In today’s digital landscape, the storage of data is almost as important as the contents of the data itself. We’ve seen large organizations struggle to keep a handle on their users’ private information with multiple leaks and database hacks, but when it comes to healthcare data, that outcome can’t be an option.
That’s why the industry of healthcare data storage has ballooned in recent years, with new vendors and technologies filling the need for data storage, protection, and privacy. The different moving pieces all need to work together with each other if an employer wants to efficiently provide healthcare for its member lives. Let’s take a look at how data storage and accessibility affects overall employee healthcare outcomes.
The question that this section of the larger health tech stack all boils down to is: how easily can I access this healthcare information? Picture it this way: if you put your life savings into a bank vault and weld the door shut, you can imagine that it’s going to be relatively safe, but very difficult to access. However, if that bank vault has a heavy steel door with a complex locking mechanism, it might be a little less secure, but it will be much easier to access what’s inside. This tug of war between access and security has lead to different ways that stored healthcare data can be processed within the warehouse in which it is stored. What has appeared to solve this problem are varying types of storage models that are useful for different organizations looking to use their data in certain ways.
Data Mart Storage Model – The data mart storage model is built modularly, keeping relevant data within separate “data marts” that are spun up as you need them. If a consumer needs to look at medical and pharmacy data, that will be stored in a separate location than the diabetes data. This method keeps the information within context, but also comes with its drawbacks.
Late Binding Storage Model – The issue of “binding” data comes into play more heavily in this storage method than the others. “Binding” data is the process of assigning information elements into categories. Once data is bound, it can be more difficult to read into other contexts. A late binding storage model keeps data more free, fluid, and available for consumers to develop queries on the fly.
Enterprise-Wide Storage Model – This is a top-down approach to data warehouse design that is used widely across the industry today. The main idea of the Enterprise Data Model is that consumers go into the project knowing the goals and outcomes they expect. With those goals in mind, the database is built to match.
Depending on the level of security needed and the amount of data analysis that needs to be done, organizations will end up choosing one of the following models. Each one has their pros and cons, but the end result of better health outcomes can ultimately be influenced by something as low-profile as data storage techniques.
If you’re curious about the other sections of the larger health tech stack, download the Blueprint we put together for HR leaders looking to better understand their healthcare tools. Our in-depth piece looks at the different pieces of the healthcare tech stack, how they work together, and how they improve overall employee health outcomes.
If you have any questions about our Health Tech Stack Blueprint or healthcare tech in general, contact us. We’ll be happy to help understand how the right healthcare software tool can make all the difference for you and your organization.