How AI Reduced Medicare Enrollment Delays by 70% for a Leading Healthcare Payer
Client Overview
Client is a not-for-profit Medicare Advantage organization founded in 1977 and headquartered in Long Beach, California. It serves more than 300,000 members across several U.S. states, offering comprehensive healthcare solutions designed to help older adults live healthy and independent lives. During the open enrollment period, they see a surge of paper-based enrollments and higher cycle time.
The Operational Drag Created by Manual Processing
Enrollment volumes surged as submissions entered through multiple intake channels like faxes, emails and handwritten forms shared as scanned images.
Most enrollment forms required manual intervention before they could be fed into the system, as handwritten submissions posed accuracy challenges during data extraction and conversion.
This led to:
01
Delays in onboarding
Due to repeated corrections, new members waited longer for activation during already time-sensitive enrollment periods.
02
Repeated compliance checks
Inconsistent data quality triggered multiple validation and rework cycles. Teams had to re-verify submissions to meet regulatory and compliance requirements.
03
Additional operational effort
Staff spent significant time interpreting handwritten fields and fixing extraction errors. This increased workload without improving accuracy.
04
Higher enrollment cycle time
Fragmented handoffs extended the processing timelines. Enrollment cycles grew longer as volumes increased, limiting the ability to scale.
Results Achieved Through the New Claims Workflow
The implementation improved day-to-day claims operations through automated dispatch workflows and faster coordination between teams. The client could respond faster and manage claim movement with less operational effort.
01
Pull forms from FTP/SFTP, shared drives, and cloud locations.
02
Extract typed and handwritten data with highest accuracy.
03
Validate and classify fields with minimal manual checks.
04
Map submissions to the structured 130+ field OEC format.
05
Move output into downstream destinations such as AWS S3.
06
Maintain transparency to align with audit and governance standards.
The Enrollment Processing Engine
Kognitos is one of the neurosymbolic AI platforms that could automate any business process workflow in simple plain English. Indium developed this GTM delivery model around a simple principle: accept the reality of how data enters the enterprise.
The AI system ingests handwritten enrollment forms as raw input and converts them into structured, compliant outputs, with human validation applied at critical phases. The workflow below shows how this comes together.
The Difference Delivered by a 7-Day POC and 98%+ Accuracy
The new workflow rebuilt the enrollment engine end-to-end, delivering measurable improvements in speed, accuracy, and operational load.
Final Takeaway
In healthcare enrollment, members need to be onboarded without friction, while the right information is processed accurately. A platform like Kognitos makes this possible, delivering measurable results without removing humans from the loop.
Claims dispatch activities moved through an automated workflow and reduced dependency on manual coordination and repetitive operational follow-ups.