Escalating demands in healthcare are rapidly changing the way organizations treat patients, support caregivers and staff, and share information. Global trends like population growth, increasing life expectancy, and widespread need for telehealth services have caused organizations to work harder and stretch their resources between numerous locations. The pressure to meet these challenges is mounting, as rising volumes of patients depend on immediate and personalized care. These patients often require more doctor's visits, sophisticated treatments, and medications, as well as the use of specialized equipment and personal devices which produce troves of medical data. A single patient can generate 80 megabytes of imaging and electronic health record (EHR) data per year.

There's been more than a little discussion of artificial intelligence (AI) in healthcare, lately, but in addition to – or, perhaps, on a more fundamental than – the myriad insights AI is expected to offer healthcare providers is the impact it's going to have on organizations' IT infrastructure. As a recent report from Tractica puts it, "while organizations are clearly recognizing the value associated with incorporating AI into their business processes, they are also encountering a number of challenges with integrating this new intelligence into operational processes." "Enabling AI at the enterprise scale is not a plug-and-play proposition," Tractica Principal Analyst Keith Kirkpatrick said in a statement. "Significant time, resources, and capital must be deployed, and in most cases, internal company teams are not experienced enough with AI, nor do they have the cutting-edge data science skills to adequately embark upon a truly transformational AI implementation." For health IT managers, among the inevitable decisions is whether their organization's storage systems are going to be able to handle the infrastructure changes needed to process and store new amounts and kinds of data.

In the last 10 years, it's become far more common for physicians to keep records electronically. Those records could contain a wealth of medically useful data: hidden correlations between symptoms, treatments and outcomes, for instance, or indications that patients are promising candidates for trials of new drugs. Much of that data, however, is buried in physicians' freeform notes. One of the difficulties in extracting data from unstructured text is what computer scientists call word-sense disambiguation. In a physician's notes, the word "discharge," for instance, could refer to a bodily secretion -- but it could also refer to release from a hospital.