Integrating Knowledge Management and Quality Risk Management
Cover: ISPE held an Expert Xchange on 18 January 2022 that included presentations and interactive exercises that generated new and useful insights into the current effectiveness of the knowledge that flows into QRM and how a knowledge map can be used to diagnose opportunities to improve KM. The exercises also helped identify the types of knowledge generated during QRM. These insights demonstrated the opportunity to improve risk-based decision-making by uniting risk and knowledge through a suitable framework such as the Risk-Knowledge Infinity (RKI) Cycle.
From Data to Knowledge Management: What to Consider
Feature: Although data and knowledge are both stand-alone disciplines that need to be systematically managed, they also must have a connection. Understanding the relationship between data and knowledge management processes and how people are leveraging advances like Pharma 4.0™ combined with these processes enables quality data transition to knowledge that can help pharmaceutical companies. The authors also want to generate understanding on how using the knowledge acquired by people through experience (tacit knowledge) can further connect both data and knowledge management systems, yield positive strategic results, and deliver more efficient processes within organizations.
Effective Knowledge Management in Mergers and Acquisitions
Feature: As the pharmaceutical industry continues to grow and evolve, a significant contributor to innovation and evolution is mergers and acquisitions. In the pharmaceutical industry, knowledge management has been identified as an enabler to a pharmaceutical quality system through the publication of ICH Q10. This article discusses, at a high level, the potential opportunities of KM contributing to the success of pharmaceutical merger acquisitions through end-to-end knowledge transfer.
AI Governance and QA Framework: AI Governance Process Design
Technical: Artificial intelligence has the potential to benefit the pharmaceutical industry and its GxP-regulated areas. However, project implementation remains limited, mostly due to a lack of robust validation procedures. Hence, there is a need to develop a robust governance framework to ensure that integration of AI into workflows is possible while simultaneously ensuring that evaluation standards are still met. The proposed framework presented in this article provides a general organizational and procedural structure for developing and sustaining AI solutions in GxP-relevant contexts.