Big data analytics can perform miracles for the cloud-enabled healthcare organization.

The fact of the matter is that most healthcare providers are under-funded, which leads to being under-automated and under-innovative.  Moreover, there seems to be a growing chasm between those who deliver healthcare to patients, and those who drive IT within healthcare provider organizations.

The statistics back this up.  According to Gartner, anticipated growth opportunities put some industries at the top when it comes to global IT spending.  However, Healthcare Providers were not in the top for growth opportunities, coming in at $15,311M. Even Utilities beat them out by a projected $18,756M.  Think about the number of changes in the world of healthcare providers.  These numbers are surprising at best, or very scary at worst.

The solution to this problem of “too much to do and not enough resources to do it” is to leverage the right new technologies, apply careful planning, and move from a reactive to proactive state in the world of healthcare IT.

The objective is to manage patient data holistically, and in new, innovative ways.  The rise of big data as a set of new technologies provides new options for both the storage and analysis of information.  This leads to better patient care and cost reductions.  The use of cloud computing provides the elastic capacity requirements at costs that almost all healthcare provider organizations can afford.  When combined, you have something that is clearly a game changer.

The data points around the use of big data for predictive analytics are beginning to show up.  In a recent story, Indiana University researchers found that a pair of predictive modeling techniques can make significantly better decisions about patients’ treatments than can doctors acting alone.  Indeed, they claim a better than 50 percent reduction in costs and more than 40 percent better patient outcomes.  (See a story by Derrick Harris over at GigaOM.)

The use case for big data, cloud computing, and predictive analytical models is compelling.  The researchers leveraged clinical and demographic data on more than 6,700 patients with clinical depression diagnoses.  Within that population, about 65 to 70 percent had co-occurring chronic physical disorders, including diabetes and hypertension.

David Linthicum, friend of and contributor to Gathering Clouds.

Leveraging Markov decision processes, they built a model used to predict the probabilities of future events based upon those events that immediately preceded them.  Moreover, they leveraged dynamic decision networks, which can consider the specific features of those events to determine probabilities.  In other words, the model looks at the current attributes of a patient, and then uses huge amounts of data to provide the likely diagnosis and the best treatment to drive the best possible outcome.

The use of core data points, along with well-designed analytical models, leads to a cost reduction from $497 to $189 per unit (58.5 percent reduction).  Also, patient outcomes improved by about 35 percent.

What’s critical to the use of predictive modeling running on cloud-based platforms is the ability to access massive amounts of data and consider that data within these models.  This is not just technology that will be nice to have.  The use of predictive analytics and the tools that support the creation of these models, along with the strategic use of data integration technology, changes the game.