How to use our pricing app

Background

First, let’s hit some brief background on healthcare “cost transparency tools”.  Studies show consumer adoption of such web-based tools or applications (apps) is really quite terrible.  So why have we spent so much time working on ours if no one is likely to use it?  Well, we actually believe that in time consumers will become more willing to use such tools.   The simple reality is these tools can save you a lot of money.  If more people elect or are forced to buy high deductible plans, it will eventually resonate that not using such apps likely means they are just leaving money on the table.  Furthermore, we believe that such tools will be appreciated a lot more once employees realize savvy healthcare consumerism can mean an earlier retirement and/or a healthier retirement income profile.

When we think of cool web apps we usually think of those we use during our leisure time – Facebook, Snapchat, etc.  Not too many people wake up in the morning and think to themselves “Oh man, let’s see which hospitals can save me money on a brain MRI!”  That would be weird.  But if understood from the perspective of ‘if I saved money in my health savings account, it may mean I can retire earlier, or at least with more money saved’, the allure becomes slightly more appealing.

HealthyHive.com

Our mission to to assist employees with not only becoming better healthcare consumers, but to also integrate the topic with retirement income savings through 401(k)s and other qualified plans.  Most people are not aware that the average couple needs almost $250,000 pay for healthcare during retirement (source: Fidelity Investments).  Tax-advantaged health savings accounts (HSAs) should be a part of everyone’s retirement income planning.  We will expand more on the healthcare and retirement income planning topic in future posts.

There are several healthcare apps that attempt to highlight price differences.  Ours is somewhat unique in that we do not rely on partnering with insurers.  The reasons are two-fold.  First, they have not been willing to collaborate with us  (See our Sunlight Foundation guest blog on this topic:  http://bit.ly/22kPWAH)  Second, we believe that partnering with them would restrict us.  As a result, our data is pure “open data”.   This means we obtain historical pricing data from the state because the state requires insurers to submit claims to a central database.

We also include hospital quality survey data, which come directly from federal government studies.  We prefer this model because we are not dependent on health insurer partnerships.  In fact, we truly believe that amidst emerging technologies coupled with consumer-friendly open data laws, the health insurance industry will continue to experience massive disruption, if not marginalization.  We don’t say this because we are pushing some ideological platform, but rather because we believe productivity bursts in healthcare are long overdo and Mr. Market is currently sniffing this out.

Case Study

OK – now let’s get to work by using an example.  Assume your doctor told you he/she would like you to get a brain MRI because you think your brain is going to explode trying to understand this topic.  Assuming you have an HMO plan based in NH – you would choose that insurance profile option on the first page:

Screen Shot 2016-05-22 at 9.06.03 PM

 

Next, let’s assume your zip is 03101 – so enter it and then select “Radiology” in the drop down menu, the click “Search”:

Screen Shot 2016-05-22 at 9.04.56 PM

Browse the drop down menu until you find “MRI scan of brain, before and after contrast”:

mri

You will then be taken to a list of local providers.  Let’s then assume you are familiar with Catholic Medical Center and Elliot Hospital.  Select those two hospitals to compare the historical prices:

 

Screen Shot 2016-05-22 at 9.08.53 PM

You will then be directed to this page (click on screen shot):

 

Screen Shot 2016-05-22 at 6.35.59 PM

You will see a lot of information.  First, observe the “Times Performed in 2014”.  Elliot is a larger hospital and has performed more of this type of MRI than Catholic.  The far right column lists the average total paid.  On average the total paid to Elliot was around $259 more than Catholic.  If you have a high deductible plan, you will see that the average deductible for Elliot patients was around $350 more vs. Catholic.  From the employee’s perspective, it is the out of pocket component that really matters the most.  We actually provide breakdowns for the average relative out of pocket expenditures covering the deductible, copay, and coinsurance amounts.  It may be information overload, but we believe the out of pocket detail is our true “value-add”.  This is where the savings are to be made.  After summing the total out-of-pocket differences between the two hospitals in our case study, you can start to understand why it may actually pay to educate yourself before booking an appointment.

The last table shows some summary data based on insurance plans.  This is added to assist consumers in confirming the relative pricing between the two hospitals based on the percentile breakpoints of prices paid to all hospitals in 2014.  It is a way to fact check the work.

 

Quality Ratings

Finally, there are 3 other tabs of information to explore.  They include the patient survey data and infection & readmission data compiled by the federal government.  It is not an amalgamation of subjective opinions posted by a small percentage of patient experiences like we often observe with popular review websites.  These are real scientific surveys.  We love them because we avoid unfairly presenting a hospital in a bad light because one person had a bad experience and wanted to vent.  We believe in the Wisdom of Crowds approach.  These data provide just that as most metrics require a response rate of at least 350.

This, my friends, is the top of the first inning in learning how to become a true consumer of healthcare services.  Stay tuned as soon we will be making it even easier to draw relative pricing inferences using data clustering approaches.

 

 

 

 

Comments are closed.