Big Data has changed the way we manage, analyze and leverage data in any industry. One of the more promising areas where big data can be applied to make a change is in the healthcare industry. Healthcare analytics have the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases and improve quality of life in general. The average human lifespan is increasing, which poses new challenges to today’s treatment delivery methods. Healthcare professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for best strategies to use these numbers. Here are a few of the ways big data can help the healthcare industry.

Reduce Costs

As with many other industries, there is enormous potential for cutting costs with big data in healthcare. There’s also an opportunity to reduce wait times, which is something that costs everyone money. By using predictive analytics to predicting admission rates over the period of a few weeks, hospitals can then allocate staff based on those numbers. There are so many other ways hospitals could cut costs using predictive analytics, but few organizations have done so yet. Hospital budgets are complex, and though the ROI potential is high, some organizations are simply not ready to invest in big data.


According to one study, the healthcare industry is 200% more likely to experience a data breach than other industries, simply because the personal data is so valuable. With this in mind, some organizations have used big data to help prevent fraud and security threats. Some measures are hardly new, but have seen great improvement in recent years, such as encryption technology, firewalls and anti-virus software. Others have arisen in response to the proliferation of devices and the Internet of the Things, including mobile device management and data loss prevention tools.

Identification of High-Risk Patients

Many healthcare systems have to contend with high rates of patients repeatedly using the emergency department, which drives up healthcare costs and does not lead to better care or outcomes for these patients. Using predictive analytics, some hospitals have been able to reduce the number of ER visits by identifying high-risk patients and offering customized, patient-centric care. Currently, one of the major hurdles to overcome in identifying high-risk patients is lack of data. Overall, there are simply too few data points, making it near impossible to get an accurate picture of the real risks, as well as the reasons for these risks.