Findings
Traditional, site-based clinical trials often create significant burdens for participants, which can hinder recruitment, retention, and the enrollment of diverse populations.
A lack of early and sustained patient engagement in trial design can lead to research protocols that are misaligned with patient needs and endpoints that are not meaningful to them.
The underrepresentation of diverse racial, ethnic, and other demographic groups in clinical trials limits the generalizability of study results and can perpetuate health disparities.
Emerging digital health technologies (DHTs) and real-world data (RWD) present significant opportunities to make clinical trials more efficient, patient-centric, and inclusive, but their adoption has been inconsistent.

Recommendations
Sponsors and research teams should engage patients and patient advocacy groups as active partners throughout the entire clinical trial lifecycle, from design to dissemination.
Decentralized clinical trial (DCT) elements should be incorporated to reduce patient burden, improve access for diverse populations, and enhance the quality of data collection.
Trial sponsors must develop and implement proactive strategies to enhance the diversity and inclusion of trial participants to ensure results are applicable to all patient populations.
Novel endpoints derived from DHTs should be developed and validated to capture more objective, real-world measures of how patients feel, function, and survive.
Multi-stakeholder collaboration between industry, academia, patient groups, and regulators is essential to address systemic challenges and improve the clinical trial enterprise.

Regulatory Considerations
Early and frequent communication with regulators, such as the FDA, is critical when implementing novel approaches like DCTs or developing new digital endpoints for pivotal trials.
Regulatory frameworks must support the use of innovative technologies and trial models while ensuring data integrity, reliability, and patient safety.
The use of a single Institutional Review Board (IRB) for multi-site trials is a key regulatory-supported mechanism for streamlining ethics review and increasing trial efficiency.
When using DHTs and decentralized methods, robust plans for data quality, privacy, and security are necessary to meet regulatory standards for trial data submission.

Findings
The use of Artificial Intelligence (AI) and Machine Learning (ML) is being applied to a broad range of drug development activities with the potential to accelerate the process and make clinical trials safer and more efficient. The inclusion of AI/ML is most common in the clinical development/research phase of regulatory submissions. Concerns exist that AI/ML algorithms could amplify errors and preexisting biases in underlying data sources, which raises issues related to generalizability and ethical considerations. Other challenges include limited explainability due to model complexity and proprietary reasons, as well as managing risks related to data quality, reliability, and representativeness. The FDA recognizes that a careful, risk-based assessment of the specific context of use (COU) is needed when evaluating AI/ML.

Recommendations
Stakeholders should adhere to practices in three key areas: human-led governance, accountability, and transparency; quality, reliability, and representativeness of data; and model development, performance, monitoring, and validation. A risk management plan should be applied to identify and mitigate risks based on the COU, guiding the level of documentation and transparency. Practices are needed to ensure the integrity of AI/ML and address issues like bias and missing data. For models, developers should use pre-specification steps and clear documentation for development and assessment criteria. Models must be monitored over time for reliability and consistency, and Real-World Data (RWD) performance can provide valuable feedback, including for potential re-training.

Regulatory Considerations
The FDA encourages early engagement through mechanisms like the Critical Path Innovation Meetings (CPIM), ISTAND Pilot Program, and Emerging Technology Program to discuss relevant AI/ML methodologies or technologies. The Verification and Validation (V&V 40) risk-informed credibility assessment framework and the principles for Good Machine Learning Practices (GMLP), while not specific to drug development, are helpful guides for evaluating models. The industry is exploring the use of a Predetermined Change Control Plan (PCCP) mechanism for AI/ML-based devices to proactively specify and manage modifications, enhancing adaptability. In general, a risk-based approach should guide the level of evidence and record keeping needed for the verification and validation of AI/ML models for a specific COU.

Findings
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.

Findings
Decentralized Clinical Trials (DCTs), which shift activities away from sites, rely heavily on technology to reduce participant burden and improve access to trials. Digital platforms are essential for this shift, providing centralized data capture, remote monitoring, and streamlined workflows. Benefits include allowing participants to be monitored remotely, which can improve self-management and clinical outcomes, and giving researchers better insight into the real-world variability of disease activity. Currently, commercial platforms are often limited in functionality and face major challenges due to a lack of interoperability and specific data standardization protocols for clinical trial platforms, making it difficult to integrate third-party modules.

Recommendations
The paper strongly recommends the adoption of unified, integrated, and DCT-specific digital platforms to fully realize the benefits of decentralization. Platform developers should adopt international standards for health data exchange, such as HL7 FHIR and CDISC standards (PRM, CDASH, ADaM), to address the lack of data standardization and improve interoperability and modularity. Platforms should incorporate features that enhance participant engagement and adherence, such as customization options, simple user interfaces (UIs), push notifications, gamification, and allowing access to participant data . Security and governance teams are paramount to manage risks associated with malware, lost devices, and ensuring compliance with local legislation and data security protocols.

Regulatory Considerations
Digital platform design must maintain digital security and compliance with local legislation and data standards. The paper notes that a fully integrated, unified digital platform in a best-case scenario would use pre-existing standards (like CDISC and HL7) to guarantee interoperability. Adopting these standards and recommendations for data sharing, privacy, and security, as recommended by organizations like the Healthcare Information and Management Systems Society, is critical for future digital components used in DCTs. Improved data integrity and accountability in platforms could be further explored using technologies like blockchain to create an immutable ledger.

Findings
Limited use of BYOD in clinical trials and evolving regulatory guidance.
Potential biases due to participant preselection based on technology access and literacy.
Challenges in technology availability for generating study endpoints.

Recommendations
Ensure appropriate technology selection to meet study objectives.
Address potential biases in study population and data variability.
Implement mitigation strategies like provisioned technologies to avoid digital divide.
Develop a comprehensive statistical analysis plan for BYOD data.
Engage stakeholders early in the study design process.

Regulatory Considerations
Manage interactions with regulatory authorities on trial design and approval.
Prepare evidence dossiers for novel assessments via digital health technology.
Ensure compliance with guidelines like those from the Agency for Health Research and Quality.

Findings
The DTRA Best Practice Evaluation Rubric uses five dimensions to determine if a DCT practice should be considered a "best practice":
Evidence of Success: Requires measurable and demonstrable success using KPIs and tangible outcomes.
Improving Patient Experience: Must address the needs of patients, caregivers, and therapeutic experts, demonstrating improved experience and engagement.
Site Impact: Must consider the implications of adoption and the practical impact on site burden and working practices.
Operational and Technical Feasibility: Ensures operational and technical aspects (including ongoing support, security, integrity, scaling, and reuse) have been fully considered when deploying new technologies.
Regulatory & Ethical Compliance: Requires appropriate consideration of global and local regulations and guidance (e.g., ICH E6/E8, GDPR, HIPAA), including adherence to privacy, consent, and ethical safeguards.

Recommendations
A practice should demonstrate several key factors across the dimensions:
Patient-Centricity: Reduce patient burden by offering the option to reduce physical visits and enable greater patient empowerment and access to information. It should strive to increase the diversity of recruited patients while mitigating bias toward technologically literate patients.
Site Support: Achieve a net reduction in burden for sites, utilizing simple, intuitive technology with minimal, on-demand training. It must provide clarity of fiduciary responsibility and use technology to increase risk-based monitoring without sacrificing data integrity.
Technical Rigor: Have a clear problem statement and a thoroughly defined strategy to mitigate operational and technical risks. It should take a holistic approach and ensure the solution is fit for use for the specific patient population, aligning with data privacy and security standards.

Regulatory Considerations
Practices must ensure compliance with both global and local regulations and Health Authority guidance. Explicit attention must be given to aligning with ICH E6 (Good Clinical Practice) and privacy laws like GDPR and HIPAA. The design must protect stakeholders providing sensitive or personal data with safeguards to ensure ethical safety.

Findings
Research is more impactful and relevant when patients and other stakeholders are treated as equal partners throughout the entire study lifecycle.
A lack of shared vision, clear communication protocols, and defined roles are significant barriers to the success of multi-stakeholder research teams.
Engaging diverse stakeholders leads to the development of more patient-centered research questions and outcome measures that reflect real-world priorities.
Institutional barriers, such as inflexible policies on compensation and data access for non-researcher team members, frequently undermine effective collaboration.
Successful patient-centered outcomes research (PCOR) requires specific skills in collaborative problem-solving, conflict navigation, and leading productive team meetings.

Recommendations
Integrate patients, caregivers, clinicians, and other stakeholders into research teams from the initial planning stages to ensure alignment with patient needs.
Establish a shared vision and clear ground rules for communication, decision-making, and responsibilities to foster a cohesive and productive team environment.
Provide training and resources for all team members on best practices for stakeholder engagement, collaborative teamwork, and patient-centered research methods.
Institutions should develop supportive infrastructure, including fair compensation policies and streamlined onboarding processes, to facilitate meaningful stakeholder participation.
Research plans should be flexible, allowing teams to adapt their engagement strategies and methodologies in response to stakeholder feedback and changing circumstances.

Regulatory Considerations
Evidence generated through patient-centered outcomes research can strengthen regulatory submissions by demonstrating that a product's benefits are meaningful to patients.
The inclusion of diverse patient populations in research, a core tenet of PCOR, helps generate real-world evidence that is more generalizable and relevant for post-market surveillance.
Regulatory bodies are increasingly emphasizing the use of patient-experience data and patient-reported outcomes, which are central to the PCORI research model.
Engaging stakeholders in the selection of clinical trial endpoints helps ensure alignment with patient priorities, which can facilitate more efficient regulatory review.
The collaborative, transparent methods promoted by PCORI can help build trust and align expectations among researchers, patients, and regulatory agencies.

Findings
Digital technologies face challenges across scientific, clinical, technological, business, ethical, and regulatory domains.
Current testing paradigms are inadequate for identifying meaningful changes in early-stage Alzheimer's disease.
Complex digital tools may not be suitable for all trial participants due to varying technology, motor, or cognitive skills.
Ethical issues such as privacy, data sharing policies, and informed consent are significant concerns.
The regulatory path for digital medical devices is unclear and needs further development.

Recommendations
Develop more sensitive and specific diagnostic tools for early-stage Alzheimer's disease.
Create adaptable and user-friendly digital tools suitable for diverse populations.
Address ethical concerns by establishing clear privacy and data sharing policies.
Engage with regulatory bodies early to understand the regulatory landscape.
Integrate digital tools into clinical trials alongside traditional measures to advance the field.

Regulatory Considerations
The regulatory path for digital medical devices is currently unclear and needs clarification.
Developers should follow design control methods and ensure compliance with relevant regulations.
Early engagement with regulatory agencies is recommended to speed up development and approval processes.

Findings
The FDA's increasing commitment to patient-focused drug development (PFDD) and patient engagement in translational research presents a significant opportunity to improve the clinical trials enterprise and enhance participation by patient groups . Patient groups can play important roles in improving the entire therapeutic development enterprise, from study endpoint selection that reflects outcomes meaningful to patients, to recruitment and retention in clinical trials, and more effective postmarketing safety . However, there is a lack of clarity about how, when, and by whom patients or patient groups should be engaged during the therapy development process, and which patients or patient groups should be engaged . Metrics by which the value of such engagement, in terms of regulatory and market success, might be measured are also lacking .

Recommendations
PFDD and patient engagement in research should be considered an effort to extend the benefits of incorporating patient insight and experiences, as well as desires and preferences, from bench to bedside and back . The therapeutic development process should meaningfully engage patients throughout, though specific guidance on implementation methods is needed .

Regulatory Considerations
The paper does not provide specific regulatory considerations or recommendations. The focus remains on identifying the opportunity and gaps in current patient engagement approaches rather than detailing regulatory pathways or compliance requirements.