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.


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.

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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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Analytics Explained: How the Internet Evaluates Info

For most, the term “analytics” refers to any kind of data that has been calculated, reformed, and translated into a usable manner. This can be in almost any form of information – from web traffic to healthcare stats collected across the globe. Crunching – or analyzing – these results are the only way to turn them into workable information.

But how exactly does the process work?

Online Analytics

Through online analytics, companies such as Google or Bing integrate various computer programs and algorithms to track website activity. This includes traffic, where web hits came from, keyword searches, and more. Website owners then receive a list of charts comparing numbers to one another, as well as previous months’ figures.

Healthcare analytics are much similar – except for how they are inputted. With the Internet, information can be automatically collected. Healthcare big data must be manually updated to a database (which can include software). However, once put into place, this data can help translate an infinite number of big data. But unlike online analytics, which shows business owners from where their Internet traffic is directed, healthcare analytics has the ability to help people. Doctors can make predictive diagnoses, see trends or similar cases, while patients can see a bigger bang for their buck.

And without analytics in the healthcare field, megabytes upon megabytes are collected without serving a purpose.

Healthcare Analytics Breakthroughs

Today, Accountable Care Organizations (or ACOs) are building models to better serve healthcare data. At once the task was seen as too large to tackle, but considering the infinite benefits to be gained by documenting big data, researchers knew the hurdle must be overcome. By incorporating these same Internet-based logistics into the healthcare field, ACOs have made serious headway.

While the end result is far different between the two entities, the process is very much the same. Check out our resources page to find more today.


What Can We Predict? Data as a Crystal Ball

In a world of future telling and crystal balls, the idea of predictive diagnosis is a simple one. We would simply look into the future, see what it held, and make the necessary adjustments. In reality, however, what’s to come is far less telling. Rather than having the answers given to us, we have to look into the given data, and use it as a tool.

The information itself – known as big data – comes from healthcare professionals from all over. Doctors take notes, see patient information, and then it’s combined into a whirlwind of numbers and facts. Then by analyzing those same numbers, patterns begin to take place, which can then be used for an educated guess into the future.

Foreseeing the Future

Today, those using these data analyzing techniques are known as early adopters. These early adopters compile and configure to help both patients and doctors. For instance, with data, doctors can see what percentage of the population has a certain disease, chronic sickness, and what symptoms they had during the process. For instance, doctors can look at recent census data and see that each physician averages 511 hyperlipidemia patients and only 145 diabetes patients. Therefore, statistically, it’s more logical for doctors to study hyperlipidemia treatment options, check for those symptoms, and have accompanying literature in stock.

Likewise, doctors can see what percentage of patients experience medicine reactions and treatment success, and base their prescriptions accordingly.

Additionally, data can be used to predict potential epidemics; patients can be informed of their susceptibility based on visit dates and their medical past. Doctors can also see which illnesses patients are more likely to catch, based on growing stats vs. pre-existing conditions. With this knowledge, preventative measures can be taken and patients, with their doctors’ help, can be better informed as to how to improve their health.

Head to our patient/customer profiling page to learn more today.


New Ways to Help Your Patient: Using Data

As healthcare professionals, the main goal is always coming up with new, more efficient ways to help others stay healthy. Whether that be in the form of a treatment option, medicines, or billing that better utilizes insurance companies, these tactics work toward a common goal. Oftentimes this means waiting for new software or scientific breakthroughs, but even when instant isn’t always available, there’s a mindset to keep moving forward.

However, that next big breakthrough just may be around the corner with big data. By analyzing numbers, figures, probabilities, and then running stylized algorithms, patterns begin to emerge. Patients with similar symptoms can be flagged, doctors can be notified of like cases, and all through the help of the computer. That means there’s no extra legwork to be done – whether on research or on patients. Additionally, entities will be given the freedom to explore their own research.

By collecting and packaging data in a central, searchable location, big data becomes infinitely helpful. Through the help of charts, unified search terms, and pattern identification – all of which contribute to multiple ways to streamline healthcare – wellness becomes an easier way of life, from both the entity and the patient’s point of view.

With Big Data Analytics, Healthcare Professionals Can:

  • Collaborate on similar cases
  • Compare patient symptoms
  • Gain easy access to charts
  • Collect sickness information
  • Predict illnesses or clinic visits
  • Perform quicker, more accurate diagnoses
  • Create charts and graphs for easy-to-follow data translation

In contrast, when big data is unformatted, it is practically useless; there are simply too many numbers floating through charts to make any sense of what they can offer. But by harnessing the information that’s already available, wellness professionals can work together to make great strides in an efficient, healthcare breakthroughs.


What is “Big” Data?

As early as toddlers, humans learn the word “big.” Small and little are used of child-size items, while adult-sized clothing, dishes, and furniture is said to be big. But just how big can we get? Physical items can only grow so large, but when “big” refers to something we can’t grasp or see, such as data collection, the size becomes seemingly infinite. It’s hard to track, impossible to stop, and requires non-stop efforts to sort out its millions of sections. Now that’s big.

This data can come from all corners of the world – the Internet, school classrooms, store statistics – the holders are almost as ongoing as the data themselves. But if this data is so absolutely huge, how do we track it?

Breaking Data Down

Within the medical field, big data comes in two forms – structured and unstructured. Structured data is that which can be found on a computer or database where it can be used in deciphering analytics. Unstructured data refers to doctor notes or patient charts that are handwritten/typed, but not yet entered into a system. There is some lag time with inputting this data – and combined with a growing number of patients and treatment options, the flow never stops.

Just how big is the data? Every year the amount of information in existence doubles, much of which is big data. As of last year, each day 2.5 quintillion (2.5X1018 ) bytes of data were created, bringing the grand total to well over 2 zettabytes (that’s over 2 billion terrabytes). Analyzing this info, however, can also provide a great deal of answers, especially in health care. Big data can help predict epidemics, allow doctors to compare symptoms with similar cases, provide more efficient patient care, and more. The benefits are almost as endless as the data themselves.

But without analyzing, it’s just data without a purpose.

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How Analytics Can Help Healthcare

When data is big, so big that it never actually stops growing, how can we learn from its information? In theory the facts are helpful, pointed, and come from so many variables that practically no group has been undocumented. But when it comes to sorting it all out, where do we find a starting point? Thanks to analytics, software that tracks down the impossible, big data, hospitals are seeing the benefits of their tedious records.

But it’s not just information these analytics are compiling, it’s what to do about them. By crunching numbers, symptoms, and other probabilities, healthcare professionals could just see actionable results. For instance, if a patient has symptoms A and B and has been to the doctor four times in the past year, analytics could help predict a specific cause. It could also compare visit dates and compile which seasonal sicknesses each patient is susceptible to catching.

Cost Effectiveness

Perhaps the biggest benefit to this big data analytics is the lack of expense. While the data itself needs to be deciphered, there’s no outside research involved. Healthcare providers can gain access to hundreds of predictable diagnoses by using the data they already compile. Doctors can also help more patients while spending fewer resources and time on individual scenarios.

Additional Perks

  • Patients can expect better value from their healthcare
  • Lowering the burden on state-funded programs
  • Reducing the impact of staff shortages
  • Increasing healthcare treatments and delivery
  • Finding patterns among diseases and their effects

To date, 90 percent of the world’s data is unstructured. However, by analyzing these numbers, specifically those related to healthcare, there is much to be gained. Professionals can host better treatment options while spending less time and money, while patients will receive better options and value – a win-win situation to adjusting the public’s view of health.