Electronic health records have improved healthcare by giving providers easier access to patient records. That data can do more than just help doctors coordinate care, however—big data and predictive analytics are making their way into hospitals.
Human health is extremely complex, and we’ve spent millennia observing, taking notes, and trying to understand risk factors and how to control disease. With such a vast amount of healthcare data available, no person could find every pattern in huge datasets—but computers can.
Predictive analytics involves taking large datasets and using these data to spot patterns. In healthcare, those patterns could help with diagnosis and treatment, potentially predicting new risk factors, improving outcomes, and saving lives. Analytics can also help healthcare organizations become more efficient and save money.
Currently, just 43% of healthcare organizations in the United States use predictive analytics, although many more are interested in doing so in the future. Common obstacles include lack of technology, incomplete data, and simply not knowing how to get started. Many organizations know the potential value of predictive analytics is there—but getting started is a challenge.
