|Experts believe that big data and healthcare must be a necessary combination. / Photo by: Kzenon via Shutterstock|
The rapid expansion of biomedical technology is proving to be beyond the ability of clinicians to incorporate this new type of knowledge into actual practice.
Charles Friedman, Ph.D., and his co-workers wrote an article in the Journal of General Internal Medicine that detailed a possible solution to the onset of this problem, according to a report published on eurekaalert.org. This solution may be able to accelerate the transformation of data into practical applications that will improve the health of patients.
In the traditional sense, it takes several years or even decades for research data to be put into actual medical practice. An example given by Friedman was about clot-busting drugs that are used to treat heart-attacks in patients that were delayed by as long as 20 years due to the slow incorporation of all the knowledge gathered about the condition.
Friedman continued, “There are lots of reasons why new knowledge isn't being rapidly incorporated into practice, if you have to read it in a journal, understand it, figure out what to do based on it, and fit that process into your busy day and complicated workflow, for a lot of practitioners, there's just not enough room for this."
Compared to the slow rate at which traditional medical research and development take, this particular article focused primarily on the need to harness the power of technology to better enable health systems to study and analyze the data that is generated during the different processes of taking care of patients. This way of data analysis is to be able to generate “local” evidence and to use this in conjunction with published peer-evaluated information to better improve overall health outcomes.
The major objective is to transform human interpretable knowledge into computable forms of information.
Friedman explained, “A lot of scientific studies result in some kind of model: an equation, a guideline, a statistical relationship, or an algorithm. All of these kinds of models can be expressed as computer code that can automatically generate advice about a specific patient."
When these different models, both “local” and published are available on a more data-compliant format, it can prove to be extremely useful for greater advancement in the medical field.
Friedman concluded, “The value of big data is to generate big knowledge. The power of big data is to provide better models. If all of those models are to sit in journal articles, no one’s going to be any healthier.”