AI Integration for Cority Injury Reporting

AI integration for Cority injury reporting focuses on three key surfaces: the initial incident report form, the OSHA recordability determination workflow, and the Form 301 (Injury and Illness Incident Report) data entry. The goal is to guide first-line supervisors through a structured, compliant reporting process at the point of entry. An AI agent can be triggered via a webhook when a new incident with a potential injury is created in Cority. This agent analyzes the free-text description, asks clarifying questions via a chat interface or dynamic form fields to fill gaps, and then suggests whether the incident meets OSHA's recordability criteria based on the extracted details (e.g., medical treatment beyond first aid, days away from work).

For implementation, the AI system acts as a co-pilot within the Cority user interface, not a replacement. It uses the Cority API to read incident objects and write back structured data to custom fields or notes. A common pattern is to deploy a lightweight microservice that hosts the AI logic (LLM calls, prompt management) and integrates with Cority's REST API. This service can auto-populate Form 301 fields like nature of injury, part of body affected, and event/exposure by parsing the supervisor's narrative. The impact is measured in reduced back-and-forth with safety personnel, fewer classification errors, and time saved—shifting data entry from a 15-20 minute manual process to a 2-3 minute review and confirmation.

Rollout requires a phased approach. Start with a pilot group of high-reporting sites, using the AI in 'assist' mode where its suggestions require supervisor confirmation. All AI interactions and data changes should be logged to a dedicated audit trail field in Cority for transparency and model improvement. Governance is critical: a safety professional must remain in the loop for final recordability decisions, and the AI's logic should be regularly validated against the latest OSHA interpretation letters. This ensures the integration enhances consistency and speed without compromising compliance rigor.

The integration is triggered when a new incident is logged in Cority with a potential injury flag. A serverless function (e.g., AWS Lambda, Azure Function) captures the initial report payload—including free-text descriptions, body part, and event type—and calls a configured LLM endpoint (OpenAI, Anthropic, or a private model). The AI's role is to act as a deterministic guide, not an autonomous decision-maker. It analyzes the narrative against OSHA's recordability criteria, asking clarifying questions if details are ambiguous (e.g., "Was there medical treatment beyond first aid?"). These questions are returned as a structured JSON payload and written back to a dedicated custom object in Cority, creating a supervisor guidance task.

For recordable cases, the AI auto-populates draft fields for the OSHA Form 301 (Injury and Illness Incident Report) within Cority. This includes generating a polished incident narrative from fragmented initial notes, suggesting a standardized injury/illness classification code, and mapping to the appropriate nature of injury and part of body fields. The draft is saved to the incident record with a clear "AI-Generated Draft" watermark. The system enforces a human-in-the-loop approval; the assigned supervisor must review, edit if necessary, and formally accept the AI's suggestions before the record is finalized. All AI interactions, prompts, and outputs are logged to a dedicated audit trail object linked to the incident for compliance and model improvement.

Rollout follows a phased, site-by-site approach, starting with a pilot group of supervisors. Governance is critical: a cross-functional team of EHS, IT, and Legal defines the guardrail rules embedded in the LLM system prompt (e.g., "Never diagnose a medical condition"). The integration is built on Cority's REST API and uses webhooks for real-time processing. For high-volume environments, a message queue (e.g., Amazon SQS) manages the flow to prevent API throttling. This architecture reduces manual data entry by 60-80% for recordable cases and ensures every injury determination follows a consistent, auditable process, directly strengthening your safety program's credibility.

A production AI integration for Cority injury reporting is built on a secure, auditable architecture. This typically involves a dedicated service layer that sits between Cority's APIs and the AI models. The service handles prompt construction, securely sends anonymized or de-identified narrative text (e.g., from the Initial Report or Description of Injury fields) to a chosen LLM provider like OpenAI or Azure OpenAI, and parses the structured response back into Cority objects like the OSHA Recordable Determination field or Form 301 details. All data exchanges are logged with full audit trails, and access is controlled via Cority's existing RBAC, ensuring only authorized supervisors or EHS personnel can trigger or view AI-generated content.

Rollout follows a phased, risk-managed approach. Phase 1 begins in a single plant or business unit, with AI acting as a suggestion engine for supervisors. The system might pre-populate the Body Part or Nature of Injury fields based on the incident description, but requires a human to review and confirm before saving. Phase 2 expands geographically and introduces more automation for low-risk, high-volume scenarios (e.g., clear-cut first aid cases), while maintaining a human-in-the-loop for all OSHA recordable determinations. Phase 3 enables broader automation, supported by continuous monitoring of the AI's determination accuracy against final, human-approved records to track performance and drift.

Governance is embedded in the workflow. Every AI-suggested field is tagged with its source, and a Human Review status is maintained. EHS leaders receive dashboards showing AI adoption rates, suggestion acceptance percentages, and any systematic discrepancies between AI and human judgments. This allows for prompt tuning, model retraining, and ensures the AI augments—rather than undermines—the organization's reporting standards and compliance posture. The final control always rests with the responsible supervisor, preserving accountability while reducing administrative burden and improving data consistency.