Privacy-Preserving AI Call Transcription & Analytics in Healthcare

The Challenge

A healthcare institute was recording thousands of patient and support calls monthly but had no scalable way to extract operational insights from them. The core challenge was not just technical — it was regulatory. Healthcare conversations contain deeply sensitive data including patient names, medical record numbers, health conditions, Medicare details, and contact information. Traditional analytics approaches posed unacceptable compliance risks under Australian Privacy Principles (APPs) and healthcare data protection standards. The provider needed a solution that could deliver actionable intelligence from call data while ensuring that patient privacy was protected by design — not as a bolted-on afterthought.

Our Approach

Corlence architected and delivered a privacy-first AI analytics pipeline with four purpose-built layers designed for healthcare-grade data sensitivity.

The high-accuracy transcription layer used Microsoft Azure Speech-to-Text APIs configured and fine-tuned for healthcare-specific terminology. The system achieved consistently high accuracy across diverse Australian accents and specialised medical vocabularies, supporting both real-time and batch transcription modes depending on operational need.

Before any AI model processed the data, a dedicated automated anonymisation layer using Named Entity Recognition (NER) and custom privacy filters automatically redacted all personally identifiable information. Patient names, phone numbers, Medicare details, medical record identifiers, and other PII were systematically removed — ensuring every downstream analysis operated on fully de-identified data. This compliance-by-design approach was fundamental to the architecture.

The AI-powered analytics layer applied generative AI and NLP models to the anonymised transcripts, performing conversation summarisation, sentiment analysis, and topic extraction. Healthcare administrators could rapidly identify trending patient concerns, recurring service quality issues, and operational bottlenecks — insights that previously took weeks of manual call review to surface.

A secure web dashboard provided administrators with access to anonymised transcripts, trend visualisations, and exportable reports — all hosted within compliant Azure cloud environments with end-to-end encryption at rest and in transit, plus comprehensive audit logging for regulatory accountability.

Technology Stack

Microsoft Azure Speech-to-Text, Python, FastAPI, Hugging Face Transformers, OpenAI GPT, LangChain, Azure Secure Storage

Business Value Delivered

• 100% of transcripts automatically anonymised before analysis, completely eliminating manual redaction effort and associated human error
• Call review and quality assurance time reduced by approximately 70%, freeing clinical administrators for higher-value activities
• Emerging patient concern trends identified weeks earlier than previous manual review processes, enabling proactive service improvements
• Full regulatory audit trail maintained with explainable AI outputs, satisfying compliance requirements under Australian Privacy Principles