorities.
Test technologies in controlled environments to validate functionality, usability, and integration before pilot studies.
Use technology pilots to collect data on feasibility, usability, and implementation challenges to inform broader rollouts.
Leverage learnings from pilots to develop a scalable strategy that addresses technical, regulatory, and cultural barriers.
Regulatory Considerations
Determine whether the technology is classified as a medical device and ensure compliance with relevant regulations (e.g., FDA, EMA, MDR).
Address data protection regulations, such as GDPR, by implementing robust privacy measures and secure data storage practices.
Account for region-specific regulatory requirements and infrastructure challenges during scaling.
Proactively consult regulatory agencies during pilots to align on technology use, endpoints, and data validity.
Keywords
Technologies
Open Resource
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.
For reference: review the relevant regulatory guidances
Regulatory spotlight
Features essential guidance, publications, and communications from regulatory bodies relevant to this section. Use these resources to inform your regulatory strategy and ensure compliance.