With the development of big data and predictive analytics, it is easier today than ever before to detect and prevent healthcare fraud. Gone are the days of lengthy, old-fashioned investigation, and here to stay are new technologies that can identify fraudulent activities automatically and instantly. By parsing through big data and analyzing payment trends, predictive analytics softwares can identify inconsistencies in payment and seemingly fraudulent activity. This article delves deeper into the system that mines data and predicts fraud by explaining the many components that make up a fraud detection software. If you are interested in learning more about how you can use technology to detect healthcare fraud, contact our attorneys today to learn more.
Link analysis focuses on measuring relationships. It mines and analyzes data relating to how individuals, healthcare providers, healthcare employees, and healthcare suppliers are related to and interact with one another. It can identify unusual interactions and even unusual identities, such as multiple or fake addresses and phone numbers.
Just as it sounds, duplicate testing refers to identifying duplicate entries in data. Since one of the most common forms of healthcare fraud is duplicate billing, the duplicate testing system can save investigators a lot of time by having a computerized system detect any duplicate entries that have been entered into the system for the same person.
On the other side of the spectrum from duplicate testing is gap testing. While duplicate testing identifies multiple entries, gap testing identifies missing entries. When evaluated in a sequence of data, a missing entry can be a sign of unaccounted for spendings or income.
Date and Time Verification
Fraud detection softwares can also include automatic date and time verifications, which identify any entries that were entered at unusual or inappropriate times. These entries will be flagged for further investigation.
Similar to a credit score, each healthcare provider will be assigned a risk score based on their activities, behaviors, and other relevant factors. Unlike a credit score, however, a higher score typically indicates higher risk, and if a healthcare provider’s score exceeds a certain level, the provider will be flagged for further investigation.
Trend analysis collects data connecting certain populations to certain medical procedures and flags any abnormalities. For example, if 20% of the elderly population nationwide require a certain procedure, but one healthcare provider is prescribing this procedure to 95% of its elderly patients, a trend analysis program will be able to detect this abnormality and flag it for further review.
Contact a San Francisco Healthcare Fraud Attorney Today
As you can see, there are many components to predictive analytics, which are what make it such a powerful tool. If you are interested in learning more about how predictive analytics are used to help prevent healthcare fraud, contact our office. If you believe you have already witnessed healthcare fraud or detected it using your existing predictive analytics software, contact the attorneys at Willoughby Brod today to learn about your options moving forward. Contact us at (800) 427-7020 or visit us online to schedule your free consultation today.
(image courtesy of Hush Naidoo)