Professor Smith has spent 45 years in our industry as a research scientist, strategist and now an academic specialising in how we make and execute strategy. In this opening presentation, he will address four questions:

• What does “strategy” mean in medical device firms?
• Why and how do the strategies of medical device firms differ?
• How does your firm’s strategy impact on the work of clinical development departments?
• What are the three questions clinical development teams should ask their strategy colleagues?

• Importance of traceability and consistency in clinical data journey for medical device development
• Exploration of key aspects including device development, clinical evaluation, trials, and regulatory pathways
• Addressing challenges and strategies for ensuring integrity and reliability of clinical data, covering data identification, collection, documentation, compliance, and benefit-risk assessment

• Regulatory Agencies: Partners, Not Opponents
  • Dispelling misconceptions about regulatory agencies
  • Emphasizing collaboration and partnership in the regulatory process
• Understanding Regulatory Perspectives
  • Exploring regulatory agencies’ objectives
  • Discussing strategies to align submission approaches with regulatory expectations
• Effective Document Presentation
  • Highlighting the importance of clear communication in submissions
    
  • Sharing best practices for organizing and formatting documents to improve readability and comprehension.

• Setting the clinical strategy
• Designing the right study for the right development phase
• Using the data gathered from all sources to support regulatory compliance, marketing claims and patient benefit

• When to use surveys for post market data collection
• Rank 4 vs. Rank 8 clinical evidence collected from surveys
• How to achieve Rank 4 for survey data for PMCF
• Best practices in conducting PMCF surveys

• Prioritizing clinical endpoints to address unmet needs
• The importance of evidence-based marketing messages
• Demonstrating market value with clinical endpoints
• Case example: cardiac company

• The excessive time on screening and reviewing
• Responsible AI: credibility and traceability of results
• Significantly reducing time and resources with AI
• Case example: burden of disease analysis

In the realm of evidence assessment in healthcare, the process of screening and reviewing data can often be time-consuming and resource-intensive. This abstract delves into a real-world case study that showcases the transformative impact of artificial intelligence (AI) in expediting evidence assessment while upholding standards of credibility and traceability.

The utilization of AI technology offers a promising solution to address the challenges posed by the cumbersome and labor-intensive nature of evidence assessment. By automating screening processes and streamlining data review tasks, AI presents a novel approach to significantly reduce the time and resources required for evidence assessment, allowing for more efficient decision-making and resource allocation.

Ensuring the responsible implementation of AI is imperative to maintain the credibility and traceability of results generated through automated processes. By adhering to principles of ethical AI design and transparent decision-making algorithms, healthcare organizations can uphold the integrity of their evidence assessment practices while leveraging the efficiencies offered by AI technology.

This abstract will present a compelling case example focusing on the application of AI in burden of disease analysis. Through this case study, attendees will gain valuable insights into the tangible benefits of AI in accelerating evidence assessment processes, promoting responsible AI implementation, and optimizing resource utilization in healthcare decision-making.

• Introduction to Real-World Data (RWD) and Real-World Evidence (RWE)
• Sources of RWD
• The Role of RWE in Healthcare
• Methodologies for Generating RWE
• Case Studies and Applications of RWE
• Challenges in Utilizing RWE Data

• Overview of Risk-Based SDV
• Identifying critical data and processes
• Implementing Risk-Based SDV

The adoption of risk-based source data verification (SDV) in clinical trials, especially under ISO 14155 guidelines for medical devices, is a step towards more efficient and reliable research. This approach focuses on checking data based on the level of risk it poses to the study’s outcomes and patient safety, making it a smart way to ensure data is accurate without wasting resources.

The session explains the basics of risk-based SDV, including how to assess and manage risks and the benefits. These benefits include better data quality, more efficiency, and smarter use of resources, all within the rules set by ISO 14155.

Moving towards risk-based SDV under ISO 14155 guidelines not only fits well with regulatory expectations but also pushes clinical research forward, making it more focused on important data, safer for patients, and more cost-effective. It encourages collaboration among those involved in clinical research to overcome hurdles and fully adopt this approach, leading to wider acceptance and use in studies.

• Criteria for site selection, focusing on infrastructure, patient population, and regulatory environment.
• Strategies to enhance compliance, including training and support
• Tools and technologies for real-time monitoring and risk mitigation.
• The importance of site engagement to boost study participation and completion rates.

• How to prepare and design data collection effectively, with risk management in mind
• How to ensure that datasets and analysis don’t ruin your study results
• How to approach the human factor in data collection plans