Implementing Machine Learning and AI for Healthcare Billing Corrections
The Inroads Advisory team helped implement a machine learning model to reduce pharmaceutical billing errors.
Veterans rely on government services for efficient, low cost healthcare. While there is always room to improve, major strides have been made in recent years with retaining patient trust compared to non-government care. Rising costs of care and prescription drugs in the U.S. continue to burden consumers. When these are incorrectly billed, the cost of correcting the issue is more expensive for everyone involved.
To alleviate the administrative burden of improper billing, the Inroads team joined a federal project aimed at determining billing error corrections at scale for Veterans. The project entailed a first-of-its-kind machine learning (ML) model with our federal client, which was an exciting opportunity to utilize artificial intelligence as this industry continues to grow.
The program implements a collaborative ML model that automatically flags wasteful pharmaceutical billing errors for proactive intervention. The model ultimately helps providers and the government deliver services efficiently and save time. Inroads has been instrumental in achieving Authority to Operate (ATO) for this model implementation and piloting the program for a test segment of consumers.
As new as machine learning and artificial intelligence are, the Inroads team understands that an implementation for government requires more calculus around determining bottom-line value for consumers and the government alike.
Excited to learn more about machine learning and how Inroads can help you navigate your program? Please reach out using our contact form below.
