Granted Patent B2

US 12,731,695 · App. 17/097,700 · Granted Sep 8, 2026

Inventors: Praveen Bhat Gurpur (Bengaluru, IN); Karteek Alluvada (Bengaluru, IN)

Assignee: CERNER INNOVATION, INC.

G16H50/80G16H10/60G16H50/30

Abstract

Claims (61)

1 . A method for automated susceptibility identification and alerting, the method comprising:

determining, by a computing device, an occurrence of an outbreak of an infectious disease that is (a) transmittable from one human to another human, and (b) occurring at a particular time or time range and at a particular location;

in response to determining the occurrence of the outbreak, automatically scanning, by the computing device, a plurality of patient records stored in a database for a plurality of patients, the plurality of patient records comprising medical history information for the plurality of patients;

in response to determining the occurrence of the outbreak, automatically scanning, by the computing device, literature sources for susceptibility and risk factors corresponding to the outbreak;

generating training data, for training a neural network model, from the susceptibility and risk factors;

training the neural network model, using the training data, to determine when a patient has at least one high risk factor corresponding to the outbreak;

determining, by the computing device, a GPS location of a mobile computing device associated with a patient of the plurality of patients, wherein the GPS location of the mobile computing device is a current location of the mobile computing device as determined by a Global Positioning System;

responsive to determining that the GPS location of the mobile computing device is within a predetermined range of the particular location of the outbreak:

determining, by the computing device, that the patient, associated with the mobile computing device, is within a predetermined range of the particular location of the outbreak;

determining, by applying the trained neural network model to the plurality of patient records, that the patient has at least one high risk factor corresponding to the outbreak;

in response to determining, by the computing device, that (a) the patient is within the predetermined range of the particular location of the outbreak and (b) the patient has at least one high risk factor corresponding to the outbreak, identifying the patient as highly susceptible to the infectious disease; and

in response to identifying the patient as highly susceptible to the infectious disease, automatically transmitting, by the computing device, an escalated alert to at least one computing device associated with the patient;

wherein the escalated alert comprises a percentage of an increased risk to the outbreak for (a) the patient, with the at least one high risk factor, that is within the predetermined range of the particular location of the outbreak compared to (b) patients who are within the predetermined range of the particular location of the outbreak but do not have the at least one high risk factor.

2 . The method of claim 1 , wherein the escalated alert comprises a location of a closest occurrence of the outbreak to a location of the patients.

3 . The method of claim 1 , wherein the method further comprises:

prior to determining the occurrence of the outbreak, receiving, by the computing device, a selection of the outbreak comprising one of influenza, common cold, severe acute respiratory syndrome, E. coli , pneumonia, tuberculosis, malaria, viral hepatitis, and Lyme disease wherein the literature sources comprise electronic literature sources, wherein at least one of the electronic literature sources is periodically updated.

4 . The method of claim 3 , wherein the selection of the outbreak was severe acute respiratory syndrome and the at least one high risk factor comprises at least one of cardiovascular and kidney diseases, obesity, cognitive and neurological disorders in patients over 65 years of age, pregnancy status, ethnicity, age, co-morbidities, geographical location, environmental factors, social behavior, food habits, physical activity, and hypertension, and

the electronic literature sources comprising one from a local health source and one from a national health source.

5 . The method of claim 1 , wherein the literature sources comprise an electronic literature source that is periodically updated, wherein the at least one high risk factor contributed to a percentage of mortality of a population above a threshold.

6 . The method of claim 1 , wherein determining, by the computing device, the occurrence of the outbreak is based on a local health source, a national health source, or a global health source.

7 . The method of claim 1 , wherein the predetermined range was determined, by the computing device, based on a distance between an address from electronic medical record information of the patient and a location of a closest occurrence of the outbreak.

8 . The method of claim 1 , further comprising determining, by the computing device, the particular location of the outbreak using a public database of at least one hotspot of the outbreak.

9 . The method of claim 1 , wherein automatically scanning comprises utilizing cognitive computing.

10 . One or more non-transitory computer-readable storage media having instructions embodied thereon, the instructions being executable by one or more processors to perform a method for automated susceptibility identification and alerting, the method comprising:

determining, by a computing device, an occurrence of an outbreak of an infectious disease that is (a) transmittable from one human to another human, and (b) occurring at a particular time or time range and at a particular location;

in response to determining the occurrence of the outbreak, automatically scanning a plurality of patient records stored in a database for a plurality of patients, the plurality of patient records comprising medical history information for the plurality of patients;

in response to determining the occurrence of the outbreak, automatically scanning literature sources for susceptibility and risk factors corresponding to the outbreak;

generating training data, for training a neural network model, from the susceptibility and risk factors;

training the neural network model, using the training data, to determine when a patient has at least one high risk factor corresponding to the outbreak;

determining a GPS location of a mobile computing device associated with a patient of the plurality of patients, wherein the GPS location of the mobile computing device is a current location of the mobile computing device as determined by a Global Positioning System;

responsive to determining that the GPS location of the mobile computing device is within a predetermined range of the particular location of the outbreak:

determining that the patient, associated with the mobile computing device, is within a predetermined range of the particular location of the outbreak;

determining, by applying the trained neural network model to the plurality of patient records, that the patient has at least one high risk factor corresponding to the outbreak;

in response to determining, by the computing device, that (a) the patient is within the predetermined range of the particular location of the outbreak and (b) the patient has at least one high risk factor corresponding to the outbreak, identifying the patient as highly susceptible to the infectious disease; and

in response to identifying the patient as highly susceptible to the infectious disease, automatically transmitting, by the computing device, an escalated alert to at least one computing device associated with the patient;

wherein the escalated alert comprises a percentage of an increased risk to the outbreak for (a) the patient, with the at least one high risk factor, that is within the predetermined range of the particular location of the outbreak compared to (b) patients who are within the predetermined range of the particular location of the outbreak but do not have the at least one high risk factor.

11 . The one or more non-transitory computer-readable storage media of claim 10 , wherein determining the patient comprises patients currently being treated in a hospital and patients previously treated in the hospital, and wherein the at least one high risk factor comprises a particular age group.

12 . The one or more non-transitory computer-readable storage media of claim 10 , wherein automatically scanning comprises utilizing cognitive computing, and wherein the infectious disease is a virus or bacteria.

13 . The one or more non-transitory computer-readable storage media of claim 10 , wherein the method further comprises:

determining that a patient is at a distance above a threshold from the particular location and has a plurality of high risk factors; and

providing an escalated alert to the patient who is at the distance above the threshold and who has the plurality of high risk factors, the escalated alert comprising information for actions to take during emergencies.

14 . The one or more non-transitory computer-readable storage media of claim 13 , wherein the distance was determined using GPS data of a user device.

15 . The one or more non-transitory computer-readable storage media of claim 10 , wherein the escalated alert is provided to a caregiver of the patient.

16 . The one or more non-transitory computer-readable storage media of claim 10 , wherein the escalated alert is provided via a healthcare application and provides a helpline number.

17 . The one or more non-transitory computer-readable storage media of claim 10 , wherein the literature sources comprise an electronic literature source that is periodically updated.

18 . The one or more non-transitory computer-readable storage media of claim 17 , wherein the method further comprises, prior to determining that the patient has the at least one high risk factor, receiving a selection of the electronic literature source that is from a global health source.

19 . The one or more non-transitory computer-readable storage media of claim 17 , wherein determining that the patient has at least one high risk factor corresponding to the outbreak is updated upon each detected change to the electronic literature source.

20 . A system for automated susceptibility identification and alerting, the system comprising:

one or more processors; and

one or more computer storage media storing computer-useable instructions that, when used by the one or more processors, cause the one or more processors to perform a method, the method comprising:

determining, by a computing device, an occurrence of an outbreak of an infectious disease that is (a) transmittable from one human to another human, and (b) occurring at a particular time or time range and at a particular location;

in response to determining the occurrence of the outbreak, automatically scanning a database for electronic medical record (EMR) data for a plurality of patients;

determining a GPS location of a mobile computing device associated with at least one patient of the plurality of patients, wherein the GPS location of the mobile computing device is a current location of the mobile computing device as determined by a Global Positioning System;

in response to determining the occurrence of the outbreak, automatically scanning, by the computing device, literature sources for susceptibility and risk factors corresponding to the outbreak;

generating training data, for training a neural network model, from the susceptibility and risk factors;

training the neural network model, using the training data, to determine when a patient has at least one high risk factor corresponding to the outbreak;

based on the GPS location of the mobile computing device and the EMR data, determining that the at least one patient is at a distance above a threshold from the particular location of the outbreak;

determining, by applying the trained neural network model to the EMR data, that the at least one patient has the at least one high risk factor;

in response to determining that (a) the at least one patient is at a distance above the threshold from the particular location of the outbreak and (b) the at least one patient has the at least one high risk factor, identifying the at least one patient as highly susceptible to the infectious disease; and

in response to identifying the at least one patient as highly susceptible to the infectious disease, automatically transmitting, by the computing device, an escalated alert to at least one computing device associated with the at least one patient or a caretaker of the patient;

wherein the escalated alert comprises a percentage of an increased risk to the outbreak for (a) the at least one patient, with the at least one high risk factor, that is within a predetermined range of the particular location of the outbreak compared to (b) patients who are within the predetermined range of the particular location of the outbreak but do not have the at least one high risk factor.

Assignments (1)

Continuity (1)

References Cited (18)