METHOD OF DELIVERING DECISION SUPORT SYSTEMS (DSS) AND ELECTRONIC HEALTH RECORDS (EHR) FOR REPRODUCTIVE CARE, PRE-CONCEPTIVE CARE, FERTILITY TREATMENTS, AND OTHER HEALTH CONDITIONS

• US 20110313790A1
• Filed: 06/14/2011
• Published: 12/22/2011
• Est. Priority Date: 06/20/2010
• Status: Active Grant

• Current Assignee: Univfy Inc.
• Original Assignee: Univfy Inc.
• Inventors: Yao, Mylene
• Granted Patent: US 10,482,556 B2
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Provided are methods of delivering decision support systems (DSSs) to healthcare providers, patients, and/or consumers with or without integrated electronic health records (EHRs) for reproductive care and other health conditions. The DSS platforms of the present invention include predication models based upon de-identified data sets and customized algorithms that may be clinic specific, region specific, and/or population specific. The DSS platforms of the present invention also include methods of providing third party payments of an individual'"'"'s medical bills, wherein the third party is not capable of viewing the personal health identifiers of the individual.

Claims

1. A method comprising the steps of:

(a) establishing an electronic decision support system (DSS) comprising a prediction model for a health condition or disease state;

(b) delivering the DSS to an individual or healthcare provider,

wherein the prediction model is developed and validated based upon clinic-specific, region-specific, and/or population-specific variables.

2. The method of claim 1, wherein the clinic-specific, region-specific, and/or population-specific variables are selected from the group consisting of demographic variables, clinical variables, laboratory variables, or combinations thereof.

3. The method of claim 2, wherein the demographic variables are selected from the group consisting of ethnicity, household income level, and education.

4. The method of claim 2, wherein the laboratory variables are selected from the group consisting of test results of body fluids, tissue level data, imaging results, and biomarker test results.

5. The method of claim 4, wherein the tissue level data is selected from the group consisting of immunoassays, quantitative PCR (qPCR), semi-quantitative measurement of transcriptome by gene expression arrays, or whole-transcriptome sequencing.

6. The method of claim 4, wherein the biomarker test results are selected from the group consisting of genetic marker tests results and genetic data extracted from discovery platforms.

7. The method of claim 1, wherein the prediction model is selected from the group consisting of machine learning, logistic regression, linear regression, and combinations of any of the foregoing.

8. The method of claim 7, wherein the machine learning comprises a boosted tree approach.

9. The method of claim 1, wherein the prediction model is validated against a set of quantitative and statistical criteria.

10. The method of claim 9, wherein the quantitative and statistical criteria is selected from the group consisting of posterior probability of an event, area under the curve, calibration, and reclassification.

11. The method of claim 1, wherein the prediction model is a Diversity Prediction Model that includes Diversity set selected from the group consisting of aggregate de-identified phenotypic data sets from clinics, phenotypic data sets with personal health identifiers from individuals, and a combination of aggregate and individual data sets.

12. The method of claim 11, wherein the Diversity Prediction Model comprises phenotypic profiles obtained from the de-identified data sets.

13. The method of claim 12, wherein phenotypic profiling for an individual is carried out in real-time against a library of known profiles.

14. The method of claim 11, wherein a clinic is compensated by a company that is using the de-identified data sets for the development of the Diversity Prediction Model.

15. The method of claim 14, wherein a clinic is compensated by the company based upon the relative size of the de-identified data set as a portion of the Diversity Set.

16. The method of claim 14, wherein a clinic is compensated by the company according to the clinic'"'"'s contribution to the establishment of phenotypic profiles obtained from the de-identified data sets.

17. The method of claim 11, wherein a clinic is compensated by the company according to the clinic'"'"'s contribution to quantitative measures of the performance of the model.

18. The method of claim 17, wherein the quantitative measures comprise predictive power, discrimination, calibration, and reclassification.

19. The method of claim 1, wherein the DSS is implemented in a business-to-business architecture, wherein the DSS is developed by a company and provided to a clinic without interaction by an individual.

20. The method of claim 1, wherein the DSS is implemented in a business-to-business-to-consumer architecture, wherein the DSS is developed by a company and provided to a clinic with interaction by an individual.

21. The method of claim 1, wherein the DSS is implemented in a business-to-consumer architecture, wherein the DSS is developed by a company and provided directly to an individual.

22. The method of claim 1, wherein the DSS is a stand-alone DSS.

23. The method of claim 1, wherein the DSS is integrated with an electronic health record (EHR).

24. The method of claim 23, wherein the EHR comprises an internet platform that supports entry of personal health data of an individual.

25. The method of claim 24, wherein the personal health data is entered into the EHR platform by the individual, a physician, a healthcare provider, or a clinic administrator.

26. The method of claim 24, wherein the EHR platform comprises support of clinic oriented services and direct to consumer goods and services.

27. The method of claim 25, wherein the individual, a physician, a healthcare provider, and/or a clinic administrator may designate sharing privileges for the EHR.

28. The method of claim 27, wherein the sharing privileges may be tailored to specify time limits for sharing.

29. The method of claim 24, wherein the DSS provides personalized predictions based upon the information entered into the EHR for a specific condition or disease state.

30. The method of claim 29, wherein the EHR is specific for fertility treatments and the DSS provides personalized predictions of at least one fertility outcome for the individual.

31. The method of claim 30, wherein the fertility outcome is the probability of a live birth event.

32. The method of claim 29, wherein the EHR is specific for menstrual and/or hormonal abnormalities and the DSS provides personalized predictions of possible conditions causing the menstrual and/or hormonal abnormalities in the individual.

33. The method of claim 29, wherein the EHR is specific for a variety of disease states and the DSS provides personalized predictions regarding treatment of the disease state for the individual.

34. The method of claim 33, wherein the disease states are selected from the group consisting of cancer, glaucoma, diabetes, heart disease, rheumatoid arthritis, and multiple sclerosis.

35. The method of claim 24, wherein the EHR platform further comprises additional platforms selected from the group consisting of consultation platforms, consumer services platforms, and consumer goods platforms.

36. The method of claim 35, wherein the consultation platform comprises features selected from the group consisting of physician biographies, physician schedules, physician consultation request modules, and payment modules.

37. A method comprising the steps of:

(a) obtaining a medical bill directed to an individual for which a third party is responsible for payment of the medical bill;

(b) entering the medical bill into an electronic health record (EHR) platform (EHR) and identifying the payor of the medical bill to be a third party;

(c) obtaining a promotional code for payment of the medical bill from the third party and entering said promotional code into the EHR, wherein the promotional code initiates payment of the medical bill by the third party, wherein the third party has no ability to access to any personal health information of the individual by way of the promotional code payment process.

38. The method of claim 37, wherein the third party is selected from an employer and an insurance company.

39. A system comprising:

(a) an electronic health care (EHR) platform by way of an internet connection, wherein a user enters information into the EHR platform;

(b) an application server (AP) that communicates with the computer terminal, wherein the AP comprises customized algorithms, wherein the algorithms are used to interpret the information entered into the EHR platform and provide a prediction model for a health condition or disease state; and

(c) a database server (DP) that communicates with the AP, wherein the DB stores the information entered on the EHR platform and provides encryption to keep the information secure.

40. The system of claim 39, wherein the user is selected from the group consisting of an individual, a physician, a healthcare provider, and a clinic administrator.

41. The system of claim 39, wherein the EHR platform is provided in an application selected from the group consisting of an electronic application on a local computer, an online web application, a mobile application, and a web-based application designed for mobile devices.

42. The system of claim 41, wherein the electronic application is located on a personal computer or a clinic server.

43. The system of claim 39, wherein the customized algorithm is comprised of de-identified data from a single clinic.

44. The system of claim 39, wherein the customized algorithm is comprised of de-identified data from more than one clinic.

45. The system of claim 39, wherein the customized algorithm provides a prediction model for a condition or disease state based upon the information provided by the user in the EHR.