Data redundancy is a problem that affects many industries, but it is especially prevalent in claims processing. Data redundancy occurs when you need to enter the same piece of information multiple times. This often happens when multiple authors are making changes in a document and reuploading said document to a repository, resulting in duplicate data with minimal edits but not archiving the older versions.
What is data redundancy?
Data redundancy is the same data stored more than once. It's a frequent problem in claims processing because it increases costs and decreases efficiency. Data redundancy can occur due to manual data entry or automated processes, but either way, it's bad for your bottom line.
Reducing data redundancy requires identifying sources of redundant information, finding ways to reduce or eliminate them (or at least ensure they are up to date), then implementing those solutions so that all parties involved always have access to accurate information.
Why is it a problem?
Data redundancy is a problem because it leads to errors, increases costs and affects the quality of service. Data redundancy not only affects the speed of service, but also its accuracy.
A claim processing system must handle large volumes of data quickly and efficiently for an organization's claims process to run smoothly. If there are duplicated fields or unnecessary fields that needlessly increase the amount of time it takes for an employee or customer representative to complete a task on his or her computer screen; then this will slow down productivity and increase costs associated with labor hours spent correcting errors caused by redundant data entry tasks
How can you reduce data redundancy?
One way to reduce data redundancy is by using analytics. Data analytics can be used to identify missing, incorrect, and duplicated information in your claims processing system. For example, you may have multiple systems with different data sources that are used for the same purpose (e.g., payroll). If those systems are not integrated with one another or with the rest of your company's systems, then it is likely that some information will not be shared between them properly or at all. This could lead to errors in calculating benefits or payments due because some fields were left blank when they should not have been--and no one noticed until after they made their calculations!
Another way you can reduce data redundancy in your company is through master data. Master data is the sole source of common business data that a data administrator shares across different systems or applications. While master data does not reduce the incidences of data redundancy, it enables organizations to apply and work the appearance of duplicate data. All repositories connected to the master data will be updated when the source of truth is updated.
Lastly, companies can reduce data redundancy through archiving outdated or unusable data. Another factor contributing to data redundancy is preserving the data pieces that the organization no longer requires. For example, organizations may move customer data to a new database and keep the same data in the old one. This can lead to data duplication and storage waste. Organizations can avoid this redundancy by promptly deleting the data it no longer requires.
Data Redundancy in Claims Processing
Claims processing involves mass amounts of ever-changing personal information. Therefore, it is one of the industries that suffers the most from complications of data redundancies. Our technologists at Veracity are often involved with redundancy audits, or setting up systems to completely negate the risk before it happens. There are several processes and technologies the industry is using, such as AI, RPA, and IPA.
Artificial Intelligence (AI)
AI technology is becoming commonplace when it comes to insurance claims processing. AI makes it possible to extract data, analyze it, and make recommendations by reviewing user profiles and creating personalized experiences. Luckily, because all AI needs to be trained with a ‘seed set’ via a human employee, companies can edit AI to fit whatever processes or best practices they need it to follow.
Robotic Process Automation (RPA)
Insurance companies are responsible for ensuring data accuracy, reliability, and privacy when it comes to meeting client commitments. Robotic Process Automation uses automation tools and workflows which can take data from insurance or healthcare documents and input them into other repositories. This reduces the need for manual data entry, does validation checks, and streamlines application reviews during claims filing. RPA also helps companies ensure legal compliance and regulatory requirements as well.
Intelligent Process Automation (IPA)
Intelligent Process Automation is being used for classifying claim policies, annotating them, and reducing operating costs. Many insurers deal with millions of documents which means there is a lot of time spent organizing, structuring, and reviewing documents. IPA cuts down on human error and work time processing this information, thus speeding up workflows and reducing the time it takes to create and use accurate information.
Conclusion
Data redundancy is a problem that can be easily addressed by implementing the right technology. We hope this blog post has given you some ideas on how to reduce your data redundancy in claims processing and make your life easier!
