2
Picture family members escorting a shaky elderly relative into the
hospital emergency department after a fall down the stairs. The patient is
complaining of a headache, but a crisis doesn't look imminent. It's a Friday, a
busy night in the local ER. There are 85 patients already on the radiologist's
work list, and a likely two-hour wait before the patient's massive—and
massively dangerous—pulmonary embolism is discovered on the scans.
That is, until now. After years of digitizing patient records and leveraging the
cloud, the healthcare industry has created a massive and still-growing pool
of data. That data, used by analytical tools and increasingly machine learning,
can drive everything from streamlining hospital workflows to promoting early
detection of cancer or a pulmonary embolism.
Companies like Cerner, Aidoc, and Arterys are taking advantage of Amazon Web
Service's (AWS) high-speed, high-volume data storage, processing and retrieval
in the cloud, and machine learning tools to develop and apply machine learning
algorithms—driving positive outcomes for patients and medical staff alike.
Digitization was really the first step. The real power
is in how you get second-order effects out of that
digitazation."
Ryan Hamilton, Senior Vice President of Population Health, Cerner Corp.
"
Cerner's voice recognition tool passively listens to clinician and patient conversations. Users speak
without interruption, while the solution processes what's said and returns essential data. The tool
provides diagnoses, one-click ordering, as well as potential allergies, all based on the conversation
between the patient and clinician.
