Which solution will meet these requirements with the LEAST operational overhead?
Use existing Python libraries to extract the text from the reports and to identify the PHI from the extracted text.
Use Amazon Textract to extract the text from the reports. Use Amazon SageMaker to identify the PHI from the extracted text.
Use Amazon Textract to extract the text from the reports. Use Amazon Comprehend Medical to identify the PHI from the extracted text.
Use Amazon Rekognition to extract the text from the reports. Use Amazon Comprehend Medical to identify the PHI from the extracted text.
Explanations:
While existing Python libraries can be used to extract text and identify PHI, this approach may require significant operational overhead for development, maintenance, and scaling, as well as ensuring compliance with PHI regulations.
Although Amazon Textract is suitable for extracting text from reports, using Amazon SageMaker for PHI identification introduces unnecessary complexity and overhead, as it requires custom model training and deployment.
This option utilizes Amazon Textract for text extraction and Amazon Comprehend Medical for PHI identification. Both services are managed, reducing operational overhead and ensuring compliance with health information regulations, making it the most efficient solution.
Amazon Rekognition is primarily designed for image analysis, not text extraction from documents. This makes it unsuitable for extracting text from reports in PDF or JPEG format, leading to incorrect identification of PHI.
I estimate that the answer is:
Use Amazon Textract to extract the text from the reports. Use Amazon Comprehend Medical to identify the PHI from the extracted text.
I opine that the answer is:
Use Amazon Textract to extract the text from the reports. Use Amazon Comprehend Medical to identify the PHI from the extracted text.