TECHNICAL FIELD

The present disclosure relates in general to computer-based disability income management methods and systems, and particularly for computer-based methods and systems related to disability insurance claims and to recovery from such claims.

BACKGROUND

Existing computer-based systems for managing disability insurance claims cannot adequately predict a return time-frame for an employee on disability leave. This is partially due to the fact that the expected return date is based upon a wide variety of factors. For example, the expected return date can be based upon a medical diagnosis or based on the employee's general conditions. In many instances, the health assessment of an employee in view of the employee's overall conditions has proven to change over time. In many other cases, the return expectation date may change during the time that the employee is on disability leave. For example, as the time that an employee is on disability leave increases, it is generally observed that the likelihood of the employee returning to work decreases exponentially; even though the expected return date remains the same and cannot account for this likelihood. Additionally, when an employee claims disability, that employee may initially assess an return expectation date. However, later during the disability leave period, the employee may adjust to the receipt of the disability payment, and lose the inclination to return to work. It is generally observed that as the disability leave time increases, employee becomes more accustomed and comfortable and are less likely to return to work thereby requiring a larger than expected disability payment. As a result, the conventional computer-based personnel management systems cannot properly calculate or determine a status of current and future employees, thereby affecting administration of disability income claims.

Disability insurance claims examiners typically handle a large caseload, and do not have the time or resources to regularly follow up with all disabled claimants. Often, a claims examiner's interactions with disabled claimants entails laborious manual procedures. Thus, claims examiners can have difficulty tracking if and when claimants within their caseload are expected to return to work. What is needed is systems and methods that enable a user (e.g., a claims examiner) to identify disability claimants that are most likely to reach full, return-to-work capacity soon, so that the user can prioritize interactions with these claimants about their plans and recovery status. Also needed are systems and methods that enable a user to identify disability claimants that have forgotten or neglected to inform the user about the claimant's return to work.

SUMMARY

The methods and systems described herein attempt to address the deficiencies of the conventional systems to more efficiently analyze disability claims that are most likely to reach full, return-to-work capacity using a predictive machine learning model, and to display a specialized graphical user interface configured to present the claim recovery analyses.

A predictive machine learning model is configured to determine a plurality of ranked claimant records and recovery scores representative of a likelihood that claimants will realize a claim termination event. The predictive machine learning model inputs disability income claim data representative of disability income insurance claims and disability income claimant data representative of claimants for disability income insurance into an event history model. The event history model utilizes discrete-time survival analysis in conjunction with a machine learning model for feature selection. In an embodiment, in modeling DI recovery the positive class in the target variable is one of a plurality of preselected claim termination events representative of a claimant achieving return to work capacity.

In an embodiment, the claimant data includes diagnosis data representative of workplace disability duration guidelines. In an embodiment, the diagnosis data are representative of Presley Reed workplace disability duration guidelines.

In an embodiment, the event history model is a predictive machine learning model continually trained using updated disability income claims data associated with the claimants for disability income insurance. In the present disclosure, disability income claims are sometimes called “DI” claims, and are sometimes called DI insurance claims. In an embodiment, the updated DI claims data is extracted from a disability income insurance claims database. In an embodiment, the continual training procedure extracts DI claims data associated with the claimants for disability income insurance from a disability income insurance claims database, and transforms the extracted data into a longitudinal format. The longitudinal format of the transformed data includes multiple person-time period records corresponding to each claimant record, wherein each person-time period records corresponding to a respective time period of observation for the respective claimant record.

In an embodiment, the machine learning model for feature selection is a gradient boosting machine learning model. In an embodiment, the gradient boosting machine learning model is an XGBoost model. In another embodiment, the machine learning model for feature selection is a logistic regression model with elastic net regularization. In a further embodiment, the machine learning model for feature selection is a random decision forests model.

In an embodiment, the claim termination event is one of a plurality of preselected recovery events representative of whether or not the claimant achieves return to work capacity in a given year. In an embodiment, continual training of the predictive machine learning model transforms model input data into a longitudinal format that includes multiple person-year records corresponding to each claimant record, wherein each person-year record corresponds to an identified year of observation for the respective claimant record.

In an embodiment, the system is configured to update and display in real time the information representative of the plurality of ranked claimant records and the recovery scores representative of the likelihood that respective claimant records will realize the claim termination event. In an embodiment, the system is configured to display, for each of the plurality of ranked claimant records, a hazard plot representative of a conditional probability over time that the respective will realize the claim termination event. In an embodiment, the system is configured to display, for each of the plurality of ranked claimant records, a bar chart in which the bars represent the probability of realizing the claim termination event during respective years of the claimant's life.

In one embodiment, a processor-based method comprises executing, by a processor, a predictive machine learning model configured to determine a recovery score for each claimant record of a plurality of claimant records stored in a disability income database, by inputting disability income claim data representative of disability income insurance claims and disability income claimant data representative of claimants for disability income insurance into an event history model utilizing discrete-time survival analysis in conjunction with a machine learning model for feature selection, the event history model outputting a plurality of ranked claimant records and recovery scores representative of a likelihood that respective claimant records will realize a claim termination event, wherein the claim termination event is one of a plurality of preselected recovery events representative of representative of a claimant achieving return to work capacity; wherein the event history model is a predictive machine learning model that is continually trained using updated disability income claimant data representative of the claimants for disability income insurance and transforming the updated data into a longitudinal format that includes multiple person-time period records corresponding to each claimant record, each person-time period records corresponding to a respective time period of observation for the respective claimant record; wherein the disability income claimant data includes diagnosis data representative of workplace disability duration guidelines; and running, by the processor, the event history model on demand to update and display, by a display device in operative communication with the processor, a graphical user interface showing a report of the plurality of ranked claimant records and the recovery scores representative of a likelihood that respective claimants will realize a claim termination event.

In another embodiment, a processor-based method comprises executing, by a processor, a predictive machine learning model configured to determine a recovery score for each claimant record of a plurality of claimant records representative of claimants for disability income insurance stored in a disability income database, by inputting disability income claim data representative of disability income insurance claims and disability income claimant data for the claimants for disability income insurance into a predictive machine learning model utilizing discrete-time survival analysis in conjunction with gradient boosting, the predictive machine learning model outputting a plurality of ranked claimant records and recovery scores representative of a likelihood that respective claimant records will realize a claim termination event, wherein the claim termination event is one of a plurality of preselected recovery events representative of a claimant achieving return to work capacity; wherein the predictive machine learning model is continually trained via updated disability income claimant data and updated data representative of the claimants for disability income insurance; wherein the claimant data includes diagnosis data representative of workplace disability duration guidelines; and running, by the processor, the event history model on demand to update and display, by a display device in operative communication with the processor, a graphical user interface showing a report of the plurality of ranked claimant records and the recovery scores representative of a likelihood that respective claimants will realize a claim termination event.

In a further embodiment, a system comprises non-transitory machine-readable memory that stores claimant records for a plurality of claimants for disability income insurance, disability income claims data representative of disability income insurance claims, and diagnosis data representative of workplace disability duration guidelines; a predictive modeling module that stores a predictive machine learning model configured to determine a plurality of ranked claimant records and recovery scores representative of a likelihood that respective claimant records will realize a claim termination event comprising one of a plurality of preselected recovery events representative of a claimant achieving return to work capacity, the predictive machine learning model utilizing discrete-time survival analysis in conjunction with gradient boosting; wherein the predictive machine learning model is continually trained via updated claimant records, updated disability income claims data, and updated diagnosis data; and a processor in operative communication with the display, wherein the processor in communication with the non-transitory, machine-readable memory and the predictive modeling module executes a set of instructions instructing the processor to: apply the discrete-time survival analysis in conjunction with gradient boosting to the plurality of claimant records for a plurality of claimants for disability income insurance, disability income claims data representative of disability income insurance claims, and diagnosis data representative of workplace disability duration guidelines, to determine the plurality of ranked claimant records and the recovery scores representative of the likelihood that respective claimant records will realize a claim termination event; and run the predictive machine learning module on demand to update and display, by a display device in operative communication with the processor, a graphical user interface showing a report of the plurality of ranked claimant records and the recovery scores representative of the likelihood that respective claimant records will realize a claim termination event.

Numerous other aspects, features and benefits of the present disclosure may be made apparent from the following detailed description taken together with the drawing figures.

