CT Cafe

As I sought out inspiring examples of computational thinking, I looked up quite a few articles on computational thinking and watched Dr. Wing’s YahooLabs presentation on the subject. Most were interesting, but still left me without a crystal clear idea of what CT is or what “inspiring examples” of it would be. Rather than be paralyzed in thought or analysis, I decided to just start jotting down my notes and thoughts and see where that takes me. I also decided to focus on HealthCare since that is my identified area of contribution to the group. So here goes:

Dr. Wing’s article identified examples of computational thinking such as robotic surgery, virtual colonoscopies, and electronic health records. I will comment on these and others I think appropriate, but before doing so I would add that I think the opportunities for using CT in healthcare are tremendous for the following reasons:

It is extremely complex. Ethical issues abound in healthcare, and the number of dollars spent at end of life can be staggering for what, in many cases, are inevitable outcomes. Technology has played a huge role in improvement of outcomes but at a huge price tag. Chronic conditions and risk management issues are enormous. I will never forget a visioning session with my staff regarding how the implementation of the electronic medical record was going to change what we do, and we concluded that though we currently manage masses of complex data, in the future would be managing more masses of more complex data.

There is currently a lot of waste. The estimated impact of poor quality or waste in the healthcare industry is staggering. This comes in many different forms, including errors, unnecessary procedures and care, etc.

We have a problem, Houston. Healthcare costs are creating a real problem for American businesses. It is a problem that definitely needs to be solved.

Okay, here are some overall comments on things that I have witnessed relative to computational thinking in healthcare.

1. We have been doing it for a long time. Over 20 years ago we were looking at how we could use computational thinking (I think it could be described as that) to improve patient outcomes. We did this in a lot of ways, but I think one good example (although very simple) was looking at our databases to ensure that diabetics were hooked up with an ophthalmologist. Diabetics have a much higher propensity for eye disease. We did this by looking at prescriptions for insulin and ensuring that anyone who had been prescribed insulin was also seen by an ophthalmologist.

2. Electronic Medical Record. This component is still very early in its implementation, but there have been several findings from it.

· Medication errors were reduced tremendously. The system provides for checks and balances on medications and automatically checks for anything outside of those parameters and requires override to be administered. This has resulted in a big drop in medication errors.

· Some unintended consequences. Redacting medical record data became much more difficult, requiring a lot of work to set up parameters for what really is the record, what should be printed out to share for a variety of sources, i.e. claim and attorney review.

· Not tremendous cost savings—yet. Perhaps that is the great test of how to utilize the data in a manner that will result in savings.


3. Google is better at predicting the flu season than the CDC (Center for Disease Control). Some comments on this are shared below, but I agree that this really opens up the idea at looking at healthcare data and outcomes in a totally different light, and probably indicates a need for expertise outside of the traditional healthcare arena. Google’s experiment in trending flu outbreaks is absolutely – and I mean absolutely – amazing. I’d bet that any professional epidemiologist would cringe at the idea of using non-clinical data to identify disease trends, but this application boggles my mind. Here’s how it works. Google meters its search queries on flu-related topics, figuring that the more that people search on a flu topic, the more likely the flu is occurring in real life. It does not measure anything clinical – nothing about positive throat cultures, visits to emergency departments, or any other traditional sentinel reporting measures. Just search queries. The demo on Google’s own website shows how, using data from the 2007-2008 flu season, it can identify flu trends two weeks ahead of anyone else, including the CDC. The data for this current season – the first full season with the H1N1 virus – confirm that many people are getting sick with the flu already, months before the traditional height of the flu season.

4. Robotic Surgery—da Vinci machines are an example of advances in Robotic surgery and I believe were invented with the intent of enabling the ability for someone across the globe to complete and do a surgery on someone on the other end. It also circumvents current limitations in the design of the human body. For example, the human wrist cannot perform a 360 degree rotation, while the robot can. This allows for a better range of performance and thus, better outcomes. Cost becomes another large issue here as it is very expensive. But the da Vinci robots eliminate two big issues in surgical care:

· Physical location of the surgeon—the surgeon could be across the globe and complete the surgery with the appropriate equipment.

· Physical limitations of the surgeon—i.e., the 360 degree rotation of the wrist.

And from the neither here nor there category, I always find quotes inspiring. I liked the following and found it while I was searching. It does not apply directly to computational thinking, but you get the idea:
“Digital fluency" should mean designing, creating, and remixing, not just browsing, chatting, and interacting.
Mitchel Resnick