Computer System and Method for Generating Trigger Alerts to Maximize Interactions With Healthcare Providers

A computer-implemented method comprising:

• receiving, at a computing system from one or more networked data sources, anonymized patient data records;
• storing the anonymized patient data records in the data storage;
• for each respective HCP of the plurality of health care providers (HCPs):
• predicting a respective time in the future at which the respective HCP is likely to make a given type of treatment decision for at least one patient by:
• accessing a respective set of anonymized patient data records associated with the respective HCP;
• inputting each anonymized patient data record in the respective set into a machine learning model that is configured to predict a next time in the future at which the given type of treatment decision is likely to be made for a particular patient, wherein the machine learning model is trained by applying one or more machine learning techniques to historical patient data records associated with past occurrences of the given type of treatment decision;
• and
• based on inputting each anonymized patient data record in the respective set into the machine learning model, determining the respective time in the future at which the respective HCP is likely to make the given type of treatment decision for the at least one patient;
• after predicting the respective time in the future at which the respective HCP is likely to make the given type of treatment decision for the at least one patient, generating a trigger alert suggesting that the respective HCP be visited prior to the respective time in the future to deliver a message related to the given type of treatment decision;
• and
• causing the trigger alert to be sent to a client device associated with a target of the trigger alert.

A non-transitory computer-readable medium having program instructions stored thereon that are executable by at least one processor of a computer system to cause the computer system to:

• receive, from one or more networked data sources, anonymized patient data records;
• store the anonymized patient data records in the data storage;
• for each respective HCP of the plurality of health care providers (HCPs):
• predict a respective time in the future at which the respective HCP is likely to make a given type of treatment decision for at least one patient by:
• accessing a respective set of anonymized patient data records associated with the respective HCP;
• inputting each anonymized patient data record in the respective set into a machine learning model that is configured to predict a next time in the future at which the given type of treatment decision is likely to be made for a particular patient, wherein the machine learning model is trained by applying one or more machine learning techniques to historical patient data records associated with past occurrences of the given type of treatment decision;
• and
• based on inputting each anonymized patient data record in the respective set into the machine learning model, determining the respective time in the future at which the respective HCP is likely to make the given type of treatment decision for the at least one patient;
• after predicting the respective time in the future at which the respective HCP is likely to make the given type of treatment decision for the at least one patient, generate a trigger alert suggesting that the respective HCP be visited prior to the respective time in the future to deliver a message related to the given type of treatment decision;
• and
• cause the trigger alert to be sent to a client device associated with a target of the trigger alert.