What is Amazon Comprehend Medical?

Patient case management and outcome— Doctors and healthcare providers can manage and easily access medical information that doesn’t fit into traditional forms. Patients can report their health concerns in a narrative with more information than standard formats. By analyzing case notes, providers can identify candidates for early screening of medical conditions before the condition becomes more difficult and expensive to treat.

Clinical research—Life sciences and research organizations can optimize the matching process for enrolling patients into clinical trials. By using Amazon Comprehend Medical to detect pertinent information in clinical text, researchers can improve pharmacovigilance, perform post-market surveillance to monitor adverse drug events, and assess therapeutic effectiveness by easily detecting vital information in follow-up notes and other clinical texts. For instance, it can be easier and more effective to monitor how patients respond to certain therapies by analyzing their narratives.

Medical billing and healthcare revenue cycle management—Payors can expand their analytics to include unstructured documents such as clinical notes. More information about a diagnosis can be analyzed and used to help determine appropriate billing codes from unstructured documents. Natural language processing (NLP) is the most critical component of computer-assisted coding (CAC). Amazon Comprehend Medical uses the latest advances in NLP to analyze clinical text, helping to decrease time to revenue and improve reimbursement accuracy.

Ontology linking —Use the ontology linking features to detect entities from clinical text and link those entities to standardized concepts in common medical ontologies. InferICD10CM identifies possible medical conditions as entities. InferICD10CM links those entities to unique codes from the 2021 version of the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM). InferRxNorm identifies medications listed in clinical text as entities and links those entities to normalized concept identifiers from the RxNorm database from the US National Library of Medicine. InferSNOMEDCT detects medical concepts such as medical conditions and anatomy, medical tests, or treatments and procedures, as entities and links them to codes from the Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) ontology.

Easy, powerful natural language processing integration into your applications— Use APIs to build text analysis capabilities into your applications for powerful and accurate natural language processing.

Accuracy— Use deep learning technology to accurately analyze text. Our models are constantly trained with new data across multiple domains to improve accuracy.

Scalability— Detect information from multiple documents, making rapid insights into patient health and care possible.

Integrate with other AWS services—Amazon Comprehend Medical is designed to work seamlessly with other AWS services like Amazon S3 and AWS Lambda. Store your documents in Amazon S3, analyze real-time data with Firehose, or use Amazon Transcribe to transcribe patient narratives into text that can be analyzed by Amazon Comprehend Medical. Support for AWS Identity and Access Management (IAM) makes it easy to securely control access to Amazon Comprehend Medical operations. Using IAM, you can create and manage AWS users and groups to grant the appropriate access to your developers and end users.

Low cost— Only pay for the documents you analyze. There are no minimum fees or upfront commitments.