Amazon Comprehend Medical

Extract information from unstructured medical text accurately and quickly

No machine learning experience required

Amazon Comprehend Medical is a natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. Using Amazon Comprehend Medical, you can quickly and accurately gather information, such as medical condition, medication, dosage, strength, and frequency from a variety of sources like doctors’ notes, clinical trial reports, and patient health records.

One of the important ways to improve patient care and accelerate clinical research is by understanding and analyzing the insights and relationships that are “trapped” in free-form medical text, including hospital admission notes and a patient’s medical history.

Today this is achieved by writing and maintaining a set of customized rules for natural language processing software, which are complicated to build, time-consuming to maintain, and fragile. A change to a single classification code name, for example, can impact dozens of hard-coded rules and failing to update a single one of them can result in missed or incorrect data. Machine learning can change all that with models that can reliably understand the medical information in unstructured text, identify meaningful relationships, and improve over-time.

Amazon Comprehend Medical uses advanced machine learning models to accurately and quickly identify medical information, such as medical conditions and medications, and determines their relationship to each other, for instance, medicine dosage and strength. You access Amazon Comprehend Medical through a simple API call, no machine learning expertise is required, no complicated rules to write, and no models to train.

You can use the extracted medical information and their relationships to build applications for use cases like clinical decision support, revenue cycle management (medical coding), and clinical trial management. Because Amazon Comprehend Medical is HIPAA eligible and can quickly identify protected health information (PHI), such as name, age, and medical record number, you can also use it to create applications that securely process, maintain, and transmit PHI. You pay only for what you use, and there are no minimum fees or upfront commitments.

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Benefits

Extract medical information quickly and accurately

Protect patient information

Lower medical document processing costs

Powered by state-of-the-art machine learning models, Amazon Comprehend Medical understands and identifies complex medical information quickly and more accurately. For example, Amazon Comprehend Medical can extract “methicillin-resistant Staphylococcus aureus,” often input as “MRSA,” and provide context, such as whether a patient has tested positive or negative, to make the extracted term meaningful.

Amazon Comprehend Medical provides a number of capabilities to help healthcare providers stay compliant and protect patient data. The service is HIPAA eligible and can identify protected health information (PHI) stored in medical record systems while adhering to the standards for General Data Protection Regulation (GDPR). Amazon Comprehend Medical allows developers to implement data privacy and security solutions by extracting and then identifying relevant patient identifiers as described in HIPAA’s Safe Harbor method of de-identification. Finally, the service does not store or save any customer data.

Amazon Comprehend Medical makes it easy to automate and lower the cost of processing and coding unstructured medical text from patient records, billing, and clinical indexing. It offers 2 APIs that developers can integrate into existing workflows and applications with only a few lines of code, costing a penny or less for every 100 characters of analyzed text. You pay only for what you use, and there are no minimum fees.

How it works

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Customer and Partners

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“Curing cancer is, inherently, an issue of time. For cancer patients and the researchers dedicated to curing them, time is the limiting resource. The process of developing clinical trials and connecting them with the right patients requires research teams to sift through and label mountains of unstructured clinical record data. Amazon Comprehend Medical will reduce this time burden from hours to seconds. This is a vital step toward getting researchers rapid access to the information they need when they need it so they can find actionable insights to advance lifesaving therapies for patients.”

Matthew Trunnell, Chief Information Officer - Fred Hutchinson Cancer Research Center


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"Roche's NAVIFY decision support portfolio provides solutions that accelerate research and enable personalized healthcare. With petabytes of unstructured data being generated in hospital systems every day, our goal is to take this information and convert it into useful insights that can be efficiently accessed and understood. Amazon Comprehend Medical provides the functionality to help us with quickly extracting and structuring information from medical documents, so that we can build a comprehensive, longitudinal view of patients, and enable both decision support and population analytics."

Anish Kejariwal, Director of Engineering, Analytics – Roche Diagnostics


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“Amazon Comprehend Medical provides us the ability to realize better results, quicker and with less overhead. By using Amazon Comprehend Medical, our customers are able to focus more on building smarter applications and extracting critical insights and less on annotating, training and re-training models. The ability to perform a very manual task accurately at scale, and securely, allows us to create more impactful solutions and better patient and clinical outcomes. For example, one of our pharmaceutical clients is using Amazon Comprehend Medical on a limited sample size to help extract information that allows them to identify medically relevant events. In preliminary findings, we are seeing a significantly faster throughput than in the past.”

Matt Rich, Healthcare AI Lead - PricewaterhouseCoopers


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“We are excited that Amazon Comprehend Medical is now available to help us and our customers uncover actionable insights from medical data. The new offering provides us with an integrated option for our ConvergeHEALTH products and for Deloitte’s consulting solutions. It allows us to adapt a scalable, cost-effective, and secure model that had previously been a challenge with prior medical Natural Language Processing tools. We are working to apply the information extraction and classification services towards applications in real world evidence, pharmacovigilance, competitive intelligence, and provider efficiency, which will help us to mine the information we need to extract meaningful insights and continue to drive transformation in the industry.”

Dan Housman, Chief Technology Officer - ConvergeHEALTH by Deloitte

Pricing

With Amazon Comprehend Medical, you pay only for what you use. You are charged based on the amount of text processed on a monthly basis. Amazon Comprehend Medical provides two APIs: Medical Named Entity and Relationship Extraction (NERe) and Protected Health Information Data Extraction and Identification (PHId).

The Medical NERe API extracts entities, entity relationships, entity traits, and Protected Health Information (PHI). If you want to only identify PHI for data protection, you can request the PHId API. All API requests are measured in units of 100 characters, with one unit (100 characters) minimum charge per request.

Feature Price per unit
Medical Named Entity and Relationship Extraction (NERe) API $0.01
Medical Protected Health Information Data Extraction and Identification (PHId) API $0.0014

Free Tier

Amazon Comprehend Medical offers a free tier covering 25k units of text (2.5M characters) for the first three months when you start using the service for both the Medical Named Entity and Relationship Extraction (NERe) API and the Protected Health Information Data Extraction and Identification (PHId) API.

Use cases

Perform medical cohort analysis

In oncology, it is critical that the right selection criteria are quickly discovered to recruit patients for clinical trials. Amazon Comprehend Medical understands and identifies complex medical information found in unstructured text to help make indexing and searching easier. You can use these insights to identify recruit patients to the appropriate clinical trial in a fraction of the time and cost from manual selection processes.


Support clinical decisions

Amazon Comprehend Medical extracts medical information from patient data stored in Amazon S3 and returns structured results that you can integrate into a healthcare dashboard a care support team can access. For example, a developer can build an early warning system to help identify individuals at risk of multiple sclerosis by extracting diagnosis, sign, and symptoms from more than 100,000 clinical notes using Amazon Comprehend Medical. By providing a “single lens” into the patients’ medical history, clinical teams can make decisions that are more informed.


Improve medical coding in revenue cycle management

For a hospital, the process of finding the right diagnosis in the patient notes that should be mapped to the correct code in the International Classification of Diseases (ICD) can be time-consuming and tedious. It is particularly challenging to extract diagnoses that can be represented in different ways. For example, “atrial fibrillation” is sometimes written as “AF.” Amazon Comprehend Medical can accurately identify abbreviations, misspellings, and typos in medical text. This reduces the time a medical coder must spend analyzing unstructured notes, decreases the time burden on clinical staff, and improves efficiency. 

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Refer to developer guide for instructions on using Amazon Comprehend Medical.

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