Seven Leading Practices for Risk Management in Medical Device

Medical device manufacturers must follow a highly structured and rigorous risk management process, mandated by the FDA, EU and ISO. To track risk-related data over the entire product lifecycle, companies currently use a variety of risk management solutions, from sophisticated software packages to simple spreadsheets and shared drives.

As regulatory requirements continue to evolve and get stricter, it is more important than ever to effectively manage risk. Despite this increased need, leading risk management practices are not implemented routinely in their product lifecycle management (PLM) systems due to more foundational elements like change management, BOM management, regulatory reporting, and quality management taking priority. This is a missed opportunity for the industry, because those that can do this well will generally be ahead of most of their peers.

Even for companies where sophisticated risk management is implemented, information is trapped in siloed systems, which makes it difficult to analyze, escalate and resolve safety issues. Things are now more challenging from a business governance perspective as well, with companies acquiring each other more rapidly than ever. This makes the enterprise technology landscape for large medical device manufacturers very complex and broad, even for basic company functions.

Effectively managing risk in the medical device industry can be challenging and complex, but it’s becoming increasingly important. Doing it well is often a key strategic differentiator, so we’ve outlined seven things that we’ve seen industry leaders do that set them apart.

Seven Things Risk Management Leaders Do

1. They use FMEA, 5-Why and Hazard Analysis as their main risk management techniques. They integrate risk data – regardless of source – into their PLM solution to harmonize design control, process control and post market surveillance components. This supports recent regulatory emphasis on improved trending, management review and escalation of risk management-related issues after release to manufacturing and the market.
2. They layer advanced analytics on top of structured quality and product data stored in PLM to move from corrective to predictive risk management. This speeds time-to-benefit during a PLM implementation and greatly extends the value. Machine learning can also be used to verify the risk controls are working correctly, improving the safety and efficacy of the products themselves.
3. They are making moves to capitalize on the Internet of Medical Things (IoMT) and smart connected products. They use predictive analytics to improve healthcare outcomes, driving huge business value.
4. They leverage advanced analytics on top of structured product, quality and risk data to answer quality-related questions, moving from descriptive to prescriptive insights. With basic analytics, they can explore and explain how often quality issues arise and where they typically occur. Advanced analytics applied to structured data across the life cycle is called product lifecycle intelligence (PLI) and it enables manufacturers to ask much more advanced questions that yield prescriptive results. For example, they may gain insight on what design features they should avoid to make sure their products don’t fall victim to common failure modes. PLI can also drive data-driven insights to answer similar questions for other business functions, including R&D, manufacturing and supply chain. This in turn leads to more efficient and accurate root cause analysis by enabling quality personnel to diagnose multi-mode complex failures, which often occurs when sophisticated device and manufacturing data is managed in multiple databases.
5. They design their risk control system for minimal risk. They use a PLM system with integral QMS and post market surveillance risk-based information. This is the most complete closed-loop methodology for designing out previous failures, safety issues and nonconformances.
6. They use role-based app connectors along with IoT and analytics. This helps enable a closed-loop vision. For now, these tools can be a competitive advantage over traditional risk management methods, but they will soon become expected and enforced by regulatory bodies as an additional way to improve patient safety.
7. They take a pragmatic approach to risk management. They create and follow a roadmap that leads to more refined levels of automation over time instead of a big bang approach. With this approach, value can come early and often throughout the journey – not just at the end. Data is becoming the "oil" of the 21st century, but leveraging data to derrive key risk insights requires short, mid and long-term strategies. Companies that lead the way start by connecting siloed data sources and continue to strive towards a long-term harmonized data landscape goal.

Current risk management system maturity is irrelevant. Companies must harmonize risk management data with PLM processes and data, regardless of the complexity of their current solution. Embracing digital capabilities such as advanced analytics, machine learning, and role-based apps can move companies from corrective risk management to predictive risk management.

The journey to closed-loop risk management may be intimidating, but it is critical and very much possible. All companies want to better understand, balance and quantify risk when bringing devices to market, in turn optimizing the safety and efficacy. Our downloadable eBook, The Future of Risk Management in Medical Device, provides additional information on the evolution of risk management. Check it out for detailed examples and advice on getting started with a pragmatic approach.

More Resources

eBook: The Future of Risk Management in Medical Device

Smart Connected MedTech: Transform Healthcare with IoMT

Turn PLM Data into Insights with Product Lifecycle Intelligence

Regulatory Information Management: Enhance Device Regulatory Strategy and Accelerate Global Registrations