Tag Archives: Amazon Translate

AWS expands HIPAA eligible machine learning services for healthcare customers

AWS HealthcareAWS announced that Amazon Translate, Amazon Comprehend, and Amazon Transcribe are now U.S. Health Insurance Portability and Accountability Act of 1996 (HIPAA) eligible services.

This announcement adds to the number of AWS artificial intelligence services that are already HIPAA eligible– Amazon Polly, Amazon SageMaker, and Amazon Rekognition. By using these services, AWS customers in the healthcare industry can leverage data insights to deliver better outcomes for providers and patients using the power of machine learning (ML).

To support our healthcare customers, AWS HIPAA eligible services enable covered entities and their business associates subject to HIPAA to use the secure AWS environment to process, maintain, and store protected health information. Healthcare companies like NextGen Healthcare, Omada Health, Verge Health, and Orion Health are already running HIPAA workloads on AWS to analyze numerous patient records.

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The addition of Amazon Translate, Amazon Transcribe, and Amazon Comprehend to the list of HIPAA eligible services will allow customers to leverage these AWS ML services to better streamline customer support and improve patient engagement. Customers can use these three services to leverage the following ML capabilities:

Amazon Transcribe: A speech-to-text service that automatically creates text transcripts from audio files will allow healthcare organizations to create text transcripts calls with patients.
Amazon Translate: A neural machine translation service that delivers fast, high-quality, and affordable language translation. This service can be employed to easily translate large volumes of text efficiently and enable patients to chat with their healthcare provider in their preferred language.
Amazon Comprehend: A natural language processing (NLP) service that can find insights and relationships in unstructured text. It can analyze sentiment (e.g., negative, positive, and neutral), and extract key phrases from patient interactions to better understand and improve engagement.

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