Bringing Confidence to Complex Correspondence Processing Workflows

Posted by Brinna Hanson on April 29th, 2021
Author
Brinna Hanson
Brinna is a marketing professional and recent graduate of the University of Minnesota. Brinna joined Smart Data Solutions in 2019 to assist the marketing department reach new heights with a focus on the HubSpot inbound process. From her time at Smart Data as well as at previous internships, Brinna has been able to gain knowledge in many different aspects of marketing as a whole.

Enhanced correspondence processing workflow with 99.5% accuracy and 37% cost reduction in the second year.

Correspondence processing workflows always have room for optimization, especially when it comes to large Blues. In this client’s case, the workflow included categorization and indexing of their Medicaid and Medicare Advantage correspondence with the ultimate goal of improving turnaround time. 

The Challenge

The current workflow included complex categorization and indexing of correspondence including member, claim number, market and sender ID. The process required combing through large documents which took a significant amount of time. 

The Solution

Smart Data Solutions worked alongside our client to establish the initial goal to lift and shift their current workflow, as well as the long-term goal to automate and streamline the process over time. Examples of documents processed included: 

SDS focused on automation technologies to achieve a lower cost and Medicaid market compliance. These technologies included machine learning to enable document classification, full document optical character recognition (OCR), proprietary matching logic, real-time document association, and automated acknowledgement letter generation.

The Results

Bringing Confidence to Complex Correspondence Processing Workflows 1

The machine learning Smart Data Solutions incorporated into this client’s workflow enabled a 37% cost reduction in year two of our partnership.

Our client also achieved 99.5% document level accuracy because of SDS’ hybrid double key and integrated audit which automated a second review of documents that the machine learning engine was not confident in.

Lastly, SDS automated the capture of member information which, in-turn, aided identification of markets for prioritization. 

Ultimately, machine learning combined with ongoing workflow optimization resulted in lower cost, higher accuracy, and Medicaid market compliance to avoid penalties.

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