CPC G06Q 40/08 (2013.01) [G16H 20/00 (2018.01)] | 8 Claims |
1. A system for dynamic healthcare navigation, comprising;
a server computer including a data warehouse, a memory, and a processor; and
at least one database comprising at least one secure data set;
wherein the server computer further includes a platform for receiving additional information from a user device and transmit the additional information to the server computer;
wherein the platform is operable to capture an image of a bill and/or receipt and convert the image of the bill and/or receipt into a digital data set using natural language processing (NLP);
wherein the at least one database is integrated with at least one automated call center system, operable to automatically collect audio data from one or more remote user devices;
wherein the at least one automated call center system includes at least one NLP module configured to automatically transcribe and semantically analyze the collected audio data;
wherein the at least one database requires at least one authorization to access the at least one secure data set;
wherein the server computer is configured to implement the at least one authorization to access the at least one secure data set;
wherein the server computer is configured to access the at least one secure data set and store the at least one secure data set in the data warehouse;
wherein the data warehouse implements a machine learning model (MLM), wherein the MLM is configured to analyze the at least one secure data set and tag predictors within the at least one secure data set;
wherein the predictors include medical equipment implemented by a healthcare provider and data the medical equipment generates;
wherein the predictors include a price prediction for a medical appointment based on the medical equipment and the data the medical equipment;
wherein the server computer is configured to receive a request associated with the predictors within the at least one secure data set;
wherein the server computer is configured to generate a first output based on the predictors within the at least one secure data set;
wherein the server computer is configured to receive a designation of a plurality of individuals and automatically aggregate components of the at least one secure data set concerned with the designated plurality of individuals;
wherein the server computer is configured to generate a second output based on predictors for the aggregated components of the at least one secure data set;
wherein the MLM includes at least one artificial neural network (ANN);
wherein the at least one ANN includes a plurality of node layers;
wherein the plurality of node layers include an input layer, one or more hidden layers, and an output layer;
wherein each of the plurality of node layers connects to one another and includes an associated weight and threshold;
wherein an output of any one node of the plurality of node layers must exceed a specified threshold to be activated;
wherein activating the any one node of the plurality of node layers includes sending data to a next node layer of the plurality of node layers; and
wherein the at least one ANN produces the predictors.
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