Collaborative Care Coordinator

The Collaborative Care Coordinator interface is designed to showcase how AI is integrated throughout the care coordination process.

1. AI-Assisted Treatment Conflicts Detection

   • The interface shows how AI proactively identifies potential medication conflicts between the patient's antihypertensive and diabetes medications
   • A warning banner appears with the detected conflict and suggested resolution

2. AI Treatment Recommendations

   • A dedicated section shows AI-generated treatment recommendations based on the patient's specific situation
   • Alternatives to problematic medications are suggested with reasoning

3. Natural Language AI Assistant

   • A search/query bar allows clinicians to ask questions about the patient in natural language
   • The AI Assistant appears in the team communications with insights about potential issues

4. Care Team Suggestions

   • AI analyzes the patient's condition and suggests additional specialists (like a Diabetic Educator) who should be added to the care team

5. Outcome Prediction Engine

   • AI-powered prediction of treatment outcomes with probability scoring
   • Patient population comparison showing how this patient's expected outcomes compare to similar demographic groups

6. AI-Generated Tasks

   • The system automatically creates and assigns high-priority tasks based on detected issues
   • AI-suggested tasks are visually highlighted to differentiate them from manually created tasks

7. Real-time Communication Monitoring

   • AI monitors care team communications to identify important clinical information
   • The system can flag important discussions that might require follow-up

This feature integrates these AI capabilities seamlessly into the clinical workflow, making them feel like a natural extension of the care coordination process rather than separate tools. The interface is organized into three columns to provide comprehensive care coordination functionality while maintaining clarity and reducing cognitive load.

This feature demonstrates how a multi-disciplinary care team can leverage AI to improve collaboration, detect potential issues early, and create more effective treatment plans based on evidence and predictive analytics.