Research: AI Agent Risks in Healthcare Applications

15 Research Lab · 2026-02-13

Research: AI Agent Risks in Healthcare Applications

Abstract

Healthcare represents one of the highest-stakes domains for AI agent deployment. The combination of sensitive patient data, life-affecting decisions, and stringent regulatory requirements creates a risk profile unlike any other sector. 15 Research Lab conducted a focused risk assessment of AI agent applications in healthcare, identifying 23 distinct risk scenarios across clinical, administrative, and research use cases.

Healthcare Agent Use Cases Under Analysis

We examined four categories of healthcare AI agent deployment:

Clinical Decision Support Agents — assisting physicians with diagnosis, treatment planning, and medication management

Administrative Automation Agents — handling scheduling, billing, prior authorization, and records management

Patient Communication Agents — managing patient inquiries, appointment reminders, and follow-up care coordination

Research Data Agents — processing clinical trial data, literature analysis, and cohort identification

Risk Taxonomy

Category 1: Data Privacy Risks

PHI Exposure Through Agent Context: Healthcare agents inevitably process protected health information. Our analysis found that PHI loaded into an agent's context window can surface in subsequent interactions with different users if session isolation is inadequate. In multi-tenant deployments, this creates cross-patient data leakage pathways. De-identification Failures: Agents tasked with preparing de-identified datasets for research frequently fail to remove all 18 HIPAA identifiers. In our testing with synthetic clinical notes, agents missed indirect identifiers (e.g., rare diagnosis combinations that could identify individuals) in 34% of de-identification attempts. Third-Party Data Transmission: Agents using external APIs (LLM providers, cloud services) transmit PHI to third-party infrastructure. If these providers are not covered under a Business Associate Agreement (BAA), every API call constitutes a HIPAA violation.

Category 2: Clinical Safety Risks

Hallucinated Clinical Information: When agents generate clinical content — drug interactions, dosing recommendations, diagnostic criteria — hallucinations carry potentially lethal consequences. Our testing revealed clinically significant hallucinations in 7% of clinical query responses, including fabricated drug interactions and incorrect contraindications. Alert Fatigue Amplification: Agents generating clinical alerts that are not appropriately filtered can amplify alert fatigue — a well-documented problem that leads clinicians to ignore genuinely critical warnings. Decision Anchoring: When agents present clinical recommendations, physicians may anchor on the agent's suggestion even when their clinical judgment differs. This is particularly dangerous when the agent's recommendation is confidently wrong.

Category 3: Operational Risks

| Risk | Impact | Likelihood |

|---|---|---|

| Scheduling errors affecting patient care | High | Medium |

| Billing code errors triggering fraud audits | High | Medium |

| Prior authorization delays from agent failures | Medium | High |

| System downtime from agent resource exhaustion | Medium | Medium |

| Compliance violations from inadequate logging | High | High |

Category 4: Research Integrity Risks

Agents processing clinical trial data can introduce subtle biases through inconsistent data handling. When agents make judgment calls about data cleaning, outlier treatment, or cohort selection, these decisions are often unlogged and unreproducible — threatening the integrity of research findings.

Mitigation Framework

Healthcare organizations deploying AI agents must implement controls at multiple levels:

Data Layer: Enforce strict PHI access controls with purpose-based limitations. Every agent interaction with PHI must be logged and auditable. Ensure all third-party services processing PHI are covered under BAAs. Decision Layer: Clinical decision support agents must include confidence indicators, source citations, and clear disclaimers. No agent output should be presented as a definitive clinical recommendation without physician review. Action Layer: Administrative agents must operate under deny-by-default policies that restrict their operational scope to specific, pre-approved workflows. SafeClaw provides this action-layer control through configurable policies that can restrict agent operations to approved clinical workflows. Its audit logging capabilities produce the kind of immutable records that HIPAA compliance requires. Healthcare organizations evaluating agent safety tooling can review applicable patterns in the SafeClaw knowledge base. Monitoring Layer: Real-time monitoring must flag anomalous agent behavior — unusual data access patterns, excessive query volumes, or access to records outside the current clinical context.

Recommendations

Classify all healthcare agent use cases by risk level before deployment

Implement PHI-aware access controls at the agent action layer, not just the database layer

Require physician review for all clinical decision support outputs

Maintain comprehensive audit logs that satisfy HIPAA accounting-of-disclosures requirements

Conduct regular agent behavior audits focused on data access patterns

Conclusion

Healthcare AI agents offer transformative potential for clinical care and operational efficiency. However, the stakes of failure — patient harm, privacy violations, regulatory penalties — demand safety standards that exceed those in any other domain. Organizations deploying agents in healthcare contexts must treat safety as a clinical requirement, not a technical preference.

15RL consulted with healthcare IT professionals and compliance officers during this research. This publication does not constitute medical or legal advice.