Risk Adjustment: Precision Beyond the Diagnosis

In the 2026 regulatory landscape, simply finding a diagnosis mention is not enough; accurate risk adjustment requires a deep understanding of the clinical, temporal, and contextual relationship of every finding.

Context is the Core of Accuracy

Our platform recognizes that not all mentions of a diagnosis qualify as valid clinical indicators. We distinguish between a patient’s true status and non-diagnostic references:

• Non-Diagnostic Filtering: We automatically exclude family history references, clinical guideline discussions, and conditional “rule-out” statements that do not reflect the patient’s actual condition.
• Supporting Evidence: Our AI differentiates between “Documented Opportunities” — diagnoses in the chart that do not yet satisfy MEAT criteria and “Suspected Opportunities” (potential conditions supported by labs, medications, or clinical indicators).

The Clinical Relationship Layer

MST’s proprietary architecture operates with a sophisticated understanding of complex clinical relationships:

• Temporal Distinction: We differentiate between acute, chronic, and post-condition states (e.g., excluding an MI if it falls outside the acute coding window).
• Primary vs. Complication: Our system understands the link between conditions, such as recognizing neuropathy as a complication of diabetes or identifying transient secondary effects like AFib during sepsis.
• Clinically Relevant vs. Transient: We filter out temporary, context-driven findings to focus on the true indicators of a patient’s ongoing health profile.

Audit-Ready Performance

We deliver results designed for seamless clinical validation and absolute audit readiness:

• Full Provenance: Every identified HCC code includes the source of the finding (e.g., PCP visit vs. Specialist note) and flexible contextual snippets to ensure clinicians can verify the logic immediately.
• High Precision Targets: Our models target less than 5% false positives and less than 1% false negatives by leveraging comprehensive contextual analysis and a transparent model that allows every rejected finding to be audited.
• Closing the Gap: We automatically flag missing charts and documentation gaps to ensure a complete and accurate risk profile for every member.

 

Anomaly Detection: Uncovering the “Long Tail” of Financial Risk

Traditional healthcare payment integrity relies on static, pre-defined rules that only catch expected billing errors. At MST, we believe rules catch what you expect, but our AI catches what you never imagined. Our solution moves beyond simple upcoding to identify the high-impact, novel, and collusive schemes that traditional defenses miss.

The MST AI-Powered Advantage

• Proactive Holistic Detection: Unlike reactive systems that require manual updates to add rules for new schemes, our machine learning models analyze behaviors and patterns to identify complex, emergent anomalies.
• Targeting the “Long Tail”: While common “Head” errors are well-understood and contained by simple checks, a single “Long Tail” event—novel, undetected for years, and highly complex—can result in catastrophic financial damage that surpasses all common fraud combined.

Iterative Implementation for Immediate Value: We roll out our models in phases, targeting high-value/high-risk categories first to ensure value capture can be realized even before full build completion.