In the US, the cost of healthcare services has long been kept a secret. But thanks to the Transparency in Coverage Rule, US health insurance issuers must now disclose the prices for their ‘non-grandfathered’ health insurance services, i.e., those issued after March 23, 2010. We now have access to a wealth of healthcare data, which was previously kept hidden.
Here’s the catch: accessing this data isn’t as straightforward as you expect.
Health insurance issuers are required to release this data in the form of machine-readable files. This means that you cannot simply view the information in these files like you can in a PDF document or a webpage. Instead, you need something to render it in a human-readable format.
Making machine-readable files human-readable is a cakewalk with Gigasheet. Keep reading to learn how.
But first, what are machine-readable and human-readable data files?
Machine-readable data, or computer-readable data, is data presented in a format that can be extracted, processed, and analyzed by a computer without human intervention. On the other hand, a human-readable data file is any form of data that a human can read naturally.
Machine-readable data files can be intended mainly for processing by machines, like JSON or CSV files. Or, they can contain human-readable data that is marked up so that it can also be read by machines, like HTML and XML files.
One important thing you should note here is that machine-readable data is always structured data.
A machine can only “read” machine-readable files if the data contained within them are structured in a way that the machine understands. So, if you have an XML file with semantic errors or a poorly structured CSV file, they are not machine-readable.
Making machine-readable files human-readable with Gigasheet
Gigasheet is a secure, online tool you can use to quickly read healthcare data, even if it is in a machine-readable file format like JSON.
To open your JSON file in Gigasheet, create your account and visit your Library.
Click on “Add Data” and import your JSON file. In this example, we are using data from Aetna’s Machine Readable Transparency in Coverage website, but these are available from all providers including United Healthcare and Humana.
Once you have imported the data, you’re done. Now the fun begins. Gigasheet enables you to play around with this data all you like.
An Example: Let Us Group Data With Gigasheet!
With Gigasheet’s “Group Data” feature, you can separate out rows of data by groups of values within a column. Gigasheet is a no-code platform, so you don’t need to sweat over writing queries. Just click on “Groups” and select the column name from the drop-down menu.
For example, here, we are grouping the data by the column billing_code_type
Here is what you get:
But what if you want to observe trends by going a bit deeper and grouping the data further? Click on “Add Column” and select your value from the drop-down. It is that easy!
Let us now group this data once again by adding the column billing_code.
After grouping by billing_code_type and then by billing_code, here is what you get:
Gigasheet groups the data rows by the values you select and displays them to you in an easy-to-read format.
At a glance, you can view all the rows that have the billing_code_type as "HCPCS" and all the billing_code values that come under HCPCS.
Try it for Yourself!
Do you want to convert machine-readable healthcare data to human-readable formats? Give Gigasheet a try. If you are not sure where to start, check out our data community here and start analyzing large, complex data sets without any coding.
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