Uncover 5.4X more DRG value with AI-driven clinical review

Background

DRG Validation (DRGV): Our inpatient coding safety-net solution is designed to identify DRG inaccuracies and reimbursement risk. DRGV delivers approximately $900M in annual client value across more than 160 health system customers, with a 95%+ agreement rate on recommendations.

Phare Audit: Our AI-native clinical reasoning engine analyzes the full medical record across 100% of cases to generate a comprehensive and clinically grounded code set. Phare Audit enhances coding precision, identifying documentation improvement opportunities and revenue integrity risks earlier in the revenue cycle.

Situation

An established DRGV client sought to expand its revenue integrity strategy by evaluating our AI capability through Phare Audit. The organization wanted to know if an AI-driven clinical review layer could identify incremental opportunities beyond its existing validation framework.

We proactively engaged the client in sharing clinical data to support next-generation DRG validation capabilities with the goal of improving DRGs as quickly as possible. Anticipating the growing role of AI in coding accuracy and clinical review, we worked with the client early to ensure the right data foundation would be in place as new capabilities emerged.

Because of the long-standing relationship between the organizations, the client agreed to provide a clinical data feed ahead of the launch of R1 Phare Audit. This early partnership enabled us to develop and validate more advanced, clinically informed reviews , positioning both organizations to move quickly as AI-native capabilities became available.

Solution

By leveraging Phare Audit’s comprehensive AI results, our auditors and CDI specialists were able to expand the review beyond traditional opportunities and identify more nuanced documentation gaps and coding recommendations.

The current tool was unable to flag some clinical scenarios that AI uncovered, expanding the depth of review and quality of coverage.