Amazon Comprehend Medical Customers

“When you're an early-stage, fast moving software start-up, time to value is important when considering the many build versus buy decisions your technology company will face. For us, switching to Amazon Comprehend Medical's RxNorm and ICD-10 entity recognition tools was a no-brainer. The APIs allowed us to quickly replace several hand-stitched solutions that were difficult and costly to maintain while also improving performance with a higher rate of accuracy and recall.”

"By using Amazon Comprehend Medical, we can normalize information from disparate sources and across different formats into a common format that we can analyze with our ML models. With this solution, we are able to more quickly and efficiently improve the care that our members receive."

“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.”

"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."

“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.”

“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.”

“One of our most important missions is to provide an accurate way to inventory all sensitive PHI or PII data, and clearly keep track of what systems the data passes and stores in case of an incident, breach, or in an effort to adhere to General Data Protection Regulation (GDPR) on behalf of our customers. Using Amazon Comprehend Medical, we are able to harness theses unstructured datasets to derive life-saving insights about patient treatment and medical history. Using its PHId API, we can also identify patient sensitive data and mask for data security.”

"We have been using Amazon Comprehend Medical with our healthcare customers like Baylor College of Medicine, to extract value from unstructured medical data like progress, referral, and operation notes. These are a goldmine of rich and meaningful patient information which can provide a clinician with more complete context for a patient’s care plan, help researchers in decreasing study time or cost, ensure accurate billing for revenue officers, and save time for busy clinicians. Building on top of the existing capabilities of Amazon Comprehend Medical, the new feature to map to common ontologies is a game-changer. Being able to pull with high-accuracy ICD-10-CM or RxNorm codes from unstructured/free-form text makes this immediately relevant for our customers. This feature significantly reduces our time to implement solutions for these use cases, going from months to days, resulting in immediate return-on-investment for administration and clinicians with improved revenue cycles and clinician workflow improvements."

"At Cloudticity, our mission is enabling healthcare organizations to provide better patient care, and to ultimately help make every human on Earth healthier. We use Comprehend Medical to find hidden insights in unstructured medical text - in real time, from sources such as HL7 messages - to discover hidden relationships that help clinicians make informed decisions about treatment plans."

“Amazon Comprehend Medical enabled us to build a deep learning platform that reduces the time it takes insurance companies to process health and supplemental benefits claims from 5 days to less than 8 minutes."

“Veeva’s vision is to build the industry cloud for life sciences, including software, data, and services, to help our customers get medicines to the patients that need them faster. By incorporating artificial intelligence into the end-to-end drug safety lifecycle, the Vault Safety Suite accelerates and scales pharmacovigilance processes. Vault Safety.AI builds upon Amazon Comprehend Medical to automate extraction of data from unstructured sources. Leveraging natural language processing and machine learning enables Veeva to rapidly develop more powerful pharmacovigilance solutions.”