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Healthcare & Life Sciences

Find the hallucinated drug interaction before patients do

Automated red teaming for clinical AI, patient-facing systems, and pharmacy applications

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Clinical Accuracy Testing

Patient safety-focused red teaming

Purpose-built test scenarios for the unique risks of AI in clinical decision support, patient communication, and medical documentation

Medical Hallucination

Fabricated studies, fictional medications, invented drug interactions, non-existent treatment protocols

Dangerous Misinformation

Incorrect dosing, contraindicated treatments, outdated clinical practices, harmful medical advice

Triage Failures

Missed emergencies, inappropriate symptom downgrading, delayed care recommendations

Clinical Anchoring Bias

Fixation on irrelevant information while missing critical diagnostic signals

Off-Label Guidance

Inappropriate medication recommendations without proper disclaimers or specialist referrals

Medical Sycophancy

Agreeing with incorrect patient self-diagnoses to appear helpful rather than accurate

Pharmacy Safety Testing

Pharmaceutical-specific risk coverage

Specialized testing for medication management, prescription verification, and controlled substance compliance

Drug Interaction Detection

CYP450 interactions, QT prolongation risks, serotonin syndrome, dangerous polypharmacy

Dosage Calculation Errors

Weight-based dosing mistakes, renal adjustment failures, pediatric calculation errors

Controlled Substance Compliance

DEA schedule violations, early refill red flags, prescription diversion indicators

Regulatory Alignment

Tests mapped to the audits you face

Purpose-built scenarios for healthcare's most demanding compliance requirements

HIPAA Compliance
Privacy RulePHI disclosure attempts, social engineering, unauthorized access
Security RuleAccess control bypass, session persistence, auth weaknesses
Breach NotificationDetect exposures before they trigger reporting
Business AssociatesInappropriate third-party sharing, vendor leakage
FDA & Clinical Safety
AI/ML SaMD GuidanceClinical accuracy, scope limitations, required disclaimers
GMLP PrinciplesBias detection, hallucination, model boundary violations
PCCP FrameworkBehavior consistency across updates, drift detection
Post-Market SurveillanceAccuracy regression, emerging failure modes

Also supports

HITECH ActState Privacy LawsHITRUST AI AssuranceEU AI Act + MDR

Applications

Tested across the healthcare enterprise

Clinical Decision Support

EHR-embedded diagnostic assistants, treatment recommendation engines, and clinical documentation tools.

Patient-Facing Systems

Symptom checkers, triage bots, patient education tools, and care navigation assistants.

Pharmacy & Medication

Prescription verification, drug interaction checking, dosage calculation, and controlled substance compliance.

Comprehensive PHI protection testing

Healthcare AI systems handle the most sensitive patient data. Our specialized testing identifies PHI exposure risks across multiple attack vectors before they become breaches.

  • Cross-patient PHI leakage detection
  • Social engineering vulnerability testing
  • Provider impersonation attack scenarios
  • Session data persistence checks
  • Unauthorized third-party disclosure risks
Risk report showing PHI vulnerability findings

Patient safety is non-negotiable

Healthcare plugins developed with clinical informaticists and healthcare security teams to address real-world patient safety risks.

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Why healthcare organizations choose Promptfoo

HIPAA-compliant deployment

Run entirely within your infrastructure with no PHI leaving your environment. Self-hosted options meet BAA requirements and data residency policies.

Continuous safety monitoring

Integrate with CI/CD pipelines to catch clinical accuracy regressions before deployment. Track safety metrics across model updates and prompt changes.

Audit-ready documentation

Generate structured reports for FDA submissions, HIPAA audits, and clinical validation requirements. Demonstrate due diligence with reproducible test results.

Secure your clinical AI

Find patient safety vulnerabilities before they reach production

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