So, you go to a doctor for some health issue. The doctor observes you, gathers information, performs tests, and then reaches some conclusion about diagnosis and treatment. This occurs hundreds of thousands of times each day, and there is evidence that cloud computing can improve this process using technology.
So, why not hit the accelerator? Last month we talked about the business case for leveraging big data analytics for clinical use. The objective is to improve upon the delivery of care, and, at the same time, minimize cost and risk. This will be the larger push within health care technology over the next several years as health care provider resources become more saturated and demand increases.
Some of the more interesting work in this area has been the integration of the cloud, big data, and artificial intelligence, such as IBM’s recent work around Watson. As GigaOm’s Derrick Harris reports, “After about a year of training its Watson system on more than 600,000 pieces of medical evidence and 2 million pages of medical research, IBM is now offering a cloud-based Watson service to help oncologists develop the best-possible treatments for cancer patients.”
Watson is designed as a question-answering system that emerged victorious on Jeopardy! in 2011. It’s just artificial intelligence (AI) technology at its core, using natural-language processing, machine learning and other data-analysis techniques. It’s able to understand written questions, and then analyze them against the source material to find the best possible response. In this case, Watson will assist those in the health care sector in the fight against cancer.
While this seems to be very “2010: A Space Odyssey,” this technology is nothing new. However, the commoditization and power of processing and storage has scaled these systems to super intellectual levels. This is due largely to the availability of commodity processing for rent in the public cloud, and the emerging use of big data systems.
Massive amounts of data can be analyzed using new divide and conquer distributed databases that can quickly pump AI technology with the right information. This is where the technology has improved greatly in just the last few years.
The use of cloud-based platforms is a natural location for this technology. Consider the immense amount of processing and storage requirements, and the ability to provide these services at a reasonable price. However, most health care providers will consider this technology as “damned scary” for now, citing compliance and legal issues that may emerge around the use of this technology.
For those of you who think that technology is looking to replace physicians, that won’t be the case. This technology becomes another tool to be leveraged for validation, verification, and even diagnostics.
Moreover, now that we’re also talking about the “Internet of Things” (device connectivity and analysis of the data produced from these connected devices) it’s a forgone conclusion that diagnostic devices, such as MRI machines, will be connected to these AI/big data systems as well. Again, even more “damned scary.”
The application of this technology in the healthcare space should prove productive. The marriage of AI and the cloud will make this technology affordable. Thus, we should be able to put it to good use.
The problem is that it takes a good bit of time for this kind of technology to move from the skunk-works, as Watson is now, to production, where this technology will save lives and money. What are we waiting for?
