We’re living in a time when illnesses that used to have a high mortality rate are starting to become chronic. The global life expectancy increased by five years between 2000 and 2015, the fastest increase since the 1960s, and a growing number of scientific advances are allowing us to live not just longer, but also better. In other words, the healthy life expectancy is also increasing.
In this context, science is facing the challenge of a global population that is growing ever older, and medicine needs to evolve to address it.
One tool the health sector has is technology. For the research, prevention and treatment of diseases, using data means adding knowledge. But, as Nuria Oliver, Director of Research in Data Science at Vodafone, pointed out at a debate organised by The Collider about AI and companies, “Big data is digital garbage if we don’t know what to do with it.”
After all, uninterpreted data is just data. But deciphered data is knowledge. Scientists and engineers, as well as the medical sector, need to work on this.
The American Medical Informatics Association (AMIA) says that the health industry needs to focus on developing tools for analysing big data. This could be achieved by creating a kind of “periodic table of data elements,” which could support the research and analysis of data by building a common language around it.
The construction of this accessible language would, of course, require input from both the engineering and health sectors. Health sector professionals would need to be proficient and familiar with computing language, and computer scientists, engineers and statisticians would need to work on their health literacy in order to share information and achieve a space for common knowledge.
On the other hand, as Nuria Oliver said in this interview with El País, we currently have a health system based on episodic experiences. “The doctor only gets to know you when something’s wrong; they have no idea what you’re like when nothing hurts, so they can’t compare data points. Technology will allow us to move to a preventive, personalised and continuous model. We’ll be able to monitor all these physiological signs continually over time— not just once a year when you go to the doctor for a checkup or because you feel sick.”
Improving analytics and research through data can have an influence on issues as important as prevention, both individually and collectively. By observing how people live in a given region, how they move around, what their habits are and what unusual activities they’ve performed, the healthcare system would obtain important information that could, for example, help it stay ahead of pandemics.
However, big data has its drawbacks. So far we’ve talked about the sharing, reception and study of data, of its usefulness for constructive and positive ends, but companies are also facing another significant challenge when it comes to using personal information: confidentiality and ethics. Just because a person is sharing data and leaving a digital fingerprint from the moment they wake up until they go to sleep, that doesn’t mean the information is simply free to be used. Will we be able to build a smart healthcare system without violating people’s privacy?