There are nearly 20 medical specialty areas for healthcare professionals, and the act
of a primary care provider referring a patient to a specialist for a particular
condition is routine. This is great for patients who require care from experts,
but not so great when it comes to coordinating overall patient care or a
holistic approach to patients that looks at underlying causes of diseases, and
not just treating symptoms.

Consequently, it hasn’t surprised me that for the many industry
verticals I have consulted with on IT, nowhere is there a greater disconnect on
communications coordination and information management than in healthcare. There
are reasons why this is the case.

The high degree of specialization in the industry creates
many information silos that are difficult to reconcile in the push for a
universal electronic medical record (EMR). There are also a high number of mergers within
the industry that leave the unfinished business of disparate and incompatible medical
systems to the end.

All of this naturally begs the question of whether big data
can help healthcare improve performance and become more patient-centric.

Healthcare institutions recognize the importance of patient
centrism, as evidenced by the growth of patient-centered medical home initiatives intended to transform
care coordination and communication to “what patients want it to
be.” 

Unfortunately, these initiatives are stymied if data can’t
be amalgamated to show the entirety of a patient’s care so as to preclude the daily
misses of medical communication—like the orthopedic doctor who prescribes a
cortisone shot for an arthritic knee without knowing that the patient is already
on heavy doses of high blood pressure medicine as prescribed by his heart
doctor, and which could be adversely affected by the cortisone. This is where a
universal, all-inclusive information source like the EMR is supposed to
function as a big data repository that knows everything about a patient; it is
also where healthcare is stumbling.

The good news is that, even though the U.S. lags other
countries in EMR implementation, over 80 percent of U.S. primary care physician now use an EMR. Equally important
to medical practitioners is being able to troubleshoot patient medical records
on both a very granular basis and as part of a broader trends basis.

At
Memorial Sloan Kettering Cancer Center in New York, patient information is fed into an EMR and is then run through an IBM Watson database
of thousands of medical journals, industry association guidelines, and hospital
best practices to provide a list of treatment options (accompanied by
confidence scores) and clinical trials for which the patient might be eligible.
“Watson’s capability to analyze huge volumes of data and reduce it down to
critical decision points is absolutely essential to improve our ability to
deliver effective therapies and disseminate them to the world,” said Dr.
Craig Thompson, president and CEO of Memorial Sloan Kettering.

In Cleveland, cloud solutions provider Explorys publishes medical trends information gleaned from its big data analytics.
The information enables healthcare providers to better understand the
conditions of individual patients, and even to determine how a particular
patient is likely to respond to a specific treatment. (Note: The Explorys article is published on The Profitable Practice, which is produced by Software Advice.)

Big data solutions are also being deployed to track and evaluate
patient outcomes of treatment—an overlooked area in the past that is extremely
significant, since 20 percent of hospital admissions are patients who were discharged within the past 30 days.

“Physicians have traditionally used their judgment when
making treatment decisions, but in the last few years there has been a move
toward evidence-based medicine, which involves systematically reviewing
clinical data and making treatment decisions based on the best available
information,” said McKinsey director Nicolaus Henke. “Aggregating individual data sets into big-data algorithms often provides the most robust evidence, since nuances in subpopulations (such as the presence of patients with gluten allergies) may be so rare that they are not readily apparent in small samples.”

There’s the potential to transform healthcare with big data,
but the industry must still answer two fundamental questions going forward:

  • Can big data improve the quality of care and the success of
    treatment outcomes for patients?
  • Does big data have the power to cure broken healthcare information
    systems?