System and methods for generating physician profiles concerning prescription therapy practices with self-adaptive predictive model

• US 20040249669A1
• Filed: 07/16/2004
• Published: 12/09/2004
• Est. Priority Date: 11/14/2001
• Status: Abandoned Application

• Current Assignee: IMS Software Services Ltd. (IQVIA Holdings, Inc.)
• Original Assignee: IMS Software Services Ltd. (IQVIA Holdings, Inc.)
• Inventors: Wynne, Brian, Pollack, Richard D.
• Application Number:
  US10/494,841
• Publication Number:
  US 20040249669A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

The invention relates to systems and methods for analyzing prescription claim histories for physicians, and creating profiles of the prescription therapies of such physicians.

Claims

1. A method for generating a profile concerning the prescription therapy practices of at least one physician in a therapeutic area of interest, comprising the steps of:

(a) receiving data for a continuous variable corresponding to prescriptions issued to at least one de-identified patient by at least one physician;

(b) for each number of levels of a predetermined range of levels, converting said continuous variable into a categorical variable having a respective number of levels;

(c) for each said number of levels of said predetermined range of levels, measuring the degree of statistical relationship of said categorical variable with an occurrence of an event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest;

(d) identifying one number of levels of said predetermined range of levels of said categorical variable having the greatest statistical significance with the occurrence of the event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest;

(e) repeating steps (a)-(d) for each one of a plurality of additional continuous variables; and

(f) estimating the probability of the occurrence of the event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest by running a predictive model using said number of levels of said categorical variables identified in steps (a)-(e).

2. The method of claim 1, wherein the step of converting said continuous variable into said categorical variable having the respective number of levels comprises using a cumulative percentage distribution function.

3. The method of claim 1, wherein the step of converting said continuous variable into said categorical variable having the respective number of levels comprises converting said continuous variable into a categorical variable having two levels, a categorical variable having three levels, a categorical variable having four levels, and a categorical variable having five levels.

4. The method of claim 1, wherein the step of measuring the degree of statistical relationship of said categorical variable with the occurrence of the event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest comprises running a logistic regression model, for each said number of levels of said predetermined range of levels, using said categorical variable having the respective number of levels as an independent variable.

5. The method of claim 4, wherein the step of running the logistic regression model comprises calculating, for each said number of levels of said predetermined range of levels, a p-value for said categorical variable having the respective number of levels.

6. The method of claim 4, wherein the step of measuring the degree of statistical relationship of said categorical variable with the occurrence of the event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest further comprises discarding said categorical variable that is not statistically significant.

7. The method of claim 6, wherein the step of discarding said categorical variable that is not statistically significant comprises discarding said categorical variable having a p-value greater than 0.05.

8. The method of claim 5, wherein the step of identifying one number of levels of said predetermined range of levels of said categorical variable having the greatest statistical significance comprises determining which one number of levels of said predetermined range of levels has the lowest associated p-value.

9. The method of claim 1, further comprising generating alert messages, after step (g), indicative of the estimated probabilities calculated in step (f).

10. The method of claim 9, wherein the step of generating alert messages further comprises associating the estimated probabilities with the alert messages.

11. A system for generating a profile concerning the prescription therapy practices of at least one physician in a therapeutic area of interest, comprising:

(a) a mass storage device for storing continuous variable data corresponding to prescriptions issued to at least one de-identified patient by at least one physician;

(b) an input device, coupled to the-mass storage device, for receiving data for a plurality of continuous variables;

(c) a filter, coupled to the input device, configured to convert, for each number of levels of a predetermined range of levels, each said continuous variable into a respective categorical variable having a respective number of levels; and

(d) a statistical model, coupled to the filter, configured to receive each said categorical variable from said filter and to determine, for each said number of levels of said predetermined range of levels, the degree of statistical relationship of each said categorical variable with the occurrence of an event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest,

said filter configured to supply each said categorical variable to the statistical model, to receive said degree of statistical relationship of each said categorical variable data with the occurrence of the event as determined by the statistical model for each said number of levels of said predetermined range of levels, and to identify one of said number of levels of each said categorical variable having the greatest statistically significant relationship with the occurrence of the event,

said statistical model configured to determine, for each said number of levels of said predetermined range of levels, the degree of statistical relationship of each said categorical variable in conjunction with all said categorical variables with the occurrence of the event,

said filter configured to identify one of said number of levels of each said categorical variable in conjunction with all said categorical variables having the greatest statistically significant relationship with the occurrence of the event, and

said statistical model further configured to estimate the probability of the occurrence of the event by using all said categorical variables having the respective number of levels identified by the filter.

12. The system of claim 11, wherein the filter is configured to convert, for each number of levels in the said predetermined range of levels, said continuous variable into said categorical variable using a cumulative percentage distribution function.

13. The system of claim 12, wherein said number of levels in said predetermined range of levels of the categorical variable consists of two levels, three levels, four levels, and five levels.

14. The system of claim 11, wherein the statistical model is configured to run, for each number of levels in the said predetermined range of levels, a logistic regression model using said categorical variable data having the respective number of levels as an independent variable.

15. The system of claim 14, wherein the statistical model is configured to calculate, for each number of levels in the said predetermined range of levels, a p-value of said categorical variable data having the respective number of levels.

16. The system of claim 15, wherein the filter is further configured to discard a categorical variable that is not statistically significant.

17. The system of claim 16, wherein the filter is configured to discard a categorical variable having a p-value greater than 0.05.

18. The system of claim 15, wherein the filter is configured to identify the number of levels of the categorical variable having the greatest statistical significance by determining the number of levels of the categorical variable having the lowest associated p-value.

19. The system defined in claim 11, wherein the filter is configured to provide alert messages associated with the probability of the occurrence of the event relevant to the prescription therapy practices of the at least one physician in the therapeutic area of interest.