I know, here’s that word again, but it is important today. Everybody knows that Netflix is and their success revolves around the use of algorithms to predict and create models that will appeal to their clientele. You use their algorithms every time you view a movie or order one online. This is the way business works. Healthcare is no different, except that we have a multitude of businesses with all kinds of algorithms used to predict and better market their product, be a medical device or health insurance.
The algorithms are why they are doing so well. Actually at one point the company’s restructuring was based on their technology working so well.
We have managed to botch up the introduction of technology in healthcare, to the degree where standards are falling by the wayside. Everybody has a better system. Instead of working with a common goal, competition is perhaps driving this to where folks are confused, overwhelmed, and frankly just checking out. We need better interfaces, but do we see any commonality here? No. The algorithms are getting more complicated and it’s a shame as it’s difficult to find and locate ground zero anymore.
The one item that Netflix has been successful with is to use algorithms to blend and bring continuity with old non technology methodologies and blend with the new. As the PC learning process grows, more customers are moving away from the mail and using technology to get their movies. Granted this is the entertainment business which has a strangle hold right now over any learning as a whole here in the US, so there’s a bit of a challenge with healthcare being from the other side of the planet, but overall they are successfully appealing to what drives their business with consumer appeal and taking advantage of the information and analyzing what works, healthcare is too fragmented to allow this to occur right now.
So perhaps healthcare might have some lessons to learn here with using algorithms to appeal to both doctors and patients in a positive manner and allow all not to have to worry about the not so good algorithms that deny claims and coverage from the insurance side of the business. I don’t know how the 2 can really continue to co-exist and allow us to have a valuable healthcare plan as a country. Nobody minds if Netflix uses your basic information to study how to make things better, but in healthcare, those algorithms come at a cost of human lives, a completely different business.
So why can’t healthcare get it together? Another part of the issue is entertainment, as we can’t seem to tear ourselves away for a few minutes each day to learn something new, so in the meantime Netflix is using their business intelligence to capitalize with algorithms to give us what we want, entertainment, not education. Perhaps when education really becomes a focus we can make some real progress, but right now without mentors and leaders, the movie business is what we have. Netflix did their job with algorithms and as a result is enjoying some real success, it’s a shame that the element of curiosity can’t be tweaked to also add intelligence to healthcare. If you can’t touch that element of curiosity, then intelligence will now grow. BD
When the Netflix Prize was awarded last month, it ended three years of intense competition aimed at finding a better algorithm for predicting users' movie preferences.
Gavin Potter, who gained recognition for his breaking the top 10 of the Netflix prize in 2008 under the name "Just a guy in a garage," says that a few key realizations allowed the winning algorithms to meet the goal. First, a powerful algorithm for searching for patterns in datasets, a technique known as collaborative filtering, was streamlined so that it could be used on the large Netflix dataset. Second, participants learned to pay attention to certain new types of details, for example the fact that ordering a movie at all indicates some preference for it, even if the customer didn't rate it. Date and time information also proved significant. But the biggest realization, Potter notes, was that blending a variety of approaches yielded the best results.
The winning team, BellKor's Pragmatic Chaos, was the first to forecast Netflix customers' movie ratings with 10 percent better accuracy than the company's in-house system--a feat that many experts believed would be impossible when the million-dollar prize was announced. Netflix plans to offer a second prize, this time for algorithms that predict movie preferences using more user information, such as gender, age, and zip code. But experts say that the real challenge is to find ways to apply the lessons learned through the original Netflix challenge to other recommendation systems.
Participants in the original Netflix competition trained their algorithms using an enormous collection of data: more than 100 million ratings covering almost 18,000 titles from nearly half a million subscribers. To test their results, their algorithms were tested on a set of data maintained by Netflix and kept secret from the contests to prevent cheating.