We’re in the midst of a transformational shift in the healthcare industry. Likely you have experienced it yourself, and it’s probably already hit you in the pocketbook. It’s the shift to what is called consumer-directed healthcare.

While on the surface consumer-directed healthcare may seem like nothing more than an attempt by employers to shift some of their spiraling healthcare costs onto their employees, there is much more going on behind the scenes. There is a lot of public policy driving this shift. The general idea is that healthcare costs are out of control because those buying healthcare services traditionally haven’t been the ones paying for them. By shifting healthcare costs to the consumer, the reasoning goes, consumers will demand better value for their money by becoming smart healthcare shoppers, and healthcare costs will begin to decline.

It all makes sense on paper, but there is one huge stumbling block in making this approach work: it’s hard to be a smart shopper when none of the things you are buying have price tags on them.

Data entrepreneurs have already seen this opportunity. Companies like Healthcare Blue Book and ClearCost Health have made real strides, but it’s a big and enormously complicated problem to solve. In part, that’s because hospitals don’t like to disclose their prices and insurers are often contractually prohibited from sharing what they pay specific hospitals for specific procedures.   

 Recognizing the issue, the federal government had mandated that as of January 1 of this year, hospitals must post their pricing for common procedures on their websites in an easily downloadable format.

 There’s a quick opportunity here to put your website scraping tools to work to gather all this pricing data in one place and normalize it. Certainly, there is an analytical product in there somewhere. But it’s less of an opportunity than it seems because what hospitals are generally posting are their list prices – and virtually nobody pays these prices. 

The challenge in hospital pricing is to find out what a specific insurance plan pays a specific hospital for, say, a hip replacement. This could be an ideal opportunity to turn to the crowd.

 One approach might be to aggregate all the pricing data that hospitals are now required to publish and use it as a data backbone – essentially a starting point. Then you could turn to consumers and ask them to anonymously submit their hospital bills and insurance statements. Take those images, use optical character recognition to get them into raw data format, then develop software to extract the valuable pricing data. When specific price data isn’t available, you could back off to list price data that would at least show if a hospital is relatively more or less expensive.

 Obviously it will take a long time to build a comprehensive database consisting of millions of price points, but there are a lot of consumer groups and other constituencies that would be very interested in your success and would work with you to increase the number of bills submitted. Hospitals won’t like this a bit, but as is so often the case, if one group doesn’t want the data out there, you have immediate confirmation that the data are valuable to some other group. Ironically, hospitals submit their price quotes for medical devices to a fascinating data company called MDBuyline to make sure they aren’t over-paying for their purchases.

 Sure, there is lots of complexity hiding under this simple framework. Also, it’s obvious that it will take a long time to build a comprehensive database. But the bromide “don’t let the perfect be the enemy of the good” nicely describes a key to success in the data business. As long as your database is the best available, it doesn’t have to be either complete or perfect. In almost every case, data is so important to decision-making that buyers will take what they can get, warts and all. This is not an invitation to be lazy or sloppy. Rather, it is recognition that you’ll have a marketable product long before you have a complete and perfect product. Just one more reason data is such a great business. Should hospital price data be on your New Year’s resolution list?

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