People’s life expectancy is increasing year by year along with the number of people suffering from chronic illnesses. These two factors are obviously interrelated. It is estimated that there are now 150 million chronic patients and by 2030 this figure is likely to rise to 171 million, according to the RAND Corporation report “Projection of Chronic Illness Prevalence and Cost Inflation”. Lack of adherence to prescribed treatment, affecting over 50% of chronic pathologies, costs the Spanish health system more than 11.25 billion euros a year, according to the Spanish Hospital Pharmacy Society (Sociedad Española de Farmacia Hospitalaria).

To address this public health problem the Treatment Adherence Observatory’s 5th annual conference brought together all healthcare organizations and companies that are now working on adherence-improvement initiatives. GMV showcased its telemedicine platform for chronic patients and also its artificial intelligence platform to cut down the prescription of unnecessary treatment, additional tests and head off the increasing number of GP visits or hospital stays that could be avoided if the patients took their medication properly and in line with their doctor’s indications: in developed countries 50% of chronic patients fail to do so.

Carlos Royo, GMV’s healthcare business development manager, pointed out during his speech that “the transformation of all types of health-record data into useful information directly helps to improve medical attention and ipso facto the welfare of the general public”. He also explained that Big Data systems for decision-making will not replace clinical systems since it is the latter “that have to translate medical knowledge for engineers to be able to convert it into algorithms”. As a physician himself he has carried out this task in developing the company’s suite of telemedicine and epidemiology control products, antari.

Performance features

The application of Big Data techniques to healthcare, according to McKinsey’s Big Data study: "The next frontier for innovation, competition, and productivity", could save up to 250 billion euros for Europe’s public-health systems. Experts argue that massive data analysis, by establishing predictive models driven by specific algorithms, will help to improve therapeutic compliance and therefore the prognosis and quality of life of patients.

Obtaining mass target-data on therapeutic compliance and non-compliance in specific pathologies, recorded in various centers, makes it possible to take the necessary systematic action to reduce the rate of negligence in self-care procedures.

Furthermore, predictive analyses lying behind the generation of Big Data algorithms will also favor more efficient resource use, generating action procedures and protocols that make treatment more effective. Likewise, it will also help to improve therapeutic action, generating specific and qualified knowledge. This all makes it possible to dispense ad hoc, individualized treatment.

The application of Big Data techniques to healthcare could save health systems huge sums of money not only due to the ensuing improvement in healthcare attention but also the reduction of administrative and clinical inefficiency. This will bring about an across-the-board improvement in all healthcare aspects such as diagnosis, logistics and the management of healthcare material.

At the end of his speech Carlos Royo argued that, “Big Data applied to healthcare represents a game-changing shift in medical practice, based on the 4 Ps: personalized, predictive, preventive and participative”.