Strategy and Solutions
The client is a mid-sized health plan focused on improving value-based care outcomes and revenue cycle performance across multiple provider networks. Inaccurate coding, missed risk adjustment opportunities, and inconsistent documentation created financial leakage and operational inefficiencies. The organization required an intelligent solution capable of identifying under-coded encounters, improving HCC capture, and supporting coding teams with real-time clinical decision support. To address these challenges, the health plan partnered with Zymr.
The health plan faced increasing pressure to improve risk adjustment accuracy while maintaining compliance and documentation quality across distributed provider networks. Existing coding workflows relied heavily on retrospective reviews, making it difficult to identify missed diagnoses and under-coded encounters in real time.
Limited visibility into coding gaps reduced the organization’s ability to accurately capture patient risk profiles, directly impacting reimbursement and value-based care performance. Clinical and coding teams also struggled with fragmented workflows and inconsistent documentation practices across provider groups.
The growing volume of claims data further complicated manual review processes, creating delays in identifying revenue opportunities and increasing administrative overhead. The organization needed an AI-driven solution capable of predicting coding gaps, improving HCC capture, and supporting proactive revenue integrity workflows.
Zymr implemented an AI-powered predictive CDS platform designed to improve coding accuracy, strengthen risk adjustment performance, and recover missed revenue opportunities at scale.
Zymr developed and deployed an AI-driven revenue cycle intelligence platform that integrated predictive analytics, clinical decision support, and automated coding workflows to improve operational and financial outcomes.
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