What if doctors had more time to spend addressing their patients’ concerns? That’s the thrust behind the push for integrating () into medicine.
By using complex algorithms to detect patterns in large datasets—like lab test results, current medications, and symptoms, to name a few— might actually make medicine more personable—not less.
“By the time patients come in, we would already know what they’ve been experiencing,” says Yale Medicine cardiologist and data researcher Harlan Krumholz, MD. “Especially for patients with chronic conditions, we could detect their need for medical attention before they do.”
The definition of varies among industries and even from one dictionary to another. But broadly speaking, in the realm of medicine, refers to the use of computer systems to create algorithms based on patterns in raw data to find connections (such as between a genetic mutation and a medical condition—or clusters of symptoms to a particular disease) that would be very hard, if not impossible, for a person to identify.
To illustrate what an -assisted future in medicine will look like, Dr. Krumholz gives a hypothetical example of a patient at risk of heart failure, a condition where a weakened heart muscle struggles to pump enough oxygenated blood throughout the body.
o start, the patient would start the day by stepping on an internet-connected scale that would monitor changes in weight for possible signs of fluid retention—a hallmark of heart failure. He or she would strap on a smart watch or other sensor to track steps and activity level, and would use a phone app to log specific symptoms, such as shortness of breath. All of this data would stream directly to the electronic health record (EHR), which would “take all of that information and categorize the patient’s risk, rather than wait for the patient to come to us,” Dr. Krumholz says. The doctor then could alert the patient that he or she is approaching danger and take steps to avert it, allowing medical care to be given proactively rather than reactively.
This kind of -assisted interaction in medicine has been years in the making, Dr. Krumholz explains. Since 1995, he has directed the Center for Outcomes Research and Evaluation (CORE), whose research has helped improve patient care by gathering, measuring, and analyzing all kinds of data, from billing records to traditional medical records and now EHRs.
Before patients’ records were digitized and able to be analyzed by computers via algorithms and , Dr. Krumholz and his team worked to extract insights from the paper records. This expensive, labor-intensive work took years to complete. The research—a result of a collaboration with federal agencies, medical professional organizations such as the American College of Cardiology and the American Heart Association, as well as clinicians, hospitals, and others—led to dramatic improvements in care. In one notable effort, which spanned years, the research and its dissemination led to drastically reducing the time it takes for heart attack patients to receive life-saving treatment that clears their blood clots.
Today, such projects could be accomplished requiring only a fraction of the time and resources. As Dr. Krumholz notes, “In this digital era, the prospect of real-time research producing real-time actionable information and timely improvements in care is just around the corner.”
and precision medicine
The Food and Drug Administration (FDA) now evaluates tools in much the same way they review drugs and medical devices for efficacy and safety. For example, one approved system analyzes CT scans of patients presenting with neurological symptoms, texting doctors when the results suggest stroke. This helps them deliver effective treatment faster, which in turn helps prevent brain damage from strokes. […]
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