Devices, Systems, and Methods for the Real-Time and Individualized Prediction of Health and Economic Outcomes

• US 20120179478A1
• Filed: 01/06/2011
• Published: 07/12/2012
• Est. Priority Date: 01/06/2011
• Status: Abandoned Application

• Current Assignee: Qualpact Incorporated
• Original Assignee: 1Emerge, Inc.
• Inventors: Ross, Allan
• Application Number:
  US12/986,027
• Publication Number:
  US 20120179478A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Provided herein are computer-implemented platforms, systems, products, devices, modules and methods for the real-time, individualized, and probabilistic-based prediction of a health or economic outcome of a patient or healthcare provider therapy using emerging health or economic data. Also provided herein is the transformation of emerging health or economic data to predict a health or economic outcome of a patient.

Claims

1. A computer-implemented system for the real-time, individualized, and probabilistic-based prediction of a health or economic outcome of a patient or healthcare provider therapy comprising:

a. a digital processing device;

b. a module executed by said processing device and configured to transform individualized emerging health or economic data that has been acquired in real-time into at least one model set for the prediction of a health or economic outcome;

c. a module executed by said processing device and configured to determine the sufficiency of the model set by comparing the model set against previously accumulated internal data for sufficiency and if necessary prompting for additional data until a preferred confidence level is achieved;

d. a module executed by said processing device and configured to analyze the model set using at least one statistical model;

e. optionally, a module executed by said processing device and configured to enhance predictive accuracy by comparing an expected result or outcome to an actual result or outcome to train, re-train, or validate at least one statistical model;

and thereby generating a real-time, individualized, and probabilistic-based output that is a prediction of a health or economic outcome of the patient or the healthcare provider therapy.

9. The system of claim 1, wherein said patient is an individual user or a third party authorized by the user.

10. The system of claim 1, wherein the system or module requests the user for an actual outcome.

13. The system of claim 1, wherein said statistical model is based on at least one of the following:

a. a linear model;

b. a logistic regression;

c. a classification and regression tree;

d. a random forest;

e. a multivariate adaptive regression spline; and

f. a support vector machine.

14. The system of claim 1, wherein said statistical model is based on at least two of the following:

a. a linear model;

b. a logistic regression;

c. a classification and regression tree;

d. a random forest;

e. a multivariate adaptive regression spline; and

f. a support vector machine.

17. The system of claim 1, wherein, said output is a health output which comprises at least one of:

a. a treatment recommendation;

b. an insurance recommendation;

c. an opinion on compliance with insurance payer standards;

d. an opinion on compliance with medical standards;

e. an analysis on economic costs associated with the risk score;

f. an analysis on a period of time associated with the risk score;

or

g. an analysis on quality of care.

2. A computer readable module comprising a computer usable medium having a computer readable program code embodied therein that is adapted to be executed to implement a method for the real-time, individualized, and probabilistic-based prediction of a health or economic outcome comprising the step of transforming individualized emerging health or economic data that has been acquired in real-time into at least one model set for the prediction of a health or economic outcome of a patient, healthcare provider therapy, or payer.

3. The system of claim 2, wherein the emerging health or economic data is combined with historical data prior to transformation into said model set.

4. The system or module of claim 3, wherein the emerging health or economic data comprises data derived from one or more of the following:

a. at least one patient;

b. at least one healthcare provider;

c. at least one healthcare expert;

d. at least one extrinsic, electronic source; and

e. at least one healthcare payer.

5. The system or module of claim 4, wherein data is acquired from said extrinsic, electronic source by utilizing a web crawler, web bot, or web robot.

6. The system or module of claim 4, wherein one or more extrinsic, electronic sources is selected from social media updates, company web sites, RSS news feeds, FDA bulletins, medical journal websites, professional healthcare society or association web sites, Medicare communications, CDC websites, federal drug agency websites, domestic or foreign government websites, numeric databases, and other Internet sources.

7. The system or module of claim 4, wherein said extrinsic, electronic source is an electronic medical record system that comprises:

a. at least one database connected to one or more healthcare providers for storing the medical records of the user;

or

b. an authorization module, wherein said user controls the data shared between at least one healthcare provider and said database.

8. The system or module of claim 7, wherein the output of the system or module is shared with the users of the electronic medical record system.

15. The system or module of claim 3, wherein a predictive accuracy is further improved by comparing an actual outcome against a predicted outcome.

16. The system or module of claim 3, wherein an accuracy is determined using at least one of the following:

a. a Bayesian prior;

b. a training and validation data;

c. a cross-validation;

d. a regularization; and

e. a bagging.

19. The system or module of claim 3, wherein one or more live experts validates an overall model set, wherein said one or more live experts are optionally, simultaneously and electronically linked.

20. The system or module of claim 3, wherein one or more live experts validates a risk score resulting in a weighted recommendation, wherein said one or more live experts are optionally, simultaneously and electronically linked.

11. A computer readable module comprising a computer usable medium having a computer readable program code embodied therein that is adapted to be executed to implement a method for the real-time, individualized, and probabilistic-based prediction of a health or economic outcome comprising the step of determining the sufficiency of a model set that was transformed from emerging health or economic data that was acquired in real-time by comparing the model set against previously accumulated internal data for sufficiency, and if necessary, prompting for additional data until a preferred confidence level is achieved.

12. The module of claim 11, wherein the sufficiency of said model set is determined by comparing the model set to a preferred confidence level from acceptable data.

18. A computer implemented method comprising a computer usable medium having a computer readable program code embodied therein that is adapted to be executed to implement a method for the real-time, individualized, and probabilistic-based analysis of a health or economic outcome comprising the step of providing an analysis that is based on emerging health or economic data that has been transformed in real-time into at least one model set for analysis of the health or economic outcome of a patient, healthcare provider therapy, or payer.