Generating intervention success probabilities and intelligent ranking of subjects

A system comprising:

• one or more data processors;
• and
• a non-transitory computer-readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform a set of operations including:
• identifying a set of subjects flagged for engagement in a potential communication workflow;
• for each subject of the set of subjects:
• accessing subject data that identifies one or more historical medical events or one or more characteristics of a subject;
• generating one or more probabilities, by leveraging one or more causal models, wherein the one or more probabilities include:
• a first probability associated with a positive outcome if an intervention is performed,
• a second probability associated with a positive outcome in an absence of the intervention, and
• a third probability representing a likelihood of the intervention being performed under standard care;
• generating, using a first mathematical function, a net-benefit bound by combining the one or more probabilities;
• predicting, via one or more machine-learning models, one or more costs and revenue amounts including:
• a cost associated with performing the intervention,
• a cost associated with an acute-care utilization or an emergency department visit due to a negative outcome, and
• a revenue generated from closing care gaps;
• and
• computing, using a second mathematical function, a net-cost saving bounds based on the net-benefit bound and the one or more costs and revenue amounts;
• ranking the set of subjects based on the net-cost saving bound to generate a ranked list;
• and
• initiating one or more preventative measures for one or more subjects of the set of subjects having a high ranking in the ranked list, wherein the one or more preventative measures include scheduling follow-up appointments or checkups, calling the subject for additional diagnostic lab tests, or providing personalized healthcare recommendations.

A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform a set of operations comprising:

• identifying a set of subjects flagged for engagement in a potential communication workflow;
• for each subject of the set of subjects:
• accessing subject data that identifies one or more historical medical events or one or more characteristics of a subject;
• generating one or more probabilities, by leveraging one or more causal models, wherein the one or more probabilities include:
• a first probability associated with a positive outcome if an intervention is performed,
• a second probability associated with a positive outcome in an absence of the intervention, and
• a third probability representing a likelihood of the intervention being performed under standard care;
• generating, using a first mathematical function, a net-benefit bound by combining the one or more probabilities;
• predicting, via one or more machine-learning models, one or more costs and revenue amounts including:
• a cost associated with performing the intervention,
• a cost associated with an acute-care utilization or an emergency department visit due to a negative outcome, and
• a revenue generated from closing care gaps;
• and
• computing, using a second mathematical function, a net-cost saving bound based on the net-benefit bound and the one or more costs and revenue amounts;
• ranking the set of subjects based on the net-cost saving bound to generate a ranked list;
• and
• initiating one or more preventative measures for one or more subjects of the set of subjects having a high ranking in the ranked list, wherein the one or more preventative measures include scheduling follow-up appointments or checkups, calling the subject for additional diagnostic lab tests, or providing personalized healthcare recommendations.