Healthcare and life sciences organizations mine unstructured data for insights to drive advances in precision medicine and enhance patient care
PALO ALTO, Calif., July 2, 2018 — Cloudera, Inc., (NYSE: CLDR), the modern platform for machine learning and analytics optimized for the cloud, and MetiStream, a healthcare analytics provider, jointly announce products to improve patient outcomes. MetiStream announces it is introducing an end-to-end interactive analytics platform for healthcare and life science industries built on Cloudera’s machine learning platform. By combining machine learning and analytics from Cloudera Enterprise and Cloudera Data Science Workbench, MetiStream states that its Ember product can deliver insights across massive volumes of handwritten clinical notes as well as genomic data providing a path for healthcare organizations to cost-effectively improve genomic research and accelerate time to patient insight.
Click to tweet: From x-ray images to clinical notes, 80% of data collected by #healthcare orgs is unstructured. @Cloudera & @MetiStream provide #analytics platforms to gain insights from previously underutilized data source: http://bit.ly/2Kia1bL #ML
Because 80% of healthcare data is unstructured, it becomes difficult for legacy data storage and analytics platforms to process, analyze and correlate across a patient population. The advances of Cloudera’s platform with MetiStream’s healthcare analytics solution now give organizations the ability to capture relevant information from diverse healthcare datasets such as unstructured clinical notes, genomics, imaging, and EHR data and correlate the data for insight, allowing them to identify patient risk, improve service quality, and enhance physician-patient collaboration according to Chiny Driscoll, chief executive officer at MetiStream.
MetiStream’s Ember product works with the Cloudera Shared Data Experience (SDX), a software framework powering Cloudera Enterprise, that allows healthcare providers to bring together data from different sources and gain insight into patient health profiles. With the power and scale of the Cloudera Enterprise platform, the ability to merge clinical datasets with genomics information and apply advanced analytics now becomes possible and the result is impactful for healthcare organizations focused on enhancing genomics medicine and patient care.
“We believe that machine learning and analytics are powerful tools for understanding diseases, improving outcomes, containing costs and delivering better care where it’s needed most,” said Mike Olson, founder and chief strategy officer at Cloudera. “Today, healthcare organizations can do what was previously impossible. They can integrate complex data sets from EHR, genomics, and imaging with machine learning and analytics at massive scale for momentous transformations in patient care, engagement, and outcomes.”
Healthcare organizations must access and process many complex and multi-structured data sets to be more prescriptive and proactive with patient care, as well as be able to more accurately report codes that impact financials and regulatory compliance. With comprehensive information that is now easier and faster to access, providers can share disease risk and prevention techniques with patients at the time of care instead of days or weeks later.
“With Cloudera, we have a unique opportunity to transform how healthcare organizations can use more comprehensive and diverse datasets to improve quality and patient outcomes. Our Ember platform delivers a better approach to how physicians and patients can interpret and analyze healthcare findings and discoveries. We believe analytics is even more powerful when insights can be shared in real-time and are interactive and evidence-based, which is why we brought Ember to the healthcare marketplace,” said Chiny Driscoll, CEO at MetiStream.
Healthcare Organizations Speed Analytics to Improve Patient Care
Together, Cloudera and MetiStream empowered a U.S. News & World Report top ranked U.S. hospital, Rush University Medical Center, which is an academic health system comprised of Rush University Medical Center, Rush University, Rush Oak Park Hospital and Rush Health. The Chicago-based medical system needed a healthcare analytics platform to process a backlog of clinical notes. Using solutions from both Cloudera and MetiStream, the medical center was able to process 7.2 million records in less than thirty-six hours. As a result of using the healthcare analytics platform, the medical center improved the standard of care by identifying patients with certain disease risks earlier than ever before possible.
“With Cloudera and MetiStream on Microsoft Azure, we can quickly spin up and down resources as our data processing needs change and evolve, and we can load huge volumes of data in days that would have taken weeks on premises. We have also been able to apply machine learning to discover new insights from our data, and by using Cloudera technologies, we are working to make development of new models easier and faster for our data scientists,” said Dr. Bala Hota, Chief Analytics Officer at Rush University Medical Center.
Additionally, Cloudera and MetiStream empowered Sharp Healthcare and Sharp Rees-Stealy Medical Group specialists to process more than 10 years of clinical notes and index them for search. By leveraging the scalable, massively parallel, in-memory power of Apache Spark, Cloudera and MetiStream support the end-to-end process of extracting, processing, storing and analyzing Sharp's clinical text data in a fraction of the time this once-manual job required. With the new solution, Sharp can now leverage NLP to discern clinical terms and then normalize these terms to well-known ontology codes, most notably UMLS CUI, Snomed-CT, and RxNorm. The result is a solution that gives Sharp the ability to flexibly search their entire notes history for any text, phrase, term, acronym, or code and return the date and time stamp along with other patient information within milliseconds. Coupled with open source Apache Spark, the now annotated clinical data can be used to train a model in Cloudera Data Science Workbench, and develop risk predictions, allowing Sharp to capitalize on machine learning and AI.
“If you had asked me two years ago what I could have done with machine learning, deep learning, and Spark, it is exactly what MetiStream and Cloudera have packaged and outlined in their joint offering. I see this as an exciting breakthrough for the future of healthcare,” said Dr. Randall Hawkins, neurologist, Sharp Rees-Steal Medical Group, San Diego.
The Cloudera Enterprise platform and MetiStream healthcare analytics solution are currently available.
Rush University Medical Center gains new insights to support customized patient treatment plans and deliver precision medicine using Cloudera on Microsoft Azure for analytics and machine learning. Read the Rush customer success story on how they tap into unstructured data from clinical notes to double the accuracy of their machine learning models.
- Visit Cloudera and MetiStream in person at Cloudera Sessions, DC on July 31
- Learn more about Cloudera’s modern platform for healthcare and life sciences
MetiStream is a healthcare analytics platform and solutions provider. We deliver innovation at the intersection of big data, artificial intelligence and healthcare analytics. Our mission is to help healthcare organizations maximize the use of their data assets to improve patient outcomes, decrease risks and accelerate healthcare discoveries. Our platform provides a complete end-to-end , allowing organizations to ingest, process, and analyze large volumes of complex healthcare data sets within a single platform. Learn more at Metistream.com.
At Cloudera, we believe that data can make what is impossible today, possible tomorrow. We empower people to transform complex data into clear and actionable insights. We deliver the modern platform for machine learning and analytics optimized for the cloud. The world’s largest enterprises trust Cloudera to help solve their most challenging business problems. Learn more at Cloudera.com.
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