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Savana Builds World’s Only Natural Language Processing Clinical Research Network on AWS


Madrid-based Savana helps healthcare providers to unlock the value of their electronic medical records (EMRs) for research purposes. It combines research-grade methodology with natural language processing (NLP) and predictive analytics to obtain relevant results for healthcare and life science providers investigating disease prediction and treatment. Using Amazon Web Services (AWS), it can process the large volumes of data required to run machine learning algorithms. It can also scale its infrastructure to support its rapid growth across global markets, including the US, Latin America, and Western Europe, while satisfying local regulatory requirements. It has reduced IT costs by up to 90 percent and cut processing times for studies by 25 percent compared to its previous technology.

Savana AI + RWE

AWS and Savana work together as trusted cloud service providers and our customers benefit from that trust as well. Hospital IT departments are realizing that the cloud is safer than the traditional on-premises approach.”

Jorge Tello
Chief Executive Officer, Savana

Operating One of the Largest Artificial Intelligence, Multicentric Research Networks in the World

International medical technology company Savana provides a cloud-based solution that processes and analyzes unstructured clinical information contained in electronic medical records (EMRs) combined with other structured databases as genomics. Based in Madrid, Savana operates one of the largest artificial intelligence (AI), multicentric research networks in the world, which is comprised of more than 180 hospitals across 15 countries.

Savana processes billions of EMRs and data points through an automatic data pipeline populated by hospitals that are investigating disease prediction and treatment. Doing this successfully requires a reliable architecture that supports the volume of information and has the processing power needed to execute complex algorithms with the highest security standards. As the business is growing, its infrastructure also needs to scale and satisfy regulatory requirements across global markets around how data is stored, managed, and transferred.

Savana built its infrastructure using AWS because of its ability to scale and its breadth of services. It’s created a reliable and scalable solution while reducing costs by up to 90 percent and data processing times by 35 percent, so it can quickly deliver results for customers’ pioneering medical research.

Building the World’s Only Natural Language Processing Research Network on AWS

Savana uses natural-language processing (NLP) to obtain the most relevant information about different diseases, conditions, and drugs from the medical language used by clinicians in EMRs. It then combines this information with other data layers, such as genetics and biomolecular data, and information sources such as clinical trials. It identifies patterns and trends that can inform predictive analysis—for example, detecting which types of patients would benefit the most from a specific treatment. It can also allow for biomarkers identification, which shortens drug discovery processes.

The company supports research studies conducted by healthcare providers and life science companies that want to use their own data to improve medical knowledge around a wide range of conditions and diseases, including onco-immunology, neurological, and cardiovascular disorders. 

Using AWS, Savana has implemented the world’s only multilingual NLP-based research network. The network’s data harmonization process involves making sense of information located in the sections of electronic medical records where clinicians add notes. The notes are written in natural language and follow no predetermined structure, and must be contextualized with structured data sources.

Running Complex Medical Data and More Than 100 AI Models

Medical records can include a wide range of data, such as the number of treatments, the effect on patients, and when an illness was identified. “This is really the source of information with which you can evaluate the effectiveness of a treatment or a follow-up,” says Jorge Tello, chief executive officer (CEO) at Savana.
The information can be complex and use acronyms, some of which can mean different things—for example, ‘mm’ can mean ‘millimeters’ or ‘multiple myeloma’. In addition, the same medical condition can have several different names. Savana brings research-grade methodology to this data curation, combining both structured and unstructured pieces of information generated over a specific timeframe. Before the data can be processed, Savana standardizes the data sources—which come in a range of formats—and removes personal details from the information so individual patients can’t be identified. After data is anonymized, Savana manages it using Amazon Simple Storage Service (Amazon S3), an object storage service, and Amazon Athena, an interactive query service. Savana can then execute more than 100 AI–NLP models that recognize complex medical entities, through a pipeline built on AWS.

Innovation Means Studies Are Now Cheaper and Faster to Run

Savana has reduced costs and processing times on AWS. Savana can run fault-tolerant workloads for up to 90 percent less compared to on-demand pricing, using Amazon Elastic Compute Cloud (Amazon EC2) Spot Instances, which allows it to access spare Amazon EC2 compute capacity. It has also reduced data processing time by 25 percent, with a typical study now being processed in 9 weeks, compared to 12 weeks using previous technology.

This rapid turnaround means customers get their results for medical research more quickly, and Savana can service more studies and customers. “We can adapt the instances that we use at a really low cost and we can maintain the time the processing takes,” says Tello. “It’s really useful to have this predictability about how much money we will spend in the next month, or the next year.”

Savana has seen a 37 percent saving in total IT costs by using AWS services such as Amazon EMR, Amazon Relational Database Service (RDS), and AWS Fargate. In addition, it has achieved a 73 percent reduction in the overall time needed to execute and complete tasks across all Savana teams.

Supporting Growth with Scalability and Agility

Savana is growing rapidly, and requires technology that can easily scale as it serves more customers and markets. “We receive new contracts from new customers every month and are dealing with millions of data points, so we need to be able to quickly increase our compute and storage resources,” says Tello. The company predicts the volume of medical records it will need to process could increase by a factor of 10 over the next 2 years.
The infrastructure also needs to be agile enough to process documents in five different languages—English, French, German, Portuguese, and Spanish—and to meet customers’ changing requirements. “This is a new technology for our customers, so they change their requirements almost every week or month,” says Tello. “AWS services are really flexible, and we can easily adapt our service to the study we are working on.”
Using AWS, Savana can manage and process large volumes of documents from different locations, with the high level of security needed for health records. It can fully anonymize this sensitive data, ensuring maximum privacy for hospitals and patients.
In addition, it can process medical data in compliance with local regulations, due to the presence of AWS in these markets. “It’s important for us to work with a global provider such as AWS, because when we work with US institutions, we can process data in the US, and when we work with European institutions, we can process data in Europe,” says Tello. “Data is a critical asset, and using AWS means our customers know where their data is located.”

Expanding in the US and Offering NLP as a Service

Savana continues to optimize its services on AWS, with the aim of further reducing processing time and costs. It’s also developing a solution built on AWS that will offer its NLP capabilities as a service. This will enable customers to directly upload electronic medical records into Savana’s system and quickly receive the analysis.
The Spanish company is also expanding its presence in the US, as its customers learn to rely on cloud services. “AWS is our trusted cloud service provider and our customers benefit from that trust as well,” says Tello. “Hospital IT departments are realizing that cloud computing is safer than the traditional on-premises approach.”

About Savana

Savana provides services for the processing and analysis of electronic medical records (EMRs). Based in Madrid, Savana operates one of the largest artificial intelligence (AI), multicentric, and multilingual research networks in the world, comprising more than 180 hospitals across 15 countries.

Benefits of AWS

  • Scales medical record processing capabilities as the company grows
  • Reduces costs by up to 90%
  • Processes medical records 25% faster
  • Innovates to offer new natural-language processing services

AWS Services Used

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

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Amazon Simple Storage Service (S3)

Amazon Simple Storage Service (Amazon S3) is an object storage service offering industry-leading scalability, data availability, security, and performance.

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Amazon EC2 Spot Instances

Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices.

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