The development of a quality assurance signal optimisation cycle

Abstract

Intorduction: Effective management and optimisation of quality assurance (QA) signals are central to sustain organisational accountability and continuous improvement, yet no standardised model currently exists to serve as a QA signal optimisation action tool. This research addresses the regulatory and operational need for a structured, signal-oriented model to optimise QA signal governance, clarify responsibilities, and strengthen conformity within quality management system (QMS) practices.

Aim : To develop a Responsibility, Accountability, Consulted and Informed (RACI) supported Quality Assurance Signal Optimisation Cycle (QASOC). A structured model aimed at: i. Strengthening QA signal management within pharmaceutical regulatory systems ii. Optimising QA signal detection, evaluation and response through a systematic approach

Results: The Quality Assurance Signal Optimisation Cycle (Figure 1) demonstrates the following: ▪ Cyclical Design, composed by 4 quadrants: i. Strategic Quality Assurance Signal Scoping ii. Quality Assurance Signal Contextualisation iii. Quality Assurance Signal-to-Theme Analytical Mapping iv. Quality Assurance Signal Optimisation; ▪ Progress Monitoring : Each quadrant functions as a phase track and repository for qualitative insights from QA signal identification, classification and evaluation.; ▪ Functional Visual Mechanism : To assess the implementation progress of quality records within QA signal management; ▪ Actionable Quality Intelligence Initiatives : Translate QA signals into quality improvement initiatives supporting continuous improvement and scientific evidence-based decision-making.

Conclusion: This research introduces a QA signal optimisation tool that facilitates the differentiation and visualisation of advancements across the phases of QA signal management. The QASOC contributes to a transparent depiction of the implementation status of the activities undertaken and supports the translation of QA signal data into actionable quality intelligence initiatives, fostering process accountability, collaborative engagement, and scientific sustainability across interdisciplinary regulatory functions.