AWS, Cloud Computing

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Automated Clinical Notes Generation with AWS HealthScribe

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Overview

Developers creating healthcare applications can incorporate AWS HealthScribe into their clinical software, enhancing their ability to deliver crucial visit highlights to medical professionals.
Example: 3M, ScribeEMR, and Babylon. In this blog, we introduced AWS HealthScribe. This HIPAA-eligible machine learning feature leverages speech recognition and generative AI to transcribe patient-clinician conversations and produce efficient clinical notes for healthcare applications.

Introduction

AWS HealthScribe represents a fresh HIPAA-eligible machine learning (ML) feature that merges speech recognition with generative AI. This fusion is designed to transcribe discussions between patients and clinicians while producing readily understandable clinical notes for efficient review.

AWS offers advanced functionalities that empower the construction of high-performance, population-scale applications capable of storing, converting, and retrieving healthcare and life science data. Through tailor-made features, users can effectively oversee and extract insights from diverse healthcare and scientific data categories, encompassing clinical records, genomic and various omics data, medical imagery, and unstructured medical text and speech.

AWS HealthScribe integrates the functionalities, eliminating the necessity for integrating and fine-tuning distinct AI services. This capability allows you to accelerate the implementation process.

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Use Cases

  • Accelerated documentation time – Facilitate clinicians in swiftly concluding clinical documentation using AI-generated clinical notes that are easily reviewable, adaptable, and ready for finalization within your application.
  • Enhanced efficiency for medical scribes – Provide medical scribes with AI-generated transcripts and clinical notes, accompanied by consultation audio, to expedite the documentation turnaround time.
  • Streamlined patient visit summaries – Create an interface enabling users to rapidly recapitulate vital conversational highlights within your application.

At present, AWS HealthScribe offers its support for the fields of General Medicine and Orthopedics.

JSON output files

An AWS HealthScribe job analyzes medical consultation to produce two JSON output files:

A Transcript file and a Documentation Insights file

1. Transcript file

In the Transcript file, AWS HealthScribe provides with

  • Participant role identification allows differentiation between patients and clinicians within the conversation transcript.
  • Transcript segmentation involves organizing dialogue sections within the transcript according to their clinical significance, such as casual conversation, subjective information, objective information, and more. This segmentation can be utilized to display specific segments of the transcript.
  • Clinical entities encompass structured data like medications, medical conditions, and treatments referenced in the conversation.

2. Documentation Insights file

  • The summary contains concise notes for significant portions of clinical documentation, including Chief Complaint, History of Present Illness, Assessment, and Plan.
  • Evidence mapping establishes connections between each sentence within the AI-generated note summary and its source in the original consultation transcript. This facilitates users in verifying the accuracy of the summary within your application.

Initiating an AWS HealthScribe Job

AWS HealthScribe can be started with AWS CLI or AWS SDK

AWS CLI

Example:
With a request body and additional settings

First-medical-scribe-job.json file contains the below request body

AWS SDK – Python (Boto3)

The AWS SDK for Python (Boto3) is used in the following example to start a start_medical_scribe_job request.

Alongside a transcript, StartMedicalScribeJob requests also produce a distinct JSON-formatted documentation insights file. Both files are stored in the output location specified in your request.

  • Transcript output

An AWS HealthScribe transcript file has the below format:

  • Documentation insights output

A documentation insights file has the following format:

Conclusion

AWS HealthScribe generates and divides detailed conversation transcripts, distinguishes between patient and clinician speakers, extracts medical terminology, and creates initial clinical notes. By combining these functions, AWS HealthScribe streamlines implementation, lessening the necessity for integrating and enhancing individual AI services. The platform encourages responsible AI adoption for healthcare software vendors by linking AI-generated clinical notes to the original patient transcripts for each sentence. Security and privacy measures are inherently integrated into AWS HealthScribe, safeguarding sensitive patient data.

Drop a query if you have any questions regarding AWS HealthScribe and we will get back to you quickly.

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FAQs

1. How does AWS HealthScribe collaborate with various other AWS services?

ANS: – Amazon Simple Storage Service (Amazon S3) can be employed to save both the input (audio files) and output (transcription and notes text files) produced by AWS HealthScribe. AWS HealthScribe leverages Amazon Bedrock’s extensive language model functionalities alongside Amazon Comprehend Medical and Amazon Transcribe Medical to create refined clinical notes through comprehension of patient-clinician interactions.

2. Does AWS HealthScribe provide support for all medical specialties?

ANS: – AWS HealthScribe is fine-tuned for the domains of general medicine and orthopedics.

WRITTEN BY Abhilasha D

Abhilasha D is a Research Associate-DevOps at CloudThat. She is focused on gaining knowledge of Cloud environment and DevOps tools. She has keen interest in learning and researching on emerging technologies.

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