Data is arguably the greatest resource that any organization has, but it can only be used effectively if it’s kept up to date constantly and can be analyzed quickly. As businesses continue to move ahead with digital transformations, they have more data to collect from more sources than ever. A great example of this is seen in the health insurance industry.
On any given day, an insurance company will have to collect data from industry statistics, smart devices, internet of things (IoT) technology, credit reports, customer databases, security camera footage, and the list goes on. The big data that modern enterprises deal with is too much to fathom using old, legacy systems. Fortunately, computer science has come a long way in recent years, and data scientists are able to create machine learning algorithms to handle data in real-time and integrate multiple, disparate data points using data science. Here are some of the most important ways that a health insurer will use data science.
Integrations and Master Data
Insurers collect information from a wide variety of data sets, including enrollment guidelines, health insurance plan classification policies, customer data, sales metrics, demographics information, provider networks, and more. In the past, organizations may have gotten away with collecting each of these data sets using disparate, or siloed, systems, but that approach doesn’t cut it anymore. Having to manually transfer data from different systems into one source is a time-consuming process that’s also prone to human error, and it’s difficult to gain actionable insights from data that may already be out of date by the time the process is done. This is why data science techniques are so important.
With modern software integrations, once disparate solutions are able to transform their datasets and communicate automatically, meaning insurers no longer have to rely on a manual process to gather insights. Data shared across multiple platforms is called master data, and utilizing this in a single source of truth is crucial for data scientists. With data analysis backed by artificial intelligence and data constantly updated in real-time, it’s much easier to monetize a huge amount of data, no matter the scope of the enterprise.
Insurance Pricing
Naturally, the biggest factors that come into play when customers compare health insurance are the level of coverage they’ll receive with each available policy, what out-of-pocket costs they may face, and how high the monthly premiums are. Data science makes it easier than ever for insurers to collect useful information from prospective customers, including location, age, tobacco use, medical records, etc. Advanced algorithms can analyze all this information during open enrollment and come up with predictive models for how big of a risk each potential client is in order to correctly configure plans and offer health insurance quotes.
These techniques even allow for simple visualizations of data to walk customers through the levels of coverage available and how premiums and deductibles will scale accordingly. They’ll also be able to view network providers, and predictive models can show how a subsidy or Medicare will work alongside the health plan, so potential customers have full context of their health care costs along with their summary of benefits.
HIPAA Compliance
The Health Insurance Portability and Accountability Act (HIPAA) was founded as a way to simplify the administrative processes in health insurance and ensure the protection of individual’s medical information. Cybersecurity and HIPAA compliance are top concerns for insurers, and data scientists are able to use deep learning models and statistical techniques to protect information. Every large enterprise these days is a potential target for cyberattacks, but advanced analytics are able to detect suspicious or anomalous behavior in complex systems far faster than a human ever could. This makes it easier for insurers to focus on the core aspects of their businesses, knowing that compliance and security are taken care of, thanks to data science.
