Over 950 Cleansweep Auctions End Tomorrow 05/02 - Bid Now Over 800 Total Lots Up For Auction at Four Locations - TX 05/03, TX 05/06, NJ 05/08, WA 05/09
When it comes to AI’s potential to transform claims management, we’re missing the real story.
There is a lot of buzz about the impact artificial intelligence could make on healthcare, from improving care management to speeding drug development to driving efficiency and revenue. New research estimates wider use of AI across healthcare could save $200 billion to $360 billion annually. Today, revenue cycle and finance leaders are leaning into ChatGPT to optimize coding for cleaner claims on the provider side or accelerate prior authorizations on the payer side.
But AI’s promise in strengthening payment integrity for health plans lies heavily in machine learning. Harnessing this potential will depend on a higher degree of collaboration between clinicians and data scientists and a thoughtful approach to operationalizing machine learning in claims management.
A new frontier Machine learning isn’t as new as generative AI, but our understanding of how to leverage it to strengthen payment integrity is becoming more sophisticated.
There are two types of machine learning models:
• With supervised models, labeled datasets are fed into a machine to train it to identify similar examples from raw data or make predictions based on the data, such as whether a claim is likely fraudulent. These models also “score” the value of the prediction according to how likely it is to be true.
• Unsupervised models analyze unlabeled data to identify aberrant behaviors. These models demonstrate strong potential to identify fraudulent claims, according to recent research. Moreover, the models get better the more data they consume.
When combined with natural language processing (NLP), a type of AI that derives meaning from written text and verbal discussions, these models speed healthcare claim processing through the use of both structured and unstructured data. For instance, NLP can quickly detect information that supports a medical diagnosis and determinations around medical necessity. It can also spot anomalies that could point to inappropriate billing, such as when the codes do not match the patient’s condition or the volume of codes is unusually high.
There is room for both supervised and unsupervised machine learning models in healthcare claims management as they complement one another. However, unsupervised machine learning in particular is a transformational capability for claims management. It positions health plans to broaden their aperture to gain deeper insights from disparate datasets—and flag patterns that might otherwise be missed, including patterns related to fraud, waste and abuse.
