Health Informatics & Health IT
Building AI that makes healthcare data systems smarter, interoperable, and safer
Industry Challenge
Health IT vendors, EHR developers, and health informatics platforms need AI to improve clinical documentation, automate workflows, enhance interoperability, and surface insights from structured and unstructured data. This requires diverse annotated datasets across clinical text, structured records, and user interface interaction logs.
How SCILabel Serves This Industry
Data Collection
We source de-identified EHR structured data exports, HL7 FHIR resource datasets, clinical order sets, and patient portal interaction logs from health IT vendor and academic medical informatics research partnerships.
Data Annotation & Labeling
Our health informatics specialists annotate FHIR resource quality, clinical order appropriateness, medication reconciliation discrepancy flags, clinical alert fatigue indicators, and patient portal message intent and urgency classifications.
Data & Model Evaluation
Evaluators benchmark EHR AI model outputs — clinical decision support alerts, order suggestions, documentation completeness scores — against physician and informaticist ground truth, and test for workflow integration safety.
Annotation Types & Formats
- HL7 FHIR resource quality and completeness annotation
- Clinical alert relevance and fatigue classification
- Medication reconciliation discrepancy labeling
- Patient portal message intent and urgency classification
- Clinical order appropriateness annotation
Specialist Workforce Tracks
Track 1 (Medical NLP) and Track 5 (AI Safety): Health Informatics Specialists, Clinical Officers with EHR experience, Pharmacists.