CHALLENGE

The client had several disparate, manual applications relying on a human agent to pull data to deliver a service intake of faxes and scanned document images into manual queues. The inputs consisted of different sources and format types, and working through repetitive processes was a less-than-optimal use of employees’ time. With the client’s average auto-forward rate of four per month, this led to a negative impact on their Star ratings. There were over 100,000 claims per month, and seasonality in data added further complexities to the issue.

SOLUTION

For Misdirects and Transfer Reduction, Sagility leveraged our OCR/NLP (Intelligent Content Processing) for intake efficiency. Route identification of resolutions was flagged as standard or true appeals specific to a line of business and within them potentially expedited or urgent cases at the onset into the resolutions intake bucket and identification of misroutes within the same intake bucket accordingly to avoid a duplicate processing. Domain-specific lexicons, semantic and contextual inferences and entity mining within the documents, drive the NLP here. ML based classification models facilitate the triaging activity.

We developed provider relationship dashboards to empower network teams to work with providers proactively to provide requisite information up front for an appeal resolution.