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After studying cases and training for years in medical school, doctors are able to identify patterns in a patient’s test results, compare them to that of a healthy individual and come up with a diagnostic and treatment. Better yet, they know how to adapt that treatment based on the patient’s age, weight, allergies, past medical history as well as known dangerous drug interactions for example.
Now, we’re on the verge of a health data breakthrough, in which computers will be able to do similar diagnostic tasks, by analyzing massive amounts of data, including genome sequences, risk factors, medical histories, drug interactions, and more.
Looking at this trend last year, venture capitalist Vinod Khosla made the bold claim that technology will replace 80 percent of companies eventually. The reality is probably more nuanced: Far from threatening to put doctors out of jobs, the falling prices of data analysis and genome sequencing are enabling them with tools they could only dream of even a few years ago.
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At the Mount Sinai Hospital in New York, Joel Dudley, Ph.D. uses Ayasdi’s products to discover how patients with certain genes are more likely to develop some diseases (diabetes, cardiovascular conditions…) as well as how genes influence the performance of a treatment, or may reveal risks of later relapses that can be prepared for.
Already 11,000 patients at Mount Sinai have had their genome sequenced, a pool large enough for meaningful analysis, although Ayasdi tells us “those are still early days for the industry. There are no plans to act on that data directly with individual patients just yet.”
Right now the Mount Sinai community is working at organizing itself to make the useful information available to the frontline staff. And another 30,000 patients may soon sign the consent form and opt in to participate in this new way to explore which care is best for them.
The marvelous complexity of the human body means that there is still a great deal of progress to be made to continue to tailor treatments at ever-finer levels, while minimizing risks and unexpected adverse reactions.
We are now at the tipping point where hospitals like Mount Sinai can afford to sequence genomes for a majority of patients. This means these hospitals are generating a pool of information on the patients that is several order of magnitude richer than anything they had before. What’s more, much of this data has direct links to the efficiency of treatments, susceptibility to drugs, and the development of new symptoms.
The best part is that doctors can start seeing these correlations without having to run costly clinical tests or conduct research in the lab. By simply crossing digitalized genetic information (now cheaper than ever to sequence) with clinical information (length of stay at the hospital, potential readmission, drug efficiency, potential bad reactions…) using big data analysis tools, doctors can see trends emerge.
Even for industries notoriously slow like healthcare, the ability to quickly extract patterns from heaps of data is accelerating the change towards personalization, risk reduction and performance optimization.
The exploration of big data by the enterprise is becoming less of a competitive edge and turning into more of a must-have. Similarly, hospitals may have to adopt genetic analysis as a rule of thumb sooner rather than later. Mount Sinai is unusual today in pioneering regular genetic screenings, but it soon may become commonplace.
To learn more about how data science is revolutionizing various industries, from healthcare and media to manufacturing and finance, come join Joel Dudley and 40 other pioneers at DataBeat/Data Science Summit, Dec 4th-5th in Redwood Shores, Calif.
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