Healthcare data interoperability
Transform HL7, FHIR, and X12 with AI, without running PHI through one.
Describe the mapping and bring your spec. Data Shepherd writes a tested, reviewable transformation script during the build phase. After that, every run executes the sealed script in an isolated sandbox. No patient data is sent to an AI model at run-time.
Free plan available. No card required.
MSH|^~\&|LAB|HOSP|EHR|CLINIC|202606051200||ORU^R01|0001|P|2.5 PID|1||MRN-000123^^^HOSP||DOE^JANE||19800101|F OBX|1|NM|GLU^Glucose||96|mg/dL|70-99|N
| patient_id | test | value | flag |
|---|---|---|---|
| MRN-000123 | Glucose | 96 mg/dL | Normal |
| MRN-000124 | Glucose | 112 mg/dL | High |
The problem
Your team is already pasting PHI into ChatGPT.
When a mapping is due and the integration queue is backed up, someone drops a patient file into a chatbot to reshape it quickly. It works, and it is a HIPAA incident waiting to happen. Your team needs the speed without the exposure.
of workplace AI interactions involve sensitive data, often through unmanaged personal accounts.
Source: Cyberhaven
How it works
From a messy interface file to a tested pipeline, in minutes.
No integration engineer, no six-week mapping project.
Describe it and bring your spec
Plain English plus your implementation guide, companion guide, or data dictionary. A de-identified sample sharpens the result. Cannot share data at all? Build from the spec alone.
AI writes transparent scripts
You get reviewable, versioned Python, never a black box. Inspect every line before it touches a record.
Run it anywhere, with no AI
Trigger by API, schedule, or click, or pull from SFTP, S3, or Azure Blob. The sealed script does the work; no PHI goes to a model.
The AI builds it. A sealed script runs it.
Most AI data tools push records through a language model on every run. We keep the model in the build phase only, so production never sends patient data to a model.
Full data-flow table, sub-processor list, and retention schedule in our Trust Center →
Built for healthcare workflows
The transformations your team does by hand today.
Claims normalization
Flatten and validate 837P and 837I claims into a clean schema for your clearinghouse or analytics.
Remittance posting
Turn 835 ERA files into postable rows for your AR system, with adjustment codes mapped.
Eligibility flattening
Convert eligibility responses into a row-per-member table your ops team can actually read.
ADT to FHIR R4
Map HL7 v2 ADT messages to FHIR resources, or whatever shape your EHR or downstream system expects.
Lab results to analytics
Parse ORU^R01 results into an analytics-ready table with units and reference flags.
Enrollment reconciliation
Diff 834 enrollment files against your roster to surface adds, terms, and changes.
Security and compliance
What your security team needs, by default.
No PHI to AI at run-time
Guaranteed by architecture
HIPAA-ready
BAA program · in progress
SOC 2 Type II
in progress
Full audit trail
Every run and change logged
Stop choosing between fast with AI and HIPAA-safe.
Map a sample HL7 or X12 message in minutes, and hand your security team a data-flow table instead of a risk-acceptance form.