Abstract: A computer-implemented method for digitizing clinical documents comprises receiving a clinical document and a policy type. The method further comprises de-skewing and removing noise from the page. The method further comprises generating extracted text from the processed page using OCR, analyzing the extracted text to generate a classification label for the page using a multinomial classifier model comprising an LSTM neural network, which has been trained to recognize different classes of page based on similarities to a corpus of historical pages that have been previously classified. The method further comprises recognizing a specified keyword within the extracted text, using an NER that is trained using policy data relating to a policy having the policy type, and displaying a visual representation of the clinical document with a visual indicator of the presence of the specified keyword on the page, and a visual indicator of the classification label.

Abstract: A first risk score for each member in a population is generated using a first predictive model. A second risk score is generated for each member in the population using a second predictive model. The population is stratified into a plurality of risk groups based on: (i) predefined manageable medical conditions; (ii) the first risk score; and (iii) the second risk score. Based on the plurality of risk groups, the population is segmented into a high-risk group and a low-risk group. A panel of care management associates of a plurality of care management associates is assigned to each member in the member population based on whether the member belongs to the high-risk group or the low-risk group.

Abstract: A method for authorizing a treatment may include receiving a treatment authorization request, creating an extracted text of a historical record using optical character recognition on the historical record, determining whether to analyze authorization performance of the treatment using a machine learning authorization process, in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process: identifying authorization criteria for the treatment based on treatment authorization guidelines; identifying a natural language record processing model corresponding to expense authorization guidelines, and performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record, determining whether the relevant record data meets the authorization criteria and in response to a determination that the relevant record data meets the auth

Abstract: Systems, methods and user interfaces are provided for generating real-time and/or diagnostic omnichannel interaction insights. The method may include obtaining transcripts corresponding to digital service channels. The method may also include generating and inputting channel-specific prompts to machine learning models to obtain insights. The method may also include generating and/or displaying analytical insights. The method may also include obtaining a natural language question, via a conversational interface, directed to a benefits database. The method may also include parsing the question. The method may also include ranking benefits using a recommendation algorithm. The method may also include generating a context by applying a language template. The method may also include inputting the context to a large language model. The method may also include providing a response to an agent to cause the agent to perform one or more actions. The method may also include generating and displaying a dashboard.

Abstract: The present disclosure regards an electronic device configured to generate personalized communications regarding patient treatment plans. The electronic device includes a processor configured to perform various operations. These operations include receiving data regarding a patient, where the data includes treatment plan data regarding a treatment plan prescribed for the patient and other data regarding a healthcare provider, a clinician, a medical record, a social media account, a laboratory, or a pharmacy associated with the patient. The operations also include providing the data to a plurality of machine learning (ML) models configured to generate a personalized communication for the patient based on the data. Additionally, the operations include receiving the personalized from the plurality of ML models, where the personalized communication is addressed to the patient and regards the treatment plan prescribed for the patient.

Abstract: An improved machine learning based method for authorizing the performance of a treatment, comprising the steps of: receiving a treatment authorization request, the treatment authorization request including a historical record of the person who will receive the treatment and treatment identifying information relating to the treatment; creating an extracted text of the historical record using optical character recognition on the historical record; determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the identified treatment; in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process: identifying authorization criteria for the treatment based on the treatment authorization guidelines, wherein the authorization criteria includes records data condi

Abstract: A system and method of predicting a medical diagnosis is disclosed. The method includes receiving claims data, clinical data and demographic data and detecting a prediction target for the diagnosis. If present, inputting the prediction indicator and the clinical data into a machine learning model to predict diagnosis risk, to create a diagnosis risk score; determining a care seeking propensity score, from the demographic data; weighting the diagnosis risk score by the care seeking propensity score to create a weighted diagnosis risk score; determining whether the weighted diagnosis risk score indicates a likelihood of the medical diagnosis; and, in response, transmitting a recommendation for further evaluation. The machine learning model may be trained using historical claims data, clinical data, and demographic data and may be trained to detect correlation between medical diagnosis signals identified from the training data, and a positive result from a screening mechanism the medical diagnosis.