Navina

Navina is an artificial intelligence assistant designed specifically for clinicians and healthcare providers navigating the complexities of value-based care. By integrating into existing clinical workflows, the platform aims to reduce the heavy administrative burden often associated with medical documentation and patient chart reviews. It automatically synthesizes unstructured medical data, patient histories, and clinical notes into actionable, structured insights. This allows healthcare professionals to quickly capture a complete picture of a patient's health status at the point of care, ensuring more accurate documentation and better-informed clinical decisions. Ultimately, the tool helps medical practices optimize their clinical workflows, improve patient outcomes, and reduce physician burnout by minimizing the time spent on manual data entry and record retrieval. By streamlining these administrative tasks, clinicians can focus more of their attention on direct patient interaction and high-quality care delivery.

Key Features

  • Clinician-centric dashboard layout
  • Care check tracking analyzer
  • Code validation recommendation tool
  • Direct EHR database integrations
  • Executive healthcare analytic widgets

Use Cases

Use Case 1: Clinical Chart Synthesis

Problem: Doctors spend too much time reviewing chaotic patient history logs before visits.
Solution: Consolidates unstructured histories into one structured, clinical dashboard.
Example: A physician understands a patient's complex decade-long history in two minutes.

Use Case 2: Precise Care Gap Closing

Problem: Providers miss preventative care checks due to fragmented patient data.
Solution: Identifies missing diagnostic tests and suggests care opportunities automatically.
Example: The copilot alerts an internist that a diabetic patient's eye check is overdue.

Use Case 3: Risk Adjustment Accuracy

Problem: Inaccurate medical coding leads to compliance penalties and revenue loss.
Solution: Scans documents to suggest accurate, clinically-supported diagnostic codes.
Example: A clinic captures appropriate risk adjustment scores by verifying histories.

Target audience: Best for: Primary Care Physicians, ACO Directors, Health System Administrators

Pricing: Paid · Categories: Assistant, Healthcare, Productivity

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Tags: AI, assistant, healthcare, productivity, SmartAssistant

Visit Navina

What is Navina?

Navina is an AI-powered assistant designed for healthcare providers in value-based care. It integrates with EHR systems to synthesize unstructured patient charts, medical histories, and clinical notes into an accessible dashboard. This gives clinicians an immediate overview of patient health data, identifying care gaps and supporting accurate documentation at the point of care.

Who should use Navina?

Navina is intended for healthcare professionals managing value-based care workflows. Its core users include primary care physicians, internists, Accountable Care Organization directors, and health system administrators who need to review patient records quickly, monitor preventative care opportunities, and track clinical quality metrics across their medical practices.

What features does Navina include?

The platform offers direct EHR database integrations, a clinician-centric dashboard, and care check tracking analyzers to flag missing diagnostic screenings. It also includes a code validation recommendation tool that suggests accurate diagnostic codes from existing patient records, as well as executive healthcare analytic widgets for clinical performance review.

What is the pricing model for Navina?

Navina is offered under a paid pricing model. It is designed for medical groups, health systems, and healthcare organizations, requiring an enterprise agreement or commercial subscription. For specific tier details, contract options, and implementation quotes, organizations must contact Navina directly through their official website.

How does Navina assist with medical coding and risk adjustment?

Navina uses a code validation recommendation tool that scans unstructured historical records and clinical notes to identify documented conditions. By cross-referencing patient data, it highlights clinically supported diagnostic codes, helping providers maintain accurate risk adjustment scores and avoid compliance penalties caused by incomplete coding.

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