Medtronic and Mercy to develop data sharing network for medical device innovation

17 October 2017 (Last Updated October 19th, 2017 10:13)

Medtronic has partnered with US-based Mercy’s IT division Mercy Technology Services to co-develop a new data sharing and analysis network to gather clinical evidence required for medical device development, safety and patient access.

Medtronic has partnered with US-based Mercy’s IT division Mercy Technology Services to co-develop a new data sharing and analysis network to gather clinical evidence required for medical device development, safety and patient access.

The new network will be designed to obtain valuable clinical information during routine patient care.

The network will initially capture deidentified data from around 80,000 heart failure patients to assess real-world factors that impact a patient’s response to cardiac resynchronisation therapy (CRT).

Medtronic strategic scientific operations senior vice-president Dr Rick Kuntz said: “Having the ability to study patient care pathways and conditions before and after exposure to a medical device is crucial to understanding how those devices perform outside of the controlled clinical trial setting.

“By partnering together, Mercy and Medtronic have set out to create a comprehensive and economical evidence generation model that ultimately allows patients to benefit from the latest therapies and technologies as early as possible.”

“This model will lead to evidence-based insights for our clinical teams and better health for our patients.”

The new project is expected to provide a base for designing a model for the US Food and Drug Administration’s (FDA) National Evaluation System for Health Technology (NEST) initiative to provide fast, cost-effective regulatory decisions and rapid therapy innovation.

FDA’s initiative for a national system is intended to obtain quick and meaningful information from routine clinical care findings.

Mercy outcomes research director Dr Joseph Drozda said: “This has the potential to improve patient care by using advanced data analytics.

“To more effectively treat patients, we need a better understanding of how they are responding to treatment and what leads to better health.

“This model will lead to evidence-based insights for our clinical teams and better health for our patients.”