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William Osler, an influential 19th century Canadian physician and co-founder of Johns Hopkins Hospital, once observed that “the good physician treats the disease; the great physician treats the patient who has the disease.” While this standard is upheld as the goal of almost all caregivers, it is notoriously difficult to achieve in an era of large patient loads, demands for quick turnaround, and best practices derived from datasets. Technology, which has drastically improved overall patient outcomes, has paradoxically made it more difficult for physicians to focus on the individual.

Or at least, that was the case. But now Big Data solutions are making it possible for doctors to tailor care to the individual patient’s needs in ways never previously envisioned.

Dr. Hee Hwang, CIO of Korea’s SNU Bundang Hospital, reports  that the adoption of a next-generation medical data warehouse built on the SAP HANA platform has transformed how doctors are able to interact with and treat patients. Bundang Hospital has long been a technology leader, going fully paperless more than a decade ago and implementing their first medical data warehouse at about the same time.  Consolidating data from a wide range of sources including treatment records and clinical research, the data warehouse proved a tremendous resource for physicians. But it was not without problems.

One such problem was speed of retrieval. Dr. Hee explains that vital queries could take an hour or more to complete, severely limiting a doctor’s ability to explore relevant data for the treatment options best suited for the individual patient.  Moreover, the existing system struggled with unstructured text and image data, which often contains the most critical information for making diagnostic and treatment decisions.

The new data warehouse, implemented in July of last year, has changed all that. The most complex of queries can now be completed in a matter of seconds. Perhaps more importantly, the availability of real-time data has fundamentally altered treatment strategies in several key areas. For example, real-time patient data has made it possible for doctors to administer pre-surgical antibiotics in a much more customized and individualized way. Within three months of adopting the new data warehouse, Bundang Hospital achieved an astounding 79% reduction in the administration of unneeded antibiotics.  This reduction not only cuts costs and improves the patient’s treatment experience (via not having to take drugs they don’t need), it produces a long-term benefit for patients by preventing the development of drug-resistant agents of infection, which would likely cause future complications.

In a similar vein, the difference that Big Data can make in helping individual patients combat a virus can be witnessed  at St. Paul’s Hospital in British Columbia, where the Centre for Excellence in HIV /AIDS is implementing a revolutionary diagnostic and treatment planning system. In the case of HIV/AIDS, treating the patient rather than disease begins with the recognition that patients are never infected simply with HIV, but with a genetically distinct strain of the virus.

Sorting through the vast amounts of genetic data to isolate both the particular strain of the virus and the best treatment plan for the patient is a task custom-made for Big Data technologies.   Dr. Julio Montaner, who heads up the Centre for Excellence, reports that the new approach will reduce the sequencing time for patient blood samples – which currently can take up to 10 days – by a factor of 100. A trial of the system planned to begin a few months from now is expected to set a new standard for individualized care in the treatment of HIV/AIDS with markedly improved patient outcomes and a sharp reduction in the number of new AIDS cases.

Another area of medical treatment where physicians face the challenge of unique signatures, and the staggeringly large datasets that they define, is in the treatment of cancer. In addition to the individual profiles of each strain of cancer, there is a vast amount of clinical data to be weighed. Doctors looking to analyze this data face the challenge of disparate sources and clumsy access – often in the form of spreadsheets.

To address these challenges, SAP has announced the first deployment of Medical Insights, which leverages a healthcare data model and semantic capabilities to extract patient data from a wide variety sources:  clinical information systems, tumor registries, biobank systems and text documents such as physician’s notes. Built on the SAP HANA platform, Medical Insights performs real-time advanced analytics on the extracted data, providing doctors the most up-to-date and reliable information possible on which to base diagnosis and a course of treatment.

I noted above that technology has previously played a role in standardizing medical practice, making it less individualized or, as Osler might have said, more focused on the disease than the patient. But in these examples we see that Big Data technologies are helping to reverse this trend. Deep within the largest and most widely dispersed medical datasets lie the specific answers needed for the treatment of specific, individual patients. Increasingly, thanks to these technologies, we have the tools to find those answers.

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