Systems and methods for utilizing wireless physiological sensors

• US 10,806,404 B2
• Filed: 06/27/2016
• Issued: 10/20/2020
• Est. Priority Date: 03/05/2004
• Status: Active Grant

• Current Assignee: Terumo Medical Corporation (Terumo Corporation)
• Original Assignee: Health Outcomes Sciences, Inc. (Terumo Corporation)
• Inventors: Soto, Gabriel Enrique, Spertus, John Albert
• Primary Examiner(s): Koharski, Christopher
• Assistant Examiner(s): Moss, James
• Application Number:
  US15/194,215
• Publication Number:
  US 20160354039A1
• Time in Patent Office: 1,576 Days
• Field of Search: 600300
• CPC Class Codes: A61B 2560/0214 of power generation or supply
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• CPC Class Codes: A61B 2562/028 Microscale sensors, e.g. electromechanical sensors [MEMS]
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02444 Details of sensor A61B5/02427 takes precedence
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/4836 Diagnosis combined with treatment in closed-loop systems or methods A61B5/0036 takes precedence
• CPC Class Codes: A61B 5/6824 Arm or wrist
• CPC Class Codes: A61B 5/6831 Straps, bands or harnesses
• CPC Class Codes: A61B 5/6846 specially adapted to be brought in contact with an internal body part, i.e. invasive
• CPC Class Codes: A61B 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/74 Details of notification to user or communication with user or patient indicating measured values G01D7/00; user input means input or output arrangements for computers G06F3/00
• CPC Class Codes: G06N 5/046 Forward inferencing; Production systems
• CPC Class Codes: G06Q 40/08 Insurance
• CPC Class Codes: G16H 10/20 for electronic clinical trials or questionnaires
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 15/00 : ICT specially adapted for medical reports, e.g. generation or transmission thereof
• CPC Class Codes: G16H 20/00 : ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 : for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Physiological sensors may be utilized to obtain physiological data for a user. The sensor data may be utilized in predicting a user'"'"'s outcome to a medical intervention using one or more models. The models may be automatically executed in response to receiving certain types and/or amount of data, such as data received from one or more physiological remote sensors, such as Internet of Things sensors. The sensors may include heart rate sensors, arterial pressure sensors, glucose sensors, temperature sensors, weight sensors, blood oxygen sensors, urine sensors, saliva sensors, skin conduction sensors, muscle sensors, brain signal sensors, and/or other sensors. A sensor may communicate over the 2360-2400 MHz and/or the 30-37.5 MHz radio frequency (RF) band. The data may be received from a networked data store. Execution of the models may identify health issues in substantially real time, and the operation of one or more medical devices may be modified and/or a communication may be generated.

Claims

1. A system, comprising;

a sensor interface configured to receive sensor signals from one or more physiological wireless sensors;

one or more computing devices;

a non-transitory memory medium that stores instructions that when executed by the one or more computing devices causes the system to perform operations comprising;

register a plurality of physiological sensors configured to be worn by or implanted in a user via a device registry service, wherein respective physiological sensor Internet Protocol (IP) addresses are stored;

associate in memory respective identifiers, comprising respective IP addresses, of the plurality of physiological sensors with the user;

receive over a network sensor data from the plurality of physiological sensors associated with the user, the plurality of physiological sensors comprising at least a first physiological sensor of a first type and a second physiological sensor of a second type,

store the received sensor data in a medical information data store;

encrypt Protected Health Information contained within the network sensor data using a symmetric or asymmetric-key block cipher unique to the user;

receive in association with the network sensor data at least one unique identifier, comprising at least one physiological sensor IP address;

utilize the at least one unique identifier to locate information regarding treatment information for the user;

access, by a rules execution engine, rules and respective rule data quantity thresholds, including at least two quantity thresholds comprising;

a first quantity threshold for sensor data from the first physiological sensor, and

a second quantity threshold for sensor data from the second physiological sensor,

wherein the first quantity threshold is different than the second quantity threshold;

perform unit conversions, normalization, formatting, and data filtering on sensor data from at least one physiological sensor;

evaluate, by the rules execution engine, the accessed rules and data quantity thresholds, including;

the first quantity threshold for sensor data from the first physiological sensor, and

the second quantity threshold for sensor data from the second physiological sensor to determine;

which biological event predictive models, in a data store of biological event predictive models, are to be used to predict respective future user outcomes to respective medical interventions or respective failures to provide respective medical interventions, and

whether the determined biological event predictive models in the data store of biological event predictive models to respective medical interventions or respective failures to provide respective medical interventions are to be executed,

wherein a given biological event predictive model predicts a future user outcome at least one year in the future to a medical intervention or a failure to provide a medical intervention, and the given biological event predictive model is based at least in part on medical survey data for a plurality of patients, and comprises at least one of;

a linear model,

a logistic model,

a cumulative logit model,

a multinomial model, or

a proportional hazard model;

at least partly in response to the rule execution engine determining that a given biological event predictive model is to be executed, execute the given biological event predictive model using at least a portion of the received sensor data to provide a biological prediction of a first future user outcome for the user at least one year in the future to a potential first medical intervention or a failure to provide the potential first medical intervention for the user;

based at least in part on the biological prediction for the user made by the executed given biological event predictive model, determine whether a first action is to be taken, the first action comprising transmission of an electronic notification and/or a modification of an operation of a first medical device;

at least partly in response to determining that the first action is to be taken, transmit the electronic notification and/or cause the operation of a first medical device of the user to be modified

utilize the medical data store, storing received sensor data, to update at least one biological prediction model;

wherein the first physiological sensor and the second physiological sensor are configured to communicate with each other over a body area network, and at least one of the physiological sensors comprises a biomedical microelectromechanical (MEMs) system configured to perform sample preconditioning and wirelessly communicate preconditioned sample data over;

a 2360-2400 MHz radio frequency band, a 30-37.5 MHz radio frequency band, and/or Bluetooth.

2. The system as defined in claim 1, wherein at least one of the physiological sensors comprises an implanted sensor or a sensor surface-mounted on a body of the user in a fixed position.

3. The system as defined in claim 1, wherein at least one of the physiological sensors is worn using a wrist band.

4. The system as defined in claim 1, wherein at least one of the physiological sensors comprises a processing device, a battery, and a wireless transceiver.

5. The system as defined in claim 1, wherein the system is configured to:

dynamically generate a data interface for a first selected biological event predictive model based at least in part on parameters of the first selected model to collect a plurality of types of health status information, demographic information, and clinical information, wherein the interface comprises a human-readable interface and/or a machine-readable interface;

collect the plurality of types of health status information, demographic information, and clinical information using the dynamically generated data interface;

identify potentially significant predictors of future health states at least one year in the future based at least in part on information collected using the data interface;

generate the first selected biological event predictive model using the identified potentially significant predictors of future health states.

6. The system as defined in claim 1, wherein the system is configured to:

generate treatment recommendations based at least in part on generated predictor results obtained from executing a first biological event predictive model.

7. The system as defined in claim 1, wherein the system is configured to:

at least partly in response to determining that the first action is to be taken, cause the operation of a first medical device of the user to be modified.

8. A computer-implemented method, comprising;

registering a plurality of physiological sensors configured to be worn by or implanted in a user via a device registry service, wherein respective identifiers, comprising physiological sensor Internet Protocol (IP) addresses, are stored;

associating in memory the respective physiological sensor identifiers, comprising respective IP addresses, of the plurality of physiological sensors with the user;

receiving, at a sensor interface, over a network sensor data from two or more physiological sensors associated with the user, the two or more physiological sensors comprising at least a first physiological sensor of a first type and a second physiological sensor of a second type;

storing the received sensor data in a medical information data store;

receiving, at a computer system, in association with the network sensor data at least one unique identifier, comprising at least one physiological sensor IP address;

utilizing, by the computer system, the at least one unique identifier to locate information regarding treatment information for the user;

accessing, by the computer system, rules and respective rule data quantity thresholds, including at least two quantity thresholds comprising;

a first quantity threshold for sensor data from the first physiological sensor, and

a second quantity threshold for sensor data from the second physiological sensor,

wherein the first quantity threshold is different than the second quantity threshold;

evaluating, by the computer system, the accessed rules and data quantity thresholds, including;

the first quantity threshold for sensor data from the first physiological sensor, and

the second quantity threshold for sensor data from the second physiological sensor to determine;

which biological event predictive models, in a data store of biological event predictive models, are to be used to predict respective future user outcomes to respective medical interventions or respective failures to provide respective medical interventions, and

to determine whether the determined biological event predictive models are to be executed, including at least a first biological event predictive model, wherein a given biological event predictive model predicts a user outcome comprising a level of risk of hospitalization or a level of risk of mortality, to a medical intervention or a failure to provide a medical intervention, and the given biological event predictive model is based at least in part on medical survey data for a plurality of patients;

at least partly in response to determining that the first biological event predictive model is to be executed, executing, using the computer system, the first biological event predictive model using at least a portion of the received sensor data to provide a biological prediction of a first user outcome, the first user outcome comprising a first level of risk of hospitalization or a first level of risk of mortality, to a potential first medical intervention or a failure to provide the potential first medical intervention for the user;

based at least in part on the biological prediction for the user, determining, using the computer system, whether a first action is to be taken, the first action comprising transmission of an electronic notification and/or a modification of an operation of a first medical device;

at least partly in response to determining that the first action is to be taken, transmitting, using the computer system, the electronic notification and/or causing the operation of a first medical device of the user to be modified;

utilizing the medical data store, storing received sensor data, to update at least one biological prediction model;

wherein the first physiological sensor and the second physiological sensor are configured to communicate with each other over a body area network, and at least one of the physiological sensors comprises a biomedical microelectromechanical (MEMs) system configured to wirelessly communicate preconditioned sample data over;

a 2360-2400 MHz radio frequency band, a 30-37.5 MHz radio frequency band, and/or Bluetooth.

9. The method as defined in claim 8, wherein at least one of the physiological sensors comprises an implanted sensor or a sensor surface-mounted on a body of the user in a fixed position.

10. The method as defined in claim 8, wherein at least one of the physiological sensors is worn using a wrist band.

11. The method as defined in claim 8, wherein at least one of the physiological sensors comprises a processing device, a battery, and a wireless transceiver.

12. The method as defined in claim 8, the method further comprising:

dynamically generating a data interface to collect a plurality of types of health status information, demographic information, and clinical information, wherein the interface comprises a human-readable interface and/or a machine-readable interface;

collecting health status information, demographic information, and clinical information using the data interface;

identifying potentially significant predictors of health states with respect to a corresponding level of risk of hospitalization or a corresponding level of risk of mortality based at least in part on information collected using the data interface;

generating the first biological event predictive model using the identified potentially significant predictors of health states.

13. The method as defined in claim 8, the method further comprising:

generating and providing treatment recommendations based at least in part on generated predictor results obtained from executing the first biological event predictive model.

14. The method as defined in claim 8, the method further comprising at least partly in response to determining that the first action is to be taken, causing the operation of a first medical device of the user to be modified.