Method and System to Assess an Acute and Chronic Disease Impact Index

• US 20080015891A1
• Filed: 07/12/2006
• Published: 01/17/2008
• Est. Priority Date: 07/12/2006
• Status: Abandoned Application

• Current Assignee: MEDai, Inc. (RELX PLC)
• Original Assignee: MEDai, Inc. (RELX PLC)
• Inventors: Lee, Diane
• Application Number:
  US11/457,089
• Publication Number:
  US 20080015891A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

A system (100) and method (300) is provided to assess an acute and chronic healthcare impact index of a patient. The impact index identifies patients having a highest potential impact for reducing program health-care costs. The method can include forecasting a health-care resource use of the patient, converting the health-care resource use to a monetary value, ranking the monetary value by an opportunity cost, and generating a score from the ranking. The score can identify patients having high health-care cost savings potential. The opportunity cost can be the projected cost of a health-care benefit, such as the cost of the emergency room visit or an in-patient length of stay.

Claims

1. A computer implemented method to assess an acute healthcare impact index of a patient comprising:

forecasting a health-care resource use of a patient;

converting said health-care resource use to a monetary value;

ranking said monetary value by an opportunity cost; and

generating a score from said ranking, wherein said score identifies patients having high health-care cost saving potential.

2. The method of claim 1, wherein said forecasting further includes

collecting health-care data of said patient for providing a statistical review;

performing a data integrity scrubbing of health care data to facilitate said statistical review; and

submitting said scrubbed data after said statistical review to a disease-specific model to generate a forecast use of said health-care resource,

wherein said disease-specific model employs a blend of linear and non-linear statistical predictive modeling technologies.

3. The method of claim 1, wherein a health-care resource use is one of an emergency room visit or an in-patient length of stay.

4. The method of claim 1, wherein said opportunity cost is the cost of a health-care benefit incurred by said patient.

5. The method of claim 1, further comprising converting emergency room (ER) and in-patient Length of Stay (LOS) measures to a monetary value.

6. The method of claim 1, further comprising presenting said score in an interactive web-based interface, wherein said score includes one of said forecasted resource use, said monetary value, said ranking, said opportunity cost, and said patient.

7. A computer implemented method to assess a chronic healthcare impact index of a patient comprising:

identifying a disease of the patient;

identifying a level of compliance of said patient for treating said disease;

determining a severity score of said patient in view of said disease; and

assigning a compliance score to said patient based on said level and said severity score.

8. The method of claim 7, wherein said forecasting further includes

collecting health-care data of said patient for providing a statistical review;

performing a data integrity scrubbing of health care data to facilitate said statistical review; and

submitting said scrubbed data after said statistical review to a disease-specific model to generate a forecast use of said health-care resource,

wherein said disease-specific model employs a blend of linear and non-linear statistical predictive modeling technologies.

9. The method of claim 7, wherein said determining a severity score includes predicting a severity of illness that incorporates primarily diagnostic and demographic independent variables and a cost-related dependent variable.

10. The method of claim 7, further comprising

determining a chronic health-care cost associated with patient'"'"'s said disease;

and

assessing a cost savings potential based on said compliance score in view of said chronic cost.

11. The method of claim 7, wherein said monitoring includes following provided guidelines to comply with a treatment plan for said disease.

12. The method of claim 10, wherein said cost savings potential is maximal for patients not following said guidelines, and said cost savings potential is minimal for patients following said guidelines.

13. The method of claim 12, further comprising creating a model that assigns a cost savings potential in monetary terms by

evaluating a first difference in cost from a first year to a second year for patients that followed guidelines;

and

evaluating a second difference in cost from a first year to a second year for patients that did not follow said guidelines; and

during these evaluations incorporating the patient'"'"'s disease, severity of illness, and guideline compliance.

14. A computer implemented method for:

ranking a cost savings potential for each patient within a group of patients; and

presenting said ranking through a web-interface for identifying patients that have the greatest potential for saving chronic healthcare costs.

15. A software system for identifying patients for health-care cost savings comprising:

a data collection unit for collecting a patient'"'"'s health-care data for providing a statistical review;

a scrubber unit for performing a data integrity scrubbing of said data prior to a statistical review;

a prediction engine for

processing said scrubbed data,

generating a forecast of a health-care resource used by said patient,

converting said health-care resource use to a monetary value,

ranking said monetary value by an opportunity cost, and

a user interface for presenting a score from said ranking, wherein said score identifies patients having high health-care cost saving potential.

16. The software system of claim 15, wherein said prediction engine assesses a chronic impact index of said patient by:

identifying a disease of the patient;

identifying a level of compliance of said patient for monitoring and treating said disease;

determining a severity score of said patient in view of said disease;

assigning a compliance score to said patient based on said level and said severity score;

determining a chronic health-care cost associated with patient'"'"'s said disease; and

assessing a cost savings potential based on said compliance score in view of said chronic cost.

17. The software system of claim 15, wherein said prediction engine provides an overall disease-specific severity score, a benchmark value for at least one measure within a disease category, and a severity-adjusted expected value for each said measure.

18. The software system of claim 15, wherein said prediction engine applies a weighted average for each said measure to establish said severity-adjusted expected value.

19. A computer implemented method to assess a severity of illness comprising:

identifying one or more dependent variables;

identifying one or more independent variables;

creating a disease-specific model from said dependent and independent variables;

validating said disease-specific model; and

applying the disease-specific model to client datasets.