Abstract: MAIA Multi-Validation Computing System provides users with a real-time, document guidance, interface to upload medical claims, in multiple different formats. Document classification is performed on the uploaded data to determine a document type. Claim features are identified and extracted specific to each contextual document type, forwarded to a pre-approval and feedback manager and used to perform semantic and keyword searches on knowledge databases specific to each document type. The claims, and relevant data are input into a machine learning model, trained to predict medical billing codes using medical claims and generate: a confidence score and results summary for each billing code. Based on a comparison of the confidence score to a threshold, one of a plurality of validation processes is performed on each billing code, results are output to a user. Validated codes may be automatically submitted to third-party insurance providers, monitored for denials, and automatically appealed.

Abstract: A closed-loop vision system is disclosed that utilizes a concept known as Dynamically Reconfigurable Vision (DRV), which is adaptive image sensing driven by a computer or human operator's response to changing scenery. The system reduces the amount of irrelevant video information sensed and thus achieves more effective bandwidth and computational resource utilization, as compared to traditional vision systems. One or more reconfigurable photodetector arrays sensitive to either visible, infrared or ultraviolet radiation are present in the DRV system. These photodetector arrays feature on-chip means for spatial and temporal data reduction implemented through multiple independently controllable, time-correlated, frequently overlapping windows on the photodetector array that may be programmed according to their size, location, resolution, integration time, and frame rate. All photodetector array windows are dynamically reconfigurable in real time on a frame-by-frame basis.