However, that doesn’t mean the market isn’t growing at an impressive pace. According to insurance and data experts, big data is the next solution in healthcare. With a potential to create more than $300 billion in value every year – by leveraging the facts and results it provides – more and more patients can see the benefit from this ongoing analysis. This is true both of physician awareness and of preventative measures.

In New York’s Presbyterian Hospital, computers have been programed to analyze ongoing risk factors of its patients. (The same factors that are most often overlooked by human error.) By integrating that software with big data, the hospital has already seen a decrease in potentially fatal blood clots by 30 percent. And that’s only the beginning – imagine what these computers could do when programed to catch multiple human oversights, and receiving a constant flow of updated figures.

Big data can also work to target specific risk factors by population, age, location, race, and more. By combining virtually every factor into a common structure, healthcare can work together with its patients to find more effective and efficient long-term solutions.

By crunching and analyzing it to find repetitions and similar situation outcomes.

For instance, in 2008, the California Public Employees’ Retirement System (CalPERS), the second-largest healthcare purchaser in the nation, set out a plan to reduce their costs. Within its first year, the plan did not increase patient fees (previously costs increased 8-12 percent per year), while saving more than $15.5 million.

Through the help of analytics, CalPERS was able to lower expenses just by predicting subsequent patient care. This study included 41,000 of CalPERS’ 1.3 million employees, and reduced fees through:

• 15 percent reduction in inpatient readmissions – within 30 days of plan enactment
• 15 percent reduction in inpatient days per 1,000 hospitalized study participants
• 50 percent reduction in inpatient stays of 20 or more days
• A half-day reduction in average patient length of stay

The study looked to monitor:

• Population-specific utilization management – through a coordinated operational infrastructure (such as big data analytics)
• The elimination of unnecessary utilization and non-compliance
• Improved clinical and resource variation among physicians
• Reduced pharmacy and utilization costs, among other areas of data

By combining efforts and recreating CalPERS study on a wide-scale scheme, their success rates can grow only respectively.

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