Method, system, and predictive computing platform for generating a reduced patient-specific data subset of drug-related adverse reaction alerts
A platform accessible by a user from a web browser/HMO's electronic medical record (EMR) for providing the user with information regarding a patient's drug regimen as well as generating alerts concerning potential adverse effects to a patient from taking a cluster including a plurality of pharmaceutical preparations and various food supplementals/herbals may be in data communication with and configured to obtain information from at least two databases and at least one tool for processing the cluster of pharmaceutical preparations in accordance with the information to generate the alerts to the user.
1. A method comprising:
utilizing, by a predictive computing platform, a plurality of Application Programming Interfaces (APIs) to obtain, over a computer network in real time, a plurality of data-sets from a plurality of disparate data sources,
wherein the plurality of data-sets comprises patient-related data in a plurality of distinct data formats;
integrating, by the predictive computing platform, the plurality of patient-related data sets into an integrated patient-related data set;
transforming, by the predictive computing platform, a data-set of drug-related adverse reaction alerts to a reduced patient-specific data-subset of drug-related adverse reaction alerts by utilizing the integrated patient-related data set and a decision algorithm,
wherein the utilizing of the decision algorithm comprises, for each drug-related adverse reaction alert of the data-set of drug-related adverse reaction alerts:
i) determining whether a patient is:
1) at an elevated risk to experience a particular drug-related adverse reaction; or
2) not at the elevated risk to experience the particular drug-related adverse reaction,
wherein the determining is based on:
a) current drug intake data,
b) candidate drug intake data,
c) a plurality of patient specific parameters,
d) drug-related adverse reaction data from the data-set of drug-related adverse reaction alerts, and
e) at least one pre-determined severity level for at least one of:
I) a drug-drug interaction (“DDI”),
II) a drug-patient interaction (“DPI”),
III) a drug-food interaction (“DFI”), or
IV) any combination thereof;
ii) including, into the reduced patient-specific data-subset of drug-related adverse reaction alerts, each drug-related adverse reaction alert where the patient is at the elevated risk; and
iii) removing, from the data-set of drug-related adverse events, each drug-related adverse reaction alert where the patient is not at the elevated risk; and
causing, by the predictive computing platform, to output, over the computer network in real time, the reduced patient-specific data-subset of drug-related adverse reaction alerts to a graphical user interface on a screen of a computing device.
2. The method of claim 1 , wherein the plurality of disparate data sources is chosen from at least two of: a source of genetic data, electronic medical records, a database of pharmaceutical drugs, a database of nutraceuticals, a database of herbal remedies, a database of dietary supplements, or any combination thereof.
3. The method of claim 2 , wherein the source of genetic data is a source of genetic test data.
4. The method of claim 2 , wherein the database of pharmaceutical drugs is a database of prescription pharmaceutical drugs.
5. The method of claim 2 , wherein the database of pharmaceutical drugs is a database of over-the-counter (OTC) pharmaceutical drugs.
6. A predictive computing platform comprising:
at least one processor, and
a non-transitory computer memory, storing a computer program that, when executed by the at least one processor, causes the at least one processor to:
utilize, by a predictive computing platform, a plurality of Application Programming Interfaces (APIs) to obtain, over a computer network in real time, a plurality of data-sets from a plurality of disparate data sources,
wherein the plurality of data-sets comprises patient-related data in a plurality of distinct data formats;
integrate, by the predictive computing platform, the plurality of patient-related data sets into an integrated patient-related data set;
transform, by the predictive computing platform, a data-set of drug-related adverse reaction alerts to a reduced patient-specific data-subset of drug-related adverse reaction alerts by utilizing the integrated patient-related data set and a decision algorithm,
wherein the utilizing of the decision algorithm comprises, for each drug-related adverse reaction alert of the data-set of drug-related adverse reaction alerts:
i) determining whether a patient is:
1) at an elevated risk to experience a particular drug-related adverse reaction; or
2) not at the elevated risk to experience the particular drug-related adverse reaction,
wherein the determining is based on:
a) current drug intake data,
b) candidate drug intake data,
c) a plurality of patient specific parameters,
d) drug-related adverse reaction data from the data-set of drug-related adverse reaction alerts, and
e) at least one pre-determined severity level for at least one of:
I) a drug-drug interaction (“DDI”),
II) a drug-patient interaction (“DPI”),
III) a drug-food interaction (“DFI”), or
IV) any combination thereof;
ii) including, into the reduced patient-specific data-subset of drug-related adverse reaction alerts, each drug-related adverse reaction alert where the patient is at the elevated risk; and
iii) removing, from the data-set of drug-related adverse events, each drug-related adverse reaction alert where the patient is not at the elevated risk; and
cause, by the predictive computing platform, to output, over the computer network in real time, the reduced patient-specific data-subset of drug-related adverse reaction alerts to a graphical user interface on a screen of a computing device.
7. The system of claim 6 , wherein the plurality of disparate data sources is chosen from at least two of: a source of genetic data, electronic medical records, a database of pharmaceutical drugs, a database of nutraceuticals, a database of herbal remedies, a database of dietary supplements, or any combination thereof.
8. The system of claim 7 , wherein the source of genetic data is a source of genetic test data.
9. The system of claim 7 , wherein the database of pharmaceutical drugs is a database of prescription pharmaceutical drugs.
10. The system of claim 7 , wherein the database of pharmaceutical drugs is a database of over-the-counter (OTC) pharmaceutical drugs.