Abstract: Techniques for operationalizing predicted changes in risk based on interventions are disclosed. In an example method, a computing system stores information about a plurality of patients. The computing system receives, from an ensemble machine learning model, intervention information for a patient including a prediction of risk and change in risk for certain interventions. The computing system generates a prioritized intervention list and provides it to a client device. The computing system receives, from the client device, updated patient engagement data for the patient and adds it to the patient engagement data. The computing system receives, from the ensemble machine learning model that is re-trained using the updated data, updated predictions. The computing system generates an updated prioritized intervention list and provides it to the client device to cause a graphical user interface (“GUI”) to be automatically refreshed with the updated list.

Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.

Abstract: A claim adjudication system including an automatic adjudication pipeline that uses pipeline rules to automatically adjudicate a claim associated with a benefit plan at one or more stages. The automatic adjudication pipeline can redirect the claim to a user interface for manual review when a pipeline rule associated with a manual review condition is triggered. The user interface can present information about the claim relevant to the manual review condition in an integrated format, including highlighting the reasons for the manual review. A user can evaluate the claim in the user interface and provide user input that addresses the manual review condition, and the claim can be routed back to the automatic adjudication pipeline. The user input can also be used as training data for machine learning to adjust pipeline rules that are used to automatically process claims and to redirect future claims for manual review.