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Design a Secure Medical Audio Pipeline

AbridgeSoftware Engineer · Technical Screen
MediumSoftware EngineerTechnical ScreenSystem Design

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This problem tests your ability to design a secure, HIPAA-compliant audio processing pipeline that handles sensitive PHI (Protected Health Information) while ensuring scalability and reliability.

Scenario

You are tasked with designing a backend system for a healthcare application that receives, transcribes, and stores audio recordings from medical consultations. Physicians record audio via a mobile app, which must be uploaded, transcribed using a third-party Speech-to-Text (STT) service, and made available for clinical review.

Requirements

  1. Security & Compliance: The system must adhere to HIPAA standards, meaning all data must be encrypted at rest and in transit. Access to audio and transcripts must be strictly controlled and audited.
  2. Scalability: The system should support up to 5,000 concurrent uploads during peak clinic hours.
  3. Reliability: If the third-party STT service is down or fails to process a file, the system must retry gracefully without losing data.
  4. Data Privacy: Raw audio should be deleted after a retention period, while transcripts are stored indefinitely.

Your Task

Design a high-level architecture for this system. Your response should address:

  • Data Flow: How does an audio file move from the client to the transcript database?
  • Security Architecture: Describe your approach to encryption, identity management, and PHI segregation.
  • Asynchronous Processing: How will you manage the long-running transcription tasks to prevent system bottlenecks?
  • API Design: Define the basic endpoints for uploading, status checking, and retrieval.

Constraints

  • Assume the mobile client has intermittent connectivity.
  • Use a standard cloud environment (e.g., AWS/GCP) approach.

Hints

Hint 1

Consider using an object storage service with pre-signed URLs to offload heavy file uploads from your primary application server.

Hint 2

Utilize a message queue (e.g., SQS/RabbitMQ) to decouple the request ingestion from the asynchronous transcription job.

Hint 3

Think about the lifecycle of the data: how to ensure PHI is not leaked in application logs or monitoring metrics.

Follow-up questions

  • How would you handle a situation where a physician needs to re-process an audio file because the initial transcript was inaccurate?
  • What specific mechanisms would you implement to ensure that an unauthorized user cannot access audio files even if they guess a file ID?
  • How would you monitor the system for 'silent' failures, such as the transcription service returning low-confidence scores?