Driving network-wide value via connected data

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Plans lose billions of dollars each year to administrative complexity and information siloing:
14%
Proportion of revenue spent by payers and providers on claims processing
40
days
Average payment cycle
$65b
Annual losses to fraud

Next generation solutions to long-standing problems

The combination of knowledge graphs and graph machine learning enables solutions which are difficult or impossible via deep learning or other conventional ML techniques. This enables new approaches to old problems such as master member indexing, automatic claims adjudication and fraud detection.

Dyad is building a suite of applications that leverage the power of connected data to solve these problems while simultaneously enabling cross-domain interoperability and automation.
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Example Application: Risk Adjustment for MA Plans

Building on our knowledge graph and graph ML platform, we have developed an application that provides enhanced risk adjustment for Medicare Advantage plans. The product scans for incomplete and incorrect diagnostic coding and documentation in clinical encounter notes, and enables corrective nudges to be inserted into the provider workflow via CodePilot.
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