This is Part 2 of a three-part blog post. Emphasis of Big Data acquisition and analysis is supposed to improve healthcare. We emphasize that healthcare collaboration is a way to deal with the massive amounts of data considered to be medical knowledge that has grown beyond mastery of anyone. That said, we also are concerned that attempts to improve quality and profitability remove focus from care and caring. The first post described one source of errors. This second post will show how this error can happen in clinical practice -- with or without healthcare collaboration. The third will suggest a solution. The whole set of the three should bring fair warning to those who hear the sirens of Big Data which is done poorly, and help them look to solutions which are done well.

The past few weeks have brought huge attention to the analysis of data to health care. Much promise has been given to disease detection, epidemiology trends, bioterrorism, and business intelligence. Savings are promised as well as improved care.

iClickCare helps erroneous interpretation by including images, words, discussion and judgment.

EMR and EHRs with financial metrics are the foundations upon which Big Data is being built. They are taking up a considerable part, even a majority, of the day in the life of the provider. What of the rest of the day, a day like this one? To be kind in description, less than user friendly EMR's are being inserted into the office work flow. This process is called “Go Live” which is a stretch of definition of zombie-like software.

All of a sudden four exam rooms are filled and three more patients sit anxiously in the waiting room. The first exam room is occupied by an elderly, somewhat demented, 86 year old with black and blue eyes following a fall. His facial fractures are multiple, and not classic. There are fractures across and within the edentulous maxilla, the nasal bones are involved. There is an extension to the orbit on one side. The zygomatic arch is displaced. With difficulty, they could be classified as LeFort II and a half. Not exactly, but sort of close enough. For patient care, the classification does not matter, the treatment will be the same. We will need to code this visit properly, we know. Why? Because and it will take searching through lists of codes.

The second room corrals a crying 13 month old who has just been sent to the office from the emergency room with a dog bite of the left lower eyelid. She is accompanied by two distraught parents and one distraught grandmother. The next room in line finds a pleasant demanding 54 year old who has body dysmorphic syndrome and is very happy from the scar after the facial basal cell carcinoma that was removed but is also very concerned about her aging body, skin turgor, wrinkles and cheek bones.

The fourth room has another child with a facial deformity and what his mother describes as moderate to severe autism. He is accompanied on the visit by his brother who shares the same spectrum disorder. There are multiple sharp-edged drawers being opened and slammed closed. The mother’s third child is to be picked up from preschool in just a few minutes. Is surgery to be done?

The fifth room is quiet. Just a calm patient who is thankful for the care, and doesn’t mind waiting. Who would want to keep her waiting? Another form of pressure rests within.

We know that all who care for patients find themselves in similar storms. In part, this exacerbated by patient expectation, such as this Scrubs blog from one our Pinterest followers. In part, it is further caused by the frustration of not doing as smooth a job of healthcare delivery as was possible with less mechanical documentation.

Can you see the potential for error in this scenario? More to follow in Part 3 when we will describe the error and suggest a solution...

This a three part blog post. Emphasis of Big Data acquisition and analysis is supposed to improve healthcare. We emphasize that healthcare collaboration is a way to deal with the massive amounts of data considered to be medical knowledge that has grown beyond mastery of anyone. That said, we also are concerned that attempts to improve quality and profitabilility remove focus from care and caring. This first post will describe a source of error. The second will show how this error can happen in clinical practice -- with healthcare collaboration or not. The third will suggest a solution. The whole of the three should bring fair warning to those who hear the sirens of Big Data which is done poorly, and look to solutions which are done well.

Here is the problem. Many companies are moving from providing big data services for government to big data services for healthcare.

These companies need to understand what they are getting into.

We, as health care providers, need to realize that we affect the outcome of big data as we provide care alone or as we are part of healthcare collaboration.

Since healthcare collaboration is about taking care of the problem at hand, together, and patient care is about helping another human being either get better, be comfortable, or die peacefully, data entry and codes have no relevance.

On the other hand, we providers are asked to be data input specialists even though we don’t see a benefit for our patients. We have the suspicion that we are working for government -- and thus a boss who regulates; or insurance, and thus a boss who underpays. 

And as happened in the office last week, we are sorely tempted to underperform as data input specialists. Thus, all of you big companies who have migrated from the TSA, CIA and NSA with your Big Data tools should be forewarned. Any of you providers, who see no value in coding precision, should consider the consequences.

Let’s use rounding errors as an example. Then compare the rounding errors to what we sometimes are tempted to (or need to) do as clinicians.

Kees Vuik of the Delft Centre for Computational Science and Engineering clearly and understandably highlights some disasters caused by numerical errors. These range from a Patriot Missile failure which killed 28 soldiers after it missed intercepting a Iraqi Scud missile, to errors on conversion of the Euro. In essence, they all (and there are many other examples) have the same root cause: the magnification of minute inaccuracies of a number by repeated calculation.

iClickCare helps erroneous interpretation by including HIPAA compliant images, words, discussion and judgment. While healthcare collaboration is taking place, accuracy is being increased.

The next post, Part 2,  will describe in detail how simple rounding errors will bring Big Data, Bad Data...

A second thought about Big Data, EMRs and healthcare collaboration.

Another plausible explanation is that the management of Big Data is doing what it is supposed to do. However, the unintended consequence is that billings increased.

Maybe what has been discovered is that a whole lot of free care is now being caught in the documentation sieve. Rightful reimbursement is given for work done now, it is just that no one was aware of it before.

Below is a non-medical analogy involving 3 ways to act when a stranger approaches and asks for directions. Assume the stranger is not a native speaker. 

#1. You tell him the directions -- walk one-half block until you see the store with the red front, turn right. You bill for Code ZDP994564.

#2. You quickly and sternly walk him to the corner, a half block away, and point down the street. You bill for the same code, and add the modifier -- 77 because you are completing the EMR form to exactly describe what you did.

#3. Or, you smile, ask him where he is from and how his visit is going, walk him down to the corner, turn right, and walk with him another half a block until he sees what he is seeking. Was there a modifier for that? Was that billable as “public relations," assistance at a higher level, or counseling? 

In either of the 3 ways, you provided a service. If you coded at different levels, isn't that what was asked for? The Garbage-In is just different. Are we as a society getting what we wanted or just getting what we have asked for -- more precise data? And what was free (free as a smile) is accounted and billed for.

iClickCare enables healthcare collaboration by combining words and images with discussion. 

We write a lot about healthcare collaboration. But this is interesting. An editorial in the Wall Street Journal, and report in the NY Times notes that EMRs and EHRs increase healthcare costs.

Design, promote, and sell something which is based on billing and regulations rather than on patient care.

Teach its efficient use over 10 to 20 hours.

Is there a surprise that billings increased? And productivity has decreased? 

Is this hyperinflation of big data?

Sometimes a well aimed gun misfires and explodes backward (the breech). Is this a breech explosion? Really, probably not, the gun may not have been aimed right to begin with. The best aim would be designing better care for the patient that is also cost efficient for the country. EMRs and EHRs are doing what they were designed to do -- bill and regulate.

Big data is a large part of our lives as medical professionals. And the demand for big data is growing. Here are but a few examples that each of us encounters every day. 

• Usual and customary fees -- their determination
• Best Hospitals, Best Doctors
• Length of Stay, Outliers 
• Meta-analysis studies
• Prescription surveillance of narcotics
• Prescribing patterns by big pharma
• Disease outbreak control
• Payment and incentives based on percentile of RVUs (relative value units)
• Free -- something gets measured so somebody else can pay (Nielsen Ratings)

How does that affect us every day?  

As the healthcare debate continues, and numbers are described in billions and trillions, we will see more big data. There are a lot of beans to count. As healthcare collaboration feeds more accurate information to big data, the value of analysis is improved. 

Learn more how ClickCare can assure that the data you use is rich, focused and related. 

References and attribution:

Bean Counter

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