The Race to the Cloud - Changes Afoot for Life Science Research

Storing health data in the cloud is predicted to be an industry worth $1 billion by 2018. Google Genomics and Amazon Web Services are said to be in a race against each other to work with the genomics industry, each hoping to appeal with their expertise in cloud storage, scalability and accessibility. They aren't the only household names in the pursuit of DNA and genetic profiling – IBM is collaborating with hospitals to allow its Watson AI to provide genetic insights from patient DNA, and Apple continues to explore opportunities with its HealthKit technology.

As cloud storage giants increasingly become an integral part of the healthcare industry, life science research is on the verge of some big changes.

Life Science Research Revolution

First it’s important to look at why there is such a booming interest in cloud technology for healthcare. The industry is not satisfied with health outcomes as they currently stand – costs are continuing to rise and true personalized medicine is still out of reach. When other industries such as finance adjusted to become information-centric and take a big data approach, they were able to achieve much more as a whole; thus, the vision for most researchers and healthcare professionals is that if the industry becomes more information-centric and data driven, outcomes will improve.

In parallel to this shift, there has been global growth in genomics, and the amount of data that needs to be stored and processed for genomics is astronomical. Additionally, most health data actually resides in images, and those images are increasingly becoming digitized for more effective analysis. Recognizing the rapidly growing amounts of healthcare data and need for greater storage abilities, it’s no surprise that cloud companies like Google, Amazon and IBM are jumping to become cloud providers for the industry.

As cloud technologies have emerged, researchers have viewed them as an ideal solution for the skyrocketing problem they face with storing and analyzing the vast amounts of data being gathered. There are several ways cloud will impact life sciences research:

• Speed of Analysis: Cloud, in theory, enables researchers to access unlimited amount of storage and processing power. In years past it could take months for researchers to complete a large-scale genome analysis project, for example, just taking into consideration computing time alone. Cloud technology, however, provides researchers the option to expand the amount of computing power based on their needs; they can elastically increase the size and power of their cloud based on the requirements of the projects they’re working on. This represents a huge benefit for researchers conducting large-scale studies.

• Data Correlation: While at first glance cloud is most applicable to storing genomic data, researchers are realizing that genomics only goes so far. Analyzing tissue data is equally important, particularly in oncology since the tissue holds all the vital information about how a cancer has manifested, what stage the tumor is, and spatial specifics of the tumor microenvironment. Thus, having the ability to store digitized, “datafied” tissue images in the cloud, which can then be correlated with genomic data and clinical outcomes, is very useful. Putting the genomic and tissue data in the cloud together allows for more collaboration, better correlations and stronger analyses of the two in parallel.

• New Ways of Analyzing: Cloud companies all bring something different to the table, and the technologies they provide have the potential to dramatically shift the way researchers analyze and compare data. For example:

Google & Search: Google is most often equated with its online search capabilities, which essentially determine how relevant a web page is to what the searcher is seeking. Applying this core competency to healthcare informatics, its algorithms could help determine how relevant a treatment is for a particular patient.

Amazon & Predictions: Amazon is an expert in developing algorithms to predict what consumers will do, like or buy. As the industry moves toward information-centric, predictive healthcare, companies like Amazon will be important partners in applying predictive algorithms to drug discovery and development efforts as well as clinical routines.

IBM & Real-Time Patient Data: IBM’s Watson Health Cloud is being used by Apple, Johnson & Johnson and Medtronic to provide researchers with open data storage and tailored data analytics based on patient data gathered through wearable devices. Technology built on these kinds of partnerships, which provides researchers with real-time patient data from a diverse global population, will facilitate faster discoveries and more personalized care.

Of course, there are a few challenges with cloud that are still being addressed. Security is one of the main hesitations to using cloud computing, but most people in the industry – and patients – recognize that the majority of data and healthcare IT systems will all inevitably be cloud-based at some point, and security concerns are likely a solvable issue.

The biggest challenge to fully implementing cloud, however, will be inter-operability. There are not yet effective standards in place with regard to electronic medical records, medical coding, and healthcare data in general. The industry will need to standardize how data is entered and utilized, and ensure inter-operability between systems in order for cloud to effectively support collaboration, data correlation and decision-making.

Despite the inherent challenges, the industry is looking toward a more information-centric, data-enabled approach to research and patient treatment, and the only way to get there is through the cloud. On the R&D; side, researchers need easy access to immense amounts of data in order to make stronger correlations and better decisions. On the clinical side, physicians need technologies that enable them to review tissue samples from anywhere, at any time; they are also in need of better databases to store patient information, share patient data across hospitals, and personalize treatments based on a review of how other similar patients have responded. Providing clinical decision support for oncologists is becoming an extremely important task for the industry due to the level of complexity it’s moving to with regard to data, diagnostics and personalized therapies.

Accomplishing these goals and meeting the increasing demands of personalized medicine will require bringing together many different data sets, and having the ability to conduct better, faster, real-time analysis and collaboration. In other words, it will require cloud computing.

About the Author

Merrilyn Datta is President and General Manager of Definiens, the Tissue Phenomics company, and has more than 15 years of commercialization experience within the life sciences and biotechnology industry. Dr. Datta joined Definiens from Life Technologies where she held a variety of roles in marketing leadership, strategy, and mergers/acquisitions.

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