Big Real World Data: a healthcare Big Brother?

According to Spain’s National Transplant Organization (Organización Nacional de Trasplantes), which has now celebrated its 25th anniversary, 2016 recorded an all-time donor high. A 10% growth rate added up to a total of 4769 transplants, consolidating Spain’s 24-year track record as a world donation leader.

This stands testimony to the great generosity and solidarity of Spain’s society and would certainly argue against any “stinginess” in terms of releasing that society’s healthcare information in the interests of improving the quality of life or even, in some cases, boosting longevity. Small wonder, then, that a European-wide study conducted this year by the Vodafone Institute, shows that 86% of Spain’s citizens are in favor of mass gathering and analysis of healthcare data by healthcare institutions to improve the detection and treatment of illnesses.

Patients are not chary of been spied on. They take for granted the responsibility and ethical uprightness of institutions, researchers and healthcare professionals to process their healthcare information with security and confidentiality. There is no fear of a healthcare Big Brother. Healthcare professionals, for their part, are finding out just how useful intelligent systems are for decision-making purposes and increasing accuracy levels in such crucial processes as diagnoses.

In this overall context, healthcare systems that are canny enough to take up Big Real World Data or Smart Real World Data options would then be able to pursue proactive, results- and behavior-forecasting policies by analyzing the healthcare data of local patients. This would then facilitate preventive, predictive and personalized medicine that would in turn help to make systems more sustainable, with patients being treated early in the onset of their illness rather than in the final stage.


Today’s huge ICT breakthroughs are driving data-analysis techniques ever onward, advancing from the mere analysis of descriptive data, limited to answering questions of the “who-what-when-where” type, to true predictive analysis. The takeup of advanced statistical techniques like machine learning, data mining, random forests, artificial neural nets or SVMs (Support Vector Machines) is now making all this possible.

Spain now has experience of insightful smart systems that are helping to cut down medical errors, identify disease-prone people, lever a more evidence-based medicine, improve healthcare processes, hone diagnoses, pinpoint uncatered-for needs, set up preventive medicine and forestall epidemics.

One of these systems has been set up by the Regional Healthcare Ministry of Galicia: HEXIN, a clinical- and epidemiological-data mining platform drawing on and standardizing many different data sources, is driving the use of Big Real World Data in the healthcare field.  HEXIN, set up with GMV technology, is also a trailblazing public procurement system.

Some are already making bold to argue that “within a few years the failure to use artificial intelligence for diagnosis purposes will be seen as sheer negligence.”

Author: Maole Cerezo Caballos

Las opiniones vertidas por el autor son enteramente suyas y no siempre representan la opinión de GMV
The author’s views are entirely his own and may not reflect the views of GMV

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