Industry Context
Healthcare organizations are increasingly looking for AI-driven solutions to improve patient engagement, clinical decision support, and operational efficiency. One of the critical areas is capturing and analyzing patient feedback in real time, particularly in native languages where off-the-shelf models are insufficient.
For Sonomed, operating in Türkiye and serving healthcare providers across the region, the ability to process patient feedback in Turkish with high accuracy is essential. Global AI services such as Amazon Transcribe or Polly provide generic speech-to-text (STT) and text-to-speech (TTS) capabilities, but they lack the linguistic nuances and medical domain specificity required for effective adoption in the Turkish healthcare ecosystem.
This context created the need for a customized AI architecture, capable of:
• Accurate speech recognition and synthesis in Turkish, optimized for medical terminology.
• Real-time feedback analysis using AI/ML pipelines tailored to local requirements.
• Secure data residency within AWS regions aligned with KVKK (Türkiye’s data protection law) and HIPAA compliance for handling sensitive health data.
Business Challenge
Sonomed set out to address a critical challenge faced by healthcare providers in Türkiye: capturing, analyzing, and responding to patient feedback in Turkish in real time. Existing solutions presented multiple limitations:
1. Language and Accuracy Barriers
o Global STT/TTS services (e.g., Transcribe, Polly) were not optimized for Turkish, especially for medical terms and patient-specific vocabulary.
o Misinterpretations in speech recognition created risks in accurately capturing patient sentiment.
2. Scalability of AI Workloads
o Handling large volumes of patient voice and text feedback required high-performance infrastructure.
o Event-driven architectures like AWS Lambda were not suitable due to heavy GPU inference needs.
3. Regulatory and Data Residency Requirements
o Compliance with KVKK (Türkiye’s Personal Data Protection Law) and HIPAA demanded that all patient data—including audio, transcripts, and derived insights—remain within approved AWS regions.
o Offloading inference to fully managed external APIs was not acceptable for sensitive healthcare workloads.
4. Operational Complexity
o Healthcare feedback flows included multiple steps: capturing patient voice, converting to text, classifying, generating responses, and optionally synthesizing speech output.
o Without a well-structured pipeline, latency and reliability issues would reduce system adoption by doctors and patients.
Sonomed needed a scalable, compliant, and locally optimized AI platform that could:
• Provide end-to-end patient feedback processing in Turkish.
• Run custom speech and NLP models on GPU infrastructure for high performance.
• Deliver real-time insights with secure data handling and integration into existing healthcare reporting systems.
Technical Challenge
Building an AI-driven patient feedback solution for the Turkish healthcare ecosystem required solving several complex technical problems:
1. Custom Speech Processing in Turkish
o Off-the-shelf STT/TTS models were inadequate for Turkish medical language.
o Sonomed needed domain-adapted speech-to-text and text-to-speech models, trained with Turkish datasets and deployed on GPU-based infrastructure.
2. High-Performance Inference
o Real-time transcription and feedback classification demanded low-latency GPU inference.
o Serverless compute was insufficient; the architecture required GPU-based EC2 Auto Scaling Groups running containerized models.
3. Retrieval-Augmented Generation (RAG)
o To provide context-aware feedback analysis, historical patient data had to be combined with real-time input.
o This required embedding generation, vector indexing in Amazon OpenSearch, and secure storage of raw data in Amazon S3.
4. Security and Compliance
o Data residency required that all inference and storage remain within the AWS Europe (eu-central-1) region.
o Integration with AWS KMS, IAM, and Secrets Manager was necessary for end-to-end encryption and access control.
5. Operational Observability
o A healthcare-grade system needed continuous monitoring.
o Integration with Amazon CloudWatch and OpenTelemetry was critical for logging, tracing, and alerting.
Solution
Sonomed implemented a GPU-accelerated, containerized AI platform on AWS:
1. Custom Turkish STT & TTS Models
o Models trained with Turkish medical speech corpora using Amazon SageMaker.
o Deployed as Docker containers on GPU-based EC2 instances (g5/g6 families)with optional Triton Inference Server.
2. Backend & NLP Pipeline
o All patient voice and text feedback processed via a backend service running on GPU EC2 Auto Scaling Groups.
o Natural Language Processing pipeline includes classification, summarization, and feedback tagging.
3. RAG Architecture
o Amazon S3 stores patient voice recordings, transcripts, and historical logs.
o SageMaker batch jobs generate embeddings.
o Amazon OpenSearch provides vector similarity search to retrieve context.
o Amazon Bedrock (optional) integrates LLMs for context-aware responses when supported models are available.
4. Security & Compliance
o IAM, KMS, and Secrets Manager enforce strict identity and key management.
o WAF + CloudFront + ACM secure the application edge.
o Aurora PostgreSQL stores metadata and reporting data in encrypted form.
o Data residency ensured by keeping inference and storage fully within eu-central-1.
5. Observability & Reliability
o Amazon CloudWatch and OpenTelemetry provide real-time monitoring.
o Logs archived in a dedicated S3 bucket for compliance and audit.
o Multi-AZ Aurora PostgreSQL and Redis (ElastiCache) provide resilience and high availability.
Outcomes
The new platform delivered measurable improvements:
• High accuracy in Turkish speech recognition → Custom STT models reduced transcription errors by >30% compared to generic services.
• Improved patient engagement → Real-time voice feedback converted into structured insights, enabling clinicians to act faster.
• Operational scalability → GPU-based EC2 Auto Scaling Groups allowed the system to handle peak loads during clinic hours.
• Regulatory compliance → All sensitive health data remained within AWS Frankfurt region, ensuring KVKK and HIPAA alignment.
• Reduced latency → End-to-end pipeline optimized to deliver responses in under 1 second for typical feedback flows.
Business Impact
• Enhanced Patient Experience
Patients could provide feedback in their native language with natural speech, leading to higher participation and more accurate sentiment capture.
• Empowered Healthcare Staff
Doctors and administrators received real-time structured insights, reducing the time spent on manual interpretation of patient comments.
• Competitive Differentiation
Sonomed positioned itself as a pioneer in AI-driven patient engagement for Turkish healthcare, with a fully localized solution.
• Scalable Business Model
The architecture enabled Sonomed to serve multiple hospitals and clinics simultaneously, paving the way for expansion beyond Türkiye.
Future Outlook
Looking ahead, Sonomed plans to extend the platform by:
• Expanding Language Support → Adapting STT/TTS models for other regional languages.
• Integration with EHR Systems → Seamlessly embedding patient feedback insights into electronic health records.
• Generative AI Extensions → Leveraging Bedrock-hosted LLMs for summarization and patient communication in a compliant, regionally hosted manner.
• Continuous Optimization → Fine-tuning models on growing datasets via SageMaker training pipelines for ever-improving accuracy.