Authored by Jack Ray Gallagher
Editorial
Ischemic heart disease and stroke have remained leading
causes of death globally since the turn of this century, accounting
for 15.2 million deaths in 2016 [1]. Alzheimer’s disease and related
dementias became the fifth leading cause of death in 2016 but were
not in the top 10 in 2000, [1] and a study published this month in
The Lancet notes that while cardiovascular disease is still the No.
1 cause of death worldwide, cancer has surpassed it in wealthier
nations [2]. Would you pick a cure for one of these deadly diseases
as the most pressing healthcare issue to solve, or would you give
higher priority to development of a spectacular technological
advancement such as artificial neural networks with the broad
potential to resolve a number of fatal diseases? Before deciding
on the healthcare issue most in need of an immediate solution,
consider these lines from English poet Samuel Taylor’s “The Rime
of the Ancient Mariner” [3]:
Water, water everywhere, / Nor any drop to drink. To be
relevant to today’s healthcare issues, this line could be rephrased
as: Data, data everywhere, / Nor any know to think.
Like the sailor stranded on a ship surrounded by saltwater that
he cannot drink, we are surrounded by inconsistent or spotty realworld
data that we cannot use to help find cures and solutions to
our most serious diseases and healthcare issues. Our collective task
is to determine how to “desalinate” our oceans of real-world data
to generate the useful and actionable real-world evidence needed
to help resolve critical healthcare issues. The relatively recent
emergence of big data in healthcare is an organized attempt to
accomplish this onerous task.
Big data analytics potentially offer great benefits to all
spectrums of medical and clinical care including improved
efficiency and effectiveness of medical treatments, accelerated
drug discovery, and enhanced personalization of patient care [4].
Big data in healthcare are derived from such sources as electronic
health records (EHRs), claims and other payor records, public
records, research studies, government agencies, web and social
media, specialized commercial databases (e.g., medical imaging,
genomic sequencing), smart phones, and wearable devices. Big
data are distinguished from traditional data used in healthcare
decision making by their high levels of the “3 Vs”-velocity, volume,
and variability-and are far too large to manage with traditional
software and/or hardware [5]. Artificial intelligence, machine
learning, and natural language processing are among tools that
have evolved to help accommodate big-data demands. Compared
with other industries, healthcare has been slow to adopt and
leverage these and other new analytical technologies, possibly due
in part to the special data challenges in healthcare including issues
of privacy, security, and standardization [6].
I maintain that curing our big-data woes is as important as curing
even the most prevalent deadly disease and could provide the
tools to cure or ameliorate many diseases. I also believe scientific
journals have a responsibility to highlight the shortcomings of
existing big data and to share solutions. Current Trends in Clinical
& Medical Sciences is committed to this task.
None.
The author has no conflict of interest.
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