Real-Time Medical Dictation to Text for Electronic Health Records

Question

Pulumi · Accepted Answer

To accomplish real-time medical dictation to text, we can utilize cloud services that provide speech-to-text capabilities, particularly services that have been trained on medical terminology. Amazon Web Services (AWS) offers such capabilities through its Amazon Transcribe Medical service, part of AWS Transcribe, designed to convert speech to text and cater specifically to medical professionals.

In this scenario, we are going to build a cloud infrastructure that continuously processes audio streams from medical dictation and converts them into text, which can then be integrated into Electronic Health Record (EHR) systems.

We'll use the following AWS resources to create this solution:

1. **Amazon Transcribe Medical**: Takes in live audio and converts it to text with high accuracy, recognizing medical terms. We will use this service to transcribe medical dictation in real-time.
   
2. **Amazon S3**: Stores the text output from the transcription. Transcribed text files will be saved in an S3 bucket, where they can be retrieved and integrated into EHR systems.

3. **AWS Lambda**: This serverless compute service can process the output of Transcribe Medical and then perform post-processing, such as pushing the text into EHR systems.

4. **Amazon API Gateway**: Provides a RESTful API endpoint that medical dictation devices or applications can call to start streaming audio data to Transcribe Medical.

Below is a high-level Pulumi code in Python that provisions these services and wires them together to create the real-time medical dictation to text solution for EHRs:

```python
import pulumi
import pulumi_aws as aws

# Create an S3 bucket to store the transcribed text files
transcripts_bucket = aws.s3.Bucket("transcripts-bucket")

# Define the IAM role which will be used by AWS Lambda function
lambda_role = aws.iam.Role("lambda-role",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Action": "sts:AssumeRole",
            "Effect": "Allow",
            "Principal": {
                "Service": "lambda.amazonaws.com"
            }
        }]
    }""")

# Attach the AWSLambdaBasicExecutionRole policy to the IAM role
lambda_policy_attachment = aws.iam.RolePolicyAttachment("lambda-policy-attachment",
    role=lambda_role.name,
    policy_arn=aws.iam.AmazonTranscribeFullAccess)

# Create a Lambda function that is invoked when new transcription job finishes
transcribe_processor = aws.lambda_.Function("transcribe-processor",
    code=pulumi.AssetArchive({
        '.': pulumi.FileArchive('./transcribe_processor')
    }),
    role=lambda_role.arn,
    handler="lambda_function.lambda_handler",
    runtime="python3.8",
    environment=aws.lambda_.FunctionEnvironmentArgs(
        variables={
            "S3_BUCKET": transcripts_bucket.bucket
        },
    ))

# Create an API Gateway to receive and handle requests
api_gateway = aws.apigatewayv2.Api("api-gw",
    protocol_type="HTTP")

# The Lambda integration for the API Gateway
integration = aws.apigatewayv2.Integration("lambda-integration",
    api_id=api_gateway.id,
    integration_type="AWS_PROXY",
    integration_uri=transcribe_processor.invoke_arn)

# Define the route for the API Gateway
route = aws.apigatewayv2.Route("api-route",
    api_id=api_gateway.id,
    route_key="POST /transcribe",
    target=pulumi.Output.concat("integrations/", integration.id))

# Deploy the API Gateway
deployment = aws.apigatewayv2.Deployment("api-gw-deployment",
    api_id=api_gateway.id,
    lifecycle={
        "ignore_changes": ["triggers"],
    })

# Links the deployment to a stage which captures all of the settings for deployment.
stage = aws.apigatewayv2.Stage("api-gw-stage",
    api_id=api_gateway.id,
    deployment_id=deployment.id,
    name="v1")

# Export the API endpoint URL
pulumi.export("api_url", api_gateway.api_endpoint)

```

### Explanation

- **S3 Bucket**: We create an S3 bucket to store the transcripts created by Amazon Transcribe Medical. It's the destination for the transcribed text files.

- **IAM Role and Policy Attachment**: We set up an IAM role that AWS Lambda will assume to perform actions on our behalf. Specifically, the role needs permission to access Amazon Transcribe.

- **Lambda Function**: We define a Lambda function that processes the output from Amazon Transcribe Medical. It's triggered when a new transcription job is finished. This function will likely have the logic to post-process the transcript, such as adding it to an EHR.

- **Amazon API Gateway**: We establish an API endpoint that external services can call to start a transcription job with Amazon Transcribe Medical. The device (or application) used for medical dictation will send audio streams to this endpoint.

- **API Gateway Integration and Route**: We create an integration between our API Gateway and Lambda function, along with the necessary routes. This setup directs the requests from the API endpoint to the Lambda function.

- **API Gateway Deployment and Stage**: We then deploy the API Gateway and link it to a stage, effectively making the API live and accessible.

This program sets up the necessary cloud infrastructure with Pulumi to perform real-time medical dictation transcription and output the text for use in EHR systems. This is a foundational setup, and custom logic would likely be needed in the Lambda function to integrate with specific EHR systems or to handle the format of the transcribed text according to EHR requirements. Additionally, you would need to implement the raw audio streaming part that captures dictation and sends it to the API, which would likely involve further development work on the dictation device or application.