How to shift from reactive to proactive quality management and meet FDA software requirements
FDA leaders have acknowledged that artificial intelligence (AI) and machine learning (ML) can improve performance and spur innovation.
Dr. Scott Gottlieb, who served as the FDA’s commissioner until April 2019, said it could even lead to the development of novel medical devices.
Yet many life sciences companies, feeling constrained by traditional validation approaches, have avoided adopting tools that could lead to major advances.
They’ve opted instead to operate in a safe zone: rigorously collecting and hoarding data without greater purpose, testing, or results.
For too long, the life sciences industry has been data-rich, but intelligence-poor.
A Shift in FDA Focus
The FDA recognizes that modern medicine requires modern technologies and new approaches to validation.
Newer FDA guidance shifts the focus toward computer system assurance (CSA) versus computer system validation (CSV), opening the door to a streamlined approach to digital transformation.
The new guidelines offer a risk-based approached based on critical thinking, not just record-keeping.
This can help life sciences companies to innovate, if their software delivers the intended outcomes for patient safety, product quality, or quality system integrity.
Quality management is a prime place for life sciences companies to leverage the CSA approach. Digital quality management systems (QMS) are already data rich; applying AI and ML can turn volumes of information into actionable insights — proactive quality.
Effective Automation
AI- and ML-enabled QMS software can intake and process quality events faster and suggest preventive actions like maintenance tasks more effectively.
Assisted by technology, life sciences companies can improve patient outcomes and limit costly downtime, errors, and other risk factors.
Rather than reading back the play-by-play of “what happened,” digital QMS can leverage AI and ML to spot trends and predict challenges before they turn into incidents.
The ability to learn from quality events – quickly and based on enormous amounts of information – creates new opportunities (and critical saves) for quality teams.
Not to mention the time and financial savings gained from speeding up and automating data-heavy tasks or preventing errors.
With ML- and AI-enabled QMS, leaders can make smarter decisions across the company, ranging from risk assessment and root cause analysis to supplier scoring.
Since digital QMS software makes it easy to see and share intelligence organization wide, companies can embed (and even automate) best practices into daily workflows.
QMS software has always supported quality, compliance and efficient decision-making.
With new approaches for computer system assurance, digital tools can play an even larger role in quality, support patient safety and deliver more value to the industry.
For more information, read the ebook by Axendia and Sparta Systems: AI Drives Shift from Reactive to Predictive Quality; Regulatory Consideration for AI-enabled QMS
