Healthcare providers are swamped with false alarms from remote patient monitoring systems. Here’s how AI can help solve the problem.
Remote patient monitoring systems have made enormous inroads in healthcare in recent years, especially for chronically ill patient populations. The ability to passively monitor patients for adverse events and warning signs can save lives, but it also creates substantial logistical hurdles for healthcare providers– especially cardiologists, electrophysiologists, and their support staff.
In particular, we’re seeing increased adoption of implantable loop recorders (ILRs) which are implanted underneath the skin of a patient’s chest and then used to detect abnormal rhythms that can be warning signs for stroke. Such devices are now connected and able to transmit data to the ‘cloud’ in order to be reviewed by healthcare professionals. Obviously, the data from these devices must be remotely monitored for alerts. ILRs are designed to be extremely sensitive so that they don’t miss any critical events, but this sensitivity often leads to an unwieldy number of false positives – which can quickly overwhelm healthcare teams.
Read the full article on Medhealth Outlook website.