Can Big Data help to reduce medical errors or diagnose illnesses?

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Physicians are nowadays able to foresee a coming illness and prevent it thanks to automatic, health-information learning algorithms. One of the pioneers of using artificial intelligence in medicine is Doctor Homer Warner. In 1961 he successfully developed computer diagnosis of congenital heart disease. If Big Data is enabling online commerce to find out the behavior of its customers, offering them a unique, tailor-made experience, it likewise has the potential for predicting, for instance, whether a patient is likely to suffer an illness on the basis of indications given by several analyzed variables and then heading off this outbreak by means of personalized treatment and therapy.

Today’s technology has a largely untapped potential for extracting huge volumes of healthcare data. As a first step, however, all this information has to be suitably standardized and processed; depending on the type of data involved this process might be of great complexity. Working hard on this task are members of the EIT-Health-promoted(*) PAPHOS consortium, including GMV. Its results will help to cut down the proportion of errors by applying evidence-based medicine while also inputting information for developing new therapies for chronic illnesses and even preventing them.

The main technological data-analysis trends like automation, cybersecurity, intelligent communities, omni-channel communications and cloud computing are all here to stay; indeed, they are already changing the relation between patients and health management. As mainstays of digital transformation within healthcare they all help to boost medical efficiency and improve collaboration between healthcare “clients” and their providers. Likewise, they encourage an active patient attitude, helping them to take their own evidence-based health decisions and accept responsibility for their own health.

The healthcare 4.0 project PAPHOS will work with structured and unstructured data to generate clinical decision-making evidence. At the same time, all this data will be processed under the strictest cybersecurity criteria to ensure protection of the sensitive information being dealt with.

In a scenario where preventive healthcare and wellbeing are coming into their own and diagnostic and illness-management procedures are achieving an increasingly high level of certainty, GMV is working in the PAPHOS project to set up a cybersecure platform applying cutting-edge analytical technology to allow all healthcare stakeholders to move on from the reporting phase (what happened?) to the predictive phase (what might happen?) and finally to prescription (why it will happen?).

Author: Maole Cerezo

(*): EIT Health is a Knowledge and Innovation Community (KIC) established by the European Institute for Innovation and Technology (EIT), an independent EU body set up in 2008 to promote innovation and entrepreneurship across Europe. The participants in the PAPHOS project are: GMV, ATOS, Ceateach, L’université Pierre-et-Marie-Curie (UPMC) of Sorbonne, Bull, Aventyn, Universidad Politécnica de Madrid, Université Grenoble Alpes and the Royal Institute of Technology (Kungliga Tekniska Högskolan: KTH) is one of the world’s biggest healthcare technology initiatives.

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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