MedSignal AI builds AI-assisted communication systems that help clinicians and care teams manage patient messaging with more speed, clarity, and consistency—without compromising security, auditability, or operational control.
We focus on workflow-first automation: turning inbound patient messages into structured tasks, suggested drafts, routed escalations, and traceable outcomes.
- Draft suggestions for patient responses (human-in-the-loop)
- Tone and policy-aligned templates for consistent communication
- Summaries of long threads into actionable clinical context
- Classify and route messages to the right queue/team
- Priority detection (time-sensitive vs routine)
- Automated follow-ups and reminders where appropriate
- Convert conversations into tasks, tickets, or care actions
- Escalation paths and SLAs for operational reliability
- Structured data extraction (intent, symptoms, dates, next steps)
- Email providers + messaging systems
- Webhooks + event-driven pipelines
- Observability and audit logging for every decision
- Safety and control by default: clear boundaries, human approval where required
- Auditability: traceable inputs/outputs, deterministic logs, versioned prompts and rules
- Security-minded design: least privilege, token hygiene, secure configuration practices
- Production realism: monitoring, retries, rate limits, and failure-safe fallbacks
Note: Compliance requirements depend on deployment context. MedSignal AI is built with secure engineering practices to support regulated environments where applicable.
medsignal-core— core domain models, routing, shared librariesmedsignal-api— REST/GraphQL APIs, auth, webhooks, integrationsmedsignal-worker— async jobs, triage pipelines, schedulersmedsignal-ui— dashboards and inbox workflows for care teamsmedsignal-infra— deployment, CI/CD, IaC, observability
- Backend: Python (Django/FastAPI), Node.js services where needed
- APIs: OpenAPI/Swagger, JSON Schema validation, webhooks
- Data: PostgreSQL, event-driven queues, background workers
- Platform: Docker, CI/CD, cloud deployment patterns
- AI: model + prompt evaluation, guardrails, output verification
We value contributions that improve:
- reliability and testing
- security and configuration hygiene
- integration quality and documentation
- evaluation rigor (datasets, metrics, regressions)
- Open an issue describing the change
- Submit a PR with tests and clear rationale
- Keep changes small, reviewable, and production-oriented
For collaboration or integration inquiries:
- GitHub Issues (preferred)
- Email: support@medsignal.ai (replace this)
© MedSignal AI