Nobel in Economics Is Awarded to Richard Thaler

WASHINGTON — Richard H. Thaler, whose work has persuaded many economists to pay more attention to human behavior, and many governments to pay more attention to economics, was awarded the Nobel Memorial Prize in Economic Sciences on Monday.

The Perks of Analyzing Unprocessed Data

When stopping to consider the logistics of unprocessed data, it can be compared to that of unprocessed food. Let’s say a squash. In its original state it’s whole, untouched, and has possibilities galore just waiting to be explored. It could be fried, baked, or turned into spaghetti or soup; if it can be thought of, it can be made. However, if it sits long enough, the squash will rot and ruin the entirety of its potential. Just wasting away.

The same can be said of unprocessed data – when filtered correctly, it becomes a delicious, helpful, entity. But when ignored for too long, it’s just another mess that needs cleaning up.

Which is why it’s all the more important to process important medical data while it can still be used. This information already exists, it simply needs to be picked, sliced, and cooked into a helpful, learning process.

Through the help of specialized computer applications, this data is crunched and made to create patterns and figures. Those results then tell doctors which patients are most likely to become sick, be cured, and what medicines can help them along the way. Then that patient’s info is also added to the stats, and so on and so forth.

Added Benefits to Crunching Data

  • Better utilization of existing numbers
  • Improved patient care
  • Reduced doctor visits
  • Reduced medical treatment fees
  • Help to eliminate prescription side effects
  • Earlier diagnosis rates
  • Better utilization of doctors’ and medical facilities’ time
  • More thorough understanding of patient risks and outcomes

Considering this information already exists within medical facilities, there is a goldmine of benefits to be had. All that’s needed is a little bit of software for patients and healthcare providers alike to start seeing these overwhelming positive effects.

Ready to start connecting the dots? Check out our healthcare expertise page to see how medical analytics are helping others.


Why it’s Time to Start Reading Doctor’s Handwriting

For decades patients have been making jokes about doctors’ handwriting. Their chicken scratch prescriptions, scribbled notes, and barely readable John Hancock, have long since been filling notepads and medical charts. And while there may be plenty of evidence as to just how bad their handwriting may be, it’s high time we all get past it. 

Why? Patient symptoms, medicine side effects, and more all sit within these doctor diaries, which could mean countless pieces of helpful information. And rather than assuming the notes can never be read, doctors and medical workers alike need to extract that information for the common good. By imputing those pages of data into a computer, it can then be used by preventative and predictive analytics. Specialized software can interpret patterns, and then use it to help keep others well.

Translating the Unreadable

Taking handwritten notes and putting them into a computer may sound like a great job for an intern, but there’s no guarantee the data can be accurately read. (Or understood.) For doctors whose handwriting is so supremely bad that they’re the only ones that can read it, that may mean extra work. However, by using voice recognition programs or even dictating to a person, medical professionals can make short work out of moving important texts. And once in typed format, the notes can then easily be translated into the software or other program.

Whether on notepads or stored away in a personal computer file, it’s time for doctors notes to be shared. Without placing this data into growing analytics programs, there is a huge chunk of data missing from predicative accessibility. In contrast, there are hundreds of studies to be advanced, along with the patients they represent.

Head to our strategic insight page to see how you can get started.


10 Unbelievable Stats On Big Data in Healthcare

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.


Mining Through the Unformatted Data in Healthcare

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?

With the help of computers. Even with machines this may not be an easy task. But, with the help of specifically written software, computers can sort through numbers at lightening speed, turning them into something useful. Set up much like online software analytics, algorithms allow computers to recognize specific patterns – most importantly, those that indicate danger or the onset of sickness.

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.


Who’s Analyzing Big Data in Healthcare?

In the world of big data, it can be hard to pinpoint who’s doing what, and when. There are simply too many sectors to consider. However, that’s also big data’s MO; it’s BIG – 30 billion pieces of content shared every month on Facebook, 40% global data growth projected per year, and 235 terabytes of data collected by the US Library of Congress in 2011. And that’s just a small piece of the pie.

Between all the collecting and analyzing news, it has to be done by someone, right?

So who is exactly behind all the number crunching here? It depends on how you look at it. On a broad scale, virtually everyone contributing to data is helping with the insight. That means websites, social media platforms, cell phone providers – and that’s just in the non-medical sector. But if we dig a little deeper into healthcare analytics, that list of who’s doing what is reduced greatly. Medical software companies, healthcare facilities, insurance providers, and data crunchers (such as Scriplogix), are all working toward a similar goal: to redefine healthcare data.

In the U.S. alone, the potential for revenue in healthcare is $300 billion, with the potential for $600 billion in annual consumer surplus. All with the help of analytics. This also means more jobs, more efficient care, and fewer costs for the patient and insurance companies – winning all around.

Today, Scriplogix, along with other early adopters, are working hard to ensure this data goes to good use, and will continue to do so as the information pool grows.

Ready to learn more? Check out our About Us page to see how analytics are helping our clients.


Doctor Collaboration Tips to Cut Working Hours

Even the man with the best job in the world gets tired of working. No matter how wonderful, fulfilling, or well paying the job may be, people need breaks. Think about it, Willy Wonka made plans for retirement, Tom Hanks hated being an adult – even with all of those toys – and Richie Rich just wanted his parents back. Despite all the incentives and the love of the field, there really can be too much of a good thing.

Add in patients, fees, and lives that are at risk, and it’s less about taking a break and more about helping others. But thanks to breakthroughs in analytics, doctors no longer have to pour over books and data – exclusively at least. With predictions and numbers that outline scads of patients with similar symptoms, doctors have a fall back to help predict the future. And they can do it by working together.

But even with help, the best doctors still need a plan for efficiency.

To help cut hours without cutting results, look to the following:

  • Closed circuit networking – find others in a similar field or compare cases by reaching out online. Doctors can then schedule meetings or share data to cure patients in less time.
  • Look to analytics – with big data in play, remember to use analytics’ findings for helpful insights to virtually any medical situation.
  • Ask for help – talk to colleagues, call specialists, or email former contacts. If they worked on a similar case, they may just be able to help.
  • Consider an assistant for collaboration scheduling. This way you’re not wasting time sending emails or leaving messages with others.

With just a few simple, yet meaningful steps, doctors can help treat more patients in less time. By collaborating with others and with analytics, we can start to fill in many of the growing medical gaps.


Catering to Patients: How Data Can Cut Patient Costs

In virtually every medical bill that’s ever been mailed, there is a breakdown of charges. Fees for one’s hospital stay, charges for tests and medicines, and possibly even bills from different departments. Because of the way healthcare entities are set up, it’s virtually impossible to send a single bill without showing where each dollar is due. Though it may take more time to decipher, the current set up protects both the service providers and their patients.

However, that doesn’t mean the fees themselves can’t be reduced. With big data prediction and analyzing, services (and therefore their fees) can be greatly reduced. By having a better idea as to what will happen – symptoms, diseases, treatment options, etc. – doctors can use fewer resources to pinpoint each issue.

One of the most effective ways to reduce patient costs is that of preventative care. This can come in the form of patient education, or by using data to determine who is more likely to develop which symptoms. Ages, past health, demographics, and more will also work to make these predictions more accurate. Obviously, the more tests and doctor time that’s needed to diagnose a patient, the higher the bill.

Predictive diagnosis also comes in lieu of the upcoming healthcare reform (PPACA), which is meant to encourage doctor collaboration and reduce patient fees … for both individuals and government-funded accounts. By incorporating software that performs more efficiently than humans ever could, more results are had for less money.

Over time, it’s projected that these perks will only grow. As more data becomes available, as well as the ways in which it’s analyzed, the journey to reduce patient fees becomes a steady, streamlined process.

To learn more about big data and what it can do, click the tabs above.


The Doctor’s Hidden Tool: Cutting Diagnosis Time with Data

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.

Data Crunching in Action

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.


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.