Edge processing Edge processing, which often includes AI and machine learning models, makes personal health data and insights immediately accessible to patients. Semiconductor manufacturers are highly focused on this right now. It allows insights to occur closer to the patient and requires less bandwidth and data to be exchanged with the cloud.
Edge computing brings data processing, analysis, and storage closer to the source, such as a patient’s cell phone, watch or iPad. Edge computing works as a complement to the cloud. This strategy can help both patients and health systems optimize the collection, storage and analysis of data—which requires the consideration of privacy, storage space, and costs. But above all, the desire for immediate access to information is the real motivator.
For patients, the easier they can access their personal health data, the better. Someone who wants to actively monitor how long or deep they are sleeping each night, can collect those data points continuously and have them pushed to their preferred device through edge computing. Knowledge is power, and today’s technology is proving you don’t have to be a doctor or nurse to review data and understand some of the things happening in your body on a daily basis. Wireless devices and sensors keep improving, allowing patients to self-serve more.
Wireless modules and devices The acceptance and popularity of wireless devices grew exponentially when the COVID-19 pandemic necessitated remote access and, in turn, spotlighted many other benefits, including more efficient healthcare delivery and continuous patient monitoring. Heart monitors and continuous blood glucose level monitors are examples of healthcare devices that have gained widespread acceptance in improving patient outcomes and remote patient management.
This all feeds into the Internet of Medical Things (IoMT), where networks of patients with portable and/or wearable medical devices and sensors and their corresponding healthcare systems and providers are connected through the Internet. IoMT devices help automate data transfer, thereby reducing human errors.
The wireless concept here is important because it allows for near-field and/or short-range communication. Wireless modules also allow a broader aggregation of data in the cloud, which enables practitioners to remotely monitor patients and access a wider sample for benchmarking and diagnosing.
Using anomaly detection, certain events or triggers throughout a patient’s day can select periods where more verbose diagnostic data can be helpful and provide extra data for those events. Having all this data available in the cloud to a patient’s full bench of providers is instrumental in more complete and accurate patient management.
