Tuesday, November 5, 2013

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.

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Posted by Bethaney at 6:17 AM
Labels: Analytics, Consumer Engagement, mHealth, Population Health | Leave a comment
Tuesday, November 5, 2013

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.

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Posted by Bethaney at 6:08 AM
Labels: Analytics, Connected Health, Consumer Engagement, Population Health | Leave a comment
Tuesday, November 5, 2013

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.

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Posted by Bethaney at 5:27 AM
Labels: Analytics, Connected Health, Consumer Engagement, Population Health, Uncategorized | Leave a comment
Thursday, September 19, 2013

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.

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Posted by Bethaney at 4:42 AM
Labels: Analytics, Connected Health, Consumer Engagement, Health Information Exchange, mHealth | Leave a comment
Monday, September 16, 2013

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.

Check out our analytics offering to learn more.

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Posted by Bethaney at 4:39 AM
Labels: Analytics, Connected Health, Consumer Engagement, Health Information Exchange, mHealth | Leave a comment