Augmenting clinical incident analysis through GenAI agents

Despite robust incident reporting, the healthcare provider we partnered with had a traditional process of analysis that failed to generate actionable insightsDespite robust incident reporting, the healthcare provider we partnered with had a traditional process of analysis that failed to generate actionable insights at scale. Incident reviews relied on manual interpretation, with limited standardization across teams. This led to three main challenges:

• Inconsistent classification of incidents and actions
• Delays in identifying high-severity cases
• Lack of visibility into recurring problems and missed learning opportunities

“Thousands of incidents were being logged, but there was no scalable way to make sense of them all. Insights were getting lost in unstructured narratives.”

To improve safety outcomes and operational efficiency, a new solution was needed—one that could automate the extraction of meaning from incident data while supporting expert validation and analysis.

We co-developed an advanced Generative AIplatform that transforms how incidents are processed. The solution automateskey tasks in the incident analysis workflow while enhancing transparency andexplainability.

Core functionalities include:

• Incident Classification: Each incident is classified into a specific type based on unstructured descriptions provided by the reporter on the incident registration. The model also evaluates how complete the original description is, the certainty of the classification, and provides the rationale for its decision. Incidents can even be reclassified, therefore correcting what the reporter filled in the incident registration.
• Critical Event Identification: The system assesses each case for its likelihood of being a severe event, based on standardized clinical procedures.
• Action Plan Categorization: Incidents are often closed without action plans when, in fact, an action plan is devised and implemented, due to bureaucratic and processual reasons, resulting in a lack of visibility and skipping steps in the process flow. Now, both explicit and hidden action plans are extracted and categorized. If an action plan is embedded in the analysis text but not logged separately, the AI detects and classifies it.

Explainability and Certainty Scores: All outputs include reasoning fields and certainty scores (1–5 scale), providing context and confidence in the automated suggestions.

“Instead of black-box predictions, the systems hows its work. It doesn't replace human oversight—it enhances it.”

The solution runs on a scalable cloud-based architecture, integrated with daily data pipelines. 

Key components include:

• Automatic definition of the types of incidents and categories of action plans, based on their similarity across several events, followed by Human-in-the-loop (HITL) clinical validation
• A GenAI pipeline that processes new and modified incident records in batches
• Structured prompts tailored by incident type, guiding the AI in classification, explanation, and triage
• A visual interface where incident data is enriched with GenAI-generated fields, enabling side-by-side analysis of human-filled data and AI-generated information

With this GenAI-powered platform, the organization moved from reactive incident handling to proactive and preventive risk management.

Tangible outcomes include:

• A significant reduction in time spent manually analysing incidents and action plans
• Daily triage of new events, identifying potentially critical/severe cases automatically
• Discovery of previously unregistered action plans, enabling more complete operational insights
• Standardization of classifications, improving comparability, and the identification of common trends and root causes across the organization
• Stronger cross-team coordination, with unified views of incident patterns over time and location

“With structure, scale, and AI explainability, the solution turned complexity into clarity—helping teams focus on what really matters.”

The platform has become a foundational tool for enhancing safety and operational excellence. By advancing how incident data is processed and utilized, the organization strengthens its decision-making with deeper insights and greater confidence.