Amazon Comprehend Medical: The what, How and Why
Health care is such an inevitable industrial sector, Statista reports that the revenue generated in the year 2022 alone hits 52 billion USD! It vividly showcases how powerful and crucial this sector is. AWS entered the health care industry earlier. as a result, they offer plenty of services but AWS Comprehend is developed to do much more than anything else. It’s definitely not an alternative for healthcare experts however it does some wonderful things. Let’s get started.
What is Amazon Comprehend Medical?
Amazon Comprehend Medical analyze and detects the valuable information from the unstructured medical text like prescriptions, case notes, test results, etc., and derive fruitful insights from it. It uses natural language processing to process medical information or Protected Health Information (PHI). Through ontology linking operations, Amazon Comprehend Medical also enables users to connect these discovered items to standardized medical knowledge bases like RxNorm and ICD-10-CM.
AWS provides services in nearly 70 languages over the globe but AWS comprehend medical is supported only in English language. The prime beneficiaries of this service are the healthcare industry, insurance firms, drug makers, pharmaceutical firms, and hospitals. It also generates confidence score to indicate how confident they are with the results generated. The high the confidence score the high the accuracy.
Use Cases of Aws Comprehend Medical
Case Management for Patients:
Medical data that doesn’t fit into conventional forms can be managed and quickly accessed by doctors and other healthcare professionals. Patients have the option to describe their health concerns in narrative form with more details than in traditional formats. Providers can select patients for early screening of medical issues before the condition worsens and becomes more expensive to treat by reviewing case records.
Research:
Researchers may enhance pharmacovigilance, conduct post-market monitoring to track adverse medication events, and evaluate therapy effectiveness by simply recognizing crucial information in follow-up notes and other clinical texts using Amazon Comprehend Medical.
Billing:
Payors can broaden the scope of their analytics to incorporate unstructured materials like clinical notes. From unstructured documents, additional information regarding a diagnosis can be assessed and used to assist in choosing the proper billing codes. In computer-assisted coding, natural language processing (NLP) is the most important factor.
Ontology linking:
Utilize the clinical text entity detection and ontology linking features to identify entities and connect them to standardized terms in widely used medical ontologies. Potential medical diseases are recognized as entities by InferICD10CM. The 2021 edition of the International Classification of Diseases, 10th Revision, Clinical Modification relates such entities to specific codes (ICD-10-CM). Using the RxNorm database from the US National Library of Medicine, InferRxNorm recognizes drugs mentioned in clinical writing as entities and correlates them to normalized concept identifiers. InferSNOMEDCT identifies medical concepts as entities and relates them to SNOMED CT (Systematized Nomenclature of Medicine, Clinical Terms) ontology codes, such as medical conditions and anatomy, tests, treatments, and procedures.
Key Benefits
Integration of NLP into your application: For effective and precise natural language processing, use APIs to incorporate text analysis functionality into your apps.
High accuracy: Utilize deep learning technologies for perfect text analysis. To increase accuracy, models are continuously trained using fresh data from numerous fields.
Scalability: Identify data from numerous documents to enable quick insights into patient care and health.
Sync with additional AWS services: Other AWS services like Amazon S3 and AWS Lambda are effortlessly integrated with Amazon Comprehend Medical. You can use Amazon S3 to store your documents, Kinesis Data Firehose to analyze real-time data, or Amazon Transcribe to convert patient tales into text that Amazon Comprehend Medical can then evaluate. Access control for Amazon Comprehend Medical activities may be safely managed thanks to support for AWS Identity and Access Management (IAM). You can create and manage AWS users and groups using IAM to give your engineers and end users the proper access.
Economical: Pay for only the documents you actually analyze. There are no upfront obligations or minimum fees.
Also Read: AWS ECS VS Azure VM
How Amazon Comprehend Medical works?
Through entity detection, Amazon Comprehend Medical analyzes unstructured clinical text using a pretrained natural language processing (NLP) model. A textual reference to medical data, such as diseases, treatments, or PHI (Protected Health Information), is known as an entity. Some procedures go a step further by finding entities and then connecting those things to accepted ontologies. You don’t need to give training data because the model is continuously trained on a sizable corpus of medical texts. A confidence score is provided for each result, letting you know how sure Amazon Comprehend Medical is that the entities it has identified are accurate.
It is performed in two ways like synchronous operation and asynchronous operation
Synchronous operation
Permits examination of a single document and returns the result of the analyzes to your applications. It is suitable for an interactive application that only works on a document at a time.
Asynchronous operations
It does analyze on a bunch of documents stored with Amazon S3. The analyzes results are also provided to the S3 bucket.
About Continuuminnovations
We continuuminnovations, Managed Cloud Service provides offers A to Z cloud solutions for health care industry. Our experienced cloud engineers recognize your needs and come up with the perfect solutions. You need not to go through the complex technical phrases and process. We have good skill set with AWS Comprehend medical.