AI and Workers' Compensation: Efficiency, Risk, and the Human Touch

How AI is changing workers' compensation claims

AI is moving from buzzword to workflow. In workers' compensation, it is cutting waste, surfacing insights, and giving legal teams faster visibility into medical and claims data-without replacing human judgment.

If you advise carriers, employers, or TPAs, the opportunity is clear: automate low-value tasks and improve decision quality. The risk is equally clear: privacy, accuracy, and professional responsibility.

Where AI adds value today

Underwriting automation: Carriers use AI to analyze historical loss data and exposure variables, assess risk, and set policy terms faster. The result is shorter cycle times and fewer manual handoffs.

Case management and document workflow: Tools handle data entry, classify and summarize records, and triage files. For medical-heavy matters, models can surface likely treatment paths and flag milestones like maximum medical improvement-subject to human review.

Risk management and prevention: By scanning unstructured data (claim notes, photos, videos, incident reports), AI can spot patterns and predictors of workplace injuries. That supports targeted safety interventions and fewer future claims.

Key challenges counsel must address

• Privacy and confidentiality: Workers' comp files include PHI and PII. Any system used for claim processing or risk analysis must meet HIPAA requirements, including access controls, audit logs, and secure storage. See the HHS HIPAA Privacy Rule.
• Open vs. closed systems: Open models can be used for general research, but do not feed them confidential client data. For live claims work, use closed or enterprise deployments that support encryption, logging, and data residency commitments.
• Security posture: Expect breach-prevention controls: role-based access, MFA, encryption at rest/in transit, DLP, redaction pipelines, and vendor security audits (SOC 2/ISO 27001). Require incident response SLAs.
• Accuracy and parameter control: AI can rely on irrelevant or stale inputs. Mitigate with retrieval from approved sources, low-variance generation settings, and required human validation. Document sampling parameters and data sources.
• Professional responsibility: Verify any legal analysis or citation. Record the basis for recommendations and ensure medically and legally material factors are considered. Monitor for bias and keep explanations sufficient for benefit decisions and appeals.
• Carrier and regulator acceptance: Some insurers restrict AI in claims handling. Align use with policy language, regulatory guidance, and internal claims-handling standards.

Practical checklist for legal teams

• Define approved use cases (e.g., intake summaries, medical record synopses, claim triage). Ban unapproved data flows.
• Select HIPAA-capable vendors; execute BAAs/DPAs; verify data residency and retention limits.
• Implement de-identification/redaction before model calls when feasible; restrict PHI access to need-to-know roles.
• Require human-in-the-loop review for any decision affecting coverage, compensability, treatment authorization, or settlement value.
• Set quality gates: benchmark accuracy, false-positive/negative rates, and citation validity on representative claims.
• Log prompts, sources, outputs, and reviewer sign-off in the claim file for auditability.
• Establish an incident and model-drift response plan; revalidate models after updates or data changes.
• Train attorneys, adjusters, and experts on proper use, limits, and confidentiality protocols.

Open vs. closed systems: quick guidance

• Use open systems for general research and brainstorming only-no client identifiers, no PHI/PII.
• Use closed/enterprise systems (or on-prem/private cloud) for claims work with PHI. Enforce encryption, audit trails, retention controls, and access reviews.
• Prefer APIs that let you pin sources (policies, medical guidelines) and keep data out of model training.
• Treat prompts and outputs as work product; preserve where relevant to the claim record.

Will AI replace workers' comp professionals?

No. Doctors still provide medical opinions. Attorneys still set strategy and ensure legal accuracy. Adjusters still exercise judgment on compensability, reserves, and settlement.

AI is a force multiplier: fewer clicks, faster files, better pattern detection. Keep people in control and the gains show up in cycle time, cost, and safety outcomes.

References and resources

• HIPAA Privacy Rule (HHS)
• NIST AI Risk Management Framework

If your team needs structured upskilling on safe, effective AI use by role, see AI courses by job.