Practical Steps for Building Your RWE Strategy

In One (1) 1 of the top medical device companies, the VP of Regulatory Affairs walks into a meeting and says, "We've got 50,000 users wearing our device 24/7. That's millions of data points. Can we use this for our label expansion instead of running another $3 million clinical trial?"

Everyone looks confused.

The honest answer? Probably not.

Not because the data isn't there. But nobody thought about Regulatory submissions when they designed the data architecture. There are no audit trails.

Version control is a mess. Half the users have missing data because connectivity drops weren't flagged in real-time. And the validation study you ran? It was 30 healthy college students sitting still in a lab, not the elderly diabetic patients who would actually be using your device.

So, more simply, Real-world evidence isn't something you track on at the end. It's something you plan from the beginning.

J&J didn't accidentally discover they could use real-world data for their ThermoCool catheter label expansion. They built the ability systematically—working with established research networks, ensuring data quality met clinical trial standards, and planning their analysis before moving a single data point.

The result? FDA approval in 6 months instead of 12. Half the typical timeline. No new clinical trial required.

The gap between "we have data" and "we have regulatory-grade evidence" is broader than most companies realize. But it's not impossible.

If you're developing a wearable device and someone just asked whether your real-world data can support Regulatory submissions, this roadmap will show you precisely what you need to build—and when.

Phase 1: Design for RWE from Day 1

The greatest mistake is thinking about RWE after you have finished with your device; by that point, it is usually too late to establish the necessary data infrastructure. Think about the end from the beginning:

• What Regulatory claims will you want to make?
• What clinical endpoints support the claims?
• What data do you need to collect to support those clinical endpoints?

Design your device/app/cloud infrastructure to collect that data at regulatory-grade quality from the first user. By this, we mean:

1. Validated data capture system with audit trails - where you can see who viewed the data, at what time, and what data was changed.
2. Data quality checks - that identify missing data, changes of out-of-range values, or disconnections, all while the user is using the device.
3. Version control - so you can tell exactly what algorithm version, firmware, and app version generated the data point.
4. Data dictionaries that define exactly what each parameter means, how it is calculated, and in what units it is expressed.

Phase 2: Validation against Clinical Standards

To establish trustworthiness in your real-world data, you must demonstrate that your device is accurate and reliable. This will require you to conduct formal validation studies comparing your wearable against clinical gold standard measures.

You should create accuracy in controlled conditions, then increase the complexity of the real world. For instance, validation for a cardiac wearable might require:

• Phase 1: Compare to a 12-lead ECG (electrocardiogram) while in a lab setting and at rest
• Phase 2: Compare to Holter monitors while ambulatory and undergoing normal daily activities
• Phase 3: Conduct exercise testing comparing your device to clinical-grade monitors during graded exercise
• Phase 4: Conduct multi-day home-based testing in a variety of patient populations

When validating across populations, include those who represent the patients for whom you intend your device to be used. This is not limited to college-aged, healthy volunteers. If you expect elderly patients to use your device, validate it with elderly patients.

If your device is intended for use in patients with diabetes, heart failure, or other diseases that might impact physiology, or an individual's skin characteristics, validate your device in these patients.

Essentially, your validation report should resemble a clinical trial protocol, encompassing everything from pre-specified endpoints and justified sample sizes to statistical analysis and data management. The FDA should be able to review your validation and understand precisely how you determined that your device is accurate. 

Phase 3: Build Your Post-Market RWE Infrastructure

The post-market opportunity begins, and your device is an opportunity for you. Now you could potentially have thousands or tens of thousands of devices generating continuous real-world data.

Now, how do you turn that into evidence that you could use in marketing a device? You should start with –

• Device performance metrics (how often are sensors within specifications?)
• Usage patterns (how are patients actually using the device?)
• Clinical outcomes (when wearable data indicates a problem, what happens clinically?)
• Safety signals (adverse events, malfunctions, or unexpected findings?)

Establish a data governance process, which may include:

• Data review committees that monitor and assess emerging data for quality and safety issues
• Operating protocols for investigating anomalies
• Protocols for when and how to leverage real-world evidence to update algorithms
• Documentation protocols that establish traceability from raw data through to your conclusions.

Create feedback loops from RWE to product development if real-world evidence indicates that features improve outcomes or that accuracy degrades in particular situations, which must inform your design improvements and validation testing.

Phase 4: Identify Strategic Partners for Credibility

The FDA places greater trust in RWE generated by established research networks, academic medical centers, or clinical registries, based on the methodological rigor, clinical oversight, and independence that these partnerships provide. ​When your company is evaluating strategic partners, you must think about the following:

• Clinical research networks, such as the National Evaluation System for Health Technology (NEST), with established data quality and Regulatory credibility​.
• Academic medical centers, which have deep knowledge of the clinical area related to the use case for your device, have published peer-reviewed research​.
• Disease registries already collecting longitudinal data on specific patients and diseases, who could capture your device data and add it to their clinical setting​.
• While having limited experience with others, patient organizations will have a unique view in helping you to recruit patients, ensuring a plan for other stakeholders includes patient perspectives, and for community engagement​.
• The experience of J&J in working with NEST ​is an excellent example of using a proven arrangement and reliability, rather than building new partnerships internally. ​

Phase 5: Prepare Regulatory Submissions

When you're ready to submit RWE to the FDA, your submission package should include:​

• Comprehensive data quality documentation - showing how data was collected, validated, monitored, and managed. The FDA needs confidence that your data is trustworthy.​
• Detailed study methodology including pre-specified protocols, statistical analysis plans, definitions of all variables and endpoints. Even retrospective analyses should be planned and documented prospectively.​
• Clinical validation evidence demonstrating your device measures what it claims to measure with sufficient accuracy for the Regulatory decision at hand.​
• Clear connection to Regulatory question showing how your RWE specifically answers the question you're asking the FDA to decide (e.g., "Does our device detect atrial fibrillation reliably enough to warrant an indication?" or "Does our glucose monitor's real-world accuracy support use in insulin dosing decisions?")

Consider scheduling FDA pre-submission meetings before making significant investments in RWE studies. Pre-submission meetings enable you to initiate your approach and seek feedback from the FDA on the appropriateness of your study design, endpoints, and methods, ensuring data quality, with the understanding that these will conform to regulations.

This is a good front-end investment with significant payoffs in terms of time and cost savings, as it enables clear approaches that the agency is unlikely to accept.

Future of RWE for Wearables:

Hybrid Trial Designs Are Becoming the Norm:

We are noticing a growing number of trials that combine traditional prospective randomized controlled design with real-world monitoring arms.

For example, in a trial of a cardiac device, patients might be randomized to treatment or control; however, instead of attending scheduled visits, we would use wearables to capture their outcomes in both groups continuously.

This hybrid design enables you to maintain the accuracy of randomization and designed comparison while also benefiting from the richness and continuity of real-world data.

Patients still participate in a formal trial, but the burden on them has been reduced - no frequent visits to the clinic, no large, bulky monitoring equipment; wear your device in your everyday life.

AI and Machine Learning are Eligible to Analyze RWE at Scale

Traditional statistical techniques can handle similar datasets that contain thousands of data points, but wearables create millions. A single patient may generate 20,000 heartbeats in a week. If we multiply that by thousands of patients, manual analysis becomes weak.

AI and machine learning are being used to find developments and patterns in massive datasets of RWE, developing from finding subtle changes that suggest adverse events before they happen to segmenting patients who respond differently to a treatment, to simply knowing that something in the real world impacts the performance of a device.

However, we will also face new Regulatory challenges; in this case, these are questions that the FDA's Digital Health Center of Excellence and its equivalents around the world are focusing on.

• How do we validate an AI algorithm that is sifting through RWE?
• How do we ensure we are not creating bias when testing RWE?
• How do we ensure the algorithm continues to learn with respect to its network as it evolves after deployment?

How Freyr Solutions Helps You Get RWE Right the First Time

At Freyr Solutions, we've guided dozens of medical device companies through successful RWE strategies—from initial design decisions through FDA clearance. We've seen what works, what fails, and what the FDA actually cares about in RWE submissions.

The Freyr difference? We combine deep Regulatory expertise with practical implementation experience. We don't just tell you what the FDA wants—we show you exactly how to build it, document it, and submit it.