How Machine Learning Is Revolutionizing Healthcare Posted on : Jul 12 - 2019

To take advantage of emerging software tools that incorporate artificial intelligence, healthcare organizations first need to overcome a variety of challenges.

Some leading-edge organizations are beginning to do just that, focusing on machine learning, a subset of artificial intelligence (AI) that encompasses statistical methods in which computer systems recognize patterns or correlations in data by ingesting large sets of training data. They improve their performance, or “learn,” over time as they incorporate new data; revising their approach as needed without human programmers updating the rules.

In the healthcare industry, most machine learning applications are in the research stage. “There is not a ton of clinical use,” according to Brian Edwards, independent validation consultant for AI vendors.

One area with a lot of research activity is radiology, where the industry is investigating how to use machine learning to detect signs of disease from digital images. “Wherever you have crisp clean data is where you should start. Images are the highest quality data that you have in a health system in terms of reliability,” Edwards says.

Machine learning has been applied to other areas, such as assessing patients’ risk of a hospital readmission, exacerbation of a chronic medical condition, or coming down with sepsis during a hospital stay.

Preparing for machine learning

“I think planning is where it starts,” says Bob Fuller, managing partner for healthcare at Clarity Insights, an information technology consulting firm focused on data analytics. Fuller says healthcare organizations should assess their overall business strategy and how AI could be deployed to solve specific problems, such as hospital readmissions or claims fraud. View More