How Predictive Analytics Can Increase Efficiency and Cut Costs

About a third of the world’s electronic data are healthcare data. And 97 percent of that data are unanalyzed.

Chris Gough, general manager of health and life science at Intel, told Inside Digital Health™ that using technology, advanced analytics and artificial intelligence to analyze data can shift the healthcare industry from being reactive to more proactive and preventative.

>> READ: Mayo Clinic's Predictive Tool Identifies Risk of Recurring Kidney Stones

At HIMSS 2019, Gough talked about the challenges with the Internet of Things (IoT) and some of the biggest problems that hospitals are facing with their IoT network.

Gough also discussed how such technology has the potential to increase efficiency and value and cut costs.

For Gough, predictive analytics and machine learning give health systems and physicians an opportunity to identify high-risk patients. Hospitals can enable risk stratification to determine if a patient needs to be transferred to a higher level of care, like the ICU, or if the patient requires intubation.

The technology allows the system to stratify the patients according to the risk of the events that are happening and take action and possibly prevent the events from happening if caught soon enough.

Using predictive analytics and machine learning to catch something before it happens could reduce the amount of time spent in hospital care and can improve patient outcomes, as the technology gives deeper insights and more data to physicians. These tools can also improve physician workflows as the technology gives physicians data in real-time and does not require them to stop what they are doing.

Navigate the digital transformation with confidence. Register for our newsletter.

Keep on Learning

How Machine Learning Predicted Post-Cardiothoracic Surgery Complications

Mobile Machine-Learning Program Predicts Autism with High Accuracy

5 Emerging Technologies that Will Change Healthcare