Abstract: Systems, methods and computer program products to identify and close gaps in care are provided. Aspects of the present disclosure disclosed and described herein enable a payer may identify gaps in care based on information in the payer database. The payer can send this information to the provider prior to patient scheduling. The scheduling system at the provider can use this information to schedule patients and their medical needs (e.g., medical test, lab tests, imaging tests, etc.) before the next patient appointment. Primary care providers (PCPs) can then be made aware of the gaps in care information, as well as Hierarchical Condition Category (HCC) coding gap information, so they can perform a complete diagnosis using all available clinical data. PCP notes along with the treatment the patient receives can be combined with the RAF (Risk Adjustment Score) shared by the payers to ensure patient is assessed for all relevant conditions during the visit.

Abstract: Apparatus, systems, devices, other articles of manufacture and associated methods are disclosed and described herein to process medical data to generate a classification of the medical data using artificial intelligence. An example apparatus includes a processor to execute instructions to implement a history of past illness (HPI) receiver to receive an HPI formatted as a string, the string including one or more words, the words organized in sentences, a natural language processor to tokenize the one or more words into tokens based on a context associated with at least one of the one or more words and a tensor generator to convert the tokens into hashes, each of the hashes forming a dimension of a tensor based on the context. The apparatus further includes a neural network to embed each of the hashes into vectors, process the vectors to classify the HPI as extended or brief based on a similarity to a set of classified HPIs and output a classification for the HPI.