Welcome


Welcome to Understanding Link Analysis. The purpose of my site is to discuss the methods behind leveraging visual analytics to discover answers and patterns buried within data sets.

Visual analytics provides a proactive response to threats and risks by holistically examining information. As opposed to traditional data mining, by visualizing information, patterns of activity that run contrary to normal activity surface within very few occurances.

We can dive into thousands of insurance fraud claims to discover clusters of interrelated parties involved in a staged accident ring.

We can examine months of burglary reports to find a pattern leading back to a suspect.

With the new generation of visualization software our team is developing, we can dive into massive data sets and visually find new trends, patterns and threats that would take hours or days using conventional data mining.

The eye processes information much more rapidly when information is presented as images, this has been true since children started learning to read. As our instinct develops over time so does our ability to process complex concepts through visual identification. This is the power of visual analysis that I focus on in my site.

All information and data used in articles on this site is randomly generated with no relation to actual individuals or companies.

Use Visual Analytics to Detect Medical Fraud

Discovering fraudulent trends and patterns within medical data is the modern day equivalent of finding a needle in a haystack. Private medical and property/causality insurers as well as government agencies are tasked with discovering and preventing medical fraud from within millions of submitted bills daily.

With health care and medical fraud costing consumers over $100 billion dollars in the United States alone, there has never been a more important time for fraud prevention. The question has always remained, how can I as an analyst proactively identify emerging trends across large volumes of medical billing.

By leveraging visual analytics, analysts gain the ability to holistically examine large amounts of medical billing across geographies, allowing for the intuitive identification of medical billing and provider to claimant trends which are indicators of fraud. It is through an holistic visual analytic approach that adverse trends surface within visual analysis when compared against normal medical billing traffic.

Medical Fraud Visual Analysis

To provide an example of leveraging visual analytics for medical fraud we will utilize SynerScope to examine volumes of medical billing data across a specific geography to surface any irregular patterns.

The process begins by importing and holistically examining the providers, claimants and CPT codes within the relationship diagram to look for any unusual relationships or velocities which may exist. From a wide view, we can determine which providers have the highest velocity of medical billing by CPT code for this area.










Next, by leveraging the sequence diagram within SynerScope, we can hover over the relationships between providers and claimants either by the provider as a whole or isolating specific CPT codes to determine when in time the treatments are taking place. As an analyst this helps me understand any unusual velocities of billing from short time spans that would be impractical under normal circumstances.










As an analyst, I want to confirm that the association being viewed within the relationship diagram is suspect. Within SynerScope I can quickly view the underlying medical billing data that is represented within the relationship at the bottom of the user interface. This provides me a preview of all the relevant data attributes that exist within the actual billing database for validation of my analysis.












By focusing on the individual claimants and their corresponding relationships, within SynerScope I can highlight and compare the treatments being rendered across multiple claimants to individual providers. As an analyst this helps me understand if multiple claimants are receiving identical treatments regardless of injury or diagnosis code (ICD9). I also want to understand that if multiple claimants are receiving the same treatments, if they are receiving them in the same time periods. Within my SynerScope visualization, I can interactively compare the relationship between claimant, provider and CPT code billed within the relationship diagram and also view within the sequence diagram if treatments are being rendered in the same velocity or span of time.










Sequence of events are just as important as the relationships as it assists the analyst in understanding if a provider is attempting not only to bill for services not required or rendered, but also if treatments billed are in a condensed time period in an effort to maximize or exhaust policy limits.

Conclusion

As compared to traditional data mining or statistical analysis, by leveraging visual analytics we can identify adverse trends more rapidly and through fewer occurrences by providing an holistic visual representation of all medical billing for an area which causes abnormal trends to surface against normal billing patterns within SynerScope. These trends can be discovered in as few as three or more occurrences, where within statistical analysis from the same number of records would require a deviation of at least 2% or more. This means by leveraging visual analysis, fewer occurrences of fraudulent billing must occur before detection and intervention by the insurer resulting in a significant risk reduction.

For an interactive example of this principal, please view the video below: