Driving Continuous Quality Improvements

This video, presented by Mike Jovanus, VP of Quality at Veeva, offers a comprehensive exploration of driving continuous quality improvements within the life sciences industry. It highlights the critical need for modernizing quality systems by connecting people, processes, and technology on an intelligent, data-driven, end-to-end digital platform. The presentation delves into the industry trends acting as catalysts for this transformation, the drivers for adopting modern quality systems, and specific opportunities for modernization, including the strategic application of advanced technologies like AI.

Jovanus begins by outlining significant industry shifts, such as increasing regulations and complex enforcement, globalization and externalization of operations, growing complexity due to virtual work environments, and the emergence of personalized medicine and gene therapies. These factors, combined with the prevalence of legacy quality solutions often 20+ years old, create a "perfect storm" for modernization. He emphasizes the shift from fragmented, incremental investments in quality systems to a more singular, transformative project approach, stressing the importance of evaluating the total cost of ownership (TCO) to demonstrate value generation.

The core of the discussion revolves around four key modernization opportunities. First, the digitization of quality content moves beyond traditional document management to content-centric approaches, leveraging collaborative authoring, mobile access on the shop floor, process-centric navigation, and video-based content delivery. Second, unifying and connecting quality processes involves streamlining end-to-end product technical complaints, integrating change control with regulatory approvals, and automating training. Third, improving third-party collaboration focuses on seamlessly integrating suppliers and contract partners into quality systems, particularly for audits and deviations, by overcoming the limitations of legacy on-premise systems. Finally, the video addresses enabling proactive quality management with advanced technologies and AI, navigating the hype to focus on practical, value-adding applications that assist, rather than replace, human efforts.

Key Takeaways:

• Industry Catalysts for Modernization: The life sciences industry is being driven to modernize quality systems by increasing regulatory complexity, globalized and externalized operations, the demands of virtual work, and the unique requirements of personalized medicine and gene therapies. Many legacy systems are 20+ years old, creating a "perfect storm" for change.
• Shift to Transformative Programs: Companies should move away from small, incremental quality system projects towards singular, transformative programs that modernize business processes, unify disparate systems, and leverage cloud-based solutions for long-term value.
• Total Cost of Ownership (TCO) Analysis: When evaluating modernization, it's crucial to conduct a thorough TCO comparison between legacy on-premise systems and new cloud solutions, as cloud models consolidate many hidden costs into a single footprint, often revealing significant value generation opportunities.
• Digitization of Quality Content: Modernization extends beyond basic document management to content-centric approaches, including collaborative authoring (e.g., Google Doc-like experience for SOPs), mobile access on the shop floor (e.g., Veeva's Station Manager with tablets), process-centric content navigation, and video-based content delivery for work instructions and training.
• Unified Quality Processes: Significant efficiencies can be gained by connecting end-to-end processes, such as linking inbound product complaints from medical inquiry or drug safety systems to the complaint handling process, and unifying change control with regulatory approvals and automated retraining.
• Seamless Third-Party Collaboration: Cloud-based systems like Veeva Vault enable secure, out-of-the-box collaboration with contract manufacturers, test labs, and thousands of suppliers, streamlining processes like external audits and deviation management by allowing self-provisioning and electronic responses.
• AI Continuum in Quality: AI capabilities range from basic automated intelligence (triaging, routing, duplicate identification) to advanced machine learning/NLP (identifying patterns) and, eventually, autonomous intelligence (system making decisions independently), though the latter is still emerging for highly regulated quality environments.
• AI Myths vs. Reality: AI will not magically deliver intelligence on day one, fix broken data or processes, or replace humans. Instead, AI requires a long-term strategy, best-in-class processes, high-quality contextual data, and serves to assist humans in performing tasks more efficiently and effectively.
• Foundational Requirements for AI: Before layering AI, organizations must simplify and harmonize their processes, align on future-state operations, and ensure a robust, high-quality foundational data model to maximize the value from AI investments.
• Veeva's Practical AI Use Cases: Veeva is implementing AI to identify potentially matching quality events (deviations, complaints) using semantic NLP, presenting similarity scores to users for validation and learning. They are also using Robotic Process Automation (RPA) to automate the processing of inbound cases in complaint handling, categorizing and routing based on predefined criteria.
• Organizational Change Management: Implementing new, connected systems, especially for processes like quality and regulatory integration, requires significant organizational change management to ensure adoption and overcome historical silos (e.g., reliance on emails, phone calls, faxes).
• Implementation Scalability: Veeva's solutions cater to a wide range of company sizes, from emerging biotechs (implementations in weeks by adopting best practices) to top-tier pharma companies (multi-year transformative journeys due to the scale of organizational change).
• Data-Driven AI Validation: Veeva emphasizes a high bar for releasing AI capabilities, ensuring they are tested and verified with real customer data to prove product relevance and quantifiable value before being put into production.
• 21 CFR Part 11 Impact: The emergence of 21 CFR Part 11 in the late 1990s was a key driver for the initial adoption of legacy electronic quality solutions, replacing manual and homegrown processes.

Tools/Resources Mentioned:

• Veeva Vault Quality: An end-to-end digital platform for quality management.
• Veeva Vault QMS: Quality Management System within Veeva Vault.
• Veeva RIM (Regulatory Information Management): Veeva's application for regulatory tracking and approvals.
• Microsoft Office: Mentioned in the context of collaborative authoring for documents like Word and Excel.
• Amazon: Used as an example of autonomous intelligence in customer service.
• Samsung Biologics: Mentioned as a collaborator in pioneering the "Station Manager" capability.

Key Concepts:

• 21 CFR Part 11: Regulations concerning electronic records and electronic signatures, which historically drove the adoption of electronic quality systems.
• Collaborative Authoring: The ability for multiple users to simultaneously edit and update documents (e.g., SOPs, work instructions) within a controlled system, akin to Google Docs.
• Station Manager: A mobile capability (e.g., on a tablet) that displays only relevant content for a specific job function at a manufacturing station, promoting paperless environments.
• Process-Centric Content Navigation: A user interface paradigm where content is organized and accessed based on steps within a business process, rather than traditional free-text searches or filters.
• Robotic Process Automation (RPA): Technology used to automate repetitive tasks, such as triaging and routing inbound cases in complaint handling.
• Natural Language Processing (NLP) / Semantic NLP: AI techniques used to understand and process human language, applied here to identify similarities in unstructured data from quality events.
• Autonomous Intelligence: The most advanced form of AI where a system makes decisions and operates independently of human intervention.
• Total Cost of Ownership (TCO): A comprehensive assessment of all direct and indirect costs associated with a system or asset over its entire lifecycle.
• Change Saturation: The point at which an organization's capacity to absorb and adapt to new changes is overwhelmed, leading to potential breakdowns in adoption and operations.

Examples/Case Studies:

• Veeva's Internal RFP Response: Collaborative authoring was a "killer application" for Veeva's own team when responding to RFPs, allowing multiple contributors to work concurrently on Word documents or Excel spreadsheets.
• Samsung Biologics & New Pharma Plant: Collaborated with Veeva on the initial design and adoption of the "Station Manager" capability, aiming for paperless manufacturing environments.
• Amazon Customer Service Bot: An example of autonomous intelligence where a bot fully resolved a damaged item issue (refund, reorder) without human intervention.
• Customer with Broken Process: A real-world example of a customer with a fundamentally broken process and poor data attempting to layer "a ton of AI" on top, which proved ineffective until foundational process and data issues were addressed.