High school English classes are the bane of procrastinators. Cliff Notes was made for those who chronically put off for tomorrow what they should have done today, and the small yellow books have saved many students from failure.
These days Cliff Notes are needed for more than just procrastinating student with senioritis. America’s most proactive consumers need help on a daily basis to navigate through the prose provided by brands. Take, for example, the iTunes agreement. It contains more words than Shakespeare's Macbeth. It was hard enough to read Macbeth when you had weeks to do so in school. Do you really think you’re going to take time to read 20,000 words of fine print when you’re waiting to checkout? That’s where TLDR comes in.This is an idea that started online and has spread rapidly across the internet since its initial usage.
TLDR, or Too Long Didn’t Read, is today’s version of cliff notes. It’s what happens when someone else takes the time to read a long piece of content and consolidates it into snackable bits of information that can be easily consumed.
There are a wide array of applications where this can be useful. As we’ve spoken about previously, TOSDR (Terms of Service Didn’t Read), summarizes concerning points of user agreements consumers are forced to sign in order to access goods or services they wish to “purchase.” If you’d like to learn more about this topic, see our blog that details how ownership has changed over time.
Machine learning also has TLDR capabilities in a variety of ways. The most obvious, of course, is related to big data and a machine’s ability to digest a seemingly infinite amount of data in order to process it to pull key insights and understand any relative correlations that may exist. We talked about this idea in a recent blog when we looked at how machines could one day cure cancer. As we learned in that piece, machines can’t read the data for us if it doesn’t have the data to read. That wins the award for most obvious statement of the year, I know. With good data, though, the potential looks unlimited.
There are other things that machine learning could do to help benefit our bloated prose based society. IBM and Watson are working hard to maximize some of these benefits within the global healthcare space. If they’re able to accomplish all their goals then healthcare will have truly been revolutionized.
Consider this. Doctors are having to do more than ever before. According to a study by The Physician Foundation, that’s why 81% of physicians feel over extended today. So much of that is based on Electronic Health Records (EHR) that the topic blows up when searched on Google. Results through the engine return several pages of articles on the love hate relationship that exists between doctors and the records they are forced to use.
According to HealthIT.gov, EHR contains patient health information, such as:
- Administrative and billing data
- Patient demographics
- Progress notes
- Vital signs
- Medical histories
- Immunization dates
- Radiology images
- Lab and test results
If doctors are forced to take time away from their day for data entry then they lose time to see their patients. It’s a big part of the reason why doctors saw 15% fewer patients in 2014 versus 2013. It means that doctors have a choice. They can either see more patients or spend time managing their EHR. The answer may sound easy, but it isn’t.
We talked in detail about the importance of Electronic Health Records last year and how much money, and how many lives, they could save if used properly. The numbers we uncovered were staggering. The system loses $12 billion per year because of communication and 70% of accidental deaths and injuries in hospitals are caused by similar issues.
That data is important not only for the reasons listed above but for machine learning to take its course. Seeing fewer patients will be an accepted practice if the data can recommend health care providers the proper treatments for patients based on past history and historical data. That opportunity could even make Telehealth, an already growing healthcare strategy, even more, attractive for doctors and patients.
IBM also believes that, with Watson’s help, the data can identify care opportunities in order to connect with the right patients at the right times. Imagine a world where an appointment could be proactively scheduled with patients who “need recommended care and initiate customizable notifications regarding visits, tests, procedures or other follow-up care.” Identifying health care opportunities ahead of time can, and should, reduce the need for follow up appointments later should illnesses, both chronic and otherwise, set in for patients.
Both of the above are examples of the power behind data and how it can impact the future of our healthcare system. Machine learning’s ability to take time out of the equation for preventative, current, and even future care, by reading the data on behalf of doctors will put time back into their pockets.
So, what’s the TLDR version of this story? Machine learning has the potential to impact your healthcare and your doctor’s ability to provide it. It’ll put your life in your doctor’s hands in ways like never before. Now, though, it’ll be the Cliff Notes version to help get you better, faster. If that isn’t the true nature of TLDR, I don’t know what is.