A Guide To Pharmaceutical Quality Management

By James Jardine

In the pharma world, a fundamental shift is taking place in the way companies approach quality and compliance. An increasing number of pharma companies are moving beyond digitized documentation and embracing an approach that capitalizes on data-driven insights. 

When pharma companies have unstructured data, they face a few hazards: first, that poor data quality is a multimillion-dollar waste. Second, data will play an increasingly important role in regulators' policy enforcement. And third, unstructured data is a massive blind spot in today's quality and compliance models. 

Quality is integral to every function throughout an enterprise, and despite the challenges of connecting quality and compliance data, there are tools that can help you ensure quality's connectedness and central bearing. Tools like connected applications, advanced analytics, and AI are becoming essential among the focus on data and predictive insights. These tools are empowering companies to simplify the adoption of a product life cycle approach to quality by converging data and processes within a centralized system that provides true quality intelligence and enables real-time decision making. 

A digitally empowered workforce is important for many reasons, especially after the COVID-19 pandemic shifted employees to working remotely. When pharma companies use AI-enabled technologies, the quality department is made more effective regardless of where they are working from.

There is an increasing emphasis on human-machine collaboration and getting the best of both worlds by letting the two complement each other. Pharma professionals won't be losing their jobs to intelligent machines -- rather, modern tools will streamline processes so that human experts can focus on ensuring high-quality pharmaceutical products.