The incorporation of data-driven testing is changing the medical device industry especially in precision engineering and robotics. It is enhancing medical device reliability by enabling real-time performance tracking. It also helps to identify issues earlier, improves accuracy, and uncovers problems missed by traditional methods, leading to safer, more reliable devices.
As a professional in the field of Systems Engineering, specializing in robotics and controls of medical devices, Shashank Pasupuleti has seen how this shift is redefining how reliability and performance of these systems are built. With the help of real-time data and analytics, data-driven testing is refining all the stages of development, from the design and manufacturing of the device to post-market monitoring.
In the field of robotics, where automation, accuracy, and durability are important, conventional approaches to testing have not been very effective. Previously, the validation of robotic medical devices was performed using mechanical stress tests and simple tests of functionality, which did not allow for the consideration of clinical conditions. On the other hand, data-driven testing is comparatively more flexible and all-embracing in its approach. The use of sensors and diagnostic tools enables the engineers to monitor the performance and identify any problems more effectively.
Another advantage of data-driven testing is that it is real time. The use of large amounts of data such as sensor readings and machine performance logs can help engineers identify potential problems at a very early stage in the design process. This anticipatory approach is effective in making certain that the medical devices are safe, effective and dependable in almost any conditions of use.
Shashank’s initiatives of testing have resulted in cost reduction, time optimization, and integration with other departments within the organization. By implementing automated, data driven testing, his new approach enhanced both the accuracy and speed of the tests, enabling faster and more efficient product development.
He explained, “Introducing real-time data analytics in testing, helped reduce post-market device failures by approximately 15% by enabling quicker identification of potential issues during the testing phase.” He also noted that testing time was cut by up to 20%, which contributed to the acceleration of the development of the cycle. Further, he highlighted, “The development of specialized test fixtures and prototypes reduced the time required to perform key tests by 25%, which resulted in faster iterations and product refinement”.
These efforts in enhancing the robotic arm systems also helped to minimize on defects during manufacturing hence increasing product quality. There has been increased test documentation standardization and enhanced communication between departments that enhanced product development.
The integration of data analytics platforms helped to identify alignment problems in the testing phase, which are not easily identifiable by conventional procedures, and make the necessary corrections before they reach critical levels. This change has been facilitated by the advancements in software systems, enabling engineers to use MATLAB and Python scripts to automate the testing processes to minimize the impact of human factors. These software systems allow the engineers to ‘run’ through several situations in which the robotic systems will be used, at different speeds, orientations and load bearing conditions. This flexibility is especially helpful when testing intricate systems as automated calibration or firmware updates, to make sure that new changes do not trigger problems.
Data-driven testing also encourages collaboration across multiple teams. This way, data is collected uniformly and stored in a central database where other departments such as engineering, clinical research, software development to access the same data. This alignment ensures that communication is more effective and that problems are solved with ease. It also guarantees that the test procedures are well documented and repeatable, which is crucial for regulatory filings and future product servicing.
However, Shashank concludes that the integration of data-driven testing in traditional process has some difficulties. Ensuring that the mountains of data generated are useful and can be used to drive change was challenging. This entails the processing of data by developing sophisticated algorithms and the availability of teams that can analyse them. As the practices of managing data continue to change, so will the capacity of these insights for even more accurate and more efficient medical devices.
Moving forward, the advancements in artificial intelligence and machine learning will improve the possibilities of data-driven testing, as is believed by thought leaders like Shashank Pasupuleti. These technologies will allow better prediction of the models, which in turn will enhance device performance and durability. With the medical device industry adopting these trends, there will be an even further enhancement of medical devices in terms of safety, efficacy and precision especially in areas such as surgery and diagnostics.
