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GenHealth.ai

GenHealth.ai builds generative AI for healthcare operations, powered by a Large Medical Model (LMM) trained on sequences of medical events. Its products automate the administrative back office for providers, health plans, and DME suppliers: fax intake routing, prior authorization and medical-necessity review (UMPA), and revenue cycle management. The developer platform exposes an Inference API that generates simulated patient futures from demographic, ICD diagnosis, CPT/HCPCS procedure, and NDC medication codes, an Embeddings API for semantic search over medical sequences, and a UM/PA API that uploads, extracts, and adjudicates prior-authorization PDFs. GenHealth integrates with any FHIR server implementing the HL7 Da Vinci prior-authorization guides (CRD, DTR, PAS). The platform is SOC 2 and HIPAA compliant with a public trust center and status page.

agent aware

Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.

Kin Score

API Evangelist profiles GenHealth.ai the way a machine reads it — 4 machine-readable artifacts, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.

Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — GenHealth.ai scores 40.6/100 (thin), with a separate agent-readiness read of 26/100 (agent aware). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.

Kin Score

This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.

Kin Score Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 40.6/100 · thin
Contract Quality 0.0 / 25
Developer Ergonomics 14.8 / 20
Commercial Clarity 10.0 / 20
Operational Transparency 4.8 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Regulatory · Health 9.8 / 15
Agent readiness — 26/100 · agent aware
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 12 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 5 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

How we profile GenHealth.ai

Each block below is one kind of artifact we hold for GenHealth.ai. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.

MCP Servers 1

Model Context Protocol servers expose these APIs directly to AI agents. We profile them because agent-native access is the fastest-growing way this provider's capabilities actually get used.

Model Context Protocol servers that expose these APIs to AI agents.

Security Posture 3

Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Genhealthai Authentication

http · 1 scheme

SECURITY

Genhealthai Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Genhealthai Trust Center

SOC 2, HIPAA

SECURITY

Resources

Every other property we hold for GenHealth.ai — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.

Documentation 2

Reference material describing how the API behaves

Agent Surfaces 3

MCP servers, agent skills, and machine-readable catalogs

Design & Contract 3

Pagination, idempotency, versioning, errors, and events

Build 1

SDKs, sample code, and the tooling you integrate with

Operate 3

Status, limits, changes, and where to get help

Commercial 2

Pricing, plans, and the legal terms of use

Company 2

The organization behind the API

← All providers · Data indexed from github.com/api-evangelist/genhealthai · machine-readable index on apis.io