System, medical item including RFID chip, data collection engine, server and method for capturing medical data

• US 10,172,565 B1
• Filed: 05/31/2018
• Issued: 01/08/2019
• Est. Priority Date: 02/06/2015
• Status: Active Grant

• Current Assignee: Brain Trust Innovations I, LLC
• Original Assignee: Brain Trust Innovations I, LLC
• Inventors: LaBorde, David
• Primary Examiner(s): Blouin, Mark S
• Application Number:
  US15/994,208
• Time in Patent Office: 222 Days
• Field of Search: 340 101
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 2562/08 Sensors provided with means for identification, e.g. barcodes or memory chips
• 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 5/065 Determining position of the probe employing exclusively positioning means located on or in the probe, e.g. using position sensors arranged on the probe
• CPC Class Codes: A61B 5/6852 Catheters
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06K 19/07758 arrangements for adhering the record carrier to further objects or living beings, functioning as an identification tag
• CPC Class Codes: G06K 7/10366 the interrogation device being adapted for miscellaneous applications
• CPC Class Codes: G06N 3/04 Architecture, e.g. interconnection topology
• CPC Class Codes: G06N 3/08 Learning methods
• CPC Class Codes: G16H 10/65 stored on portable record carriers, e.g. on smartcards, RFID tags or CD
• CPC Class Codes: G16H 40/20 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/63 for local 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

A system includes a plurality of RFID chips affixed to a medical item, a data collection engine device, and a server device. The data collection engine wirelessly transmits power to a first one of the RFID chips and receives first medical data from the first RFID chip while the first RFID chip is activated by the power receiver. The data collection engine generates a first message indicative of the first medical data to be sent to the server device. The server device can determine aspects of the medical item such as position and risk states based upon the first medical data.

Claims

1. A method for monitoring a patient, the method comprising:

receiving, at a controller, sensor data from a sensor device associated with a radio-frequency identification (RFID) chip and the patient;

determining, by the controller, an initial orientation of the patient based at least on the received sensor data after determining the initial orientation of the patient, determining a series of patient orientations based on subsequently received sensor data from the sensor device;

storing patient orientation data regarding the series of determined patient orientations in a memory device;

generating patient-specific data based at least on the stored patient orientation data; and

generating, by the controller, a recommended repositioning schedule for the patient based at least on the generated patient-specific data.

2. The method of claim 1, wherein the RFID chip receives power wirelessly from a power transmission subsystem of another device, and the another device sends the sensor data to the controller.

3. The method of claim 1, further comprising storing each of the initial patient orientation and the series of patient orientations as patient attributes in a first database including a plurality of risk states and patient attributes.

4. The method of claim 3, further comprising:

generating an output value from a trained neural network model (NNM) based upon the sensor data; and

determine whether at least one of the initial orientation and the series of patient orientations corresponds with one or more of the patient attributes associated with one or more of the risk states in the first database.

5. The method of claim 4, wherein the plurality of risk states includes one of incorrect position risk, a pressure ulcer risk, and a falling risk.

6. The method of claim 4, further comprising:

training the neural network model (NNM) to generate the trained NNM, wherein the training of the NNM includes;

performing pre-processing on the plurality of patient attributes for each of the plurality of past risk state events to generate a plurality of input data sets;

dividing the plurality of past risk state events into a first set of training data and a second set of validation data;

iteratively performing a machine learning algorithm (MLA) to update synaptic weights of the NNM based upon the first set of training data; and

validating the NNM based upon the second set of validation data.

7. The method of claim 4, wherein:

the NNM includes an input layer, output layer, and a plurality of hidden layers with a plurality of hidden neurons; and

each of the plurality of hidden neurons includes an activation function, the activation function is one of;

(1) a sigmoid function f(x)=1/(1+e<sup>−

x</sup>);

(2) a hyperbolic tangent function f(x)=(e^2x−

1)/(e^2x+1); and

(3) a linear function f(x)=x,

wherein x is a summation of input neurons biased by the synaptic weights.

8. The method of claim 1, further comprising determining and storing a cumulative time spent in a particular patient orientation.

9. The method of claim 1, wherein the particular patient orientation is an orientation that pressurizes a defined area of interest comprising a wound or a defined area at risk for formation of a pressure ulcer.

10. The method of claim 1, wherein the patient-specific data comprises a measure of patient activity.

11. The method of claim 1, wherein the receiving of the sensor data further includes receiving one or more of a pressure value, a temperature value and a moisture value associated with the patient.

12. The method of claim 1, wherein generating the recommended repositioning schedule for the patient based at least on the generated patient-specific data comprises setting a value for at least one repositioning parameter selected from a group consisting of patient turn frequency, maximum time spent in a particular orientation, minimum time spent in a pressure relief orientation, and orientation angle or turn angle thresholds.

13. A method for monitoring a patient, the method comprising:

receiving, at a controller, sensor data from a sensor device associated with a RFID chip and the patient via a device, the RFID chip receiving power wirelessly from a power transmission subsystem of the device;

determining, by the controller, a series of patient orientations based on the sensor data;

generating patient-specific data based at least on the determined series of patient orientations; and

adjusting, by the controller, a repositioning schedule for the patient based at least on the generated patient-specific data.

14. The method of claim 13, wherein:

the repositioning schedule defines a turn frequency for the patient; and

adjusting the repositioning schedule for the patient based at least on the generated patient-specific data comprises adjusting the turn frequency.

15. The method of claim 13, wherein:

the repositioning schedule defines a threshold time for an amount of time spent by the patient in a defined orientation; and

adjusting the repositioning schedule for the patient based at least on the generated patient-specific data comprises adjusting the turn frequency.

16. The method of claim 13, wherein the threshold time defines a maximum time spent in a pressure orientation.

17. The method of claim 13, wherein the threshold time defines a minimum time spent in a pressure relief orientation.

18. The method of claim 13, wherein generating the recommended repositioning schedule for the patient based at least on the generated patient-specific data comprises setting a value for at least one repositioning parameter selected from a group consisting of patient turn frequency, maximum time spent in a particular orientation, minimum time spent in a pressure relief orientation, and orientation angle or turn angle thresholds.

19. A system comprising:

a sensor device including a plurality of sensors configured to sense a plurality of values associated with a patient, and a radio-frequency identification (RFID) chip, wherein the RFID chip includes;

a controller configured according to instructions in memory to generate a message including an identification information associated with the RFID chip and the plurality of sensed values; and

an antenna portion configured to transmit the message;

a communication device communicating with the RFID chip, wherein the communication device comprises;

a power transmission subsystem including a power source and an antenna arranged to wirelessly transmit power from the power source to the RFID chip;

a transceiver configured to receive the message from the RFID chip;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources including instructions for configuring the controller to generate one or more messages indicative of the identification information and the plurality of sensed values to be sent by the transceiver to a server device via a network connection,

wherein the RFID chip includes an antenna for wirelessly receiving the power from the transceiver of the communication device;

wherein the server device comprises;

a transceiver configured to receive the one or more messages from the communication device;

a controller operatively coupled to the transceiver; and

one or more memory sources operatively coupled to the controller, the one or more memory sources storing;

instructions for configuring the controller to determine a series of patient orientations based upon the sensor data in the one or more messages received from the communication device;

a first database including a plurality of risk states and patient orientations as patient attributes; and

a trained neural network model (NNM) for generating an output value based upon the patient attributes.

20. The system of claim 19, wherein the communication device is an antenna device adapted to be plugged into an electrical outlet.