The introduction of mobile devices as a standard tool in our everyday lives has fostered in a new era of consumerism where shopping is concerned. Now, anyone who sees a product they want in a store can quickly hop online and discover where they can get that same item for less.
So why hasn’t such consumerism taken hold in healthcare?
The simple answer is that many consumers don’t know that it is even possible to shop for healthcare as they do for any other product or service. As a result, the typical consumer goes into a healthcare procedure knowing nothing about what it will cost and unaware that other, less-costly options could have been available. This phenomenon is one of the reasons the Kaiser Family Foundation estimated in 2012 that 20% or more of what Americans spend on healthcare per year is spent on waste.
Take a common example: If a doctor orders an MRI, the patient may go ahead and schedule the procedure at the doctor’s facility or affiliated hospital. But the truth is that same consumer can very often get the same test done elsewhere for a fraction of the cost. Researchers at the Health Care Pricing Project at Yale have shown significant variation in the price of common healthcare procedures and services both between and within states. For example, the most expensive lower limb MRI in Columbus cost more than six times the price of the least expensive one.
In response to such variation, a number of private and publicly available tools have been created to introduce transparency in healthcare pricing. There are also health plan-based tools as well as public sites created as a result of legislation, including Colorado’s Comedprice.org, Maine’s comparemaine.org, and New Hampshire’s NHhealthcost.org, among others.
Even though price transparency tools now exist, another element is needed to nudge people to use them. That’s where big data and predictive analytics can play an enormous role. By tapping into these two sciences, it is now possible to make sure that consumers have the information they need—and precisely when they need it—to help them more effectively shop for healthcare. In fact, Time Series or Propensity Scoring models can be used to predict who is likely to be in the market for a procedure (like an MRI) within the next 60-90 days and then these users can be not only educated proactively about the variation in prices but also pointed toward tools that enable them to get the best value.
Taking this one step further, last year Target started to pilot beacons in some of its stores to alert users to deals and reviews as they walk through the aisles. Why couldn’t those same principles be applied to healthcare?
What if you checked into an emergency department and while you were waiting, you received a message that told you what to expect during the visit, what it would cost and what lower-cost alternatives were locally available (such as urgent care or Telehealth if the visit is not life-threatening). It might even provide an Uber code to help you get there. Or, under a different scenario, you make an MRI appointment and, in advance, up pops information telling you what the visit will cost, what to do to prepare and lists nearby alternatives that provide that same MRI at a lower price and at similar quality.
Wouldn’t that transform the healthcare shopping experience? Wouldn’t that make us all better and more confident healthcare consumers? The technology to do all this is here. We just need to be smarter in how we use it.
This article originally appeared in Payers & Providers News on November 17, 2016. Click here to access the original.
To download a PDF of this article, click here.