Patient Graph
Longitudinal context across visits, documents, languages, and episodes.
Platform
The patient-context layer.
MedLineage starts with one painful workflow — preparing complex records before specialist review. Each reviewed packet compounds into reusable patient context the same tenant can read back: source-cited timeline, provenance trail, missing-record state, and exportable structure for downstream workflows.
From packet to patient-context layer
The packet is the wedge. The source-cited patient-context layer is the platform.
MedLineage starts with one painful workflow: preparing complex records before specialist review. Each reviewed packet can compound into reusable patient context — source-cited timeline, provenance trail, missing-record state, and exportable structure for downstream workflows.
Provenance Ledger
Every finding remains tied to source document, page, language, and extraction path.
Workflow Layer
Packets can support second opinions, referrals, tumor boards, trial screening, payer review, and clinician-approved agents.
04Why now
Clinical AI has reached the easy part. Trust is now the bottleneck.
LLMs can now read more of the messy record layer: PDFs, scanned reports, multilingual notes, lab tables, discharge summaries, HL7 messages, FHIR feeds, and CSV exports.
But complex care cannot run on black-box summaries.
Before a specialist, tumor board, trial-screening workflow, payer review, or clinical agent can safely act, patient context has to be structured, cited, and reviewable.
MedLineage sits at that transition: after raw records, before clinical decisions.
Records are more digital, but not more usable.
Hospitals have EHRs, PDFs, portals, HL7, FHIR, and data exports — but the patient story is still scattered across systems, languages, and formats.
LLMs make extraction possible. Provenance makes it usable.
The breakthrough is not "summarize this PDF." The breakthrough is turning messy records into claim-level, source-cited clinical context a human can verify.
Interoperability increases the need for context.
More exchangeable health data does not automatically create clinician-ready understanding. Raw records still need to become structured, cited, and reviewable.
Why this becomes huge
Start with one packet. Compound into every clinical-AI workflow.
The same source-cited patient context is the prerequisite for every workflow above the packet. We start narrow on complex second opinions, then compound into referrals, tumor-board prep, clinical-trial screening, payer review, medico-legal review and human-approved clinical AI workflows.
- 1Today
Complex-care second opinions and specialist referrals — multilingual packet, missing-record checklist, provenance ledger.
- 2Next
Tumor-board briefings, surveillance signals, clinical-trial eligibility screening — same patient graph, new lenses.
- 3Later
Payer review, medico-legal review, EHR write-back, agentic clinical workflows — all gated on the same source-cited substrate humans already signed.
Talk to us about a pilot.
Early customer conversations only — no paid pilots claimed, no hospital deployments to point at yet. If your team is preparing complex multilingual cases for specialist review, we’d like to hear from you.