Business Problem

1. Low QA Coverage

   • Only <3% of calls were being manually reviewed due to limited QA staff.
   • This created blind spots in identifying performance issues, compliance gaps, and training needs.

2. Regulatory Risk

   • The healthcare industry is tightly regulated by HIPAA.
   • Lack of full call monitoring risked non-compliance, which could result in legal action or fines.

3. Inconsistent Experience

   • Without insights from the remaining 97% of calls, patient experience varied and wasn’t measurable.
   • Leadership lacked actionable data to improve service quality or agent training.

Solution: Generative AI-Powered QA Agent

A Generative AI system was implemented to automate quality assurance across every call, ensuring real-time analysis and insights.

Core Capabilities:

• Transcription: Uses ASR (Automatic Speech Recognition) to convert speech to text in real-time.

• Summarization & Evaluation: LLMs analyze transcripts using healthcare-specific QA metrics:

  • Empathy (Did the agent express care?)
  • Accuracy (Was correct information given?)
  • Compliance (Were HIPAA and internal rules followed?)

• Risk Detection:

  • Flags HIPAA violations
  • Spots escalation triggers
  • Identifies training opportunities

• Agent Feedback:

  • o Custom coaching suggestions per interaction
  • o Helps agents improve based on real conversations

Architecture Overview

1. Data Input:

   • Real-time or recorded audio calls

2. ASR Layer:

   • Converts audio to text using:

     • Whisper (OpenAI): High-accuracy open model
     • AWS Transcribe Medical: HIPAA-compliant transcription tuned for medical terms

3. LLM Layer:

   • Custom fine-tuned model (e.g., GPT-4) does:

     • Checklist compliance scoring (Was HIPAA followed?)
     • Conversational quality scoring (How well did the agent handle the call?)
     • Empathy detection (Did the agent sound compassionate?)

4. Analytics:

   • Uses tools like Power BI or Looker to generate QA dashboards

   • Tracks metrics like:

     • Compliance trends
     • Agent performance
     • Common patient issues

5. Feedback Loop:

   • Weekly feedback emails to agents

   • AI-picked call snippets highlight good and bad moments for coaching

Outcomes

Key Differentiators

1. Healthcare-Specific Prompts

   • The LLM is trained on and prompted with domain-specific knowledge, such as:

     • HIPAA rules
     • Medical triage best practices
     • Empathy in clinical communication

2. Security-First Design

   • Uses zero-trust architecture
   • Full PHI (Protected Health Information) encryption at rest and in transit

3. Easy Integration

   • Plug-and-play with existing call systems like:

     • Genesys
     • Twilio
     • No need to change core telephony infrastructure