Machine learning based clinical resource controller

A computer-implemented method, comprising:

• receiving, at a machine learning mode, from one or more laboratory information systems, one or more messages for a patient as at least one input value to the machine learning model, wherein the one or more messages provide information about a clinical workflow;
• based on the machine learning model receiving the one or more messages as the at least one input value to the machine learning model, outputting, by the machine learning model, at least one output value indicating that the one or more messages are actionable;
• extracting, from the one or more messages, clinically significant data;
• and
• controlling, based at least on the clinically significant data, at least one medical device associated with the patient to perform one or more tasks.

A non-transitory computer readable medium storing instructions, which when executed by at least one data processor, cause the at least one data processor to perform operations comprising:

• receiving, at a machine learning model, from one or more laboratory information systems, one or more messages for a patient as at least one input value to the machine learning model, wherein the one or more messages provide information about a clinical workflow;
• based on the machine learning model receiving the one or more messages as the at least one input value to the machine learning model, outputting, by the machine learning mode, at least one output value indicating that the one or more messages are actionable;
• extracting, from the one or more messages, clinically significant data;
• and
• controlling, based at least on the clinically significant data, at least one medical device associated with the patient to perform one or more tasks.