The Evidence Matters 2024 Medical Devices/IVD Best Practices session featured industry experts Rea Castro, Director of Medical Affairs at QuidelOrtho, Sepanta Fazaeli, Clinical Systems & Medical Lead at Stryker, and Christa Goode, Worldwide Scientific Operations Director, Medical Writing at Johnson & Johnson. They discussed how AI-enabled integrated evidence management is driving innovation, moderated by Julie Pritchard-Hedtke, President at AVIO Medtech Consulting. Here are the key insights from the session:
Key Takeaways from the Evidence Matters 2024 Medical Devices/IVD Industry Best Practices Breakout “Driving MedTech Innovation Through AI-Enabled Integrated Evidence Management”
Q: Using one word, what does AI mean to you?
A: Rea Castro, Director of Medical Affairs at QuidelOrtho Clinical: Assistance. Rea’s response highlights the role of AI as a support mechanism, enhancing the capabilities of healthcare professionals by streamlining tasks and decision-making processes.
Christa Goode, Worldwide Scientific Operations Director, Medical Writing at Johnson & Johnson: Data integrity. For Christa, AI’s significance lies in its ability to maintain the accuracy, consistency, and trustworthiness of data, which is fundamental for making informed decisions in healthcare.
Sepanta Fazaeli, Clinical Systems & Medical Lead at Stryker: Protection and compliance. Sepanta views AI as a tool for safeguarding patient information and ensuring that healthcare practices meet regulatory standards, reflecting a commitment to both security and adherence to legal frameworks.
Q: What are the key components of evidence management?
A: The key components include data collection, storage, processing, analysis, knowledge extraction, and the scalable and reusable storage of information. This comprehensive approach to managing evidence is critical for making informed decisions and fostering innovation in healthcare. Emphasis is placed on the need for a systematic framework that enables data sharing and reusability, thereby enhancing the efficiency and effectiveness of healthcare solutions.
Q: Why is evidence management important?
A: Evidence management is vital for regulatory compliance, a sentiment echoed by many in the healthcare industry. Informed decisions based on robust evidence improve patient health outcomes and lead to better patient care. Managing evidence means healthcare professionals must bridge the gap between technical innovation and patient-centered care and ensure that products in the market are not only technologically advanced but also clinically relevant and safe.
Q: What are the main challenges associated with data management?
A: Challenges abound when managing data: managing the sheer volume of data from various sources, ensuring data integrity to make reliable decisions, engaging stakeholders to prevent siloed data collection, and addressing data complexity to meet stringent regulatory standards. These challenges underline the need for sophisticated data management strategies that can handle the complexities of modern healthcare data, ensuring that it can be used effectively to support patient care and regulatory compliance.
Q: How can these components be integrated into a comprehensive and cohesive evidence management approach?
A: The integration of these components into a cohesive evidence management strategy involves creating a data lake that combines the flexibility of data lakes with the structured management capabilities of data warehouses. Savanta elaborates on the necessity of standardizing and harmonizing data, developing a data dictionary, and potentially creating an ontology or knowledge graph. This foundation facilitates AI models’ use and enables connections with external databases, laying the groundwork for automated, AI-driven solutions that enhance the value and usability of healthcare data.
Q: What is your vision for the AI-enabled MedTech space?
A: Rea envisions a future where AI acts as an assistant, streamlining literature reviews, ensuring regulatory compliance, and identifying trends for post-market surveillance. This vision focuses on leveraging AI to enhance efficiency and decision-making, with the ultimate goal of improving patient outcomes.
Christa’s vision is centered on AI ensuring data integrity and augmenting human capabilities to make processes faster and decision-making more reliable, all within the overarching goal of enhancing patient safety and outcomes.
Sepanta sees a future of data democratization, where standardized and harmonized data opens doors to predictive and prescriptive analytics, offering new market opportunities and enhancing patient safety. Savanta’s vision emphasizes AI’s role in supporting rather than replacing human expertise, suggesting a collaborative relationship between technology and healthcare professionals.
The panelists’ responses collectively paint a picture of a future where AI and advanced data management practices significantly impact healthcare, from improving operational efficiencies and decision-making processes to ensuring patient safety and driving better health outcomes.
