Generating Insights From Doctor Consultations Using AWS Transcribe and Medical Comprehend

The healthcare industry may be a highly regulated industry where an excellent deal of data exchange occurs via verbal communication. This audio data can contain valuable information and actionable insights. This post explores the way to integrate HIPAA-eligible AWS AI services AWS Transcribe and Medical Comprehend to store and identify insights during this data. Automating medical data extraction and comprehension helps healthcare professionals specialise in patient care.

Amazon Transcribe Medical

Amazon Transcribe Medical may be a machine learning (ML) service that creates it easy to quickly create accurate transcriptions from medical consultations between patients and physicians. Amazon Transcribe Medical automatically converts the medical and pharmacological terms utilized in physician-dictated notes, practitioner and patient consultations, and tele-medicine from speech to text to be used in clinical documentation applications.

Transcribe Medical offers an easy-to-use streaming API that integrates into any voice-enabled application and works with virtually any device that features a microphone. The service is meant to transcribe medical speech for medical care and may be deployed at scale across thousands of healthcare providers to supply affordable, consistent, secure, and accurate notetaking for clinical staff and facilities. Additional features like automatic punctuation and intelligent punctuation enable you to talk naturally, without having to vocalize awkward and explicit punctuation commands as a part of the discussion, like “add comma” or “exclamation point.” Moreover, the service supports both medical dictation and conversational transcription.

Amazon Comprehend Medical

Amazon Comprehend Medical allows developers to spot the key common sorts of medical information automatically, with high accuracy, and without the necessity for giant numbers of custom rules. Comprehend Medical can identify medical conditions, anatomic terms, medications, details of medical tests, treatments and procedures. Ultimately, this richness of data could also be ready to at some point help consumers with managing their own health, including medication management, proactively scheduling care visits, or empowering them to form informed decisions about their health and eligibility.

There are not any servers to provision or manage — developers only got to provide unstructured medical text to grasp Medical. The service will “read” the text then identify and return the medical information contained within it. Comprehend medical also will highlight protected health information (PHI). There are not any models to coach and no ML experience is required. And no data processed by the service is stored or used for training. Through the Comprehend Medical API, these new capabilities are often integrated with existing services and health systems easily. The service is additionally covered under AWS’s HIPAA eligibility and BAA.

Unlocking this information from medical language makes a spread of common medical use cases easier and cost-effective, including: clinical decision support (e.g., getting a historical snapshot of a patient’s medical history), revenue cycle management (e.g., simplifying the time-intensive manual process of knowledge entry), clinical test management (e.g., by identifying and recruiting patients with certain attributes into clinical trials), building population health platforms, and helping address (PHI) requirements (e.g., for privacy and security assurance.)

Architecture of Telemedicine Process

Process Flow

The user makes a call, at the given appointment by the medical practitioner. The process of transcribing the call i.e., conversion of Speech-to-text starts as the call ends.

The file is stored in the S3 bucket, with a unique id, to create a format where every call is treated as a new incoming file.

Lambda is a powerful tool that we can use to do calculations and perform any type of analytics. It can interact with other AWS services like sage maker, transcribe and comprehend to give intelligent responses.

Here is the excerpt from the code which invokes the Medical Transcribe function for the speech to text conversion.

response = s3.get_object(Bucket = bucket, Key = key)
stream = response[‘Body’].read()
job_url = ‘https://’+bucket+’’+key
ts = transcribe.start_medical_transcription_job(
MedicalTranscriptionJobName= job_name,
‘MediaFileUri’: job_url
OutputBucketName= bucket,
‘ShowSpeakerLabels’: True,
‘MaxSpeakerLabels’: 2,

The output from the Medical Transcribe is then stored in the S3 bucket. A little example of the output in text format:

uh, if this
Miss Stones,
Yes, it’s Ms John speaking. How can I help you? Oh,
hi. Um, so I’m so glad that you’re finally online. Ah, Mr Ones. You know, I just called the doctor’s office with the question, and I hope probably you can answer me,
uh, being seeing, you know. Ah, Doctor for the past 34 months. Uh, unfortunately, she starred in the office. You to some family emergency or something, but I want to update that, You know, my headaches are still the same.
So not sure what’s going on with me. I have a pounding headache, some days on the left side,
and sometimes it’s like my whole skull is paining sometimes, like the back of my head is spinning. So I’m just wondering, you know, water days. I’ve been taking over the counter medications, but all helping me.
Okay, First of all, let me get your name so I can pull off the records so I can just go through it, so Okay, Still be a name, please.
You’re My name is, uh, James Smith.

The stored conversion is grabbed on by the lambda function, and now invokes the MedicalComprehend function.

The output from the MedicalComprehend function.


This post reviewed how the use of AWS Medical Transcribe and AWS Comprehend helps the medical practitioner in reviewing the information and gives out information which is useful in diagnosing the patients, without the inherent hectic nature of the work.

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