System and method for automatic detection for multiple failed orders at a back end pharmacy

A method for predicting pharmacy order failures, the method comprising:

• receiving, in real-time, sensor data from one or more sensors associated with pharmacy order fulfilment of a high-volume pharmacy;
• identifying at least one pharmacy order package associated with the sensor data;
• updating, in a database, data associated with the at least one pharmacy order package based on at least some of the sensor data;
• providing, to a predictive model, the updated data associated with the at least one pharmacy order package, the predictive model being configured to predict one or more failures in a pharmacy order associated with the at least one pharmacy order package;
• and
• in response to receiving at least one prediction output from the predictive model indicating at least one failure prediction in the pharmacy order associated with the at least one pharmacy order package, initiating at least one corrective action, wherein the at least one failure prediction corresponds to a contradiction between an expected value indicated by the updated data associated with the at least one pharmacy order package and a predicted actual value indicated by the updated data the at least one pharmacy order package.

A system for predicting pharmacy order failures, the system comprising:

• a processor;
• and
• a memory including instructions that, when executed by the processor, cause the processor to:
• receive, in real-time, sensor data from one or more weight sensors associated with pharmacy order fulfilment of a high-volume pharmacy;
• identify at least one pharmacy order package associated with the sensor data;
• update, in a database, data associated with the at least one pharmacy order package based on at least some of the sensor data;
• provide, to an artificial intelligence engine configured to execute at least one machine learning model that is initially trained using at least historical pharmacy order package data, the updated data associated with the at least one pharmacy order package, the at least one machine learning model being configured to predict one or more failures in a pharmacy order associated with the at least one pharmacy order package;
• and
• in response to receiving at least one prediction output from the predictive model indicating at least one failure prediction in the pharmacy order associated with the at least one pharmacy order package, perform at least one corrective action, wherein the at least one failure prediction corresponds to a contradiction between an expected weight value indicated by the updated data associated with the at least one pharmacy order package and a predicted actual weight value indicated by the updated data the at least one pharmacy order package.