Identifying relationships among disparate pieces of information is the key to pinpointing billing practices that indicate fraud, waste, and abuse. Finding those relationships, however, tends to be difficult. Connections among claimants, providers, and other parties to a claim are usually hidden in all the available data, concealed by the sheer volume of medical files, bills, papers, and reports stored in a company’s various systems.
To unlock your data’s potential, apply the right combinations of analytics and modeling techniques to your medical bill data. Doing so — using a tool such as ISO MedSentry™ — can reveal patterns of aberrant treatment and billing behaviors. Examples include:
- excessive modalities: the frequent use of a large number of modalities every time a provider sees a patient
- mismatch of diagnosis to procedure: a claim that indicates procedures that don’t align with the condition treated (for instance, chiropractic manipulation of the lower spine in the case of a neck injury)
- template (or boilerplate) billing: a group of claims indicating every patient received the same treatment modalities
- mismatch of provider to specialty/diagnosis/procedure: procedures that have no alignment to the specialty or practice of the provider (for example, a psychiatrist providing physical therapy modalities)
- high time billing: one or more claims showing a provider billing for more hours in a single day than a person might reasonably work during that period
Medical fraud costs the property/casualty insurance industry tens of billions of dollars a year, and the predictive analytics in ISO MedSentry can help you protect your policy owners and your combined ratio.