ToolVet
83trust / 100

Scrub

by x402.getautomatedrcm.com in Weather & location

x402 APIPassing, checked 2 h ago

Healthcare text de-identification engine (demo endpoint — SYNTHETIC/TEST DATA ONLY, never send real PHI; nothing is stored). Strips names, DOBs, member/claim IDs, phones, emails, addresses, SSNs; dates become a derived interval timeline (service-to-denial days, days-ago) so denial and timely-filing math survives de-identification. Returns scrubbed text + token map for local re-identification. Production use requires the licensed in-environment engine: contact [email protected]

POST https://x402.getautomatedrcm.com/scrub

Last 30 days

All checks passedSome failedAll failedNot checked
Uptime
100%
Response time
183 ms typical, 183 ms slowest 5%
Last check
2 h ago
Next check
any minute now

How to call it

# See the payment challenge (nothing is charged)
curl -i -X POST "https://x402.getautomatedrcm.com/scrub" \
  -H "content-type: application/json" \
  -d '{"text":"Patient: Jane Sample, DOB 1/2/1980, member TEST123456, seen 6/1/2026, denied 6/20/2026 CO-50."}'
import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { ExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client().register(
  "eip155:8453",
  new ExactEvmScheme(privateKeyToAccount(process.env.AGENT_KEY)),
);
const pay = wrapFetchWithPayment(fetch, client);

// Not sure it's safe to pay? Preflight it first for $0.03:
// GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fx402.getautomatedrcm.com%2Fscrub
const res = await pay("https://x402.getautomatedrcm.com/scrub", {
  method: "POST",
  headers: { "content-type": "application/json" },
  body: JSON.stringify({"text":"Patient: Jane Sample, DOB 1/2/1980, member TEST123456, seen 6/1/2026, denied 6/20/2026 CO-50."}),
});
console.log(await res.json());

Example input

{
  "text": "Patient: Jane Sample, DOB 1/2/1980, member TEST123456, seen 6/1/2026, denied 6/20/2026 CO-50."
}

Example output

{
  "counts": {
    "DATE": 2,
    "DOB": 1,
    "ID": 1,
    "PERSON": 1
  },
  "scrubbed_text": "Patient: [PERSON_1], DOB [DOB_1], member: [ID_1], seen [DATE_1], denied [DATE_2] CO-50. --- DERIVED TIMELINE --- [DATE_2]: 19 days after [DATE_1]",
  "token_map": {
    "[PERSON_1]": "Jane Sample"
  }
}

Security scan

  • No findings. We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.

Recent checks

WhenResultHTTPTimePrice
2 h agoPassed402183 ms$0.01