AWS HealthScribe
Automatically create clinical notes from patient-clinician conversations using generative AIWhy AWS HealthScribe?
Transform the patient-clinician experience with conversational and generative AI
AWS HealthScribe is a HIPAA-eligible service empowering healthcare software vendors to build clinical applications that use speech recognition and generative AI to automatically generate preliminary clinical documentation.
Using a single API, AWS HealthScribe automatically identifies speaker roles, classifies dialogues, extracts medical terms, and generates rich preliminary clinical transcripts and notes. AWS HealthScribe removes the need to integrate and optimize separate AI services, allowing you to expedite implementation. Powered by Amazon Bedrock, AWS HealthScribe makes it faster and easier to integrate generative AI capabilities, without needing to manage the underlying machine learning (ML) infrastructure or train healthcare-specific large language models (LLMs).
How customers are automating clinical documentation with AWS HealthScribe
Benefits
Data on the clinical documentation burden
Features of AWS HealthScribe
AWS HealthScribe uses generative AI to transcribe and summarize patient-physician conversations—freeing up more time for patient care instead of documentation.
Use summarized clinical notes
AWS HealthScribe analyzes consultation conversations to generate summarized clinical notes for sections like chief complaint, history of present illness, assessment, and treatment plan. You can provide these summaries in your clinical application to help clinicians and medical scribes quickly recap highlights of patient visits and review, edit, and finalize notes for clinical documentation.
Extract structured medical terms
AWS HealthScribe extracts structured medical terms, such as medical conditions, medications, and treatment from the conversation transcript. You can use these medical terms in your clinical application to generate useful workflow suggestions for the user. Medical terms can be used to auto-suggest relevant reading material or entries for a drop-down form in your application.
Generate rich consultation transcripts
AWS HealthScribe provides a rich turn-by-turn transcript with word-level timestamps. AWS HealthScribe also identifies speaker roles, like patient and clinician, for each dialogue in the transcript.
Segment transcripts
AWS HealthScribe segments transcript dialogue into categories such as small talk, subjective, and objective, based on the dialogue’s clinical relevance. Categories can be subsequently used to help locate specific portions of the transcript.
Validate outputs with evidence mapping
Every sentence in the AI-generated clinical note includes references to the original consultation transcript, making it easier for users to validate the accuracy of the summary. Providing traceability and transparency for AI-generated insights is consistent with Responsible AI’s principle of explainability. Sharing references along with the summary notes to clinicians or medical scribes helps foster trust and encourages safe use of AI in clinical settings.
Eligible for HIPAA compliance
AWS HealthScribe is a HIPAA-eligible service that prioritizes security and privacy for patient data. The service does not retain inbound audio or output text, nor does it use your data to train AI models. You have full control to determine whether to store transcripts in local environments or self-managed cloud storage.
Use cases for AWS HealthScribe
Resources for AWS HealthScribe
Introducing AWS HealthScribe
Read more about AWS HealthScribe, a service that uses natural language processing to transcribe, analyze, and process medical conversations for improved clinical documentation and care delivery.
AWS Healthscribe demo
See AWS HealthScribe in action. Submit audio files, view the transcript and summarized clinical notes, and more.
Developer guide
Find detailed instructions for using AWS HealthScribe features and a complete API reference for developers.
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Learn how AWS HealthScribe automatically generates summarized clinical notes and transcripts from patient-clinician conversations.