Blood biomarker testing, read as one picture.

Mostly in intent and in repetition. A routine blood test is usually ordered to answer a question a clinician already has. Biomarker testing in a wellness or prevention setting is usually run without a specific complaint and repeated at intervals, so the interest is in movement between blood draws rather than in one result. That is why reading each test on its own falls short: the meaning of a biomarker panel sits between the values and between the dates.

Four steps. Parse: the result arrives as a lab PDF, a photo, an HL7 or FHIR feed or JSON, and numbers, units, ranges and flags are computed by deterministic code. Structure: every biomarker is mapped to LOINC and its unit to UCUM, and the panel becomes part of one FHIR R4 record. Understand: values are read together and against previous draws, conditions and medications on file, with named guidance cited claim by claim. Deliver: a patient view, a clinician view, a PDF or JSON through the API. Previous versions of the report stay as history, so the reading of an earlier draw can be revisited after the next one arrives.

Yes, and this is the usual case for anyone who has done biomarker testing for more than a year. Labs name the same biomarker differently, report it in different units and use their own reference ranges. Vitrubo normalises each value to one of 17,000+ LOINC codes and to UCUM units, so a test from one lab in one year and a test from another lab the next read as points on one line. Reference ranges are kept per laboratory rather than replaced, because the range that applies to a value is the one the lab that measured it published.

Rather than a table of pass and fail, the report groups flagged biomarkers into a few connected stories and places them in four zones: needs attention, worth watching, back to normal, always normal. Where the guidance calls for a repeat, the biomarker to re-test and the interval are named beside the value. For each biomarker the trend across draws sits beside the latest value and its range, with the guidance behind the interpretation one chip away. The Ryan Hayes live report is an example: 15 months of lifestyle change, read as direction of travel rather than as a list of results.

No. Vitrubo is not a diagnostic device, does not select treatment and does not predict what a biomarker will do next. It drafts a reading of the values that are there, organises the history and names tests worth considering; a clinician reads and signs where one is involved. For a person reading their own biomarker blood test it is educational context to bring to an appointment, not medical advice, and the page says so.

Any biomarker a laboratory reports as a value, in any panel: metabolic and lipid panels, blood count, inflammation, iron and vitamin status, thyroid and other hormones, kidney and liver markers, and the wider panels wellness programs order. Urine and allergy panels arrive as values too. Hospital records, prescriptions and notes are read as context, so a biomarker that a medication is known to move is read with that in mind.

From named medical guidance: EHA, WHO, NICE, KDIGO and BSH among others, with up to 30 sources behind one biomarker view. Each statement carries a chip that opens the publication, so a clinician checks the basis rather than trusting the prose. Several AI models cross-check the same case before a draft is shown, and their disagreements are resolved rather than averaged.

The program keeps running the biomarker testing it already runs; results reach Vitrubo as HL7 or FHIR from the lab, or as PDF uploads. Each new blood panel regenerates the member's report, with earlier versions kept as history. The member sees plain language and direction of travel; the program's clinician sees the full reasoning, connected medications and tests to consider, all under the program's own brand.

Through the API or a white-label portal. A user photographs a biomarker blood test or uploads the PDF, the app posts it and receives the structured FHIR R4 panel and the interpretation as JSON, or renders the branded page. Every further test regenerates the report, which is what brings a user back after each draw. Vitrudoc, an AI chat grounded only in the user's own record, can sit beside it.

Containers run on your infrastructure or in a private cloud; you own the keys and Vitrubo has no access to the data. Identity never enters the prompt: the models see values and an internal ID. A HIPAA business associate agreement for US deployments, a GDPR Article 28 processing agreement and Standard Contractual Clauses, encryption in transit and at rest. A biomarker blood test never leaves your perimeter in identifiable form.

Yes, through a lab, clinic, program or app that offers Vitrubo under its own brand; there is no self-serve signup. The person uploads the PDF or a photo of the biomarker blood test and reads the plain-language view: what stands out, why it is grouped that way, and what to discuss with a doctor. Earlier tests uploaded alongside it are read into the same trend: a single blood test is read on its own, two or more are read as a line.

In: lab PDFs, photos of a result, HL7, FHIR R4 and JSON through the API. Out: a FHIR R4 record of the panel, the interpretation as JSON, white-label PDFs, and a role-based portal. LIS, EHR or EMR connections use the same HL7 and FHIR channels, so a blood test flows in and its reading flows back without a second system.

With a call, then sandbox access and API documentation. Your team runs its own anonymised biomarker panels, ideally repeat blood draws from the same people, and validates the drafts against its own reading. There is no commitment at that stage; production follows when you are ready, inside your perimeter or a private cloud.