“Continual learning and improvement” is a quality management approach where software systems and development processes are regularly evaluated and enhanced based on new data, insights, user feedback, and technological and scientific advancements. Rather than being a one-off process at product launch, it is an ongoing cycle throughout the device’s lifecycle.
How does ORTEC Logiqcare apply Continual learning and improvement?
Our Clinical Decision Support Systems (CDSS) combined with LogiqSuite’s Medical Data Management (MDM) platform unlock continual learning and improvement, ensuring systems evolve safely with real-world use and monitoring. Utilizing real-world data collected from users and patients drives continual improvement cycles for optimal patient outcomes by:
The data collected from the application of a Clinical Decision Support System and stored securely in LogiqSuite’s MDM platform allows scientists to enrich the data with additional patient outcomes and to analyze new insights and potentially improve or recalibrate the prediction model implemented in the medical device.
Medical devices may be updated based on the latest scientific insights and advancements. This includes incorporating new or updated prediction models and adapting to changes in clinical guidelines. These updates ensure the CDSS provides accurate and reliable results that align with ongoing scientific advancements and recommendations, keeping devices at the forefront of medical practice.
In the EU, the development and maintenance of medical device software are governed by regulations such as the Medical Device Regulation (MDR 2017/745) and standards like ISO 13485 (Quality Management Systems for Medical Devices) and IEC 62304 (Software Lifecycle Processes). These require manufacturers to systematically monitor, review, and update their devices – supporting continual improvement.
Continual improvement involves the periodic review of risk management files (ISO 14971). New risks identified from post-market surveillance data can be mitigated through software updates or process improvements.
Under EU MDR, manufacturers must engage in ongoing post-market surveillance. This includes collecting user feedback, reporting incidents, and implementing corrective/preventive actions – all feeding into continual improvement cycles.Collecting the data of a medical device implemented in clinical practice constitutes additional evidence on the performance of the device, extremelyvaluable for the post-market clinical follow-up evaluation.
Medical device software such as CDSS is often updated to correct defects, address cybersecurity risks, and enhance technical and functional features. Each update goes through assessment, validation, and regulatory review, ensuring continuous compliance and improvement.We ensure rigorous documentation and traceability of all changes, maintaining integrity and transparency throughout the software lifecycle.
The synergy between our CDSS and MDM solutions not only supports existing applications but also facilitates the development of new CDSS. Through systematic data collection within MDM, we lay the foundation for new prediction models. These models are implemented into feasibility devices or investigational medical devices and evaluated in clinical practice. The real-world data collected during these implementation trials, enhanced with patient follow-up information via MDM’s eCRF and ePRO functionalities, allows for robust validation and clinical evaluation. This process culminates in the creation of certified medical devices that comply with EU MDR, ready to bring transformative healthcare innovations to market.
Continual learning and improvement in medical device software is not just recommended – in the EU, it’s a regulatory expectation. It ensures that devices evolve safely with real-world use, guided by structured processes and robust post-market monitoring. By integrating our Clinical Decision Support Systems (CDSS) with the Medical Data Management (MDM) platform, we enable development of new CDSS and data-driven improvement of prediction models. This synergy ensures that medical devices evolve safely through real-world application, supported by structured methodologies and comprehensive post-market surveillance. The result is enhanced healthcare outcomes and the promotion of innovation across medical practices.
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