Moving Towards Personalised Medicine with Big Data Strategies

personalised medicine

 

The use of Big Data strategies is thought to have the potential to help physicians and scientists move closer to treating individuals, rather than diseases. Developing treatments that are tailored to an individual’s history and sensitivities presents many challenges, and will only be possible within a digital health care system.

An article from 20 January 2011 in the U.S. News & World Report explains: “Personalized medicine is a young but rapidly advancing field of healthcare that is informed by each person’s unique clinical, genetic, genomic, and environmental information. Because these factors are different for every person, the nature of diseases — including their onset, their course, and how they might respond to drugs or other interventions — is as individual as the people who have them. Personalized medicine is about making the treatment as individualized as the disease. It involves identifying genetic, genomic, and clinical information that allows accurate predictions to be made about a person’s susceptibility of developing disease, the course of disease, and its response to treatment.”

One way big data analytics will help is making treatment more personalised, is through the use of wearables collecting data from the individual’s health in real time. The research on chronic and degenerative diseases such as diabetes or Parkinson’s for example, will considerably benefit from the use of wearables to compare data about patients’ evolving physical states across large populations.

Nearly 350 million people worldwide have diabetes, according to the World Health Organization, with about 50% of them likely to die from cardiovascular disease and stroke. Cheap blood glucose level testers, from companies such as Sanofi Aventis, can now be attached to smartphones, collecting data and recording it for analysis. This information can be combined with other data from exercise and diet apps to build a more complete picture of the patient’s lifestyle. David Sibbald, chief executive of informatics company Aridhia, explains: “We’re working with app and insulin pump providers to bring their data together with existing clinical data to allow patients to become much more involved in the management of their own care”.

Now that sequencing human genomes is getting faster and cheaper, another exciting development is the combination of big data analytics and genomics to tackle diseases such as cancer. DNA sequencing machines from Illumina can map a human genome in a day, potentially bringing cost to under $1,000. Wayne Parslow, European manager for US healthcare analytics company, at MedeAnalytics believes that: “As more and more information is known about us, the more tailored our treatments will become. Soon we’ll be carrying our own personalised health plans around with us”.

Before achieving the integration of big data in healthcare, however, key challenges will need to be addressed. The ability to align and compare multiple data points from various sources and identify previously unknown factors involved in disease requires to integrate and link data from multiple sources.

The primary challenge is the vast amount of data in existing systems that appear in different file types and currently don’t communicate with. Bringing all these disparate data sets together and standardising them is a big challenge that business analytics companies are attempting to tackle. SAS company is currently handling the aggregation and anonymisation of the Royal Brompton and Harefield NHS Foundation data. Dr Cliff Morgan, chief clinical information officer for the Trust, told the BBC: “We’re heading down the road to a completely digital health care record. But a lot of this data is in physically separated databases – there are about 400 in our hospitals alone – and so far we have brought 60% of these together into one clinical data warehouse.”

The second challenge for data in the clinical space is how to store and share these large amounts of data while maintaining standards for patient privacy. Data sharing is key to such advances, but privacy concerns continue to rise. NHS England recently postponed its care.data project for six months to give the public more time to understand it.

Although we are in the early days of healthcare big data, it is clear that strategies for big data in the integration of healthcare data have the potential to help in lowering costs, and realising the future of personalized medicine.

We are running a Big Data in Pharma Briefing on 23rd September in London. If you are interested taking part in our interactive discussions, roundtables, and workshop, on how you can implement big data strategies in pharma and healthcare, book you place to attend now.

 

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