HEALTHCARE FRAUD PROTECTION AND MANAGEMENT

摘要

Real-time fraud prevention software-as-a-service (SaaS) products include computer instruction sets to enable a network server to receive medical histories, enrollments, diagnosis, prescription, treatment, follow up, billings, and other data as they occur. The SaaS includes software instruction sets to combine, correlate, categorize, track, normalize, and compare the data sorted by patient, healthcare provider, institution, seasonal, and regional norms. Fraud reveals itself in the ways data points deviate from norms in nonsensical or inexplicable conduct. The individual behaviors of each healthcare provider are independently monitored, characterized, and followed by self-spawning smart agents that can adapt and change their rules as the healthcare providers evolve. Such smart agents will issue flags when their particular surveillance target is acting out of character, outside normal parameters for them. Fraud controls can therefore be much tighter than those that have to accommodate those of a diverse group.

主权项

1. An adaptive method for healthcare claim fraud detection, comprising:
a data reduction step for converting claim data into profile data comprising a plurality of behavioral dimensions, wherein a minimum of a hundred fold reduction in data volume is realized; an individual recognition step for identifying individual healthcare providers in said profile data and for collecting such into corresponding long term individual healthcare provider profiles; a clustering step for identifying groups of healthcare providers in said profile data and for collecting such into respective long term group profiles; a smart agent building step for feeding historical claim data through the data reduction step to the individual recognition step and the group recognition step, and for creating a plurality of individual and group smart agents therefrom and each including profile data organized into said plurality of behavioral dimensions; an updating step for using claim data fed through the data reduction step to be added to any matching long term individual healthcare provider profile; a real time fraud detection step for comparing updates of individual ones of the plurality of behavioral dimensions to their running values in the long term individual healthcare provider profiles and measuring any significant deviations; a fraud classification step for scoring said deviations as being the result of fraudulent or non-fraudulent behavior on the part of the respective individual healthcare provider having sourced the claim data; wherein, the step of updating produces a self-learning and adaptive fraud detection capability that evolves over time healthcare provider-by-healthcare provider.