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

• US 9,980,681 B1
• Filed: 11/02/2017
• Issued: 05/29/2018
• 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:
  US15/802,310
• Time in Patent Office: 208 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 device reader such as a data collection engine device, and a server device. The data collection engine wirelessly transmits power to an RFID chips and receives first medical data from the RFID chip while the 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 system comprising:

a medical item including a plurality of radio-frequency identification (RFID) chips, wherein at least a first RFID chip of the plurality of RFID chips is a passive-type RFID chip, wherein at least one of the first RFID chip and a second RFID chip of the plurality of RFID chips includes;

a controller configured according to instructions in memory to generate a message including identification information associated with the respective RFID chip and medical data indicative of a medical event; and

an antenna portion configured to transmit the message;

a mobile device communicating with the at least one of the first RFID chip and the second RFID chip, wherein the mobile device comprises;

a transceiver configured to receive the message from the at least one of the first RFID chip and the second 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 medical data to be sent by the transceiver to a server device via a network connection;

wherein the server device comprises;

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

a first database including a plurality of states for medical items and attributes for each of the plurality of states;

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

instructions for configuring the controller to determine whether the output value corresponds with one or more of the attributes associated with one or more of the states in the first database.

2. The system of claim 1, wherein the controller of the server device is further configured to:

perform pre-processing on the medical data to generate an input data set; and

generate the output value from the trained model based upon the input data set.

3. The system of claim 1, wherein in the server device:

the one or more memory sources further store a plurality of past risk state events, each of the plurality of past risk state events including a plurality of attributes and a quantifiable outcome; and

the controller is further configured to;

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

performing pre-processing on the plurality of 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 training data; and

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

wherein the MLA for updating the synoptic weights is one or more of ADALINE training, backpropagation algorithm, competitive learning, genetic algorithm training, Hopfield learning, Instar and Outstar training, the Levenberg-Marquardt algorithm (LMA), Manhattan Update Rule Propagation, Nelder Mead Training, Particle Swarm (PSO) training, quick propagation algorithm, resilient propagation (RPROP) algorithm, scaled conjugate gradient (SCG), support vector machines, genetic programming, Bayesian statistics, decision trees, case based reasoning, information fuzzy networks, clustering, hidden Markov models, particle swarm optimization, simulated annealing.

4. The system of claim 3, 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) the sigmoid function f(x)=1/(1+e<sup>−

x</sup>);

(2) the 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 synoptic weights.

5. The system of claim 3, wherein the NNM is one or more of a feed forward structure Neural Network;

ADALINE Neural Network, Adaptive Resonance Theory 1 (ART1), Bidirectional Associative Memory (BAM), Boltzmann Machine, Counterpropagation Neural Network (CPN), Elman Recurrent Neural Network, Hopfield Neural Network, Jordan Recurrent Neural Network, Neuroevolution of Augmenting Topologies (NEAT), and Radial Basis Function Network.

6. The system of claim 1, wherein the plurality of states for medical items include expiration dates for each of the medical items.

7. The system of claim 1, wherein the plurality of states for medical items include cleaning and maintenance cycles for each of the medical items.

8. A system comprising:

a medical item including a plurality of radio-frequency identification (RFID) chips, wherein at least a first RFID chip of the plurality of RFID chips is a passive-type RFID chip with no battery, wherein at least one of the first RFID chip and a second RFID chip of the plurality of RFID chips includes;

a controller configured according to instructions in a memory to generate a message including identification information and medical data associated with the respective RFID chip; and

an antenna portion configured to transmit the message;

wherein the first RFID chip includes an antenna for wirelessly receiving power from a power transmission subsystem;

a device reader communicating with the at least one of the first RFID chip and the second RFID chip, wherein the device reader comprises;

a transceiver configured to receive the message from the at least one of the first RFID chip and the second 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 medical data to be sent by the transceiver to a server device via a network connection,

wherein the server device comprises;

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

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;

a first database including a plurality of identification of medical items and usage parameters for each of the medical items; and

instructions for configuring the controller to determine data in the medical item database that is associated with the identification for the at least one of the first RFID chip and the second RFID chip; and

generate an information reply including usage parameters associated with the medical item based upon the determined data, the usage parameters include an identification of a medical professional that used the medical item and an identification of a patient for whom the medical item was used.

9. The system of claim 8, wherein the usage parameters include an expiration date of the medical item.

10. The system of claim 8, wherein the usage parameters include a cleaning and maintenance cycle of the medical item.

11. The system of claim 8, wherein the usage parameters include location data of the medical item.

12. The system of claim 8, wherein the one or more memory sources of the server device further storing:

a second database including a plurality of states for medical items and attributes for each of the plurality of states;

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

instructions for configuring the controller to determine whether the output value corresponds with one or more of attributes associated with one or more of the states in the second database.

13. A server device comprising:

a transceiver configured to receive one or more messages from an RFID chip, the one or more messages including identification information and medical data for a medical item;

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;

a first database including a plurality of identifications of medical items and usage parameters for each of the medical items; and

instructions for configuring the controller to determine data in the medical item database that is associated with the identification information from the RFID chip; and

generate an information reply including usage parameters associated with the medical item based upon the determined data.

14. The server device of claim 13, wherein the usage parameters include an identification of a medical professional that used the medical item and an identification of a patient for whom the medical item was used.

15. The server device of claim 13, wherein the usage parameters include an expiration date of the medical item.

16. The server device of claim 13, wherein the usage parameters include a cleaning and maintenance cycle of the medical item.

17. The server device of claim 13, wherein the usage parameters include location data of the medical item.

18. The server device of claim 13, wherein the one or more memory sources further storing:

a second database including a plurality of states for medical items and attributes for each of the plurality of states;

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

instructions for configuring the controller to determine whether the output value corresponds with one or more of the attributes associated with one or more of the states in the second database.

19. The server device of claim 13, wherein the medical data includes location data associated with the medical item.

20. The server device of claim 13, the one or more memory sources further storing:

instructions for configuring the controller to determine a current and any change in location over time that is associated with the identification information from data received from the RFID chip; and

instructions for configuring the controller to determine a current stage in and changes in a cleaning and maintenance cycle over time of the medical item based on changes in location of medical items derived from data received/read from the RFID chip.