Ready to Use AI with Real World Data? What Medical Device Teams Need to Know (and How Milo Can Help)
The rise of real world evidence alongside controlled clinical trials marks a major evolution in how we evaluate medical devices in everyday care. Rather than replacing traditional trials, real world data augments them by filling in gaps around usage, long-term performance and patient diversity. It helps manufacturers support benefit claims and reimbursement decisions with broader and more practical insight.
Instead of relying only on carefully selected cohorts and scheduled visits, manufacturers can now learn from electronic health records, claims databases, disease registries, remote sensors and even patient reported outcomes that flow in every day. This data is broader, richer and closer to clinical practice, but it is also messier. Artificial intelligence promises to make sense of the noise, yet success is never automatic. Before adding machine learning to a real world data strategy, teams should slow down and ask a few hard questions.
What is Real World Data and Real World Evidence?
Real world data is information collected outside the traditional clinical setting. It includes electronic health record notes, billing and claims files, imaging archives, device logs, pharmacy dispensations, registry entries and patient generated signals from wearables or mobile apps. When these sources are analysed to show a device’s safety, performance or economic value, the result is real world evidence.
Regulators and payers are paying closer attention to real world evidence because it reflects routine use, diverse populations and longer follow up. For software as a medical device, continuous learning systems and connected implants, real world evidence is often the only way to track drift, usability and durability over time.
How Can Artificial Intelligence Add Value?
Unstructured text extraction
AI tools can read free text clinical notes such as discharge summaries or operative reports to identify adverse events or outcome signals linked to a device. These insights are often not documented in standardised data entry fields like checkboxes or billing codes, but they can provide critical evidence of safety or performance in routine care.
Computer vision
AI can examine medical images such as X-rays or CT scans to detect small changes linked to how a device is working inside the body. It can spot patterns that humans might miss due to fatigue or subtle variations. This helps teams uncover evidence of device safety or performance from existing images, without needing new scans or relying only on manual review.
Time series analysis
AI can monitor streams of data from wearables or connected devices to detect patterns over time. It can alert teams to early signs of malfunction or patient risk before clinical symptoms are obvious. This is especially valuable for implants and home monitoring where continuous observation is key.
Data linkage
AI helps match patient records across systems such as hospitals, pharmacies and insurance databases, even when names or IDs do not align. This creates a fuller picture of how and where a device is used, which improves evidence quality and helps identify risks or benefits that only show up across settings.
Data completeness
AI-powered methods such as imputation can help recover missing information, reduce bias and increase the robustness of findings generated from incomplete real world datasets. This strengthens the credibility and reliability of evidence derived from diverse and sometimes imperfect data sources.
How Milo Makes Real World Data Practical
Explore Milo in a live sandbox
If you are planning to use AI with real world data and want to see how Milo can support your strategy, we offer secure sandbox access to test your data capture, monitoring and reporting workflows. Contact us to schedule a walkthrough.
Retrospective data with the right safeguards
Milo supports the use of retrospective datasets under lawful secondary use bases and provides consent tracking and de-identification tools that satisfy GDPR and HIPAA requirements.
Exploratory AI before formal models
Milo is first and foremost a smart data capture and monitoring platform. It collects structured and unstructured real world data across eCRF, ePRO, device logs and imaging. This makes it easier for other AI models to work with high quality, standardised data from the start. At the same time, Milo includes built-in tools for early pattern detection and signal exploration so teams can begin asking useful questions without building custom AI models. These early insights can help identify trends, focus validation efforts or support post market surveillance, even if a full predictive model is never deployed.
Separate AI processing and evidence reporting
Milo gives teams a secure space to explore unstructured clinical notes and imaging without affecting the main study dataset. Once insights are extracted, Milo saves both the original files and the processed results in one central location. This setup keeps the process clear and well-documented, making it easier to explain how findings were generated and used in regulatory reporting.
Direct linkage to quality and regulatory files
Milo automatically connects its outputs to your quality and regulatory documentation. This includes linking extracted insights or flagged issues directly to risk registers, PSUR templates and safety reports. It removes the need to manually transfer data between systems and helps show regulators exactly how real world findings support device monitoring and safety decisions.
