Method of delivering decision support systems (DSS) and electronic health records (EHR) for reproductive care, pre-conceptive care, fertility treatments, and other health conditions

• US 10,482,556 B2
• Filed: 06/14/2011
• Issued: 11/19/2019
• Est. Priority Date: 06/20/2010
• Status: Active Grant

• Current Assignee: Univfy Inc.
• Original Assignee: Univfy Inc.
• Inventors: Yao, Mylene
• Primary Examiner(s): Paulson, Sheetal R
• Application Number:
  US13/160,245
• Publication Number:
  US 20110313790A1
• Time in Patent Office: 3,080 Days
• Field of Search: 705 2- 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 system comprising:

(a) an electronic health record (EHR) platform comprising a secure internet connection, wherein the EHR platform has questions and modules specific for at least one health condition or disease state, wherein a user provides answers to the questions and enters information for a subject individual into the EHR platform by way of the secure internet connection, wherein the EHR platform is integrated with a decision support system (DSS), wherein the DSS comprises a prediction model for the at least one health condition or disease state;

(b) an application server (AP) comprising a customized algorithm developed with computer code comprising clinic-specific, region-specific, and/or population specific variables, wherein the AP is in communication with the EHR platform and the customized algorithm interprets information entered into the EHR platform to generate the prediction model for the at least one health condition or disease state;

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

(d) a DSS test report showing a predicted probability of the at least one health condition or disease state for the subject individual, wherein the DSS test report comprises personalized predictions regarding treatment for the at least one health condition or disease state for the subject individual, wherein the personalized predictions are based upon the prediction model for the at least one health condition or disease state from the DSS and the information entered into the EHR platform by the user and the prediction model is validated with non-overlapping training and test sets against a set of quantitative and statistical criteria generated from the clinic-specific, region-specific, and/or population-specific variables,

wherein the EHR platform comprises an interactive web portal to ordering renewals of prescriptions from specialty pharmacies;

procurement of health products related to a disease area;

healthcare provider referrals;

healthcare provider appointments; and

calendar alerts for medical appointments, lab tests, and medications.

2. The system of claim 1, wherein the user is selected from the group consisting of an individual, a physician, a healthcare provider, and a clinic administrator.

3. The system of claim 2, wherein the user may designate sharing privileges for the EHR platform.

4. The system of claim 3, wherein the sharing privileges may be tailored to specify time limits for sharing.

5. The system of claim 1, 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.

6. The system of claim 5, wherein the electronic application is located on a personal computer or a clinic server.

7. The system of claim 1, wherein the customized algorithm is developed using de-identified data from a single clinic.

8. The system of claim 1, wherein the customized algorithm is developed using de-identified data from more than one clinic.

9. The system 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.

10. The system of claim 9, wherein the demographic variables are selected from the group consisting of ethnicity, household income level, and education.

11. The system of claim 9, 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.

12. The system of claim 11, 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.

13. The system of claim 9, wherein the biomarker test results are selected from the group consisting of genetic marker tests results and genetic data extracted from discovery platforms.

14. The system of claim 1, wherein the customized algorithm is developed using a statistical method selected from the group consisting of machine learning, logistic regression, linear regression, and combinations of any of the foregoing.

15. The system of claim 14, wherein the machine learning comprises a boosted tree approach.

16. The system of claim 1, 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.

17. The system of claim 1, wherein the prediction model is a Diversity Prediction Model that includes a 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.

18. The system of claim 17, wherein the Diversity Prediction Model comprises phenotypic profiles obtained from the de-identified data sets.

19. The system of claim 18, wherein phenotypic profiling for an individual is carried out in real-time against a library of known profiles.

20. The system of claim 17, wherein a clinic is compensated by a third party that is using the de-identified data sets for the development of the Diversity Prediction Model.

21. The system of claim 20, wherein a clinic is compensated by the third party based upon the relative size of the de-identified data set as a portion of the Diversity Set.

22. The system of claim 20, wherein a clinic is compensated by the third party according to the clinic'"'"'s contribution to the establishment of phenotypic profiles obtained from the de-identified data sets.

23. The system of claim 20, wherein a clinic is compensated by the third party according to the clinic'"'"'s contribution to quantitative measures of the performance of the model.

24. The system of claim 23, wherein the quantitative measures comprise predictive power, discrimination, calibration, and reclassification.

25. The system 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.

26. The system 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.

27. The system 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.

28. The system of claim 1, wherein the information entered into the EHR platform is personal health data of an individual.

29. The system of claim 1, wherein the EHR platform comprises support of clinic oriented services and direct to consumer goods and services.

30. The system of claim 1, wherein the EHR platform is specific for fertility treatments and the DSS provides personalized predictions of at least one fertility outcome for the individual.

31. The system of claim 30, wherein the at least one fertility outcome is the probability of a live birth event.

32. The system of claim 1, wherein the EHR platform 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 system of claim 1, wherein the at least one health condition or disease state is selected from the group consisting of cancer, glaucoma, diabetes, heart disease, rheumatoid arthritis, and multiple sclerosis.

34. The system of claim 1, wherein the EHR platform further comprises additional platforms selected from the group consisting of consultation platforms, consumer services platforms, and consumer goods platforms.

35. The system of claim 34, wherein the consultation platform comprises features selected from the group consisting of physician biographies, physician schedules, physician consultation request modules, and payment modules.