The analysis of large data sets could help health care providers and policymakers make better health care decisions and predict future trends, USA Today reports.
For example, big data could:
- Help insurers revamp how they predict costs;
- Allow patients to inform physicians about which medications will not work with their particular genomes; and
- Let researchers use real-time hospital data to identify the most effective, low-cost ways to treat patients.
Comments From Experts
Eric Schadt -- director of the Institute of Genomics and Multiscale Biology at Mount Sinai Hospital -- said several factors have made such data analytics and genomic research possible, including:
- Changes in the law;
- Financial incentives; and
- Public demand for better health care and outcomes.
Schadt also noted that the cost of genomic sequencing -- about $3,000 per patient -- is "rapidly approaching the cost of a standard test, such as an MRI or PET scan."
According to USA Today, combining patients' genetic information with big data analyses could allow doctors to determine:
- Potential illnesses and treatments; and
- Which treatment will work best for which patients.
However, several barriers and concerns remain with the use of big data.
Winston Hide -- associate professor of bioinformatics and computational biology at Harvard School of Public Health -- noted that privacy concerns are prevalent with the use of such data sets. For instance, publicly available health data could lead to employment discrimination or data breaches.
In addition, Schadt said many patients and providers "simply are not aware" that such technology is available.
"I think in five years' time, we will be talking about advances in several different areas such as cancer that are routinely impacted by big data," Schadt added (Kennedy, USA Today, 11/24).