When making a diagnosis, doctors have traditionally (and logically) relied on personal data directly from patients—their lab tests, examinations, and medical histories. But what if insights from population data were able to help doctors predict a potential diagnosis months or even years earlier and be used to monitor these patients after a diagnosis is made?
A growing body of research in the exciting field of predictive and prescriptive analytics suggests that if you input large datasets—drawn from millions of healthcare claims or electronic medical records, for example—sophisticated algorithms can identify patterns that deliver meaningful diagnostic information for patients with a wide range of conditions. These technologies can be used to uncover hidden risks in a population by detecting disease, correcting misdiagnosis, and monitoring disease progression.
Author: Chase Spurlock, Michael Fleming