How AI is helping unlock data to find clues for timely treatment

How AI is helping unlock data to find clues for timely treatment

Whether it’s transmitted digitally through a HIE or arrives in the form of a fax, healthcare data is more voluminous than any other time in recent memory. A clinician may need to invest considerable time and effort, for instance, poring over the information to know whether a medication a patient was on ten years back was as yet essential at a similar dose now.

Machine automation can help. Medal is among the developing market of organizations, including edgeMED, Inovalon, Telegenisys and others, that work to extract, process and contextualize the vast amount of data that exists about patients – and present it to physicians in a timely and relevant way.

Medal founder Lonnie Rae Kurlander says she has a mantra: “Simplicity is the ultimate sophistication.” She says that unwieldy mass of patient data can be tamed with artificial intelligence, profiting doctors and patients alike by reducing workloads and speeding time to relevant data.

Andy McMurry, Medal’s co-founder and chief information officer, says machine learning can do the reading and assign context to inform the doctor about the patient’s most relevant condition.

“I was serving a patient in the ICU who was in a coma because we couldn’t get his patient info in a timely manner,” says Kurlander. “If I’m able to quickly find the hints, the clues, maybe the prior diagnosis is already there. Maybe there were similar incidents before that could have helped us.”

Kurlander sees Medal’s methodology as a collaborative tool more than a road to replacing doctors. She invokes Elon Musk’s achievement in meeting car manufacturing goals at Tesla by beginning a second generation line in the industrial facility parking garage staffed completely by people: “He said his biggest mistake was over automating.”

Computer augmentation isn’t at any point expected to replace a physician she argues – rather, it has the extraordinary potential to “put power in hands of power users of health care,” she says. She takes note of that in ancient games like chess or go, even a novice user “playing with (computer) assistance will win every time.”

McMurry has his very own mantra: “A wealth of information creates poverty of attention.”

Doctors, he says, invest excessively time as “fancy store clerks” who enter and sift through massive amounts of data. The benefit of it all is lost through the sheer volume of it; AI-driven software like Medal can help assign context and meaning to it for practitioners, bringing them back to the “joy of delivering medicine.”

Doctors feel swamped with the information side of the new healthcare landscape. Leveraging information to give further understanding into patient results has been appeared to fundamentally decrease time spent on routine information entry.

Utilizing machine learning and AI to help “read through the chaff,” as McMurry says, takes over for doctors in territories that they shouldn’t be and aren’t even truly expected to do. This leads to a greater range of potential come to by professionals in healthcare, not to say enhanced results and cash saved.

At last, utilizing AI to simplify how doctors interact with patients will prepare healthcare organizations for the future.