Navina

Automatically collects and consolidates patient health records from multiple disparate EHR systems into a unified data view. Eliminates manual chart-switching and data hunting across fragmented healthcare IT infrastructure.

Uses AI to identify clinically significant patterns, correlations, and anomalies in aggregated patient data that might be missed by manual review. Surfaces insights relevant to diagnosis, treatment planning, and patient risk stratification.

Automatically identifies and highlights the most clinically relevant information from a patient's complete medical record based on context and clinical relevance. Reduces cognitive load by filtering noise and surfacing what matters most for the current clinical encounter.

Acts as a translation and integration layer between incompatible EHR systems and healthcare data formats. Enables seamless data flow across healthcare IT infrastructure that would otherwise remain siloed.

Provides AI-driven clinical recommendations and decision support based on aggregated patient data, clinical guidelines, and pattern recognition. Assists clinicians in making evidence-based decisions while maintaining human oversight.

Automatically generates or assists in creating clinical documentation by summarizing relevant patient information and clinical events. Reduces manual documentation burden while maintaining accuracy and completeness.

Monitors aggregated patient data in real-time to detect potential safety issues, adverse events, drug interactions, and clinical red flags. Alerts clinicians to patient safety concerns that might otherwise be missed.