Treatment intelligence and interactive presence portal for telehealth

• US 10,325,070 B2
• Filed: 12/13/2016
• Issued: 06/18/2019
• Est. Priority Date: 12/14/2015
• Status: Active Grant

• Current Assignee: The Live Network, Inc.
• Original Assignee: The Live Network, Inc.
• Inventors: Beale, Ryan Gordon, Jaruzel, II, Mark Ellis
• Primary Examiner(s): Borissov, Igor N
• Application Number:
  US15/377,490
• Publication Number:
  US 20170169177A1
• Time in Patent Office: 917 Days
• Field of Search: 705 2, 705 3
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G16H 30/20 for handling medical images, e.g. DICOM, HL7 or PACS
• CPC Class Codes: G16H 40/67 for remote operation

Abstract

The present invention relates to a computerized system for and method of providing precision healthcare services such as consultation, education, assessment, diagnosis, intervention, or treatment at a distance via encrypted real-time image and audio presence where the healthcare professional'"'"'s assessment, diagnosis, and intervention activities are informed by patient feedback, smart objects, and artificial intelligence and patient outcomes are optimized through recursive system feedback. The present invention is unlimited with regard to the type of patient entity or healthcare professional entity.

Claims

1. An integrated computer-implemented system for providing secure telehealth and teletherapy services informed by artificial intelligence and patient feedback comprising:

a physical database configured to store application data, patient data including demographic, genetic, assessment, diagnosis, treatment, history, outcome, wearable, mobile device, augmented reality device, and social network, and professional data including assessment, diagnosis, treatment, intervention, prognosis, homework assignment, theoretical orientation, efficacy rating, discipline, and demographic data both in a raw form and a vectorized form where each patient has one or more vectors comprised of the aggregation of numerous, diverse, data points about them;

a physical or virtual display configured to display a patient'"'"'s real-time image and audio presence and relevant emotional, clinical, behavioral habit, and feedback data to a healthcare professional and

a computer-implemented physical processor or collection of processors configured to use artificial intelligence to analyze patterns in vectorized patient data, to transmit data to and receive data from patients across a range of devices and interfaces including smart phones, mobile devices, augmented reality displays, wearables, and smart devices, to transmit data to and receive data from a healthcare professional, and to receive and execute commands from one or more healthcare professionals, to continually learn from outputs of patient experiences that are added to said system that further refine further patient recommendations, wherein said computer-implemented system is configured to;

receive patient information chosen from the group consisting of symptom data, demographic data, genetic data, diagnostic data, treatment data, history data, outcome data, wearable data, mobile device data, augmented reality device data, social network data, and combinations thereof;

store the patient information in the database in a vectorized form;

analyze the vectorized patient information via low-rank matrix factorization and suggest at least one healthcare professional to be used;

receive a selection from the patient of a healthcare professional;

suggest intervention processes to the healthcare professional for the patient based on the patient information, healthcare professional demographic and theoretical orientation data, and vectorized data from previous patients analyzed via low-rank matrix factorization;

receive from the healthcare professional an initial plan of service for the patient, the initial plan is based on the suggested intervention processes;

connect a patient'"'"'s device to the healthcare professional to receive real-time image and audio on the computer-implemented system and to provide in-session therapeutic intervention process based on the initial plan of service;

augment the healthcare professional'"'"'s in-session therapeutic intervention process with real-time data displays about the patient'"'"'s mood derived from real-time analysis of the patient'"'"'s speech including tone analysis, and facial expressions including microexpressions, by providing said real-time data to the healthcare professional during the in-session therapeutic intervention process in which the healthcare professional providing the service for the patient over the computer-implemented system chosen from the group consisting of consultation, education, assessment, diagnosis, intervention, treatment, and combinations thereof;

analyze feedback from the patient in real-time and suggest revisions to the initial plan of service during the in-session therapeutic intervention process, and

push clinical homework activities assigned by the healthcare professional during the in-session therapeutic intervention process to at least one patient device.

2. The system of claim 1, wherein the patient is a person or other entity seeking professional consultation, education, assessment, diagnosis, intervention, or treatment related to psychotherapy or psychoeducation.

3. The system of claim 1, wherein the healthcare professional is a person or other entity seeking to offer professional consultation, education, assessment, diagnosis, intervention, or treatment related to psychotherapy, psychoeducation, or similar professional activities.

4. The system of claim 1, wherein the database has been secured through encryption.

5. The system of claim 1, wherein the transmission of the patient'"'"'s real-time image and audio presence and relevant data has been secured through encryption.

6. The system of claim 1, wherein the transmission of the healthcare professional'"'"'s real-time image and audio presence and relevant data has been secured through encryption.

7. The system of claim 1, wherein the computer-implemented processor has been configured to use artificial intelligence including low-rank matrix factorization, vectorization and skip thought vectors to analyze patterns in vectorized patient assessments, genetics, diagnoses, treatments, outcomes, wearables, mobile devices, augmented reality devices, and social networks suggest prognosis and clinical interventions for an individual patient including in-session interventions, patient homework, prescribed activities, and therapeutic orientation.

8. The system of claim 1, wherein the computer-implemented processor has been configured to transmit homework treatment recommendations to patients across a range of devices and interfaces including smart phones, mobile devices, augmented reality displays, wearables, and smart devices and receive feedback about patient adherence to the treatment recommendations across a range of devices and interfaces.

9. The system of claim 1, wherein the computer-implemented processor has been configured to transmit predictive data derived from artificial intelligence processes informed by vectorized patient data and treatment adherence data about a patient to a healthcare professional to support professional psychotherapeutic activities.

10. The system of claim 1, wherein the computer-implemented processor has been configured to receive raw and predictive data about patient treatment adherence from a range of devices including smart phones, mobile devices, augmented reality displays, wearables, and smart devices and healthcare professional input and to transmit that data to the database in both a raw form and a vectorized form for use in training and improving an artificial intelligence system.

11. The system of claim 1, wherein the computer-implemented processor has been configured to use the artificial intelligence including low-rank matrix factorization, vectorization and skip thought vectors to analyze patterns in vectorized patient assessments, genetics, diagnoses, treatments, outcomes, wearables, mobile devices, augmented reality devices, social networks, treatment adherence, and treatment response to suggest current prognosis as well as clinical interventions for an individual patient.

12. The system of claim 1, wherein the computer-implemented processor has been configured to use artificial intelligence including low-rank matrix factorization, vectorization and skip thought vectors to analyze patterns in vectorized patient assessments, genetics, diagnoses, treatments, outcomes, wearables, mobile devices, augmented reality devices, social networks, treatment adherence, and treatment response to suggest continuing or discontinuing treatment as well as discharge prognosis.

13. The system of claim 1, wherein the computer-implemented processor has been configured to use artificial intelligence including low-rank matrix factorization, vectorization and skip thought vectors to analyze patterns in vectorized healthcare professional data including assessment, diagnosis, treatment, intervention, prognosis, homework assignment, theoretical orientation, efficacy rating, discipline, and demographic data to initially suggest prognosis as well as clinical interventions for an individual patient including in-session interventions, patient homework, prescribed activities, and therapeutic orientation.

14. The system of claim 1, wherein the computer-implemented processor has been configured to use artificial intelligence including low-rank matrix factorization, vectorization and skip thought vectors to analyze patterns in vectorized healthcare professional data including assessment, diagnosis, treatment, intervention, prognosis, homework assignment, theoretical orientation, efficacy rating, discipline, and demographic data to suggest current prognosis as well as clinical interventions for an individual patient including in-session interventions, patient homework, prescribed activities, and therapeutic orientation.

15. The system of claim 1, wherein the computer-implemented processor has been configured to use artificial intelligence including low-rank matrix factorization, vectorization and skip thought vectors to analyze patterns in vectorized healthcare professional data including assessment, diagnosis, treatment, intervention, prognosis, homework assignment, theoretical orientation, efficacy rating, discipline, and demographic data to suggest continuing or discontinuing treatment as well as discharge prognosis.

16. A method of providing psychotherapy customized to a patient at a distance, including the steps of:

entering by the patient information about themself into a computing system chosen from the group consisting of symptom data, demographic data, genetic data, diagnostic data, treatment data, history data, outcome data, wearable data, mobile device data, augmented reality device data, social network data, and combinations thereof;

storing the patient information in a database in a vectorized form;

analyzing by the computing system the vectorized patient information via low-rank matrix factorization and suggesting at least one healthcare professional to be used;

selecting by the patient a healthcare professional;

suggesting by the computing system intervention processes to the healthcare professional for the patient based on the patient information, healthcare professional demographic and theoretical orientation data, and vectorized data from previous patients analyzed via low-rank matrix factorization;

creating by the healthcare professional an initial plan of service for the patient based on the suggestions of the system and entering the plan into the system;

connecting the patient to the healthcare professional via real-time image and audio on the computing system to provide in-session therapeutic intervention process based on the initial plan of service;

augmenting the healthcare professional'"'"'s in-session therapeutic intervention process with real-time data displays about the patient'"'"'s mood derived from real-time analysis of the patient'"'"'s speech including tone analysis, and facial expressions including microexpressions, by providing said real-time data to the healthcare professional during the in-session therapeutic intervention process, the healthcare professional providing the service for the patient over the computing system chosen from the group consisting of consultation, education, assessment, diagnosis, intervention, treatment, and combinations thereof;

analyzing by the computing system feedback from the patient in real-time and suggesting by the computing system revisions to the initial plan of service during the in-session therapeutic intervention process, and

pushing by the computing system clinical homework activities assigned by the healthcare professional during the in-session therapeutic intervention process to at least one patient device.