Cotiviti Completes Acquisition of Edifecs.
Read the press release.
Member Suspecting is part of Cotiviti’s
Risk Adjustment solution suite.
Health plans and risk‑bearing providers often operate with incomplete visibility into patient complexity. Fragmented data, inconsistent documentation, and evolving risk models make it difficult to identify undiagnosed conditions early enough to influence outcomes. Traditional suspecting programs frequently rely on claims alone, lack explainability, or overwhelm clinicians with low‑quality candidates. Accurate risk adjustment begins with understanding the full clinical picture, yet many organizations struggle to identify conditions not yet captured in available documentation or hidden across fragmented data sources.
increase in suspect capture compared to claims-only suspecting
By synthesizing claims, clinical notes, pharmacy data, labs, and more, it delivers transparent, evidence‑backed suspects that power stronger prospective and retrospective workflows. Member Suspecting unifies structured and unstructured data into a single AI‑driven pipeline, across clinical notes, labs, vitals, pharmacy claims, and problem list history.
Evolved natural language processing (NLP) extracts meaningful insights from unstructured documents, while machine learning and clinical rules help generate high‑quality, evidence‑backed suspects. Confidence scoring and suppression logic help ensure that only relevant, actionable candidates are surfaced, reducing noise and minimizing provider abrasion. Flexible delivery options such as flat files, CCV forms, or Epic‑integrated notifications allow organizations to operationalize suspecting in the workflows that suit them best. The result is earlier identification and support for more accurate risk capture and improved value‑based performance.
Member Suspecting offers a number of benefits for our clients, such as:
Cotiviti Member Suspecting combines multi‑source evidence, advanced AI, and operational flexibility into a single, transparent solution. Our engine synthesizes clinical and claims data with explainable logic, producing suspects backed by clear documentation. With its blend of transparency, configurability, and clinical rigor, Member Suspecting delivers a uniquely powerful approach to risk capture.
