Automate data engineering
Move streaming health events, pharmacy files, CRM syncs, provider records, model outputs, and reporting jobs through repeatable workflows with visible dependency graphs.
Run healthcare data workflows your teams can trust
Run the healthcare data workflow behind public health streams, provider records, patient activation, analytics, ML, and AI context.
Mage
I created the healthcare workflow and prepared it for review.
- ingest streams, files, public datasets, and operational exports
- validate sensitive data, lineage, permissions, and approvals
- publish dashboards, APIs, activation outputs, and ML-ready datasets
- register PHI/PII-aware context with audit evidence
You can inspect the source graph, sensitive-data checks, lineage, approval status, delivery targets, and context artifacts before downstream teams or agents use the output.
Mage helps healthcare and life sciences teams govern public health streams, provider normalization, patient activation, analytics, ML outputs, and PHI/PII-aware AI context.
Overview
Modernize healthcare data workflows from operations to AI
Mage helps healthcare and life sciences teams turn public health streams, provider records, pharmacy files, patient activation, and AI context into governed workflows instead of scattered scripts, loaders, and dashboards.
Modernize legacy data models
Replace brittle Lambda, Airflow, Dagster, manual loaders, Sheets, file parsing, and hidden transformation logic with inspectable medallion-style workflows.
Prepare AI-ready context
Package PHI/PII-aware healthcare context with lineage, row and column controls, approval evidence, audit logs, and approved outputs.
Use cases for healthcare and life sciences
Each use case starts with work healthcare teams already run: health streams that need medallion governance, public records that need normalization, and activation outputs that need PHI/PII-aware controls.
Public health medallion architecture
Move health program apps, streaming events, public datasets, validation, retries, audit logs, dashboards, and ML training outputs through one governed medallion workflow.
Health program apps and streaming events feed Mage bronze, silver, and gold workflows with offset and retry controls, validation, audit logs, government dashboards, and ML training outputs.
Program appscases + encounters
Streaming eventsHL7 / FHIR updates
Mage medallion workflowOffsets + retries
- BronzeLand raw eventsoffsets + retries
- SilverValidate dataschema + quality gates
- GoldPublish metricsdashboard + ML tables
Offset controlValidationAudit logs
Gov dashboardsfresh program metrics
ML trainingtraining-ready tables
Healthcare data workflows break where tools stop
The workflows break where healthcare teams already feel the pain: feeds arrive in many shapes, sensitive data needs safe retries, context is scattered, and outputs need to trigger governed action.
Move Stripe customer data into BigQuery every weekday before 8am.
Stripe
Sources arrive in many shapes. Healthcare data arrives as streams, files, PDFs, spreadsheets, public datasets, and operational exports with different schemas and controls.
Why did revenue freshness drop?
The Stripe ingest arrived 42 minutes late, delaying the customer revenue model.
Stripe ingest delayed 42 min
Sensitive runs need auditability. Sensitive workflows need lineage, permissions, approvals, safe retries, and audit logs before public health, patient, provider, or model outputs can be trusted.
Customer syncRun delayed
Inspect run
Late source foundRetry availableOpen logs
AI context needs governance. AI and self-service analytics need source-backed context, but freshness, permissions, lineage, PHI/PII controls, and approved outputs are scattered across tools.
Pipeline
Table
Chart
File
AnswerRevenue freshness dropped after the Stripe ingest arrived late.
Outputs must trigger safe action. Dashboards, APIs, SMS, Iterable, reporting marts, ML training, audit exports, and product systems need governed delivery, not another warehouse-only handoff.
One healthcare workflow becomes governed execution
Mage turns sensitive healthcare data work into visible dependencies, checks, approvals, handoffs, and reusable context.
Run our healthcare data workflow. Ingest health events, pharmacy files, provider records, CRM data, and model outputs, validate sensitive-data controls, publish dashboards and activation outputs, and prepare context for analysts and agents.
The healthcare workflow is ready to review. I connected the sensitive sources, added validation and approval checks, prepared governed delivery outputs, and registered reusable audit context.
Customer health workflow
3 sourcesDaily at 7amChecks enabled
Ask. Start with the health feed, pharmacy file, provider registry, model output, or activation workflow that needs stronger controls.
Sequence. Mage sequences streaming events, files, public datasets, operational systems, model outputs, and dependency rules.
Validate. Checks catch schema drift, missing files, stale public records, sensitive-field issues, failed writes, and unapproved outputs.
Standardize. Mage makes public health, provider, patient, pharmacy, and model-output logic visible before it becomes trusted workflow output.
Deliver. Governed outputs reach dashboards, APIs, SMS, Iterable, reporting marts, ML training flows, and safe AI context.
Context. Lineage, PHI/PII controls, approvals, run history, and permissions become context teams and agents can reuse.
What Mage governs across healthcare workflows
Sensitive source orchestration Streaming events, pharmacy files, provider records, public datasets, PDFs, spreadsheets, and exports can move through one governed dependency graph.
Trust and safety checks Validate schema drift, offsets, retries, PHI/PII tags, row and column controls, approvals, lineage, and output readiness.
Workflow modernization Move public health, provider normalization, reporting, and activation workflows out of manual loaders and fragile schedulers.
Healthcare action surfaces Deliver governed outputs to dashboards, APIs, SMS, Iterable, reporting marts, product systems, ML workflows, and audit exports.
Audit context registration Register lineage, approvals, sensitive-data labels, run history, quality evidence, and delivery state as reusable context.
PHI/PII-aware AI context Expose scoped public health, provider, patient, pharmacy, model, and metric context to analysts, applications, and agents.
Start a trial
Talk with Mage about production data workflows, AI-ready context, managed deployments, and security requirements for your team.