Systems and methods for reducing resource consumption via information technology infrastructure

Abstract

The present disclosure is directed to reducing resource consumption via information technology infrastructure. A server of the present disclosure receives one or more data packets including data indicating a healthcare transaction event. The server selects healthcare trend model trained by the server using previously received data packets. The server determines a correlation coefficient between each of a plurality of healthcare related recommendations and the selected healthcare trend model. The server retrieves a notification template from a notification data structure that maps to the highest ranking healthcare related recommendation. The server generates a request to deliver the notification corresponding to the highest ranking healthcare related recommendation at a destination address of a computing device of a participant.

1 .- 20 . (canceled) 
     
     
         21 . A system to manage transactions, comprising:
 one or more processors, coupled to memory and configured to:   identify a healthcare transaction event corresponding to a participant of a plurality of participants of a healthcare platform;   select, based at least in part on a type of the healthcare transaction event, a healthcare trend model trained by a machine learning engine using historical healthcare transaction events corresponding to the plurality of participants of the healthcare platform, the healthcare trend model trained by the machine learning engine to include a baseline threshold for one or more features;   determine, based on a comparison between the healthcare transaction event and the baseline threshold of the one or more features of the selected healthcare trend model, to generate one or more notifications to control an occurrence of the type of the healthcare transaction event corresponding to the healthcare transaction event;   select, based at least in part on the selected healthcare trend model trained using the machine learning engine, a notification to control the occurrence of the type of the healthcare transaction event; and   transmit, via a network, the notification to a computing device associated with the participant to control the occurrence of the type of the healthcare transaction event.   
     
     
         22 . The system of  claim 21 , wherein the healthcare transaction events include one or more of a claim payment, a card denial, a password change, or a received deposit. 
     
     
         23 . The system of  claim 21 , wherein the one or more processors are further configured to:
 determine the baseline threshold for one or more features comprising at least one of a type of event, geographic area, or time of year.   
     
     
         24 . The system of  claim 21 , wherein the one or more processors are further configured to:
 receive the historical healthcare transaction events corresponding to the plurality of participants from a device of an administrator remote from the one or more processors via an administrator interface rendered by the one or more processors on the device of the administrator.   
     
     
         25 . The system of  claim 21 , wherein the machine learning engine is configured to use at least one of a decision tree learning, a predictive model, association rule learning, support vector machines, clustering, or reinforcement learning. 
     
     
         26 . The system of  claim 21 , wherein the one or more processors are further configured to:
 select the healthcare trend model from a plurality of healthcare trend models based on a minimum vector distance between the healthcare transaction event and each of the plurality of healthcare trend models.   
     
     
         27 . The system of  claim 21 , wherein the type of the healthcare transaction event comprises a denial, and the one or more processors are further configured to:
 retrieve a denial healthcare trend model responsive to the healthcare transaction event including the denial; and   select, based on the denial healthcare trend model, a denial recommendation comprising a recommended resource allocation responsive to determining that an insufficient amount of resources resulted in the denial.   
     
     
         28 . The system of  claim 21 , wherein the type of the healthcare transaction event comprises a partial denial, and the one or more processors are further configured to:
 retrieve a denial healthcare trend model responsive to the healthcare transaction event including the partial denial; and   select, based on the denial healthcare trend model, a denial recommendation comprising an ordered list including at least one qualifying item and at least one non-qualifying item responsive to determining that the partial denial resulted from a transaction including one or more qualifying items and one or more non-qualifying items.   
     
     
         29 . The system of  claim 21 , wherein the one or more processors are further configured to:
 perform, responsive to the determination to generate the notification, a lookup in a recommendation data structure using an identifier of the selected healthcare trend model to identify a plurality of healthcare related recommendations linked with the selected healthcare trend model; and   generate the notification based at least in part on a healthcare related recommendation of the plurality of healthcare related recommendations.   
     
     
         30 . The system of  claim 21 , wherein the one or more processors are further configured to:
 identify, responsive to the determination to generate the notification, a plurality of healthcare related recommendations linked with the selected healthcare trend model;   determine a correlation coefficient between each of the plurality of healthcare related recommendations and the selected healthcare trend model, wherein the correlation coefficient indicates a likelihood that the plurality of healthcare related recommendations reduces resource consumption of information technology infrastructure by reducing the occurrence of the type of the healthcare transaction event;   select, based on a rank of each correlation coefficient, a highest ranking healthcare related recommendation of the plurality of healthcare related recommendations; and   generate the notification based at least in part on the highest ranking healthcare related recommendation.   
     
     
         31 . A method of managing transactions, comprising:
 identifying, by one or more processors, a healthcare transaction event corresponding to a participant of a plurality of participants of a healthcare platform;   selecting, by the one or more processors, based at least in part on a type of the healthcare transaction event, a healthcare trend model trained by a machine learning engine using historical healthcare transaction events corresponding to the plurality of participants of the healthcare platform, the healthcare trend model trained by the machine learning engine to include a baseline threshold for one or more features;   determining, by the one or more processors, based on a comparison between the healthcare transaction event and the baseline threshold of the one or more features of the selected healthcare trend model, to generate one or more notifications to control an occurrence of the type of the healthcare transaction event corresponding to the healthcare transaction event;   selecting, by the one or more processors, based at least in part on the selected healthcare trend model trained using the machine learning engine, a notification to control the occurrence of the type of the healthcare transaction event; and   transmitting, by the one or more processors via a network, the notification to a computing device associated with the participant to control the occurrence of the type of the healthcare transaction event.   
     
     
         32 . The method of  claim 31 , wherein the healthcare transaction events include one or more of a claim payment, a card denial, a password change, or a received deposit. 
     
     
         33 . The method of  claim 31 , comprising:
 determining, by the one or more processors, the baseline threshold for one or more features comprising at least one of a type of event, geographic area, or time of year.   
     
     
         34 . The method of  claim 31 , comprising:
 receiving, by the one or more processors, the historical healthcare transaction events corresponding to the plurality of participants from a device of an administrator remote from the one or more processors via an administrator interface rendered by the one or more processors on the device of the administrator.   
     
     
         35 . The method of  claim 31 , wherein the machine learning engine is configured to use at least one of a decision tree learning, a predictive model, association rule learning, support vector machines, clustering, or reinforcement learning. 
     
     
         36 . The method of  claim 31 , comprising:
 selecting, by the one or more processors, the healthcare trend model from a plurality of healthcare trend models based on a minimum vector distance between the healthcare transaction event and each of the plurality of healthcare trend models.   
     
     
         37 . The method of  claim 31 , wherein the type of the healthcare transaction event comprises a denial, comprising:
 retrieving, by the one or more processors, a denial healthcare trend model responsive to the healthcare transaction event including the denial; and   selecting, by the one or more processors, based on the denial healthcare trend model, a denial recommendation comprising a recommended resource allocation responsive to determining that an insufficient amount of resources resulted in the denial.   
     
     
         38 . The method of  claim 31 , wherein the type of the healthcare transaction event comprises a partial denial, comprising:
 retrieving, by the one or more processors, a denial healthcare trend model responsive to the healthcare transaction event including the partial denial; and   selecting, by the one or more processors, based on the denial healthcare trend model, a denial recommendation comprising an ordered list including at least one qualifying item and at least one non-qualifying item responsive to determining that the denial healthcare transaction event resulted from a transaction including one or more qualifying items and one or more non-qualifying items.   
     
     
         39 . The method of  claim 31 , comprising:
 performing, by the one or more processors responsive to the determination to generate the notification, a lookup in a recommendation data structure using an identifier of the selected healthcare trend model to identify a plurality of healthcare related recommendations linked with the selected healthcare trend model; and   generating, by the one or more processors, the notification based at least in part on a healthcare related recommendation of the plurality of healthcare related recommendations.   
     
     
         40 . The method of  claim 31 , comprising:
 identifying, by the one or more processors responsive to the determination to generate the notification, a plurality of healthcare related recommendations linked with the selected healthcare trend model;   determining, by the one or more processors, a correlation coefficient between each of the plurality of healthcare related recommendations and the selected healthcare trend model, wherein the correlation coefficient indicates a likelihood that the plurality of healthcare related recommendations reduces resource consumption of information technology infrastructure by reducing the occurrence of the type of the healthcare transaction event;   selecting, by the one or more processors based on a rank of each correlation coefficient, a highest ranking healthcare related recommendation of the plurality of healthcare related recommendations; and   generating, by the one or more processors, the notification based at least in part on the highest ranking healthcare related recommendation.