Method, system and program for improved health care

• US 10,592,501 B2
• Filed: 08/02/2019
• Issued: 03/17/2020
• Est. Priority Date: 03/10/2011
• Status: Active Grant

• Current Assignee: Seegnal EHealth Ltd.
• Original Assignee: Seegnal EHealth Ltd.
• Inventors: Shiloh, Ron Zeev
• Primary Examiner(s): Le, Linh Giang
• Application Number:
  US16/530,035
• Publication Number:
  US 20190370253A1
• Time in Patent Office: 228 Days
• CPC Class Codes: G06F 16/24 Querying
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

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 supplements/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.

Claims

1. A method comprising:

receiving, by at least one processor, current drug intake data of a patient;

wherein the current drug intake data comprises current drug prescription information for at least one current drug being taken by the patient;

wherein the current drug prescription information identifies for the at least one current drug, at least;

i) a current drug dosing,

ii) a current drug dosage form, and

iii) a current drug duration of treatment;

receiving, by the at least one processor, a plurality of patient specific parameters;

wherein the plurality of patient specific parameters comprises at least one of;

i) current lab results or

ii) health-related baseline parameters;

receiving, by at least one processor, candidate drug intake data;

wherein the candidate drug intake data comprises candidate drug prescription information for at least one candidate drug that is not being taken by the patient;

wherein the candidate drug prescription information of the at least one candidate drug identifies at least;

i) a candidate drug dosing,

ii) a candidate drug dosage form, and

iii) a candidate drug duration of treatment,

determining, by the at least one processor, a plurality of drug-related adverse reaction alerts between at least one current drug and the at least one candidate drug, based on;

i) the current drug intake data,

ii) the candidate drug intake data,

iii) drug-related adverse reactions data of the plurality of drug-related adverse reaction alerts, and

iv) at least one pre-determined severity level for at least one of;

1) a drug-drug interaction (“

DDI”

),

2) a drug-to-patient interaction (“

DPI”

),

3) a drug-food interaction (“

DFI”

), or

4) a combination thereof;

wherein a drug-related adverse reaction alert is caused by at least one of;

i) the DDI,

ii) the DPI,

iii) the DFI, or

iv) a combination thereof;

applying, by the at least one processor, a decision algorithm to the plurality of drug-related adverse reaction alerts to determine a patient-specific subset of drug-related adverse reaction alerts from the plurality of drug-related adverse reaction alerts, by;

for each respective drug-related adverse reaction alert;

i) determining whether the patient is;

1) at an elevated risk to experience a particular drug-related adverse reaction alert or

2) not at the elevated risk to experience the particular drug-related adverse reaction adverse alert;

ii) when the patient is the elevated risk, including the particular drug-related adverse reaction alert into the patient-specific subset of drug-related adverse reaction alerts, based on;

1) the current drug intake data,

2) the candidate drug intake data,

3) the plurality of patient specific parameters,

4) the drug-related adverse reactions data of the plurality of drug-related adverse reaction alerts, and

5) the at least one pre-determined severity level for at least one of;

a) the DDI,

b) the DPI, or

c) the DFI, or

d) a combination thereof;

iii) when the patient is not at the elevated risk, not including the particular drug-related adverse reaction alert into the patient-specific subset of drug-related adverse reaction alerts;

causing the at least one processor, to output the patient-specific subset of drug-related adverse reaction alerts to a graphical user interface on a screen of a computing device;

receiving, by the at least one processor, at least one change to the plurality of patient specific parameters of the patient;

repeating, by the at least one processor, the applying of the decision algorithm to the plurality of drug-related adverse reaction alerts to determine an updated patient-specific subset of drug-related adverse reaction alerts based at least in part on the at least one change to the plurality of patient specific parameters of the patient; and

causing, by the at least one processor, to output the updated patient-specific subset of drug-related adverse reaction alerts to the graphical user interface on the screen of the computing device.

2. The method of claim 1, wherein the health-related baseline parameters are chosen from:

medical history, food supplements, herbal supplements, genetic data, gender, race, alcohol intake, smoking, at least one imaging output, or combinations thereof.

3. The method of claim 1, wherein, when the patient is at the elevated risk, the method further comprises assigning the patient to an elevated risk population;

and wherein, when, the patient is not at the elevated risk, the method further comprises assigning the patient to a non-elevated risk population.

4. The method of claim 1, wherein the at least one current drug is a plurality of current drugs.

5. The method of claim 1, wherein the at least one candidate drug is a plurality of candidate drugs.

6. A system comprising:

at least one processor;

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;

receive current drug intake data of a patient;

wherein the current drug intake data comprises current drug prescription information for at least one current drug being taken by the patient;

wherein the current drug prescription information identifies for the at least one current drug at least;

i) a current drug dosing,

ii) a current drug dosage form, and

iii) a current drug duration of treatment;

receive a plurality of patient specific parameters;

wherein the plurality of patient specific parameters comprises at least one of;

i) current lab results or

ii) health-related baseline parameters;

receive candidate drug intake data;

wherein the candidate drug intake data comprises candidate drug prescription information for at least one candidate drug that is not being taken by the patient;

wherein the candidate drug prescription information of the at least one candidate drug identifies at least;

i) a candidate drug dosing,

ii) a candidate drug dosage form, and

iii) a candidate drug duration of treatment,

determine a plurality of drug-related adverse reaction alerts between at least one current drug and the at least one candidate drug, based on;

i) the current drug intake data,

ii) the candidate drug intake data,

iii) drug-related adverse reactions data of the plurality of drug-related adverse reaction alerts, and

iv) at least one pre-determined severity level for at least one of;

1) a drug-drug interaction (“

DDI”

),

2) a drug-to-patient interaction (“

DPI”

),

3) a drug-food interaction (“

DFI”

), or

4) a combination thereof;

wherein a drug-related adverse reaction alert is caused by at least one of;

i) the DDI,

ii) the DPI,

iii) the DFI, or

iv) a combination thereof;

apply a decision algorithm to the plurality of drug-related adverse reaction alerts to determine a patient-specific subset of drug-related adverse reaction alerts from the plurality of drug-related adverse reaction alerts, by;

for each respective drug-related adverse reaction alert;

i) determining whether the patient is;

1) at an elevated risk to experience a particular drug-related adverse reaction alert or

2) not at the elevated risk to experience the particular drug-related adverse reaction adverse alert;

ii) when the patient is at the elevated risk, including the particular drug-related adverse reaction alert into the patient-specific subset of drug-related adverse reaction alerts, based on;

1) the current drug intake data,

2) the candidate drug intake data,

3) the plurality of patient specific parameters,

4) the drug-related adverse reactions data of the plurality of drug-related adverse reaction alerts, and

5) the at least one pre-determined severity level for at least one of;

 

a) the DDI,

 

b) the DPI, or

 

c) the DFI, or

 

d) a combination thereof;

iii) when the patient is not at the elevated risk, not including the particular drug-related adverse reaction alert into the patient-specific subset of drug-related adverse reaction alerts;

output the patient-specific subset of drug-related adverse reaction alerts to a graphical user interface on a screen of a computing device;

receive at least one change to the plurality of patient specific parameters of the patient;

repeat the application of the decision algorithm to the plurality of drug-related adverse reaction alerts to determine an updated patient-specific subset of drug-related adverse reaction alerts based at least in part on the at least one change to the plurality of patient specific parameters of the patient; and

output the updated patient-specific subset of drug-related adverse reaction alerts to the graphical user interface on the screen of the computing device.

7. The system of claim 6, wherein the health-related baseline parameters are chosen from:

medical history, food supplements, herbal supplements, genetic data, gender, race, alcohol intake, smoking, at least one imaging output, or combinations thereof.

8. The system of claim 6, wherein, when the patient is at risk, the processor assigns the patient to an elevated risk population;

and wherein, when, the patient is not at the elevated risk, the processors assigns the patient to a non-elevated risk population.

9. The system of claim 6, wherein the at least one current drug is a plurality of current drugs.

10. The system of claim 6, wherein the at least one candidate drug is a plurality of candidate drugs.

11. A non-transitory computer-readable storage medium tangibly encoded with computer executable instructions, that when executed by a computing device, is configured to:

receive current drug intake data of a patient;

wherein the current drug intake data comprises current drug prescription information for at least one current drug being taken by the patient;

wherein the current drug prescription information identifies for the at least one current drug at least;

i) a current drug dosing,

ii) a current drug dosage form, and

iii) a current drug duration of treatment;

receive a plurality of patient specific parameters;

wherein the plurality of patient specific parameters comprises at least one of;

i) current lab results or

ii) health-related baseline parameters;

receive candidate drug intake data;

wherein the candidate drug intake data comprises candidate drug prescription information for at least one candidate drug that is not being taken by the patient;

wherein the candidate drug prescription information of the at least one candidate drug identifies at least;

i) a candidate drug dosing,

ii) a candidate drug dosage form, and

iii) a candidate drug duration of treatment,

determine a plurality of drug-related adverse reaction alerts between at least one current drug and the at least one candidate drug, based on;

i) the current drug intake data,

ii) the candidate drug intake data,

iii) drug-related adverse reactions data of the plurality of drug-related adverse reaction alerts, and

iv) at least one pre-determined severity level for at least one of;

1) a drug-drug interaction (“

DDI”

),

2) a drug-to-patient interaction (“

DPI”

),

3) a drug-food interaction (“

DFI”

), or

4) a combination thereof;

wherein a drug-related adverse reaction alert is caused by at least one of;

i) the DDI,

ii) the DPI,

iii) the DFI, or

iv) a combination thereof;

apply a decision algorithm to the plurality of drug-related adverse reaction alerts to determine a patient-specific subset of drug-related adverse reaction alerts from the plurality of drug-related adverse reaction alerts, by;

for each respective drug-related adverse reaction alert;

i) determining whether the patient is;

1) at an elevated risk to experience a particular drug-related adverse reaction alert or

2) not at an elevated risk to experience the particular drug-related adverse reaction adverse alert;

ii) when the patient is at the elevated risk, including the particular drug-related adverse reaction alert into the patient-specific subset of drug-related adverse reaction alerts, based on;

1) the current drug intake data,

2) the candidate drug intake data,

3) the plurality of patient specific parameters,

4) the drug-related adverse reactions data of the plurality of drug-related adverse reaction alerts, and

5) the at least one pre-determined severity level for at least one of;

 

a) the DDI,

 

b) the DPI,

 

c) the DFI, or

 

d) a combination thereof;

iii) when the patient is not at the elevated risk, not including the particular drug-related adverse reaction alert into the patient-specific subset of drug-related adverse reaction alerts;

output the patient-specific subset of drug-related adverse reaction alerts to a graphical user interface on a screen of a computing device;

receive at least one change to the plurality of patient specific parameters of the patient;

repeat the application of the decision algorithm to the plurality of drug-related adverse reaction alerts to determine an updated patient-specific subset of drug-related adverse reaction alerts based at least in part on the at least one change to the plurality of patient specific parameters of the patient; and

output the updated patient-specific subset of drug-related adverse reaction alerts to the graphical user interface on the screen of the computing device.

12. The non-transitory computer-readable storage medium of claim 11, wherein the health-related baseline parameters are chosen from:

medical history, food supplements, herbal supplements, genetic data, gender, race, alcohol intake, smoking, at least one imaging output, or combinations thereof.

13. The non-transitory computer-readable storage medium of claim 11, wherein, when the patient is at the elevated risk, the patient is assigned to an elevated risk population;

and wherein, when, the patient is not at the elevated risk, the patient is assigned to a non-elevated risk population.

14. The non-transitory computer-readable storage medium of claim 11, wherein the at least one current drug is a plurality of current drugs.

15. The non-transitory computer-readable storage medium of claim 11, wherein the at least one candidate drug is a plurality of candidate drugs.