Healthcare Complaint Processing System
Case Study Summary
Client: Confidential
Industry: Private healthcare
Role: AI Solution Architect
Impact Metrics:
- 70% reduction in manual complaint handling time
- 91% complaint categorization accuracy
- Top 5 complaint themes identified within the first week
- Unified five intake channels into one processing pipeline
- Automated anonymization before analysis to meet privacy requirements
I built an end-to-end complaint processing system that ingests text, voice, and handwritten submissions, normalizes and anonymizes them, and delivers clustered insights to a dashboard so private healthcare teams can prioritize responses quickly.
The Challenge
The entity received complaints from email, social-media platforms, voice, and handwritten letters, but every channel required different handling and manual triage. The team needed a single pipeline that could standardize content, protect personally identifiable information, and surface top themes without slowing response times.
The Solution
→ Implementation
I designed a staged pipeline (ingestion → normalization → anonymization → topic modeling → insights) on Google Cloud to keep processing consistent across formats. Cloud Functions orchestrated ingestion, Document AI handled OCR for handwritten letters, and Speech-to-Text converted voice messages into text. A small Vertex AI LLM standardized complaint language, clustered topics, and summarized themes for a lightweight web dashboard used by staff.
→ Solution Architecture
Architecture diagram placeholder per request.
→ Tech Stack
- Infrastructure: Google Cloud Platform
- Data Platform: Cloud Storage
- CI/CD: Cloud Build
- Containerization: Serverless Cloud Functions
- Backend: Cloud Functions, Cloud Run (dashboard API)
- Modeling: Vertex AI, Document AI, Speech-to-Text API
- Vector Index: Not used (topic clustering in Vertex AI)
Key Learnings
- Multichannel normalization: Standardized text, voice, and OCR outputs early to keep downstream logic simple.
- Privacy compliance: Stripped PII before any LLM processing to meet regulatory requirements.
- Cost control: Chose a small LLM in Vertex AI to balance accuracy with operating costs.
- Operational relevance: Tailored cluster summaries to the language used by frontline teams.
Measurable Impact
- 70% reduction in manual complaint handling time
- 91% complaint categorization accuracy
- Top five complaint themes identified within the first week
- Faster prioritization of response plans using cluster summaries
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