Identifying inaccurate or incomplete insurance claims is a common task for hospitals seeking to maximize reimbursement for services. While flagging errors can be difficult, using machine learning can make the process easier, according to Deepti Sharma, director of product management at HSBlox.
"By proactively scrubbing claims using machine learning, providers can head off potential issues by comparing current claims to previous patterns of denials," she told Becker's Hospital Review. "They can then make corrections, such as improving coding accuracy, before claims are submitted rather than after, helping improve cash flow as well as significantly reducing the cost of reworking claims."
If you would like to share your RCM best practices, please email Kelly Gooch at kgooch@beckershealthcare.com to be featured in the "RCM tip of the day" series.
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