Ever wish you could go back to being a kid? A time when – hopefully for most of us – our most complex decision was which flavor lollipop to get at the doctor’s office.
Back then the entire concept of healthcare – much less healthcare data analytics – was beyond our thought process. We had no idea of the enormous complexities that had to play out just to make sure those sniffles or earaches didn’t get any worse.
Yet, here we are as adults in today’s very rapidly evolving landscape – where we are all too aware that healthcare complexities certainly do exist. For regional health plans (RHPs), this means surging costs, fierce competition and workforce shortages, to name a few.
What’s more, many Americans are choosing to enroll in government-sponsored programs like Medicare and Medicaid – as well as policies like the Affordable Care Act (ACA) – instead of traditional employer-sponsored insurance. For RHPs, this only makes serving members and maintaining provider network adequacy harder.
So, short of inventing a time machine and heading back a few decades, what should RHPs do? To fully understand how to solve the problem, they need to have a more holistic view of their network.
In other words, they need data.
But not just any data. RHPs must have the ability to leverage healthcare data analytics to get a complete view of their provider networks — and how they stack up against their competitors.
Using healthcare data analytics to optimize provider networks
Payer insights are often confined to basic demographic data. By producing high-confidence data, payers can access competitor data and a comprehensive view of their own provider networks. These insights can help them optimize their networks and outpace their competitors.
If you’re new to the term “high-confidence data,” it’s how we describe a very rigorous data management process. One that’s made up of information that’s pulled from multiple sources and cleaned and standardized to improve data reliability. Plans can use high-confidence data to make better, more informed decisions.
The importance of high-confidence data can’t be emphasized enough. Members have a lot of health plans to choose from. The right insight can give you a full understanding of the landscape and where you need to grow your provider network. That insight helps you ensure they choose your health plan, not your competitors.
Yet, data analysis is sometimes easier said than done. Even if you understand the brain-twisting world of healthcare data analytics, implementing it can be a challenge.
While there are tools to make data analysis easy for you, let’s start by understanding what to include. There are three main aspects to building high-confidence data:
- Ensuring data consistency by checking multiple sources
Think of this step as triple-checking your data. A lot of us “data nerds” like to call this “triangulating” (this simply means gathering and verifying data from different places).
Here’s how triangulation works: First, collect the data from multiple independent sources, such as internal databases, public directories and third-party data providers. Once you have the data, compare and cross-reference these data sources to find discrepancies.
- Cleaning and standardizing healthcare data for reliability
Cleaning data is an important step within healthcare data analytics because it removes inconsistencies, errors and duplicates. This helps ensure your data is accurately represented and free from anomalies that could affect its reliability.
Once the data is clean, the next step is standardizing it. This means converting it into a consistent format that makes it easier to analyze and use. All the data should follow the same structure and conventions. For example, putting all addresses in USPS format.
- Confidence scoring in healthcare data analytics
A confidence score is a metric that represents the likelihood the data is accurate (for example, that a provider’s address or specialty is correct). The score is based on the number of independent sources that can confirm the information.
Data confirmed by many sources has a high confidence score, while data confirmed by only a few sources has a lower score.
By categorizing your data in this way, you can prioritize the most reliable information for decision-making and identify which data may need further validation.
Once the data is in a usable format, you can identify gaps in your healthcare provider network. For example, if you’re an RHP in the Midwest, you can use the data to understand your competitive position in the market and ensure the optimal mix of providers so you can build, optimize, manage and sell your networks effectively. This will help you improve access to care and member satisfaction.
How to harness the benefits of healthcare data analytics
There are tremendous benefits to optimizing your provider network using healthcare provider analytics. Leveraging its full potential can help you gain in-depth insights into both your networks as well as your competitors’.
The best and fastest way to do this is with data visualization tools.
The right solution can transform complex data sets to visual formats, making it easy for everyone to understand and act on those insights. Understanding healthcare analytics trends without visualization tools is like navigating a new city without a map — without them, the odds that you’ll get lost are much higher.
The right visualization platform can identify trends, spot anomalies and help you make evidence-based decisions. All these benefits make it easier to grow, optimize and market your provider network.
Explore a healthcare data analytics solution for optimizing your provider networks.
