Since 1995, Intercede Health has helped partners manage complex conditions across critical points in the care pathway. Our HCC program combines proprietary analytics with clinician-led visits—so missing risk codes are not only identified, but confirmed and documented to a standard that stands up.

Risk adjustment depends on complete, current diagnosis capture. In practice, conditions that exist in the medical record—or are strongly suggested by utilization—often never reach a claim. Suspect lists without clinical confirmation create weak documentation and audit risk. And most provider and plan teams lack capacity to outreach, schedule, and complete focused coding visits at scale.
Historical and chronic conditions drop off when they are not re-documented in the payment year—leaving RAF and care planning incomplete.
Recapture and suspect reports without bedside (or virtual) confirmation rarely produce MEAT-ready support for every code.
PCPs and internal coding teams cannot chase every gap. A dedicated clinical partner closes the execution gap.
Intercede pairs a proprietary opportunity model with licensed clinicians who confirm conditions in a focused coding visit—or, when a visit is not feasible, through a structured chart-review package.
We blend current coded risk, historical and suspect diagnoses, utilization and cost intensity (with log-transformed severity so extremes inform rank without dominating), and chronic condition load. The output is a prioritized member list ordered by expected missing risk—not a raw data dump.
Nurse practitioners and physicians evaluate the member in person, at home, or by telemedicine. They document only what is clinically supported, using MEAT standards, and return claim-ready diagnoses your plan or risk-bearing group can act on.
Codes only count when the record supports them. Every Intercede coding visit is documented to the MEAT standard. Where a listed historical code is not clinically appropriate, we document that finding so recapture lists can be corrected—protecting audit posture while improving list quality over time.
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