AI Integration for Cority Occupational Health

AI integration for Cority Occupational Health connects primarily to the Case Management and Health Surveillance modules. The core workflow involves ingesting unstructured data—such as physician notes, screening results (e.g., spirometry, audiometry), and exposure monitoring records—and using NLP to structure it into the appropriate Cority data objects like Medical_Case, Health_Surveillance_Requirement, and Employee_Health_Record. This automation reduces manual data entry from hours to minutes for occupational health nurses and physicians, ensuring critical health data is captured consistently and is immediately available for analysis and reporting.

A production implementation typically uses a secure middleware layer or a dedicated microservice that polls Cority's REST API for new records or listens for webhooks on key events, such as a new Incident linked to a potential illness. This service processes the incoming data through an LLM orchestration platform, where domain-specific prompts extract structured findings, suggest ICD-10 codes, and even draft initial fitness-for-duty recommendations. These AI-generated outputs are then written back to Cority as draft notes or tasks, flagged for human review and approval by a qualified health professional within the existing Cority workflow, maintaining necessary governance and audit trails.

Rollout should be phased, starting with a single, high-volume health surveillance program (e.g., hearing conservation). Key to success is configuring the AI's access with the same role-based permissions (RBAC) used in Cority, ensuring it only processes data for authorized employee populations. This integration doesn't replace clinical judgment but acts as a copilot, accelerating triage and allowing occupational health teams to focus on complex cases and proactive interventions, ultimately supporting faster case resolution and more robust compliance with health monitoring regulations like OSHA 29 CFR 1910.95.

The integration typically layers AI agents atop Cority's core Health Surveillance and Case Management modules. Agents are triggered via webhook from new medical screening results (e.g., audiograms, spirometry, blood lead levels) or upon the creation of a new occupational illness case. Using a secure API gateway, the agent retrieves the relevant employee record, exposure history, and previous test results from Cority. This context is packaged with a specialized prompt and sent to a governed LLM (like GPT-4 or Claude) for analysis. The LLM's task is to compare results against occupational exposure limits (OELs) and historical baselines, flag anomalies, and draft a fitness-for-duty recommendation or a case management next step (e.g., 'Schedule follow-up chest X-ray', 'Recommend temporary work restriction').

The AI's structured output—containing the analysis, confidence score, and recommendation—is posted back to a dedicated AI_Recommendation custom object within Cority via its REST API. This creates an auditable trail linked to the original health record. A configured workflow rule then routes this recommendation to the appropriate occupational health nurse or physician for review within their standard Cority dashboard. The clinician can approve, modify, or reject the AI's suggestion with one click, which updates the case and triggers any necessary downstream actions (appointment scheduling, work order generation). For high-confidence, low-risk administrative tasks—like sending standard follow-up instructions—the workflow can be configured for auto-approval.

Governance is enforced at multiple layers: RBAC ensures only authorized clinicians receive AI suggestions; all LLM interactions are logged with the employee ID and recommendation ID for audit; and a human-in-the-loop approval step is mandatory for any clinical decision. The architecture is deployed in the client's cloud environment (Azure, AWS) or a compliant Inference Systems managed VPC, ensuring health data never traverses unauthorized LLM endpoints. Rollout follows a phased pilot, starting with a single health surveillance program (e.g., hearing conservation) to validate accuracy and workflow efficiency before expanding to more complex case management scenarios.

Governance starts with data access and model selection. AI agents must operate within strict RBAC (Role-Based Access Control) boundaries, mirroring Cority's existing permissions for Health Surveillance Cases, Medical Records, and Fitness-for-Duty Assessments. This ensures a nurse case manager's AI copilot cannot access an employee's full medical history without proper clearance. All AI-generated recommendations—like a suggested case classification or follow-up action—should be logged as a system note with a clear audit trail, linking the prompt, the source data (e.g., spirometry results, symptom logs), and the user who approved or overrode the suggestion.

A phased rollout mitigates operational risk and allows for tuning. Phase 1 (Assistive Drafting) might deploy AI to summarize lengthy physician notes or draft standard correspondence for case closure, with all outputs requiring human review and sign-off within the Cority workflow. Phase 2 (Analytical Triage) could introduce AI to analyze trends across health surveillance data (e.g., identifying clusters of abnormal hearing tests by department) and surface prioritized review lists for the occupational health team. Phase 3 (Prescriptive Guidance) would cautiously introduce AI-suggested next steps, such as recommending a specific type of follow-up examination based on symptom patterns and exposure history, always presented as a draft for clinician validation.

Security is non-negotiable. The integration architecture should ensure Cority remains the system of record. AI models are called via secure APIs; no protected health information (PHI) or personally identifiable information (PII) is stored in external vector databases without explicit, audited consent and encryption. Inference Systems implements this using private, dedicated inference endpoints and prompt engineering techniques that minimize data exposure. A final governance layer is a regular review cadence, where occupational health leadership, legal, and compliance review AI-assisted outcomes for accuracy, bias, and clinical appropriateness, using Cority's reporting tools to audit decision patterns and ensure the integration supports, rather than undermines, professional medical judgment.