The Future of Risk Management for Medical Devices

Introduction

Medical device risk management has begun a significant metamorphosis based on artificial intelligence (AI), machine learning (ML), and the internet of medical things (IoMT). Data analytics created by artificial intelligence and machine learning will assess and predict adverse events and device failures that might occur. This is due to the systems’ ability to gather, organize and analyze an unprecedented amount of data at a fraction of the time.

The addition of emerging technologies such as artificial intelligence, machine learning, and the internet of things (IoT) to medical device risk management will truly be game-changing. The ability of machine learning to collect and assess possible risks from an enormous number of locations will be a useful tool for both manufacturers and regulatory bodies. This aspect of IoMT cloud computing allows manufacturers, patients, and health care providers immediate updates regarding any potential harm to the patient.

This blog post will review what medical device risk management used to be and the systems that were frequently used. It will examine the three parts of the equation to find a solution to the future of medical device risk management and the bright future with this solution in place.

What Medical Device Risk Management Was

Until recently, risk management for medical devices relied on manual repetitive tasks and health care systems were in silos. Groups and organizations worked on a more individual basis. A strong and robust risk management system integrates all these instances into one large and dynamic system that allows for multiple stakeholders to work in tandem toward the common goal of minimizing risk and creating a robust interconnected system.

Correct decisions can be made by professionals with the help of artificial intelligence. These decisions include, but are not limited to, analyzing patient biological information and categorizing the risk of a procedure within the medical device risk management system.

There are several issues with this method. The first issue is the duration of time it takes to create and prepare these documents, which entails utilizing large teams working for months to complete one document. Another issue is human error, as most wording is not consistent or can be inaccurate. Plus, different documents can have different levels of risk The final issue with the existing method is outdated information. Initially books, and then 20 year outdated online articles are being referenced. This method until today has led to an acceptable analysis. As we will see there is great potential moving forward in the present and future.

The First Part of the Equation: AI

Artificial intelligence has been mentioned frequently in the news. We have seen AI advertised and applied in new ways in the last year. Can this new technology benefit the existing risk management system that has been in place for decades? The answer is yes. AI uses algorithms to review large harms, hazards, and emerging risk datasets immediately. In essence, the software platform gathers information from countless online sites and databases in seconds. This information is then processed and displayed. In this way artificial intelligence can literally update risk management documentation with up-to-the-second information.

Artificial intelligence also could create a live trend analysis that will combine field data with risk management documentation. This will allow for predictive and preliminary action to avoid a harm that may not have occurred yet. This instance alone will not only save companies money but more importantly save lives as well. The uniformity of the data processed from artificial intelligence propagated throughout risk management documentation will allow for uniform wording in all documentation.

The Second Part of the Equation: ML

ML and AI are usually discussed in tandem. This is because machine learning is a subset of AI. Machine learning is AI’s “work horse,” gathering the information and conducting the predictive analysis that the AI will assess and review. Machine learning is also able to populate documentation and “learn” by frequent usage to recommend possible solutions. This instance would be very useful for second document revisions or product updates based on field data.

Risk management makes an enormous leap to a data-driven model by incorporating models that are predictive and use the most recent data available. Manufacturers will have an up-to-the-minute assessment of the status of their devices. This will provide much faster reaction time and response time for the patient. The process of reporting will be expedited also. This will occur when automated information is gathered from patient reports to the manufacturer using artificial intelligence.

Both AI and ML work well together and complement each other as key elements forging the future of risk management. Next, we see the role the internet of medical things (IoMT) plays in fulfilling the third part of the equation.

The Third Part of the Equation: IoMT

As we have seen, AI and ML are extremely useful for risk management in medical device design. They both significantly improve the accuracy, breadth, and scope of analysis. IoMT is the third aspect of our equation.

In general, IoMT is a system of medical devices communicating with each other and transmitting data to the cloud. Any data produced by a device or sensor will have remote patient monitoring (RPM). The monitored data will be retrieved by the ML tool and processed to the risk management system by artificial intelligence. Any irregular data that is received will generate a warning that can be sent to health care professionals from the manufacturer if needed. This early warning system can lower patients’ re-admission to the hospital.

Medical Devices Today

The medical device industry has undergone significant changes in the last few years since the COVID pandemic. This is due to changes in technology, updated regulations, and a greater focus on medical device risk management. Manufacturing data analytics are now utilized by companies to provide greater analysis to risk management and design control documentation. The U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) require greater focus on effectiveness and safety. There is a growing focus by both regulatory bodies and medical device manufactures for large teams that cross many different departments ranging from development, quality, and clinical.

Conclusion

We have seen that AI, ML and IoMT are great tools when deployed individually. When all three tools are working together, medical device risk management takes a great leap forward technologically. It is evident that IoMT transmitting data to ML and processed by AI not only solves the problem of error-prone and inconsistent risk management documentation, it also can save lives by early detection and money by reducing hospital costs.