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.


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.

Check out our analytics offering to learn more.


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.