How Cloudera Provides Predictive Data Insights in Healthcare?




The healthcare industry is leaning toward value-based services and data can help practitioners focus on positive patient outcomes without having to necessarily worry about costs. The proper review of data can help drive down expenses and eliminate waste, but in order to accomplish all this, incoming data must be properly collected and analyzed. This means organizations are turning to analytical tools to help, and many are opting for Cloudera. Cloudera helps leverage data in ways that make it easier to make informed divisions across historical and real-time patient health data.

Nine of the top 10 global pharmaceutical companies are using Cloudera to navigate through the process of predictive analytics. "The Cloudera Enterprise platform is the cornerstone of our next-generation analytics platform, and the outstanding partnership we have developed with Cloudera has allowed us to quickly aggregate, cleanse, enrich, and visualize our data in ways we struggled with in the past," explained Bill Leister, VP of Analytics, Informatics and Business Intelligence at Quest Diagnostics.

Why Cloudera appeals to healthcare organizations

Cloudera is a scalable, next-generation hybrid data management platform that collects, processes, secures and analyzes data for evaluating strategies that can positively impact patient outcomes. It uses data, analytics and AI to provide healthcare organizations with deep insights quickly. It is able to process, manage, govern and securely analyze complex, real-time streaming data at a fast pace which then enables healthcare practitioners to effectively use the data and predictive analytics to influence innovation in a variety of areas including remote patient monitoring, genomics and value-based healthcare.

Cloudera has a variety of features that appeal specifically to the healthcare field including:

·         A fast pace toward merging omit data with clinical/phenotype data. This is gathered from any existing technology, all of which can help improve precision medicine when combined. 
·         The implementation of machine learning to help create algorithms which can address specific questions within healthcare, like how to quickly identify a cancerous tumor. This is possible through the high volume of data Cloudera can ingest, store and analyze; perfect for building machine learning models. 
·         A real-time streaming analytics platform which helps make it easier to capture, combine, secure and drive analytics in specific categories such as bio-monitors or anything connected to healthcare IoT devices, at scale. This directly relates to the creation of effective machine learning models since this data helps inform those. 
·         Accelerated threat detection as well as investigation and response to help proactively secure the large volume of incoming healthcare data, including sensitive, patient-specific information.

These features come together to help healthcare organizations effectively manage their data intake and analysis in a way that can positively impact how practitioners provide treatment and patient care in the future.

How big is data in healthcare?

Big data is big in healthcare. In 2017, it was an estimated $14.25 billion dollar business. By 2025, it’s predicted to grow to almost $69 billion. This unprecedented increase in healthcare data means organizations need the right analytical tools, AI and machine learning techniques to mine helpful insights from all this data. The right analytics can help the industry reduce healthcare costs, enhance revenue streams, manage more proactive patient care and help with the creation of more personalized medicine.

“Healthcare data analytics is emerging as an essential tool for patient diagnoses, detecting epidemics, enabling genomic analysis and delivering on the promise of molecule medicine,” according to an article on yahoo! finance.

These numbers help support the need for the right tools to process big data in healthcare simply due to the value the analytics can bring to the industry both for the patients and the practitioners.

What predictive analytics bring to the table

In conjunction with the right tool to analyze incoming data, healthcare organizations rely heavily on predictive analytics to influence change. Utilizing predictive analytics is the process where you learn from historical data in order to make more intelligent predictions about the future. New data streams, including those direct from patients are beginning to be used within healthcare for predictive analytics. This information helps influence the way care develops, potentially improving the quality of personalized attention over the long term.

Predictive analytics enables practitioners and data analysts to ask questions where the answer isn’t easy to find. Rather than only looking at questions where you already know the answer, such as how many patients do you see daily on average, bringing in technology can help you find answers to more complex questions like what common traits do cancerous tumors have. This is accomplished with platforms like Cloudera which can give you a data set that goes beyond your own experience. This leads to answers which then influence better patient treatment across the board. These “answers” can also help reduce operating costs without having to sacrifice the quality of care.

Where data and patient care meet?

Rising up to meet the growing needs of healthcare’s big data; platforms like Cloudera Big Data Analytics are working hard to give the industry actionable analytics that serve the very necessary goal of improving patient care without creating additional expense.

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