While there are numerous benefits to healthcare providers being able to access a growing array of digital devices to monitor their patients, more devices invariably means having to sort through more sources of data.

To help providers make more effective use of their devices, the Mayo Clinic has launched a new technology platform initiative.

The Remote Diagnostic and Management Platform (RDMP) connects devices to AI resources that would help providers with clinical decisions support and diagnoses in what the Minnesota-based health system calls “event-driven medicine.” It’s designed to help providers in and outside the health system analyze data and make faster and more accurate diagnoses, thus coming closer to providing truly continuous care to patients.

“The dramatically increased use of remote patient telemetry devices coupled with the rapidly accelerating development of AI and machine learning algorithms has the potential to revolutionize diagnostic medicine,” John Halamka, MD, president of the Mayo Clinic Platform, said in a statement. “With RDMP, clinicians will have access to best-in-class algorithms and care protocols and will be able to serve more patients effectively in remote care settings. The platform will also enable patients to take more control of their health and make better decisions based on insights delivered directly to them.”

To help support and market the RDMP, the Mayo Clinic also unveiled two companies in partnership with other enterprises.

The first is Lucem Health, launched by the Mayo Clinic in a partnership with Commure to help providers gather and analyze data from any device involved in a remote patient telemetry setting. In this context, RDMP would be the platform upon which AI algorithms are integrated with data coming from the devices.

For the second, Anumana, developed by the Mayo Clinic and digital health company nference, is aimed at the cardiac care space. In this initiative, providers would be able to use RDMP to analyze date from mHealth devices used to collect electrocardiogram (ECG) signals.

“Undiagnosed heart disease affects millions of Americans and people across the globe,” Paul Friedman, MD, chair of the Mayo Clinic’s Department of Cardiovascular Medicine and the leader of the team that developed the algorithms, said in the release. “For many conditions, such as a weak or thickened heart pump or silent arrhythmias, effective evidence-based treatments exist that can prevent heart failure, stroke, or death. The key is to detect the disease before symptoms develop to prevent these events from happening.”

“The addition of AI to the ECG, a ubiquitous and inexpensive point-of-care test that is already integrated into medical workflows, makes this approach good for patients, convenient for clinicians, and massively scalable,” he added.