Abstract: In general, techniques for context-driven medical claim (CDMC) processing and a system implementing same are disclosed herein to enable N number of different payer entities (PEs) to receive and process medical claims and identify medical claims of interest, e.g., medical claims with at least one predetermined abnormality, preferably before adjudication and payment. In an embodiment, a pipeline implementing CDMC techniques provides a rule-based engine to detect predetermined claim abnormalities based on, for instance, rules comprising logic that, when instantiated, compares target line items/components of a medical claim against data within medical data repositories (e.g., diagnosis and treatment databases, patient outcome data, and pharmaceutical databases), and/or detects the presence of a predetermined claim abnormality based on trainable medical model(s) or other machine-learning approaches.

Abstract: A system and method of generating for determining appropriateness of a pharmaceutical prescription includes receiving, at a remote server, input data from a client device, the input data comprising at least one of a tradename of a pharmaceutical, a generic name of the pharmaceutical, drug category, drug sub category, or diagnosis data; searching, using a pharmaceutical clinical and expense module, a pharmaceutical usage/financial database (PUFD) to identify relevant data based on the input data; generating, using the pharmaceutical clinical and expense module, output data based, at least in part, on the identified relevant data; and transmitting the output data to the client device.

Abstract: A system and method for healthcare stop-loss or reinsurance assessment and planning comprising receiving input data, the input data comprising diagnosis data related to a medical condition; identifying data in a healthcare diagnosis/financial database (HDFD) based on, at least in part, the input data, the identified data comprising treatment data including a list of one or more treatments associated with the diagnosis data and expected treatment costs associated with each treatment of the list of one or more treatments, and procedure data comprising a list of one or more procedures associated with the diagnosis data and expected procedure costs associated with each treatment of the list of one or more treatments, generate output data based on, at least in part, the identified data; and transmitting the output data to a user.