ClickCare Café

Big Data Moves to Healthcare: A Description. A Warning. A Solution. 3

Posted by Lawrence Kerr on Thu, Nov 01, 2012 @ 12:06 PM

Healthcare collaboration fixes big data warning

 

As they used to say on the radio, “When we last left this topic, our hero was deeply entrenched in the office with a backlog of difficult patients. 

In part 1 we described how healthcare collaboration, providers and Big Data are not necessarily a compatible mix. In part 2, we described how this incompatibility might come about. In Part 3, here and now, we answer the hanging question from the previous parts: Can you see the potential for error in this scenario?

Let’s go back to Part 1, to Garbage in/Garbage out, and back to the job of the “Provider Coding Clerk.”  To finish treatment and sign out the first patient, codes are needed. Or the record could be held, to be revisited at the end of the day. You are no doubt aware that all providers like to stay late doing paper work. This may be an opportunity to do so. 

The Provider Coding Clerk uses the advanced search tool and looks for LeFort Fracture. Not there. Fracture of the Face is next. Some answers. Facial fracture. Now a list which extends well below the bottom of the screen. There are a whole bunch of codes, several of which when clicked on multiple times do not really describe the clinical problem that dictates treatment. Ah! There is one! Facial Fracture NOS (Not Otherwise Specified). In other words--close enough.

Close enough. Let’s round off to that. Patient discharge. Mental note of surgery planned. On to the room with slamming drawers and screaming mother. Next!

Some months later, here comes the most powerful, CIA-TSA-FBI designed Big Data software. It can answer many questions. The medical administrator, insurance actuaries and government regulators are intrigued to have answers to some burning questions. How much does it cost to treat facial fractures? What is the readmission rate? Do 86 year olds have facial fractures dysporportionately as compared to 26 year olds? How often do orbital fractures result in blindness. How much should the provider be paid? Is he doing quality work? The purchase of such software must, in their opinion, be money well spent!

The answer very likely has the same underpinnings as iClickCare which enables medical collaboration and healthcare collaboration. While Big Data machines look at collections and trends for answers, providers look toward colleagues for answers about patients. Providers have no feedback. Round off and close enough get them through the day. Accuracy has no value. 

However, providers will care about data input when it has value to each of them as an individual. Big Data should remember the individual. Let them search, compare, form regressions and statistical analyses on their own. Empower each individual. Make them part of the solution, give them time and respect. Help them grow. You never know, no longer a Provider Coding Clerk, one might come up with something you never thought of.

This may require Open Data, but at least the patient will be served. 

Big Data -- you have been warned!  And an answer has been offered.

 

Click me

 


Tags: medical collaboration software, healthcare collaboration, big data in healthcare

Big Data Moves to Healthcare: A Description. A Warning. A Solution. 2

Posted by Lawrence Kerr on Thu, Oct 25, 2012 @ 03:55 PM

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 andWaiting in line for healtcare collaboration 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.

healthcare collaboration avoids nurses as butler.jpgThe 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...

Click me

 

 

References:

Waiting in line sketch: Jonny Pickton


Tags: healthcare collaboration, EHR, EMR, big data in healthcare, big data

Big Data and Healthcare Collaboration

Posted by Lawrence Kerr on Thu, Sep 20, 2012 @ 02:00 AM

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)
We question, then, how does bad data going in, affect what comes how?
How does that affect us every day?  Healthcare collaboration is affected by Big Data
One approach that industrialists have had toward healthcare collaboration is monetizing Big Data. Hence, we find a plethora of “free” so called collaborative websites. “Free” forums, “Free” references. “Free” consultations. And better than “Free”, honoraria for one’s opinion. While not always needed, pictures and words offer a more accurate communication. But let’s spend a few minutes on “Free”. Chris Anderson has written a book, appropriately enough for this blog, entitled Free. He describes the many forms of free. One that we are all familiar with is free broadcast TV. Free to us, but paid for by a third party, those who pay $3.5 million for 30 second Super Bowl ads. We are willing to watch the ads in exchange for watching football for free. We pay for free later by purchasing what we have been shown. Of course, blogging in general is either filled with ads or offered as a gateway. It is either a lot of posters for the circus, or the barker for the sideshow. It brings new terms to old concepts. Pay Per Click and Organic Search are two of them. Competition for Eyeballs is another.

Another form of free is the free trial. ClickCare offers iClickCare as a free trial with the hope that when familiar with our service you will go ahead and buy it. If you don’t, you still have basic functions for free, and we hope that you will come back and purchase a subscription. This form of free is clear and explicit.

However, there are other forms of free which are less explicit and may be even nefarious. This is where Big Data comes into play. We are all familiar with our paying for our free searches by allowing what we search for, how we searched, and where we came from, to be used by others. Increasingly, we are offered free access to sites offered as a means to enable sharing among professionals. Someone pays for these sites. What do they get in return? Among the returns are aggregated data about what drugs we subscribe, how a marketing campaign worked, or who is a thought leader who can be leveraged/manipulated to increase sales.

This is where Big Data may be hurting us as individuals.

Conversely, as individuals, how do we hurt big data. Really it comes down to the now proverbial -- GIGO -- garbage in / garbage out. In order to satisfy the massive thirst of Big Data, we are asked to reduce our complexity of decision making and care management to CPT and ICD codes. These are unwieldy and time consuming. Imagine that if a patient needs an xray for an injured finger that was just fine before it (inexplicably) struck a wall, then we need an ICD-9 or ICD-10 code. We need to order an xray. We have patients waiting, the EMR is flashing that it has an un-filled field. We know what the film will look like and we know that the finger bone is attached to the hand bone. Knowing all of that, we might  accept that the code for fractured hand is close enough. We will still get to see the fracture. The finger is part of the hand after all, we get the xray, the patient gets the treatment, and Big Data gets the garbage. The Big Data, counting the bits and bytes delivers erroneous:

Usual and customary fees—their determination 

 Fee for treatment

 
 

Best Hospitals, Best Doctors

 

Best outcome because of wrong comparison

 

Length of Stay, Outlier

 

Simpler code for a more complex problem

 

Meta-analysis studies

 

Large numbers of pooled errors

 

Prescription surveillance

of narcotics

 

Narcotic prescribed for less painful injury.

Prescribing patternsby big pharma

Sales representative sits in wrong waiting room 

Disease outbreak control

All of a sudden there are more hand hand than finger fractures

 

Payment and incentives based on percentile of RVUs (relative value units)

Wrong unit, right treatment

Free—something gets measured so something else can pay (Neilsen Ratings)

 

Ask for free data analysis, get the data you pay for. Data gets more accurate information, and qualitative as well as quantitative data as well.

 

 

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. 

Click me 

References and attribution:

Bean Counter

http://rickmarolt.info/?page_id=276


Tags: healthcare collaboration, big data in healthcare, big data

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