Healthcare AI Go-Live Checklist: Pre-Production Validation

TL;DR

Before you deploy a healthcare AI agent, verify six categories: functional testing, integration, security, compliance, monitoring, and rollback. Skipping any one of these is how "passing tests" turns into a patient safety event.
Only 16% of health systems report that their AI deployments deliver measurable value, per KLAS Research 2024, mostly because pre-production validation is cursory.
The first 48 hours after go-live surface the majority of integration defects. Staff a dedicated observer and pre-define rollback triggers before the agent touches a live patient.
94% of physicians report that prior authorization delays have led to adverse clinical events (AMA 2024 survey), which is why operational correctness matters more than demo accuracy.

The gap between passing tests and being production-ready

Your vendor finished integration testing. The agent reads records, submits prior auths, and navigates payer portals. The pilot looked clean. Leadership wants to move. But there is a real gap between "it works in testing" and "it is safe against production systems with real patients."

This checklist is for clinical informatics teams, IT directors, and compliance officers who need to validate readiness. It covers functional verification, security, compliance, monitoring, and rollback. Assign owners. Do not skip items.

"The riskiest moment in any healthcare AI deployment isn't the model itself, it's the integration seams. That's where 80% of production incidents originate."

Dr. John Halamka, President, Mayo Clinic Platform

Category 1: Functional testing

Confirm the agent has been tested against scenarios that match your environment, not the vendor's demo environment.

FHIR conformance

Agent reads required FHIR R4 resources from your specific EHR version (Epic, Oracle Health, Meditech)
Agent handles the FHIR search parameters your EHR actually supports (not just the spec)
Agent processes FHIR Bundle pagination correctly
Agent handles both relative and absolute FHIR references
Write-back operations use proper resource validation
Tested in a realistic sandbox, not a minimal mock

Workflow completeness

Every production workflow tested end-to-end
Tested with your top 10 payers by volume
Each workflow tested with standard, edge, and failure cases
Multi-step workflows resume correctly after interruption
Time-dependent workflows tested with realistic timing

Interface coverage

Interfaces tested in combination, not just isolation
Voice/IVR tested against your phone system configuration
Payer portals tested against the actual portal versions in use
Multi-interface workflows validated end-to-end

Error handling

Handles network timeouts without hanging
Handles HTTP 429 (rate limit) with backoff
Handles HTTP 500 without data loss
Handles malformed responses (truncated JSON, unexpected HTML)
Handles expired tokens and revoked credentials with alerts
Error conditions produce actionable log entries

Category 2: Integration testing

EHR connectivity

Connects through your EHR's production proxy, not directly to the FHIR server
OAuth2 token refresh works under sustained load
Respects EHR rate limits
Handles scheduled maintenance windows
Connection pool and timeouts match your network topology

External dependencies

Production credentials provisioned (not sandbox)
DNS resolves all endpoints from production network
Firewall rules allow required outbound connections
TLS cert validation enabled on all connections
Handles upstream API deprecations

Data flow

No field truncation, encoding, or type mismatches
Patient identifiers map correctly (MRN, member ID, FHIR ID)
Timezone handling is correct across systems
Clinical values (dosages, lab values) preserve precision
Character encoding consistent (UTF-8 end-to-end)

Category 3: Security review

Authentication

Uses dedicated service accounts, not personal credentials
Least-privilege permissions
Secrets in a manager, not config files
Rotation process documented and tested
No credentials in logs at any level

PHI protection

No PHI in log files
No PHI sent to systems without a BAA
No PHI cached in temp files or browser storage
LLM API calls do not transmit PHI unless BAA is in place
TLS 1.2+ for all PHI in transit
Vendor used synthetic data for HIPAA-compliant testing

Vulnerability assessment

Dependencies and images scanned
No hard-coded secrets in deployed code
Network exposure limited
Input validation blocks injection
No arbitrary code execution from external sources

Category 4: Compliance validation

HIPAA

BAA signed with vendor
BAAs with all subprocessors (cloud, LLM, monitoring)
Audit logging captures all PHI access with timestamp, identity, action
Audit logs tamper-evident and meet retention policy
Breach notification procedures documented and tested

Regulatory

State-specific rules handled (prior auth, referrals vary by state)
SaMD or clinical decision support classification documented if applicable
CMS requirements addressed for Medicare/Medicaid workflows
Consent restrictions respected
AI decision-making is auditable and explainable

Organizational policy

Aligns with your AI governance policy
Clinical leadership approval on scope
Intended use documented and matches vendor claims
Staff training plan in place
Escalation path defined for out-of-scope situations

Category 5: Monitoring and alerting

Performance

Response time baselined per workflow
Error rate tracked per workflow, interface, and payer
Throughput captured
Queue depth monitored
Resource utilization alerts set

Operational alerts

Alert when error rate exceeds 2x baseline
Alert when no tasks complete for a defined period
Alert on external connectivity loss
Alert on authentication failures
Routing, escalation, and hours defined
Thresholds tuned to avoid alert fatigue

Clinical safety

Patient safety events tracked as a distinct category
Near-miss events logged and reviewed
Clinical accuracy measured continuously
Drift detection in place
Weekly clinical review for the first month, monthly after

Category 6: Rollback and incident response

Rollback

Incident response

AI-specific incident plan documented
On-call schedule for first 30 days
Vendor support SLAs documented
Communication plan for affected staff
Post-incident review process defined

The first 48 hours

Most issues surface in the first 48 hours. Plan for active monitoring.

Hour 0 to 4. A dedicated team member watches dashboards live. Every completed workflow gets spot-checked. Anomalies trigger immediate investigation.

Hour 4 to 24. Check error rates every 2 hours. Classify every failure. Verify successful tasks produced correct downstream effects.

Hour 24 to 48. Review aggregates. Identify any workflow with zero completions (possible silent failure). Compare clinical accuracy to baseline. Make a go/no-go decision for ongoing operation.

Documentation for the governance committee

Prepare this before final approval:

Testing summary. Functional, integration, and security results with an evaluation framework that measures more than accuracy.
Risk assessment. Likelihood, impact, and mitigation for each risk. Patient safety called out specifically.
Monitoring plan. What is watched, thresholds, responders, escalation.
Rollback plan. Steps, estimated time, manual fallback.
Staff readiness. Training evidence and reporting paths.
Vendor accountability. SLAs and contractual obligations.

Key Takeaways

Validate six categories before go-live: functional, integration, security, compliance, monitoring, rollback.
Test against your top 10 payers and your actual EHR version, not the vendor demo setup.
BAAs must cover every subprocessor, including LLM providers and monitoring services.
Staff a dedicated observer for the first 4 hours and an elevated watch for the next 44.
Pre-define rollback triggers and test the procedure in staging before go-live.
Track near-miss events separately. They predict the incidents that would otherwise surprise you.
Err toward including checklist items. Minutes to verify, months or years of operation.

FAQ

How long should pre-production validation take?

Plan for 4 to 8 weeks for a moderate-complexity agent (prior auth, scheduling). Very constrained use cases (read-only analytics) may finish in 2 weeks. Writeback and multi-interface agents routinely take 10+ weeks once security review and BAA chasing are included.

Do we need a separate BAA for the LLM provider?

Yes, if the agent transmits any PHI to the model. Major providers (Anthropic, OpenAI, Google, AWS Bedrock) offer BAAs on specific tiers. Verify the BAA covers the exact API, region, and logging configuration you use in production.

What is the single most common go-live failure?

Sandbox drift. The vendor tested against a mock or a stale FHIR sandbox, and production behaves differently. Insisting on tests run against your production-equivalent environment, using your EHR version and payer configurations, catches this before patients are affected.

How soon should we schedule the first clinical review?

Within the first week. Per a HIMSS 2024 report, organizations with weekly clinical reviews during the first month resolved issues 3x faster than those with monthly cadences.

When should we trigger a rollback?

Define triggers in writing before go-live. Typical triggers: error rate above 3x baseline for 30 minutes, any patient safety event, any data integrity issue affecting more than 1% of records, or loss of audit logging.