The Future of Predictive Healthcare

In terms of big data and how it relates to the healthcare field, numbers are constantly expanding. This isn’t only a comment on the number of patients being treated, but how the field is working to reinvent itself. Last year alone, big data in healthcare netted $30 billion – to a market that has yet to tap into a fraction of its potential.

In the mean time, however, data pools are growing as well, with the same falling-short-of-what-it-can-do results. Until both patients and healthcare providers jump on the bandwagon, this is a trend that’s apt to repeat itself. Like any great idea, predictive healthcare can’t see its full potential without user involvement.

The Future

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.


Data Pools – Where do They Come From and How Can We Use Them?

Data pools, information overloads, figure collection – whatever you wish to call them – these conglomerations of information hold a great deal of potential in the healthcare field. From predictive diagnoses to determining which treatment options provide better results, big data is working to overhaul the way healthcare is performed.

As for the figures themselves, these growing data pools are located virtually everywhere. By collecting patient information every time a person arrives for treatment (or a prescription, or enters their info online), companies can keep track of demographics and their respective ailments. Over time, patterns begin to emerge as to what ages are more likely to develop which sickness, and so on.

But how can we use that data?

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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