Interface-to-outcome intelligence for healthcare

Every handoff between clinical, pharmacy, and claims systems — watched, traced, and priced.

A prescription doesn't move through a hospital in one step. It's a chain of handoffs between systems that were never built to fully trust each other — and the failures that matter most are the ones nothing alerts on, until they're expensive. Handoff is a live map of that chain: where a message is right now, where it broke, and what the break is costing.

Request a diagnostic See how it works ↓

LIVE HANDOFF CHAIN — MEDICATION ORDER, ADMISSION THROUGH ADMINISTRATION ≈3,860 msgs/hr, fleet-wide

ORDER ENTRY

A01 · O01

PHARMACY

RDE · verify

GAP FLAGGED

PACKAGING

RDS

BEDSIDE SCAN

RAS · MAR

CLAIMS & BILLING

837 · submit

order #48213 · dose revised 5mg → 7.5mg after packaging 3rd occurrence this month

The problem

Interfaces fail quietly — and nobody watches the space between systems.

Each interface engine — Cloverleaf, Rhapsody, Mirth, Iguana — monitors only its own slice of the chain. A cross-system failure shows up as several unrelated local anomalies instead of one traceable story.

The quiet ones cost the most

A message that partially succeeds, or succeeds late, rarely trips an alarm the way a hard outage does — so it's the failures nobody's watching for that do the damage.

Nobody owns the full chain

Each engine sees its own hop. Stitching five systems' logs into one story is still a manual, war-room exercise, done after something's already gone wrong.

No line back to the dollar

IT sees an error count. Finance sees a denial rate. Nobody sees the line connecting a specific interface failure to what it actually cost.

How it works

Four layers, one evidence chain.

Sits alongside the interface engines a health system already runs. It doesn't replace them, and it never touches the production message path.

Topology & tracing live map

A live, cross-vendor map of every interface point in the environment. One order, claim, or lab result is traced hop to hop across engines and systems — the way a distributed tracer follows one request through software, except the "spans" here are HL7 v2 messages, FHIR resources, and X12 transactions.

Anomaly detection deterministic

Statistical baselines and hand-authored pattern rules decide something is actually wrong — no generative AI in the decision path. Volume, latency, error rate, and known-risky sequences like a dose changed after packaging, all explainable and auditable on their own.

AI narration scoped & redacted

A generative layer sits only on top of what detection already found — clustering related failures and explaining them in plain language. It never decides what's wrong, and every real system change still requires a human to review and approve it.

Financial impact evidence-graded

The layer that doesn't exist anywhere else in this market. Each failure pattern maps to a cost model, so a dropped message isn't just a ticket — it's a dollar figure tied to denials, delayed claims, rework, or safety risk, graded by how much evidence actually stands behind it.

Proof it's real

The incident walkthrough, end to end.

This is the exact pattern the detection engine is built to catch — worked through from the first mistimed hop to a graded dollar estimate.

INCIDENT #48213 · PACKAGING → SCAN MISMATCH SYNTHETIC DEMO DATA

14:01:58Packaging completed — order #48213 at original dose, 5mg

14:03:41Order revised: dose 5mg → 7.5mg — packaging not re-triggered

14:07:12Bedside scan confirms stale label (5mg), not current order (7.5mg)

14:07:12ANOMALY — administration does not match current order

AI narration — root cause

Third occurrence this month of the same pattern: a dose revised after packaging but before the bedside scan, with a 4–6 minute average window. A confirmation step at the scanner, flagging any order revised after packaging, would catch this before administration instead of after.

$8,400

Pharmacist + compliance review

$4,300

Delayed claims release

$1,500

Rework & documentation

ILLUSTRATIVE → CLIENT-APPROVED → RECONCILED

Why it's different

Everyone else owns one piece. Nobody owns the chain.

Interface engines

Monitor their own engine's health only — no visibility past their own hop.

Infrastructure / APM tools

Watch uptime and latency — no idea what a message means clinically or financially.

AI-driven RCM tools

Catch the symptom in the claim, after the fact — never the interface event that caused it.

Handoff

The only layer connecting one specific technical failure, across every vendor, to a proven dollar amount.

Who it's for

Start with one bounded diagnostic, not a platform rollout.

Health systems mid-EHR-transition Community & regional hospitals Payment-integrity vendors & consultancies Federal / VA-adjacent systems TPAs & smaller payers