6 Signs of a Flawed Risk Management Process

A strong quality risk management process is an investment that brings safety, quality, and compliance to life science manufacturers and their patients. 

A “check the box,” or barebones regulatory compliance, approach to risk management provides little value for life science manufacturers and may even create risk itself by taking valuable time away from subject matters experts who are working on safe and effective new product development.  

The ability for the risk management process to create long term quality value for manufacturers is hindered by legacy processes that are a messy result of ad-hoc changes and hasty compliance initiatives. 

Before the true effects of a flawed risk management are felt, such as a product recall or high severity patient impact, manufacturers can self -evaluate to see where their risk management process stands. 

These signs can signify a process that is consuming too many resources, slowing down decision-making, and ultimately not delivering the quality improvement and risk reduction necessary for patient safety.

1. Standalone documents – Standalone failure modes and effects analysis that are tracked in manual record systems such as Word or Excel
2. Subjective risk scoring – Risk scoring in different parts of the product lifecycle is subjective or undefined, leading to variable outcomes
3. Time-consuming – Risk management takes significant time from quality, R&D, operations, supply chain, distribution, clinical, and regulatory affairs staff
4. Periodic reviews – Risk review is done quarterly or monthly instead of continuous signal analysis and trend detection
5. No link to the QMS – Risk analysis is linked manually to the quality management system—or it isn’t linked at all
6. No lessons learned – Risk analysis, analysis, evaluation, control, review, and post-market decision making all start from scratch for each new product and process

It’s time to modernize your risk management process. 

Once an effective, digital risk management process is in place, quality teams can look forward to adding impactful best practices by asking themselves:  

• What high-value activities could be prioritized if your experts streamlined time-consuming risk management activities?
• Which quality triggers, signals, and trends enable you to take timely and effective action to reduce risk?
• What untapped sources of quality data could be analyzed to improve risk evaluations?
• How can existing data be used to better understand existing risk profiles and improve new products?
• What technologies, such as artificial intelligence, can be used to improve risk assessments and decision-making?