At the MedTech Forum in Lisbon, Portugal, this year, one of our customers – a global leader in in-vitro diagnostics and a medical device manufacturer, delivered a session detailing the substantial data challenge posed by the immense volume of post-market data generated each year. For instance, In 2024 alone, there were 2.6 million Medical Device Reports (MDRs) reported to the FDA globally, 102 FDA recalls, and millions of customer calls per year. Additionally, data comes from over 100,000 installed platforms, scientific literature, clinical data, and the Quality Management Systems. The combination of this massive data inflow and rigorous processes created data silos, making it difficult to realize the data’s complete value for patient safety, product performance, operational efficiency, customer experience, and new product development.
Direct access to data and insights was historically constrained for business users because data analysts served as the central point for generating and providing these insights and visualizations. To address these challenges, the company is implementing a transformation based on four pillars: Insights, Data, Business Processes, and People.
As the company shifts to an AI-driven approach, its model is designed to empower business users with integrated analytics. This is facilitated by adopting the ‘Data Mesh’ concept, ensuring processes and source systems consistently deliver reliable results and high-quality data products. Consequently, AI systems can interpret queries and provide embedded, real-time insights during process execution, making data readily available to complaint handlers and other business users, reducing sole reliance on data scientists and data engineers The presentation detailed several AI-supported use cases across the product lifecycle, demonstrating how Smarteeva’s AI offerings are changing post-market surveillance:
- Real-Time Risk Review: By leveraging a combination of AI agents, R&D product risk files are seamlessly integrated with post-market customer complaints. This integration drives proactive risk management by expediting the detection of unforeseen risks and fosters more intelligent complaint management through risk and severity-driven qualification, prioritization, and escalation. For example, an AI assistant can suggest a corresponding existing risk from complaint data, or highlight potential new risks that require further assessment if no direct match is available.
- AI-Supported Case Handling (Global Escalation Brief): The complaint journey, from local reporting to global action, is streamlined by AI. It generates structured, clear, and actionable escalation briefs by drawing from diverse inputs, including local case notes, technical data, customer allegations, chatter, and emails. This process enhances case insights, speeds up escalations, improves data quality, and accelerates case resolution. The AI assistant can also detect missing escalation information and confirm case qualification. Notably, the Smarteeva platform features a “Case Handling Assistant” with options for finding similar cases, summarizing cases, or determining related risks.
- Advanced Product Health Insights: Through trend insights, AI empowers quality monitoring by assessing contributing cases and then summarizing, translating, and clustering data. Analysts tasked with manual trend analysis benefit from the trend assessment engine’s ability to generate a comprehensive summary of all underlying cases. This results in faster and earlier assessments, profound insights into hidden patterns, and an increased frequency of trending, enabling a focus on action instead of manual data review. One example demonstrates how AI can pinpoint a sharp increase in a product’s complaint rates, then summarize the root cause, actions taken, and expected resolution.
- PMS Report Automation: Leveraging a data mesh architecture and standardized processes, AI automates the generation of Post-Market Surveillance (PMS) reports. This significantly reduces manual compilation effort and frees up resources for deeper analysis. Users can configure new PMS reports by selecting the report type, reporting period, and product(s), and then generate and access the automated reports.
To ensure effective data storage and utilization, and to establish clear AI implementation standards, the company prioritized the formation of a global data governance team within its change management protocol. This strategic move serves as a prerequisite for businesses, facilitating the deployment of private AI models or data storage tools that balance privacy protection with maximizing AI’s potential.
Smarteeva’s AI capabilities are fundamentally altering post-market surveillance by offering real-time insights, automating workflows, and enabling business users, which collectively drives product excellence and patient safety.
About Smarteeva: Headquartered in Dallas, Texas, Smarteeva Software pioneers Post Marke Surveillance for the medical device sector. We offer Complaint Handling, Adverse Event Reporting, Risk Management, and Recall Management solutions, designed for compliance and streamlined operations. Built on Salesforce.com with cutting-edge AI and Machine Learning, we are true partners to our users.
Reach us at: info@smarteeva.com | www.smarteeva.com
