This could not be more true in healthcare. Predictive modeling can improve patient outcomes and reduce costs such as the following:

Ensuring fair and accurate payments for risk-adjusted government plans by aligning reimbursement with the health status and expected costs of covered individuals

Analyzing all member claims in real time to detect and prevent fraud, waste, and abuse (FWA) before payments are made — shifting from reactive recovery to proactive prevention

Detecting and redacting PHI in clinical notes — including previously unrecognized instances — to protect patient privacy and support compliance across payer and provider systems

Identifying clinical scenarios where early detection can meaningfully impact outcomes — such as predicting which patients are at risk of becoming septic

Predicting which claims are likely to be denied — helping providers address issues proactively and reduce revenue cycle friction

Every step tracked and auditable.

Our technology is not-out of-control wizardry. It methodically tracks all of the steps it used to make decisions and is auditable. When there is not enough data to make a prediction, our software knows not to “pull the trigger.”

Using our knowledge graph, algorithms, and our inference engine, our technology can use both unstructured and structured data to make predictive modeling a reality.