For weeks we’ve been talking about just how big, big data has become. From population growth, record collection, and a growing understanding of illnesses, its numbers are quite literally growing off the nonexistent charts. But today we bring the facts. Not only do they incoporate figures and growing trends, but they let us know just how likely doctors are to jump on these analytics bandwagons. Sit back, relax, and prepare to be amazed.
- 10. New York City has more hospitals than Seattle, Houston, and Detroit combined. (They also employ the most doctors – more than twice that of Los Angeles.)
- 9. The U.S averages almost $8,000 in healthcare expenses per capita. Norway comes in second place at less than $5,000.
- 8. In 2010, 30.74% of the country’s healthcare expenses funded hospitals; in comparison, less than 2% went into research.
- 7. Over the next two years, hospitals expect their revenue sources from risk-based financial reimbursements to double – from 9% to 18%.
- 6. 75% of hospitals are not exploring accountable care organization models (ACOs).
- 5. In a controlled test, adverse reactions to pediatric drugs fell by 40% in just two months – with the help of analytics.
- 4. In 2009, the U.S. spent more on healthcare than Great Britain’s entire GDP.
- 3. Back in 1970, the average household medical expenses came in at $370 per year.
- 2. Just three years ago, the U.S. spent nearly $2.5 trillion on healthcare. It’s projected that that number will rise to a whopping $4.5 trillion in 2019.
- 1. If the United States’ healthcare system was a country, it would host the world’s sixth-largest economy.
Whether believable or not, these stats represent America’s current healthcare situation. But with the help of analytics, these fees can be evened out, along with coverage and equal care.
Stay tuned for even more facts on big data.
To say there’s an overwhelming amount of healthcare data available is an understatement. In fact, it might just be the mother of all understatements. There is more data than we can conceivably consider trudging through in a single lifetime, let alone those of generations to come. And if that’s not enough, there’s a steady flow of more coming in.
So how do we mine through this abundance of information – information that’s in no particular format?
For instance, say a patient appears fine, but has elevated blood count levels, has been sleeping more frequently, and was diagnosed with a new strain of influenza last year. By crunching other patients’ data, the computer can tell us what sicknesses this patient is susceptible to, and whether or not those few symptoms are anything to look out for.
The Nitty Gritty
As for the data mining itself, all that’s required is for doctors to input their information. Though they make take diligent notes, the data is useless without computer intervention; once the numbers and software have a chance to meet, infinite perks can be had.
If it were left to humans, numbers would come in faster than they could be analyzed, inevitably useless finds. But with software that is constantly upgrading, users are able to create progressive, positive results from the files they already keep.
Ready to learn more? Head to our Case Studies page to see healthcare analytics in action.
In the grand scheme of things, it’s always ideal to look for solutions that help everyone involved. Patients, doctors, healthcare workers – everyone down to the person who handles the billing. In most cases, that magical, cure-all answer doesn’t exist. With big data, however, helping all sides is just the beginning to what this growing solution can provide.
Because of the mere nature of big data, it’s meant to help both ends of the spectrum. Doctors can see patterns to help predict illnesses, while patients can be made aware of upcoming epidemics, likely healthcare risks, or what treatment options statistically worked best. The less time a patient has to spend under doctor or facility care, the more money they will save.
In a study performed at the University of Ontario Institute of Technology, data on premature infants was followed to see how preemptive measures could help improve patient care.
Specifically, the study surrounded nosocomial infections (hospital-acquired) in premature babies. These infections can be extremely dangerous, often fatal, to fragile patients. The study found that hospital monitors were able to record data that showed subtle changes in the infants 12-24 hours before any symptoms of infection were visible. Because the changes are so gradual and monitor data is too frequent and overwhelming, the brain can’t process it without help.
However, with data analysis, the same illness in the same timeline, can be quickly diagnosed; doctors were able to start treatment a full 24 hours before the infection would have been recognizable by humans. All that was needed was a little software to handle information that was already being collected.
These and other forms of early warning signs are working to greatly improve patient care and health – and at minimal costs to the patient.
Head to our patient and customer profiling page to learn more.
