Haematology
What are the primary bottlenecks in the acute myeloid leukemia pathway that push length-of-stay above the national average?
Three systems: a Data Engine that structures free text, Striata for analysis and reports, and Voyager for the questions nobody thought to ask.
Reads clinical notes in any European language and writes structured, coded records. Typos, abbreviations and local shorthand included.
Data Engine 01 / 06
Splits the document into anchored sections and translates where needed, preserving the original source text.
Splits the document into anchored sections and translates where needed, preserving the original source text.
Parallel agents identify clinical entities by type, capturing the exact words and their position in the note.
Brings the extractions together, removes duplicate spans and resolves conflicting entity types.
Adds timing, negation, certainty and clinical context, alongside details such as dose and severity.
Connects entities to one another: a treatment to its indication, a finding to its evidence, a condition to a body site.
Maps each entity to the appropriate clinical ontology, producing structured records linked back to the source.
Captured Entities
Conditions, medications, procedures, symptoms, family history, allergies, care plans and more. Each entity carries detailed attributes.
“Admitted for acute CHF exacerbation.”
Global Standards
Coded to SNOMED CT, ICD-10, RxNorm, LOINC and ATC. National and custom ontologies can be added — a national cancer ontology, for example.
Ask questions in plain language. Striata selects cohorts, tests associations, maps care pathways and checks guideline conformance, then writes a report with editable charts you can forward.
Clinical codes and SQL can be inspected. Reports detail the methodology and data used.
Operational, financial, and clinical questions — answered in minutes.
What are the primary bottlenecks in the acute myeloid leukemia pathway that push length-of-stay above the national average?
Which patient cohorts drive readmissions for CHF, and which interventions correlate with improved outcomes without increasing cost?
How does cost-effectiveness of minimally-invasive versus open hip replacement compare, adjusted for post-op rehab duration?
An autonomous health-data analyst that works without being asked. Voyager studies your hospital’s context, proposes hypotheses and investigates your data, then sends each finding to the person responsible.
Learns what matters to your hospital from national guidelines, hospital strategy, and policy and planning.
Generates clinical and operational questions worth investigating, guided by your priorities and data.
Tests those hypotheses against your structured clinical records using statistical analyses.
Asks follow-up questions and runs further analyses until the finding is clear.
Routes the finding and supporting evidence to the person responsible for the problem.
Amorphous combines clinical data extraction and analysis. See how that compares with tools built for individual parts of the workflow.
| Capability | AI document assistantsQ&A over notes | Clinical NLP APIsText extraction & coding | Cohort & BI toolsAnalysis of structured data | |
|---|---|---|---|---|
| Turn notes into coded clinical data | ||||
| Extract contextual attributes | ||||
| Trace clinical findings to source notes | ||||
| Build cohorts using clinical criteria | ||||
| Compare outcomes across cohorts | ||||
| Ask questions in plain language | Varies | |||
| Output FHIR R4 and OMOP | Varies | |||
| Support EHDS readiness |
Priority tasks, data sources, success metrics.
A secure connection to your records through your existing EMR.
Your teams complete real tasks on selected cohorts.
The Data Engine structures continuously and Striata rolls out across departments.
For partnerships
hello@amorphous.health