One of the big innovations in technology that has helped digital health initiatives is the advancement of machine learning. Machine learning is teaching computers a basic set of “rules” that define what “good” is and “bad” within the desired outcome. From there, the software learns and builds up its database of knowledge based on those rules. A few ML companies are now on the cusp of providing important advances in this field.
Biofourmis is a company looking at the patient biodata. New medical instruments can constantly collect, record, and transmit medical data about a patient without requiring a visit to the hospital. As medical data is collected, machine learning is evaluating all that data to find emerging patterns so medical professionals can make in-formed decisions about treatment.
Machine learning, once properly configured, excels at finding patterns, the same way humans do. But, because machines work much faster than humans, can analyze huge volumes of data to find patterns at an accelerated rate.
Health[at]SCALE uses machine learning in their Health[at] SCALE Interception software, which will be used to identify patients in the general population that may be at risk to certain conditions. The digital health software then coordinates matching them with the medical experts that can act on this “early warning” to diagnose and provide treatment at the earliest stages, when it can be most effective.
While there is a lot of medical data to take in, another major hurdle in digital health and patient-centric care is bringing in the relevant information when it’s needed. Doctors that need access to daily medical data results from an MRI, or payment information for a patient need to pull these from different sources to form a complete picture.
Innovaccer is a company that is helping medical professionals and administration to organize, keep track, and easily retrieve the data they need, when and where they need it. They are working with a combination of AI and ML to reduce the amount of time professionals spend looking for data, and to allow them to work with it quickly.