Put AI into production without losing control
Create the policies, technical controls, approval paths, and monitoring required to use AI safely across clinical and operational healthcare workflows.
- HIPAA
- GDPR
- ISO 27001
- RBAC
- Audit trail
Trusted by healthcare teams in the US and Europe since 2016
Healthcare AI governance is more than compliance
HIPAA, GDPR, and security controls are necessary. But they do not answer questions like these:
- ?What is the AI allowed to do?
- ?Which actions require human approval?
- ?Which data can the model access?
- ?How are decisions logged?
- ?What happens when the model is uncertain?
- ?How does an agent recover from a failed action?
- ?Who owns an escalation?
- ?How do you detect model or workflow degradation?
Those answers are the governance layer.
Six controls every production healthcare AI system needs
- Access
Which systems, records, fields, and tools can the AI reach?
- Permission
What may it read, recommend, create, update, or submit?
- Human oversight
Which actions require review or approval, and by whom?
- Verification
How does the system confirm the action actually succeeded?
- Escalation
When and how does the AI hand the workflow back to a person?
- Auditability
Can you reconstruct what happened, why, and which data was used?
Match autonomy to workflow risk
| Autonomy ↓ · Risk → | Low clinical risk | Medium risk | High risk |
|---|---|---|---|
| Low autonomy | Summarize | Prepare | Assist |
| Human review | Execute + approve | Recommend + approve | Mandatory review |
| Autonomous | Routine admin workflows | Limited, within guardrails | Not recommended |
- Lower risk
SchedulingEligibilityRoutingDocument classification
- Moderate risk
Coding supportPrior auth preparationClinical documentationCare-plan suggestions
- Higher risk
DiagnosisMedication changesTreatment decisionsEmergency triage
Autonomy should increase only when observability, fallback, and auditability increase with it.
Your AI governance operating model
- AI inventory
Which models, vendors, agents, and AI features are in use today.
- Risk classification
Risk level by use case and workflow.
- Data governance
PHI handling, access, retention, and model-provider policies.
- Human-in-the-loop controls
Approval, review, and exception handling.
- Model validation
Accuracy, bias, reliability, and workflow testing.
- Monitoring
Errors, drift, escalation rates, and operational failures.
- Vendor assessment
Third-party AI providers, BAAs, infrastructure, and security posture.
- Audit documentation
Clear records of decisions, permissions, and system behavior.
Policies are not enough. Controls have to exist in the product.
User / AI agent
ClinicianStaffAI agentAI feature in your product
Governance layer
RBACWorkflow permissionsApproval rulesConfidence thresholdsPHI controlsLoggingHuman escalationMonitoring
Systems and data
EHRPMSRCMData platform
Riseapps implements the controls behind the policy, not just the governance document.
Governed AI in regulated production
Human approval, EU MDR, HIPAA, and GDPR were requirements, not afterthoughts, in these projects.
- AI on clinical data
4h → 30m
to prepare a Medicare-compliant care plan
- Problem
- Clinicians spent hours assembling care plans for dementia patients.
- Built
- AI care-plan copilot with clinician approval of every plan
- Systems
- EHR + ambient visit data
- Regulated AI · EU
< 6 months
to an MDR-ready clinical AI product
- Problem
- A clinical risk-prediction product had to run inside 4 European hospitals with different EHRs.
- Built
- Integration and data layer for SAP, Epic, and regional EHRs, built for EU MDR
- Systems
- FHIR · HL7 · CSV · GDPR
- RPM exception handling
+77%
patient adherence
- Problem
- Care teams could not keep up with remote patients between visits.
- Built
- RPM module with real-time vitals, alerts, and clinician queues
- Systems
- Devices + care-team alerts
After partnering with Riseapps, we booked 462 patients, allowing the company to accrue revenue. The quality of their code was commendable.
Tara RaffiFounder, Almond ObGyn“We've seen a reduction in bugs and timely delivery.”
Andrew BellinghamDirector of Product, Healthera
Simon Liu, HealtheraWatch the video testimonial(opens in a new tab)
Where we can help
- AI governance assessment
What's already deployed, where risk sits, and which controls are missing.
- Governance framework
Policies, risk tiers, ownership, approval criteria, and escalation rules.
- Technical implementation
Permissions, logs, approval flows, observability, and agent guardrails, built.
- Use-case review
Assess proposed AI workflows before they reach production.
- Vendor evaluation
Review external AI models and platforms against your requirements.
Built for organizations moving AI into production
- Healthcare providers
Deploying AI across clinical and administrative workflows.
- HealthTech companies
Adding AI features to regulated healthcare products.
- Multi-site groups
Standardizing AI use across locations and systems.
- Enterprise teams
Where departments have already started adopting AI on their own.
From scattered AI use to governed deployment
- 1Inventory
Models, vendors, agents, and AI features in use.
- 2Risk classify
Rate each use case by workflow risk.
- 3Define controls
Access, permissions, oversight, escalation.
- 4Assign ownership
Named owners for approvals and escalations.
- 5Implement guardrails
Build the controls into systems and products.
- 6Validate
Test accuracy, reliability, and failure paths.
- 7Monitor
Track drift, errors, and escalation rates.
Healthcare AI governance, answered
Isn't HIPAA compliance enough?
HIPAA covers how protected health information is handled. It does not define what an AI system may do with it: which actions need approval, how uncertainty is handled, how failures are recovered, or how decisions are reconstructed. AI governance covers those questions.
Departments already use AI tools on their own. Where do we start?
With an inventory: which models, vendors, and AI features are in use, which data they touch, and who owns them. Then we classify each use case by risk and close the largest control gaps first.
Do you only write policies, or also build the controls?
Both. We define the framework and then implement it in your systems and products: role-based access, workflow permissions, approval flows, confidence thresholds, PHI filtering, logging, escalation, and monitoring.
How do you decide how autonomous an AI workflow can be?
We place each workflow on a risk and autonomy matrix. Low-risk administrative work can run autonomously within guardrails. Clinical decisions keep mandatory human review. Autonomy grows only as observability, fallback, and auditability grow with it.
Can you assess third-party AI vendors?
Yes. We review vendors and model providers for data handling and retention, BAA availability, hosting and security posture, logging, and how well their product fits your governance requirements.
Does this cover European requirements as well?
We work with providers in the US and Europe and map controls to HIPAA and GDPR. For EU teams we can align governance documentation with EU AI Act obligations where they apply.
How is AI monitored after go-live?
We track accuracy, drift, error and escalation rates, and failed actions, with alerts and review routines owned by named people, so degradation is caught before it reaches patients or revenue.
Know what your AI is allowed to do before it starts doing it
We can review your current AI stack, workflows, and controls and identify the gaps between pilot and production.
- Review of AI already in use
- Gap list ranked by risk
- NDA on request
Email biz@riseapps.biz