A 53-year-old woman in Southern California's Inland Empire is suffering from an un-identified neurological disorder. It started as an odd numbness in her left arm, and now she feels uncomfortable, persistent tingling and prickling pain from the bottom of her feet to the top of her eyebrows. She feels these symptoms to varying degrees at all times of the day and night.
The woman brought her symptoms to the attention of her doctor who, baffled, sent her to a specialist. The specialist ran a number of tests and ruled out all the most likely possibilities, but like the woman's general practitioner, the specialist was left puzzled. The specialist, in turn, presented the woman's case to a panel of leading neurologists from the state's top hospitals, but no one could offer up an explanation or provide effective treatment.
This woman's case is not a hypothetical situation, and unfortunately, it is not a unique situation. But the difficulties of this woman's diagnosis could soon be a thing of the past.
Where limited resources have previously left her without answers, new technologies are being pioneered to exponentially increase her doctor's access to medical knowledge and, in turn, the chance of finding a cure. These technologies are being developed by the same people who originally created the World Wide Web. They are called semantic technologies, and are currently being explored, improved, and applied to healthcare in a movement known as Health 3.0.
But what exactly are semantic technologies, and how can they improve our nation's health?
The word "semantic" is broadly defined as "meaning," and in the context of the Internet, the term is used to describe how computers can understand the meaning of words and text, which could be on a page or in a database. Traditionally computers have not been able to understand the meaning of the words and numbers they process, but with semantic technologies they can start to do so. This is not to suggest that computers are intelligent, but when they have enough information to work with they can start to make connections between different pieces of information that wouldn't otherwise be brought together. In a healthcare environment this is very valuable, because it's simply not possible for any one medical practitioner to have enough knowledge to recognize every symptom or pattern of illness and connect it to every available cure.
Because computers can process information so much faster than people, semantic technologies for data-linking start to make the correct diagnosis possible in a much shorter period of time, and perhaps reducing months of uncertainty and pain to just a few days or even hours. And the opportunities for combining and analyzing vast amounts of data are enormous: just imagine what would be possible if Electronic Health Records (EHRs) could be matched against public and private medical research, health trend data, healthcare professional profiles, and all the latest medical research. In our case, the Southern California woman could be matched to the right physician immediately, have her treatment measured against national averages, and be tapped into the latest research almost immediately.
Over the next several years, our nation's healthcare system will become increasingly digitized and semantically organized in an effort to achieve an Open Healthcare Information Architecture, also known as Health 3.0. The digitization of the nation's health records combined with the widespread utilization of semantic technologies will improve access, quality, and affordability of healthcare across the board.
Such an interconnected wealth of knowledge and resources could lead to a greatly accelerated and heightened diffusion of knowledge, a promotion of public health and preparedness, improved quality of care for all, as well as significant decreases to the general cost of healthcare. Said costs currently sit at more than double the average nation's individual cost of healthcare while our nation's average life-expectancy falls about ten percent below the average nation's.
With a Health 3.0 system, personal information would only be made publicly accessible once it has been depersonalized. In that form it could be utilized to create enhanced medical and clinical correlations, monitor public health, determine health practice efficacy, and conduct cost-benefit analysis of various modalities of treatments. These utilizations of depersonalized health data have already been seen in the California Health Interview Survey, a biannual survey of 50,000 CA residents that provides invaluable health data utilized at the local, state, and national levels. However, the information gathered in the survey is not yet semantically coded. Once it is coded the accessibility and thus the usefulness of the data will increase exponentially.
Of course, the incorporation of semantic technologies into America's healthcare system will not be an easy task and must be approached with the greatest of caution, as personal health records comprise much of the information being handled. Systems for maintaining the security of EHRs and other proprietary information are currently being implemented by Health 3.0, such as government standards like HIPPA which will help ensure personal confidentiality.
Health 3.0 also faces a significant challenge in that the application of semantic technologies to healthcare requires at least some level of cooperation between insurance companies, emergency care, hospitals, pharmacies, general practitioners, specialists, researchers, and patients. Achieving this cooperation will almost certainly require a considerable amount of time and legislation to be fully realized. But once partnerships are formed, the open healthcare information architecture of Health 3.0 would be able to take shape, creating greater collaboration, visibility, and accountability in our healthcare system which will ultimately lead to a healthier nation.
Here's to the future.