Putting Real-World Data, Evidence and AI to Work for Devices

Challenges facing device manufacturers

Adapt to multiple complexities: regulation landscape, innovative technologies, and evolving knowledge of diseases, illnesses

Speed of evidence generation matters because it is a costly process

Trials are challenged by learning curves and rapid iterations of device design, adding to the difficulties of choosing appropriate comparators and outcomes

While the market is expected to grow, unit prices and operating margins continue to decline

Dynamic and unpredictable regulatory path

Revenue models rely heavily on reimbursement (CPT codes), requiring investment from manufacturer to provide evidence that shows efficacy and provides reduced cost of care

Differentiating medical device products in a competitive market is critical for minimizing commoditization

Meet these challenges head on with RWD, AI, and Integrated Evidence Generation

Real-world data (RWD), real-world evidence (RWE) and artificial intelligence (AI) are changing the way research is conducted, evidence is generated, and care is delivered. By integrating these data and tools, we can transform challenges into opportunities and innovation.

Let’s explore how we got to where we are today and how you can cost-effectively access and use them to meet your objectives and those of your different stakeholders.

Setting the stage

Real-World Data (RWD)

Are data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources.

Real-World Evidence (RWE)

Is the clinical evidence about the usage and potential benefits or risks of a medical product derived from the analysis of RWD.

Artificial Intelligence (AI) Tools & Methods

Range from machine and deep learning, medical and natural language processing, and large language models. All can be used in different capacities for data extraction, estimation, prediction, and insights generation.

Integrated Evidence Generation (IEG)

Is a framework for generating fit-for purpose evidence to support decision-making in healthcare. It involves the integration of multiple sources of RWD to provide a more comprehensive and accurate view of the effectiveness, safety, and value of interventions.

*U.S. Food and Drug Administration. Framework for FDA’s Real-World Evidence Program. www.fda.gov/media/120060/download.

What’s most important?

Where do RWD come from?

What are the key factors when considering RWD for devices?

Are key variables, including device identifiers, recorded in routine clinical care (e.g. in EMR or claims data)?
How many types and sources of data are necessary?
What is the potential missingness for key variables? Is it feasible to reduce missingness through linkage to other sources?
Will the data be used for regulatory submissions, and, if so, in which countries?