The increase of digitized claims data allows for further advancements in the prevention of healthcare fraud. By reviewing mass amounts of claims data, analysts are starting to recognize trends of normalcy in billing, and therefore, any abnormalities that may be consistent with fraud.  Three classifications of healthcare fraud have emerged from this type of analysis:

•  Unbundling:  the process of a provider separately billing procedures that should be combined into one charge
•  Upcoding:  the consistent over-billing of procedures
•  Duplicates: the intentional submission of the same claim more than once

Researchers are also using the colossal amount of claims data to determine whether or not certain medical treatments are truly effective. If multiple claims with varying treatments or behaviors are submitted, they can be analyzed to conclude which medical solutions have the most positive impact on patients’ conditions. 

Researchers and other experts in the healthcare industry also want to use the data to establish a more efficient claims process. The focus is shifting towards improving accuracy and reducing claims processing time. Due to the increased volume of claims being submitted, it is becoming essential for the claims process to be as efficient, precise and fast as possible. 

Big data found in healthcare claims databases has potential to revolutionize the claims process. Though this is a relatively new trend, it is likely that researchers will use the data to help prevent fraud, determine the most effective medical treatments and make the claims process more efficient.