The healthcare industry is gradually adopting artificial intelligence tools for a variety of applications from clinical decision support to medical image analysis and identifying targets for drug development. But the automation of labor-intensive tasks through machine learning should also be of interest to payers, particularly to improve the accuracy of provider data.
Data inaccuracies can impact claims management, referrals, surprise medical bills, and much more. Blue Cross of California had to pay a whopping $38 million in refunds to members in 2015 as a result of inaccurate data.
A new report from Orderly Health highlights how its machine learning tools can help payers and providers verify their data and reduce costly fines.
Fill out the form below to download your free copy of Orderly Health’s The Definitive Guide to Provider Data Accuracy.
The healthcare industry is gradually adopting artificial intelligence tools for a variety of applications from clinical decision support to medical image analysis and identifying targets for drug development. But the automation of labor-intensive tasks through machine learning should also be of interest to payers, particularly to improve the accuracy of provider data.
Data inaccuracies can impact claims management, referrals, surprise medical bills, and much more. Blue Cross of California had to pay a whopping $38 million in refunds to members in 2015 as a result of inaccurate data.
A new report from Orderly Health highlights how its machine learning tools can help payers and providers verify their data and reduce costly fines.
Fill out the form below to download your free copy of Orderly Health’s The Definitive Guide to Provider Data Accuracy.
