TalkAboutHealth dba Advizmo

Advizmo automates the matching of health-plan members to condition-specific peer mentors and care-management conversations. It typically sits inside the navigation layer that members encounter after a diagnosis or a triggering event such as a cancer claim or short-term disability filing. The platform uses natural language processing to match members with trained peer guides who have lived experience with the same condition, aiming to reduce unnecessary clinical utilization and improve return-to-work outcomes. Its enterprise clients use it to supplement or replace high-cost nurse triage for initial member support.

TalkAboutHealth began as a peer-support program for cancer patients, incubated at Columbia University. It operated initially as a non-profit research pilot. As the team built a scalable technology layer — machine-learning matching, conversation analytics, enterprise integration — they rebranded the commercial side as Advizmo and began selling into large health plans and self-insured employers. The original cancer-support mission remains part of the company's identity but the go-to-market engine is now enterprise B2B health-tech.

Per the firm's communications, publicly disclosed relationships include Evernorth (Cigna's health-services division) and The Hartford. At The Hartford, the deployment is tied to disability and leave-management populations, where peer-matching conversations are designed to help claimants navigate return-to-work and condition management.

Advizmo has raised venture funding, though total capital raised has not been formally disclosed in public filings. Publicly reported sources of early backing include Columbia Business School's Lang Fund. The firm has participated in startup competitions and accelerators, but the current capital structure and any subsequent institutional rounds remain undisclosed as of the latest available public record.

Advizmo's matching engine covers oncology, fertility, musculoskeletal conditions, mental health, and chronic disease management, among other categories. The breadth of condition coverage is partly a function of its origin in cancer-patient conversations; that data set gave the team a foundation for training machine-learning models on symptom-specific language, which later generalized to other high-cost, high-touch condition areas.