Personalized Medicine & Big Data: Are We Finally Moving Forward?
Did you know that the concept of personalized medicine dates to the days of Hippocrates – the father of modern medicine? Of course, Hippocrates did not have access to the amount of data today’s medical professionals have available to them.
In our modern times, the spark for personalized medicine was lit primarily by knowledge arising from the Human Genome Project at the beginning of this century. In recent years, the pace of data generation has become staggering. The Institute for Health Technology Transformation reports that by the end of 2013, U.S. healthcare organizations had generated 150 Exabytes of healthcare data.
With all this data, it seems logical that it can and should be leveraged to revolutionize medicine and impact both public and personal health. Let’s focus in on personalized health and medicine.
What is Personalized Medicine?
The Personalized Medicine Coalition defines personalized medicine as:
An evolving field in which physicians use diagnostic tests to determine which medical treatments will work best for each patient. By combining the data from those tests with an individual’s medical history, circumstances and values, healthcare providers can develop targeted treatments and prevention plans.
Connecting the Dots Between Big Data & Personalized Medicine
The discussion around big data’s role in personalized medicine has been buzzing for several years now. However, it’s promise has yet to come to fruition as healthcare entities and providers struggle to leverage big data for personalized care.
Certainly, lessons can be learned from other industries and large companies like Google, Apple and Amazon who have developed solutions for managing and analyzing big data. However, it’s important to note that these examples center on management of mostly homogenous big data.
In healthcare, we are dealing with data from multiple sources, including:
Clinical data: e.g. medical diagnosis, medical images, patient histories, labs
Omics data: genomics, transcriptomics, proteomics, epigenomic, nutriomics, etc.
Claims data: data from public and private claims processed across health systems and providers
User-generated data: fitness apps, data from smart phones, etc.
Big data in healthcare translates to processing large amounts of both structured and unstructured data about individual patients. Traditional technologies can’t do this well – this is where big data technologies come into play. Integrating this data with new technologies into “smart data” – data that can be used in a meaningful way for clinical decision making – is no easy task, but it is increasingly possible.
Technology to Move Personalized Medicine Forward
Researchers in BMC Medical Genomics note a growing gap in our abilities to generate and interpret omics and other data, noting that the bottleneck is less about the ability to generate the data and more about managing, integrating, analyzing and interpreting it.
They go on to cite the following investments for success:
Infrastructures with cutting-edge omics facilities and analytics tools
Advanced digital technologies (high computing performance and storage resources)
Highly-qualified multi-disciplinary teams
Investments in security and privacy
The evolution and application of cloud-computing
The Business Model for Personalized Medicine
An ongoing risk for personalized medicine is that it will be contained to middle- to high-income individuals and countries. To make personalized medicine available to the many, technology platforms will need to evolve to become scalable, more efficient and affordable.
Additionally, healthcare organizations providing personalized medicine must be able to realize a return on their investment. To offer it, it must make economic sense and fit into their larger strategic plan and service line mix.
The Shift from ‘One Size Fits All’
The promise of personalized medicine is the shift away from ‘one size fits all’ medicine. In summary, shifts within healthcare, as outlined in a Gartner article ‘Personalized Medicine is Approaching Faster than You Think!’, will continue to move us in the right direction.
Falling costs: Now it takes a few thousand dollars to sequence the human genome, compared to more than a billion USD in the ‘90s.
Electronic health record adoption: Notably national EHR programs and initiatives that pull data from multiple healthcare organization, labs, etc.
Government lead directive: While the direction of U.S. healthcare is a bit uncertain, most agree we will continue to move toward a value-based versus volume-based model and government will, hopefully, continue to invest in advancements in personalized medicine.
Industry collaboration developments: Continued cross-collaborations between industries and public-private partnership will fuel momentum.
Patient-led demand: Consumerism is really taking hold in healthcare and patients will likely drive the demand for personalized medicine, wanting more choice and involvement in their healthcare.
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