Healthcare Requires Trusted AI Before Autonomous Capability.
DeliverHealth embeds governance and accountability into every product, from documentation to agentic workflows.
The Questions You Are Already Asking
Before you approve an AI vendor, your governance and security teams ask these first.
What data is used, and why?
Where does AI participate in the workflow?
Is patient data used to train outside models?
Who reviews the output, and what happens when it is uncertain?
Can the activity be audited later?
These questions decide governance reviews and contract terms. Our answer stays consistent wherever AI participates, with governance and accountability embedded from documentation to agentic workflows.
Governed by What the AI Is Allowed To Do
How much risk an AI task carries depends on what it touches. Drafting a note sits lower on the scale than recommending a code, and recommending a code sits lower than submitting a prior authorization. Governance scales the same way, so the further AI moves into the workflow, the more review, logging, and escalation we place around it.
See how the controls change across InstaNote, InstaCode, and InstaAuth.
Assistive documentation support
Supports enterprise dictation, transcription, and note generation.
Note accuracy and clinician accountability.
Human review before finalization and clinician sign-off before the legal medical record.
How Your Data Actually Moves
Each stage below is governed by embedded controls and accountability.
- Customer-authorized input
Every workflow starts with information you've authorized us to use, such as encounter audio, clinical documentation, coding metadata, and your own configuration rules.
- Approved AI processing
That information passes through approved AI processing for a defined task, whether that's clinical note generation, coding recommendations, or workflow coordination.
- Human or customer review
A human reviewer, or a member of your own team, checks the result before it moves downstream.
- Final output and downstream use
The approved result moves downstream for final output and use.
- Monitoring and governance
Monitoring continues after that, so both sides can track performance, exceptions, and control effectiveness over time.
Product-Level Safeguards
Seven safeguards run underneath every workflow, scaled to what that workflow needs.
Human review
Preserves accountability before outputs are finalized or used downstream.
Confidence signals
Prioritizes review and flags uncertain outputs.
Traceability
Supports audit readiness and exception investigation.
Customer configuration
Aligns controls with your policies and operating model.
Exception handling
Routes uncertain or higher-risk scenarios to human oversight.
Action limits
Keeps AI activity inside approved workflow boundaries.
Performance monitoring
Supports ongoing evaluation as models, data, and requirements change.
Third Parties, Our Accountability
Speech-to-text tools, language models, and workflow platforms are embedded in our products to extend what they can do. We own the governance, accountability, and responsibility for these tools. Every vendor is held to the same high standard of evaluation before it operates inside a healthcare workflow.
- Evaluate
- Contract
- Configure
- Deploy
- Monitor
What We Can Show You
The evidence behind the claims above.
HIPAA-aligned safeguards
Across documentation, coding, and authorization workflows.
HITRUST posture
Including rapid recertification qualification for our eSOne platform and AI-related controls in scope.
Workflow-level audit logs
Covering review history and exception handling.
Documented partner governance
For every third-party tool in the pipeline.
Calm, reliable clinical AI at enterprise scale that fits inside real healthcare workflows, with trust and accountability designed in from the start.
