System, method and device for predicting an outcome of a clinical patient transaction

• US 10,319,476 B1
• Filed: 08/24/2016
• Issued: 06/11/2019
• 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:
  US15/246,400
• Time in Patent Office: 1,021 Days
• Field of Search: 340 101
• 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 15/00 ICT specially adapted for medical reports, e.g. generation or transmission thereof
• 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 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 50/70 for mining of medical data, e.g. analysing previous cases of other patients

Abstract

A system that includes a plurality of RFID tags affixed to medical items, and a plurality of data collection engine devices, client devices and backend devices. The backend devices include trained machine learning models, business logic, and attributes of a plurality of patient transactions. A plurality of data collection engines and hospital information systems send attributes of new patient transactions to the backend devices. The backend devices can predict particular outcomes of new patient transactions based upon the attributes of the new patient transactions utilizing the trained machine learning models. Using business logic and the trained machine learning models, the backend devices can also make recommendations to optimize the patient flow in healthcare provider organizations.

Claims

1. A method for predicting an outcome associated with a new patient transaction, the method comprising:

receiving a plurality of input attributes of the new patient transaction;

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

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

classifying the output value into a delay risk category to predict the outcome.

2. The method of claim 1, further comprising:

storing a plurality of past patient transactions, each of the plurality of past patient transactions including a plurality of patient attributes and a quantifiable outcome; and

training 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 transactions to generate a plurality of input data sets;

dividing the plurality of past patient transactions 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.

3. The method of claim 2, 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.

4. The method of claim 2, 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.

5. The method of claim 2, wherein the performing of the MLA includes measuring a global error in each training iteration for the NNM by:

calculating a local error, the local error being a difference between the output value of the NNM and the quantifiable outcome;

calculating the global error by summing all of the local errors in accordance with one of;

(1) Mean Square Error (MSE) formula

6. The method of claim 1, wherein the trained model is a trained Self-Organizing Map (SOM) including a plurality of network nodes arranged in a grid or lattice and in fixed topological positions, an input layer with a plurality of input nodes representing the input attributes of the past patient transactions, wherein each of the plurality of input nodes is connected to all of the plurality of network nodes by a plurality of synaptic weights.

7. The method of claim 6, further comprising:

storing a plurality of past patient transactions, each of the plurality of past patient transactions including a plurality of patient attributes and a quantifiable outcome;

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

training a SOM to generate the trained model, wherein the training of the SOM includes;

initializing values of the plurality of synaptic weights to random values,

randomly selecting one past patient transaction and determining which of the plurality of network nodes is a best matching unit (BMU) according to a discriminant function, wherein the discriminant function is a Euclidean Distance; and

iteratively calculating a neighborhood radius associated with the BMU to determine neighboring network nodes for updating, and updating values of synoptic weights for neighboring network nodes within the calculated neighborhood radius for a fixed number of iterations to generate the trained model.

8. The method of claim 7, wherein normalizing of the plurality of patient attributes includes generating another SOM including the plurality of patient attributes to reduce dimensionality.

9. The method of claim 1, wherein the receiving the plurality of patient attributes of the new patient transaction further includes receiving one or more messages including a patient identification and location information associated with a first RFID tag and a medical professional identification and location information associated with a second RFID tag from a data collection engine (DCE).

10. The method of claim 1, wherein the new patient transaction is one of an admit patient order, discharge patient order, transfer patient order, or room turnover request, and the output value is one of an estimated delay time or an indication of delay or no delay for the new patient transaction.

11. The method of claim 1, wherein:

the receiving of the plurality of patient attributes of the new patient transaction further comprises receiving a plurality of new patient transactions, each including a plurality of patient attributes;

the generating of the output value and classifying the output value further includes generating a graphical image including output values for each of the plurality of new patient transactions;

the method further comprises receiving a graphical display request from a remote client device and transmitting the graphical image to the remote client device as a response;

the trained model is a trained SOM; and

the graphical image is a cluster diagram including a plurality of clusters of output values having a similar characteristic.

12. The method of claim 1, wherein the patient attributes include:

a location of a patient of the new patient transaction received from one of an RFID associated with a patient identification for the patient and a DCE in communication with the RFID;

a medical professional identification of a medical professional associated with the patient received from an RFID associated with the medical professional;

information regarding a prior relationship between the medical professional and the patient received from a memory source;

date information of the new patient transaction;

wherein the receiving of the plurality of patient attributes of the new patient transaction further comprises one or more of the following;

receiving one or more of an age of the patient, insurance information associated with the patient and employment information associated with the patient from a memory source;

receiving information indicative of a medical specialty associated with a facility in which the patient is located from the memory source;

receiving identification information indicative of an individual that signed a patient transaction order and date information of the order from the memory source;

receiving identification information of a current attending physician of record for the patient from the memory source,

receiving information indicating presence or absence of a resident physician as a participant in a patient care episode, and

receiving information indicating a number of medications on a medication administration record at a time of the patient transaction.

13. The method of claim 1, further comprising:

assigning an allocation of appropriate clinical resources to the patient transaction based upon an optimization algorithm in accordance with the delay risk category of the patient transaction and attributes of available clinical resources.

14. A Throughput Manager Device (TMD) comprising:

a transceiver for receiving input attributes associated with a patient transaction from one or more remote entities via a network connection;

the transceiver further for receiving an information request from a remote client access device via the network connection, the information request being a request for calculated quantifiable outcomes for a plurality of patient transactions;

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;

calculate a quantifiable outcome for each of the patient transactions from a trained model based upon at least two or more patient attributes of the respective patient transaction where the outcome represents a numerical, continuous or categorical outcome; and

generate an information reply including a graphical display indicating the numerical/continuous output value or the category output of each of the patient transactions.

15. The TMD of claim 14, wherein the one or more remote entities include:

a data collection engine receiving patient identification from a first RFID tag and medical professional identification and location information from a second RFID tag;

a CPOE system;

a Hospital Bed Management System (BMS); and

an Electronic medical records system.

16. The TMD of claim 14, wherein:

the information request further includes a request for an average discharge lag for each of a plurality of facilities in a system, each of a plurality of clinical services lines in a respective facility, and for the system; and

the generating of the information reply including the graphical display further includes including the average discharge lag for each of the plurality of facilities, the each of the plurality of clinical services lines, and for the system in the graphical display.

17. A client access device comprising:

a display;

a transceiver for sending an information request to a TMD via a network connection, the information request being a request for calculated quantifiable outcomes for a plurality of patient transactions based upon a trained model from the TMD;

the transceiver further for receiving the plurality of the calculated quantifiable outcomes from the TMD;

a controller operatively coupled to the transceiver and the display; and

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

generate a graphical display on the display indicating a delay risk category of each of the patient transactions based upon the calculated quantifiable outcomes.

18. The client access device of claim 17, wherein:

the memory stores display preferences for selecting metrics to be displayed;

the controller generates the graphical display to include an average discharge lag for a system and each of a plurality of subordinate hierarchical entities and members of the system based upon the display preferences stored in the memory.

19. The client access device of claim 18, wherein:

the controller is coupled to a user input device; and

the controller is further configured to interact with the graphical display based upon selections received via the user input device to increase or decrease subordinate hierarchical entities displayed in the graphical display.