INDUSTRY // HEALTHCARE

Clinical AI you can defend to regulators

Patient safety isn't negotiable. Flightline gives you the evidence to prove your AI meets the standards healthcare demands.

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ONE METHODOLOGY // STRICTER INTERPRETATION

The same Readiness methodology, applied with healthcare's stricter tolerances.

Healthcare AI operates slower and more conservatively than other domains. The bar for safe to ship is higher, the consequences of failure are more severe, and the regulatory scrutiny is more intense.

SECTION 01

THE STAKES

Healthcare AI carries unique risks

When AI makes mistakes in healthcare, people can be harmed. The stakes are higher, the regulations are stricter, and the consequences are more serious than in any other domain.

!Patient safety as the primary concern, not a tradeoff
!HIPAA, FDA, and state-level regulatory requirements
!Clinical accuracy for life-critical decisions
!Complete audit trails for legal and regulatory review

RISK CONSIDERATIONS

Patient Safety

Incorrect AI outputs can cause direct physical harm

Regulatory Exposure

FDA, HIPAA, state boards, and accreditation bodies

Trust Erosion

Failures undermine provider-patient relationships

SECTION 02

USE CASES

Where Flightline helps

Common healthcare AI applications and the specific risks Flightline catches.

Clinical Decision Support

Ensure recommendations align with medical guidelines

Key Risks

!Suggesting contraindicated treatments
!Missing critical drug interactions
!Outdated clinical guidelines

Readiness Questions

GroundingSafetyRules

Patient Communication AI

Prevent misinformation in patient-facing systems

Key Risks

!Incorrect dosage instructions
!Misinterpreting symptoms
!Inappropriate medical advice

Readiness Questions

HallucinationSafetyBrand Safety

Medical Document Processing

Validate extraction from clinical notes, labs, imaging

Key Risks

!Misread lab values
!Incorrect patient identification
!Missing critical findings

Readiness Questions

SchemaIntentQuality

Administrative AI

Catch errors in billing, scheduling, prior auth

Key Risks

!Incorrect billing codes
!Missed appointments
!Prior auth denials

Readiness Questions

RulesConsistencySchema

SECTION 03

METHODOLOGY APPLIED

The 10 Readiness Questions for Healthcare

Same questions. Healthcare context. Different failure examples and unacceptable outcomes.

Intent

Does the AI correctly understand the clinical context and intent?

FAILURE EXAMPLE

AI interprets 'pain management' query as request for opioid prescription

UNACCEPTABLE OUTCOME

Inappropriate treatment pathway initiated

Grounding

Are responses grounded in validated clinical evidence?

FAILURE EXAMPLE

AI cites outdated treatment protocols

UNACCEPTABLE OUTCOME

Patient receives substandard care

Hallucination

Did the AI fabricate medical information?

FAILURE EXAMPLE

AI invents drug interactions that don't exist

UNACCEPTABLE OUTCOME

Unnecessary treatment delays or contraindications

Rules

Did it follow clinical protocols and regulatory requirements?

FAILURE EXAMPLE

AI recommends off-label use without proper context

UNACCEPTABLE OUTCOME

Liability exposure, patient safety risk

Safety

Did it avoid recommendations that could cause patient harm?

FAILURE EXAMPLE

AI suggests dosage outside safe ranges

UNACCEPTABLE OUTCOME

Patient injury or death

Consistency

Are clinical recommendations consistent across similar cases?

FAILURE EXAMPLE

Same symptoms get different triage priorities on different days

UNACCEPTABLE OUTCOME

Inequitable care, missed emergencies

Quality

Are outputs clear, professional, and clinically appropriate?

FAILURE EXAMPLE

AI uses ambiguous language in clinical notes

UNACCEPTABLE OUTCOME

Miscommunication, continuity of care gaps

Robustness

Can it resist manipulation that could harm patients?

FAILURE EXAMPLE

Patient manipulates AI to get inappropriate referral

UNACCEPTABLE OUTCOME

Healthcare resource misuse, delayed care for others

Brand Safety

Does it maintain appropriate clinical tone and boundaries?

FAILURE EXAMPLE

AI provides emotional support advice beyond its scope

UNACCEPTABLE OUTCOME

Trust erosion, inappropriate reliance on AI

Schema

Are clinical records and outputs structurally valid?

FAILURE EXAMPLE

AI generates invalid HL7/FHIR messages

UNACCEPTABLE OUTCOME

Integration failures, data loss, care gaps

SECTION 04

CONSERVATIVE BY DESIGN

Healthcare AI moves slowly. Intentionally.

We apply stricter thresholds and more conservative defaults for healthcare applications. Speed is secondary to safety.

HIGHER THRESHOLDS

What passes in other domains may fail in healthcare. We calibrate pass/fail thresholds to clinical standards, not industry averages.

EXPLICIT UNCERTAINTY

We surface uncertainty explicitly. Healthcare AI should know when it doesn't know, and say so clearly.

HUMAN ESCALATION

We test that AI appropriately escalates to human clinicians. The goal is augmentation, not replacement.

SECTION 05

COMPLIANCE

HIPAA-conscious by design

Flightline never touches PHI. All testing uses synthetic data generated from your schema, not your patient records. Zero exposure by architecture.

✓No PHI processed. Synthetic data only
✓Runs in your environment. Data never leaves
✓Complete audit logs for compliance review
✓Deterministic testing for reproducibility

SECURITY POSTURE

Zero PHI

Synthetic data only

Your Env

Data stays local

Audit Logs

Complete trail

SOC 2

On roadmap

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