Generative Model For Creating And Presenting Medical Orders

A method comprising:

• obtaining training data sets, wherein a training data set, of the training data sets, comprises:
• a first set of patient data corresponding to a first patient;
• a first order for a prescription or treatment that has been placed for the first patient;
• training a first machine learning model to generate orders based on patient data sets;
• monitoring, in real-time, updates to a second set of patient data corresponding to a second patient;
• based on the real-time monitoring, detecting a trigger for applying the first machine learning model to the second set of patient data;
• applying the first machine learning model to the second set of patient data to generate an order for the second patient;
• presenting the order for the second patient;
• receiving feedback corresponding to the order;
• based on the feedback corresponding to the order, retraining the first machine learning model,
• wherein the method is performed by at least one device including a hardware processor.

A system comprising:

• at least one device including a hardware processor;
• the system being configured to perform operations comprising:
• obtaining training data sets, wherein a training data set, of the training data sets, comprises:
• a first set of patient data corresponding to a first patient;
• a first order for a prescription or treatment that has been placed for the first patient;
• training a first machine learning model to generate orders based on patient data sets;
• monitoring, in real-time, updates to a second set of patient data corresponding to a second patient;
• based on the real-time monitoring, detecting a trigger for applying the first machine learning model to the second set of patient data;
• applying the first machine learning model to the second set of patient data to generate an order for the second patient;
• presenting the order for the second patient;
• receiving feedback corresponding to the order;
• based on the feedback corresponding to the order, retraining the first machine learning model.