AI-Powered Features

AI-powered features using Google Gemini API to assist with risk assessment, call quality analysis, and automated summarization

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AI Transformation in Action
BEFORE (Raw CCE Note)
"9 month running. EDD-30th Sept. hai. Delivery ki tayari karneko bola hai. 30 tarikh hospital tayari ke sath jana hi hai. ye bataya. medicine chalu hai.baki thik hai."
AFTER (AI Supervisor Summary)
  • AI Risk Level: Medium
  • Key Risk Factors: Previous LSCS, Gestational Age 37w 6d
  • Supervisor Note: "Beneficiary is nearing her due date and has a history of previous LSCS. Ensure she is prepared for hospital admission and delivery as advised."

This example shows how AI transforms unstructured call notes into structured medical insights, enabling efficient case management and follow-up prioritization.

Status: AI features are fully implemented, operational, and active in the system, providing real-time assistance to call center agents. They are integrated into the call logging workflow, with risk assessment, quality analysis, and call summarization running automatically during call log saving when quality criteria are met (meaningful discussion content, successful connections). Supervisor summaries are generated automatically in the backend after office hours to avoid impacting call agent performance.

What is AI in Our System?

Our call center management system integrates Google Gemini AI to provide automated, intelligent assistance to healthcare workers. The AI automatically analyzes beneficiary data, call interactions, medical history, and IDC responses to generate risk assessments, quality analysis, and structured summaries that support clinical decision-making and care coordination.

Technology Stack
  • AI Model: Google Gemini 2.5 Flash Lite
  • Integration: Google Apps Script with Gemini API
  • Data Sources: Beneficiary records, call logs, medical history, IDC responses
  • Output: Structured JSON analysis, risk assessments, quality feedback, and automated summaries

🤖 Risk Assessment

AI analyzes beneficiary data to identify potential health risks and complications.

What it does:
  • Evaluates medical history, vital signs, obstetric data, and IDC responses
  • Identifies high-risk indicators based on clinical guidelines
  • Provides risk level classification (Low, Medium, High, Critical)
  • Suggests specific, actionable recommendations for follow-up
  • Includes urgency levels (Routine, Soon, Urgent, Emergency)
How it helps: Helps call center agents prioritize follow-ups and escalate urgent cases.
📊 Quality Analysis

AI reviews call content to assess interaction quality and completeness.

What it does:
  • Analyzes call discussion notes for completeness and structure
  • Checks if all required topics were covered
  • Evaluates communication effectiveness and protocol adherence
  • Provides detailed feedback with scores and improvement suggestions
  • Generates quality grades (A, B, C, D, F) and training recommendations
How it helps: Ensures consistent, comprehensive care delivery and identifies training needs.
📝 Call Summarization

AI generates structured summaries of call interactions.

What it does:
  • Extracts key information from call notes and beneficiary context
  • Creates concise, structured summaries in English
  • Highlights important medical updates and concerns
  • Identifies action items, follow-ups, and risk factors
  • Supports supervisor review with actionable insights
How it helps: Provides quick overviews for supervisors and maintains accurate records.

🎯 Improved Decision Making

AI provides data-driven insights to help agents make informed decisions about beneficiary care and follow-up prioritization.

⚡ Increased Efficiency

Automated analysis reduces manual review time and helps agents focus on direct patient interaction.

📈 Quality Assurance

Consistent analysis ensures all cases are evaluated against the same clinical standards and guidelines.

🔍 Early Risk Detection

AI can identify potential complications before they become critical, enabling preventive interventions.

📚 Knowledge Enhancement

Provides agents with additional insights and recommendations based on comprehensive medical knowledge.

📊 Data-Driven Insights

Converts raw data into actionable insights for program improvement and resource allocation.

Scenario 1: High-Risk Pregnancy Identification

Situation: A beneficiary reports symptoms that might indicate complications.

AI Role: Analyzes symptoms against medical history and provides risk assessment.

Outcome: Agent receives immediate guidance on whether to escalate to medical supervision.

Scenario 2: Call Quality Improvement

Situation: New agent conducting follow-up calls.

AI Role: Reviews call content and provides feedback on completeness.

Outcome: Agent learns to ask all necessary questions and provide comprehensive care.

Scenario 3: Documentation Enhancement

Situation: Complex case with multiple health issues discussed.

AI Role: Creates structured summary highlighting key points and action items.

Outcome: Supervisor can quickly understand case status and next steps.

Scenario 4: Resource Allocation

Situation: Program manager needs to prioritize cases.

AI Role: Provides risk scores for all active beneficiaries.

Outcome: Resources directed to highest-need cases first.

How AI Works in Our System
  1. Data Collection: AI receives comprehensive beneficiary data, call logs, medical history, and IDC responses
  2. Conditional Triggering: System checks for meaningful content (>10 characters) and successful call connections
  3. Asynchronous Processing: AI analysis runs in background to maintain smooth user experience
  4. Structured Analysis: Gemini AI processes information using medical knowledge to generate JSON-structured insights
  5. Result Integration: AI outputs are stored with call logs and displayed immediately in the interface
  6. Quality Assurance: Results include validation, error handling, and fallback mechanisms
Trigger Points
  • Real-time AI Analysis: Runs automatically during call log saving if all conditions are met:
    • Discussion text contains >10 characters
    • Call outcome is "Connected" (not invalid/unreachable)
    • Beneficiary is selected and data is available
  • After-hours Processing: Supervisor summaries are generated automatically in the backend after office hours to avoid impacting call agent performance
  • Manual Triggers: All AI features can be run manually using buttons in the call log form
  • Batch Processing: Bulk supervisor summary generation available for administrative use
  • Real-time Integration: AI results are displayed immediately in the call log interface
Performance Considerations
  • AI processing is fully asynchronous to prevent UI blocking
  • Intelligent conditional triggering prevents unnecessary API calls
  • Results are cached and integrated seamlessly with call logging
  • API costs optimized through content quality checks and batch processing
  • Error handling ensures system stability even if AI services are unavailable
  • Response times typically 3-10 seconds with graceful degradation

🤖 Not a Replacement for Clinical Judgment

AI provides supportive insights but cannot replace professional medical diagnosis or human expertise.

📊 Dependent on Data Quality

AI analysis accuracy relies on complete and accurate beneficiary data and call documentation.

🌐 Internet Connectivity Required

AI features require stable internet connection to communicate with Google services.

💰 API Costs

Each AI analysis incurs small API costs that should be monitored for large-scale deployment.

⏱️ Response Time

AI analysis typically takes 3-10 seconds and runs asynchronously to maintain smooth user experience.

🔒 Data Privacy

Medical data sent to external AI services must comply with privacy regulations and data protection policies.

Can AI replace human call center agents?

No, AI is designed to assist and enhance human decision-making, not replace it. Clinical judgment and empathy remain essential.

How accurate is the AI analysis?

AI provides supportive insights based on general medical knowledge and available data. Accuracy depends on data quality and should always be validated by healthcare professionals.

What happens if AI gives wrong advice?

Agents should use their clinical judgment and follow established protocols. AI recommendations are suggestions, not mandates.

Is beneficiary data secure when sent to AI?

Data is sent to Google Gemini API, which has enterprise-grade security. However, ensure compliance with local data protection regulations.

How much does AI cost to run?

Costs are minimal per analysis (typically <$0.01 per AI operation), with intelligent conditional triggering to avoid unnecessary API calls. The system only runs AI analysis when there is meaningful call content (>10 characters) and a successful connection, optimizing both cost and performance.

Can AI handle local languages or dialects?

Gemini AI supports multiple languages. If needed, we can configure it for local language processing.

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