How Healthcare Can Thrive with Cloud Computing

Cloud computing offers efficiency and the ability for deep, predictive learning. Both could play a substantial role in helping reduce administration and increase the personalization of healthcare. But, what inherent characteristics of cloud computing must we overcome before we can get there?

Efficiency in the Cloud

This support includes not only hosting the tools themselves but providing auto-scaling computational nodes that start up as needed to support the tasks you give them and shut down when idle, effectively guaranteeing that the compute you need does not cost any more than it needs to at current commodity rates.

With Amazon Web Services (AWS), you can even use the spot market to reduce those costs further by only using spare compute power available on the spot market. At last year’s AWS re:Invent conference, a couple of different organizations explored dramatic cost reductions that could be gained using AWS for genomic processing compared to dedicated in-house data centers. Reductions can also be gained in calendar time due to the ability to flex up to a larger machine count or flex down without machines sitting idle during periods of transition.

Privacy in the Cloud

What Approach to Data Privacy Is Most Secure?

Encryption. Encrypting the information does not help, because any system that wants to process the data has to decrypt it first. For that to happen, the system must have the key to do so. Decrypting data to process it means it is also vulnerable to being copied, stored or shared by whatever system was able to decrypt it. That kind of transitive trust at a large scale is unprecedented.

Bitcoin. We can imagine a couple of possible ways that technology can help. First, new technologies like blockchains (used by Bitcoin to enable financial transactions) may be able to secure information in a way that uses for shared information can be traced. While this does not by itself guarantee the security of the information, it would, in principle, allow you to trace what happened to your information up to the point it was leaked and therefore who leaked it. This could be key to giving organizations the accountability needed to build consumer confidence.

Distributed Computing. The more practical solution that delivers true privacy is a distributed computing model able to process, yet not disclose, the data. The data in our example would live in a single system used by a trusted individual where processing algorithms could be run on that data to answer questions. This is tricky to execute because any processing code that will operate on the data must itself be proven secure. Thus, there will be significant performance impacts in coordinating distributed computing this way. At least it is possible to provide a solution that can protect each contributor’s data but allow their data to be included in larger cohorts for processing.

Alternately, we could hope that cultural norms change so the sensitivity around disclosing this kind of information evolves — though, we can’t count on that happening as fast as needed to eliminate the problem.

Knowledge Management in the Cloud

Deep Learning in Healthcare

Watson for Genomics

Loose ends remain before the use of cloud systems and AI in healthcare can be more fully implemented. How many of the advertised capabilities are as fully-baked as they appear? How safe are public clouds? How has our ability to process and store data improved? As the healthcare and tech worlds continue to co-mingle, the opportunities to shave costs and increase precision will only grow.

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Authored by
Craig Knighton.

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