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

• US 9,679,108 B1
• Filed: 12/26/2016
• Issued: 06/13/2017
• 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
• Application Number:
  US15/390,695
• Time in Patent Office: 169 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 medical items, 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 items such as position and consumption based upon the first medical data.

Claims

1. A system comprising:

a plurality of radio-frequency identification (RFID) chips, wherein a first RFID chip of the plurality of RFID chips is a passive-type RFID chip;

a data collection engine (DCE) device communicating with the plurality of RFID chips, wherein the DCE comprises;

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

a transceiver configured to receive first data from at least one of the first RFID chip and a second RFID chip of the plurality of RFID chips while the first RFID chip is activated by the power received, the first data including identification information of the at least one of the first and second RFID chips, one or more pressure values and position information;

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 to be sent by the transceiver to a server device via the network connection,

wherein the first RFID chip includes an antenna for wirelessly receiving the power from the transceiver of the DCE and control logic for generating the identification information;

wherein the server device comprises;

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

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 patient identifications associated with a plurality of patients, and patient attributes for each of the plurality of patient identifications;

a trained neural network model (NNM) for generating an output value corresponding to a present patient orientation based upon one or more of the one or more pressure values and position information; and

instructions for configuring the controller to determine whether the present patient orientation corresponds with one or more of the patient attributes associated with a patient of concern in the first database.

2. The system of claim 1, wherein the one or more messages further include:

a first spatial orientation of a third RFID chip associated with an endotracheal tube in the patient;

a pressure of an air-filled cuff in the endotracheal tube; and

a plurality of temperature values associated with the endotracheal tube.

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

perform pre-processing on the one or more pressure values and position information to generate an input data set;

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

determine whether the output value corresponds with one or more of the patient attributes associated with the patient in the first database.

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

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

the controller 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 patient attributes for each of the plurality of past patient position events to generate a plurality of input data sets;

dividing the plurality of past patient position 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.

5. A server device comprising:

a transceiver configured to receive one or more messages via a connection to a network, the one or more messages including a plurality of input attributes of a new patient event, the input attributes include patient identification data originating from a first RFID chip disposed in a first area remote from the server device and location data associated with the first RFID chip, the one or more messages received via a DCE in communication with the first RFID chip or from the first 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 storing a trained model and instructions for configuring the controller to;

perform pre-processing on the plurality of input attributes to generate an input data set;

generate an output value from the trained model based upon the input data set; and

compare the output value to a predetermined variable of the new patient event to predict likelihood of a never event.

6. The server device of claim 5, wherein:

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

the controller further configured to;

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

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

dividing the plurality of past patient 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.

7. The server device of claim 6, 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 ƒ

(x)=1/(1+e<sup>−

x</sup>);

(2) the hyperbolic tangent function ƒ

(x)=(e^2x−

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

(3) a linear function ƒ

(x)=x,

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

8. The server device of claim 6, 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.

9. A server device comprising:

a transceiver configured to receive one or more messages via a connection to a network, the one or more messages including identification data originating from a first RFID chip disposed in a first area remote from the server device and location data associated with the first 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 storing;

a first database including a plurality of patient identifications associated with a plurality of patients and patient attributes for each of the plurality of patient identifications; and

instructions for configuring the controller to determine based upon the one or more messages whether a never event is likely to occur.

10. The server device of claim 9, wherein

the patient attributes in the first database include a scheduled surgical procedure and location data for the scheduled surgical procedure;

the controller is further configured to extract a patient identification corresponding to the identification data of the first RFID chip from the patient database; and

the controller is configured to determine that the never event is likely to occur when the location data associated with the first RFID chip is not in accordance with the location data for the scheduled surgical procedure associated with the extracted patient identification.

11. The server device of claim 9, wherein:

the one or more messages further include a first spatial orientation of a plurality of second RFID chips in proximity to the first RFID chip in the first area;

the controller is further configured to extract a patient identification corresponding to the identification data of the first RFID chip from the first database;

the controller is further configured to determine a patient position based upon the first spatial orientation;

the patient attributes in the first database include a scheduled surgical procedure and patient position associated with the scheduled surgical procedure; and

the controller is configured to determine that the never event is likely to occur when the determined patient position is not in accordance with the patient position associated with the scheduled surgical procedure for the extracted patient identification.

12. The server device of claim 11, wherein:

the one or more memory sources store a trained model; and

the controller is further configured to determine the patient position based upon the trained model.

13. The server device of claim 9, wherein:

the one or more messages received by the transceiver further include identification data originating from a second RFID chip disposed in the first remote area;

the controller is further configured to extract a patient identification corresponding to the identification data of the first RFID chip and a medical professional identification corresponding to the identification data of the second RFID chip from the first database;

the patient attributes in the first database include a scheduled surgical procedure and an identification of a medical professional associated with the scheduled surgical procedure; and

the controller is configured to determine that the never event is likely to occur when the medical professional associated with the scheduled surgical procedure is not in accordance with the extracted medical professional identification.

14. The server device of claim 9, wherein:

the one or more messages received by the transceiver further include a plurality of identification data originating from a plurality of second RFID chips disposed in the first area;

the controller is further configured to extract a patient identification corresponding to the identification data of the first RFID chip and a plurality of medical item identifications corresponding to the plurality of identification data of the plurality of second RFID chips from the first database;

the patient attributes in the first database include a scheduled surgical procedure and identifications of necessary medical items associated with the scheduled surgical procedure; and

the controller is configured to determine that the never event is likely to occur when the necessary medical items associated with the scheduled surgical procedure are not in accordance with the extracted plurality of medical item identifications.

15. The server device of claim 9, wherein:

the one or more messages received by the transceiver further include a plurality of identification data originating from one or more second RFID chips disposed in the first area;

the first RFID chip is associated with a patient identification tag of a patient and the one or more second RFID chips are associated with an endotracheal tube in the patient;

the one or more messages received by the transceiver further include a pressure value of an air-filled cuff in the endotracheal tube and a temperature value associated with the patient, the pressure and temperature values originating from the one or more second RFID chips;

the patient attributes in the patient database include scheduled surgical procedure and a predetermined temperature and pressure range for the patient during the surgical procedure; and

the controller is configured to determine that the never event is likely to occur when the pressure and temperature values included in the one or more messages do not correspond to the predetermined temperature and pressure ranges for the patient during the surgical procedure.

16. The server device of claim 9, further comprising:

instructions for configuring the controller to store in the first database associated with one of the patient identifications a time duration for which the first RFID chip is disposed in the first area as a patient attribute.

17. The server device of claim 16, wherein the one or more memory sources further storing a second database including a plurality of medical professional identifications associated with a plurality of medical professionals and attributes for each of the plurality of medical professional identifications.

18. The server device of claim 17, wherein:

the one or more messages received by the transceiver further include identification data originating from a second RFID chip disposed in the first area, the second RFID chip associated with an identification tag of a medical professional;

the controller is further configured to extract a patient identification corresponding to the identification data of the first RFID chip from the first database and a medical professional identification corresponding to the identification data of the second RFID chip from the second database; and

the controller is further configured to store in the first database associated with the extracted patient identification a first time duration for which the first RFID chip is disposed in the first area and in the second database associated with the extracted medical professional identification a second time duration for which the second RFID chip is disposed in the first area.

19. The server device of claim 9, wherein:

the one or more memory sources further storing a third database including a plurality of medical item identifications associated with a plurality of medical items;

the one or more messages received by the transceiver further include identification data originating from a third RFID chip disposed in the first area, the third RFID chip associated with an identification tag of a medical item;

the controller is further configured to extract a medical item identification corresponding to the identification data of the third RFID chip from the third database; and

the controller is further configured to store in the third database associated with the extracted medical item identification a time duration for which the third RFID chip is disposed in the first area.

20. The server device of claim 9, wherein:

the one or more messages received by the transceiver further include a plurality of identification data originating from one or more second RFID chips disposed in the first remote area;

the first RFID chip is associated with a patient identification tag of a patient and the one or more second RFID chips are associated with an endotracheal tube;

the one or more messages received by the transceiver further include a pressure value of an air-filled cuff in the endotracheal tube and a temperature value associated with the patient, the pressure and temperature values originating from the one or more second RFID chips; and

the controller is further configured to determine a time duration for which the endotracheal tube is inserted in the patient.