Is patient data the path to the Holy Grail of predictive medicine?

Predictive medicine is defined as anything medicine can do to predict and/or anticipate potential illnesses. At the moment, a lot of people are talking about Machine Learning and Big Data as key factors for transforming healthcare services. Their usage is changing the face of the entire medical field, especially for early detection of serious illnesses and epidemics. However, nothing can be predicted if there is no data. And herein lies the dilemma.

Machine Learning and Big Data as key factors for transforming healthcare services.

The winning combination of patient data and AI

Whilst artificial intelligence becomes increasingly part of modern medicine, traditional medical practice and patient interaction are mainstays from evolutions in healthcare. Test results and patient declarations make up the gold mine of information which researchers are keen to sift through. But, for data to be of any use, it needs to be collected and stored properly. It must be organised methodically and systematically for it to be a reliable way of creating prediction models.

 Today, global awareness of predictive medicine is still relatively low, despite it having a whole range of uses:

  •  Algorithms could provide statistics and probabilities about potential future pathologies which will lead to improved treatment in hospitals and healthcare organisations
  •  It could provide a complete overview of patient care and smooth out the road to recovery
  •  Analysing collected and stored data serves as the raw material researchers need to come up with new methods of prevention. This will then make it easier for the healthcare system and public authorities to organise treatment and information for the whole population.

Machine Learning: an invaluable tool for medical advances

With AI paving the way, data storage will help create and, above all else, lead to the development of ‘Chatbots’ or ‘Machine Learning’ which are currently almost non-existent in healthcare services. Their purpose will be to help professionals use and understand their systems. Thanks to data processing, progress has already been made in cancerology and imaging departments as machines are programmed to pick up signs of disease. AI can carry out thorough surveys of patient data to help establish potential diagnoses for diseases as well as analyse risk.

 There is no doubt that implementing this system around the world would benefit the entire human race – an opinion backed by many stakeholders in healthcare. However, pooling all patient data is not going to happen overnight. What really needs to change is for organisations and people’s mentalities to come together on a global scale.

 Every country has its own approach: a federal government is structurally different to a country like France as a federal state is more independent than a French region. The former can rule itself whereas the latter must answer to the government even despite its small degree of autonomy, as the state is inevitably the decision-maker of government operations. From a cultural standpoint, protecting personal data is trickier in certain Asian countries than in France, for instance. As a result, being able to share people’s data from around the globe is unlikely to happen any time soon. We must therefore hope that the unquestionable medical benefits of doing so will outweigh any objections people have which will hinder progress.

 Whether we like it or not, AI is on the rise. Healthcare organisations being able to work together efficiently is one piece of evidence for – and I’ll say it again – the benefit of patients. Their symbiosis will no doubt help achieve the Holy Grail of modern medicine – the 4 P’s: predictive, personalised, preventative, and participative.

Jean-Baptiste MICHON, ENOVACOM Product Marketing Manager

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