Workplace Absenteeism Risk Model

• US 20090319297A1
• Filed: 06/18/2009
• Published: 12/24/2009
• Est. Priority Date: 06/18/2008
• Status: Abandoned Application

• Current Assignee: UPMC
• Original Assignee: UPMC
• Inventors: Hernandez, Gerardina M., Peele, Pamela B.
• Application Number:
  US12/486,800
• Publication Number:
  US 20090319297A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A computer-implemented process includes: collecting a plurality of data; implementing a model that uses the data to estimate or predict lost work time at an individual level; and computing absenteeism risk at the individual level. The model can include, for example an artificial neural network or a model that uses regression analysis.

Claims

1. A computer-implemented process comprising:

collecting a plurality of data;

implementing a model that uses the data to estimate or predict lost work time at an individual level; and

computing absenteeism risk at the individual level.

2. The computer-implemented process of claim 1, wherein absenteeism comprises an amount of time away from work;

accidentally or intentionally.

3. The computer-implemented process of claim 1, wherein the data includes International Classification of Disease (ICD) codes.

4. The computer-implemented process of claim 3, wherein the International Classification of Disease (ICD) diagnosis codes are aggregated into groups as defined by the Clinical Classification System.

5. The computer-implemented process of claim 1, wherein the data includes Current Procedural Terminology (CPT) codes and Healthcare Common Procedure Coding System (HCPCS) codes.

6. The computer-implemented process of claim 5, wherein the Current Procedural Terminology (CPT) codes and Healthcare Common Procedure Coding System (HCPCS) codes are aggregated into medical services groupings as defined by the Berenson-Eggers Type of Service.

7. The computer-implemented process of claim 1, wherein the data includes Pharmaceutical Therapeutic Class Code.

8. The computer-implemented process of claim 7, wherein a variable-reduction method is employed over the Pharmaceutical Therapeutic Class Codes to generate RX-groups.

9. The computer-implemented process of claim 1, wherein the model includes an artificial neural network.

10. The computer-implemented process of claim 9, wherein the artificial neural network is implemented using Multilayer Perceptrons (MLPs).

11. The computer-implemented process of claim 9, wherein the model uses a testing set to test the performance of the artificial neural network.

12. The computer-implemented process of claim 9, wherein in a deployment phase, a fitted model is applied on a new data set where a target value is unknown, and the fitted model is used to produce an estimate of the unknown target value given the new data set.

13. The computer-implemented process of claim 9, wherein the artificial neural network has a target of Total Lost Work Hours for a year X+1, the input data are age, sex, a variable that indicates if the employee has dependents, variables for CCS codes, variables for RX-groups, and variables for BETOS codes, representing information corresponding to a year X;

and the model predicts Total Lost Work Hours for the year X+1 and assumes that each individual will be an employee for the entire predicted year time frame.

14. The computer-implemented process of claim 9, wherein the artificial neural network has a target of Total Lost Work Hours for a year X+1, the input data are age, sex, a variable that indicates if the employee has dependents, variables for CCS codes, variables for RX-groups, and variables for BETOS codes, and actual Total Lost Work Hours for a year X, representing information corresponding to year X;

and the model predicts Total Lost Work Hours for the year X+1 and assumes that each individual will be an employee for the entire predicted year time frame.

15. The computer-implemented process of claim 9, wherein the artificial neural network has a target of lost work hours for an absence category for a year X, and the input data are age, sex, a variable that indicates if the employee has dependents, variables for CCS codes, variables for RX-groups, and variables for BETOS codes, representing information corresponding to year X.

16. The computer-implemented process of claim 15, wherein the absence category comprises at least one of:

Total Lost Work Hours;

Scheduled Lost Work Hours;

Illness and Unscheduled Lost Work Hours;

Workers'"'"' Compensation Only Indemnity; and

Short-Term Disability.

17. The computer-implemented process of claim 1, wherein the model applies multiple regression analysis.

18. The computer-implemented process of claim 17, wherein the model is constructed for a selected absence category.

19. The computer-implemented process of claim 17, wherein the model employs a multiple linear regression with stepwise selection, the dependent variable is lost work hours for a year X, and explanatory variables include age, sex, a variable that indicates if the employee has dependents, variables for CCS codes, variables for RX-groups, and variables for BETOS codes, representing information corresponding to year X;

and categorical variables have two categories.

20. The computer-implemented process of claim 18, wherein the absence category includes at least one of:

Total Lost Work Hours;

Scheduled Lost Work Hours;

Illness and Unscheduled Lost Work Hours;

Workers'"'"' Compensation Only Indemnity; and

Short-Term Disability.

21. The computer-implemented process of claim 17, wherein for a deployment phase, a fitted model is applied on a new data set where a dependent variable is unknown, and the fitted model is used to obtain a predicted value of the dependent variable.

22. The computer-implemented process of claim 1, wherein the absenteeism risk for each employee is computed as a number ranging from zero to infinity, and an employee having an absenteeism risk equal to 1 has an estimated or predicted lost work time equal to a mean of an estimated or predicted lost work time for a reference population, an employee having an absenteeism risk above or below 1 indicates that the employee'"'"'s estimated or predicted lost work time is higher or lower, respectively, than the mean of an estimated or predicted lost work time for the reference population.

23. The computer-implemented process of claim 1, wherein absenteeism risks are computed separately for a definable category of absenteeism that represents a reference population.