How AI and machine learning can predict illness and boost health equity

Artificial intelligence and machine learning are key to unlocking patient data and solving some of healthcare's most complex problems. Even as the U.S. seeks to put the COVID-19 pandemic in the rearview mirror, many who survive the initial illness suffer debilitating long-term health impacts, especially those with underlying health conditions.

Technology allows easier access to disparate data sources without compromising data privacy or integrity. In addition, advanced analytics deliver real-time insights, enabling providers to predict outcomes and diagnose illness early to intervene with patients at risk of developing long-term COVID-19 and other chronic diseases.

To delve deeper into these technologies and their ramifications in healthcare, Healthcare IT News spoke with Brett Furst, president of HHS Tech Group.

Q. How can health system IT leaders leverage AI and machine learning technologies to unlock and explore patient data for the accurate, early detection and treatment of chronic disease?

A. Health system IT leaders face two principal challenges specific to healthcare data: It's very messy, and there's a considerable amount of it.

For example, much of the valuable patient information related to social determinants of health is buried as unstructured data in the notes sections of electronic health records, making it difficult to capture and analyze. Further, in addition to patient data in EHRs, a wealth of information can be gleaned from other sources, such as surveys, games, retail and social media, which in many cases no one has ever combined.

When researchers and providers combine diverse data sets, the healthcare industry can acquire new insights at the population health level. However, due to the messiness and volume of healthcare data, manual or human efforts to draw value from it are not sufficient, and that's where artificial intelligence and machine learning enter the picture.

AI, machine learning and other related technologies enable providers and researchers to obtain a wider breadth of insights in a dramatically shorter time frame than traditional data extraction, capture and analysis methods.