Patent Application Titled “Treatment And Diagnoses Of Disease And Maladies Using Remote Monitoring, Data Analytics, And Therapies” Published Online (USPTO 20220400989): Patent Application

2023 JAN 05 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor Myers, Steven F. (Rockville, MD, US), filed on June 16, 2022, was made available online on December 22, 2022.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “Despite advances in prevention, treatment, and management of diseases and maladies, racial disparities persist. For example, for cardiovascular diseases (CVD), the CVD accounts for one-third of differences in mortality rates in racial minorities compared to whites. The non-whites experience the highest mortality rates. Further complicating CVD outcomes, anxiety and depression often co-occur with and are known risk factors for the CVD, doubling the chances a patient will die from the CVD. Evidence shows that the CVD patients with untreated anxiety and/or depression experience poorer outcomes, including hospitalizations because of major adverse cardiovascular events (MACEs). For instance, increased prevalence of depression in patients with CVD is associated with a significant increase in hospitalization and death. Such phenomena have been explained by the fact that several pathological mechanisms underlying depression and anxiety are in line with pathological mechanisms of the CVD, such as elevated platelet activities, autonomic and immune dysregulation, elevated inflammatory process, metabolic abnormalities, and accumulation of oxidative stress.

“It is not known if the link between CVD and depression/anxiety is independent of comorbid medical diseases, or if these associations depend on race and ethnicity. From a treatment perspective, antidepressants have not been shown to be effective for preventing MACE’s in the CVD patients with depression/anxiety. Innovative treatments are needed, especially so, in non-whites with the goal of improving access and reducing barriers to care. Evidence-based telemedicine tools have shown efficacy when used alone and could be integrated to improve outcomes for patients with the CVD and depression/anxiety, with an emphasis on interventions designed to address barriers to care typically experienced by racially diverse, non-white patients.

“Accordingly, a system and method for prediction and diagnosis of depression and/or anxiety in non-white CVD patients using an artificial intelligence-based model are desired. This novel system and method have various applications for prediction and diagnosis of various diseases and maladies for all patient and consumer populations using unique artificial intelligence-based models and neural network algorithms.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor’s summary information for this patent application: “This disclosure relates to treatment and diagnoses of disease and maladies using remote monitoring, data analytics, and behavioral therapies. Remote monitoring of a patient can involve a distinct video-based vital sign capture capability, which utilizes a mobile device (having a digital camera embedded therein) as an optical sensor that can measure vital signs of the patient. Video-based vital sign capture can involve capturing video imaging data of one or more body areas of a patient, wherein the video imaging data is captured using a mobile device. Further, analyzing the video imaging data using one or more optical analysis techniques, where these techniques include color-based analysis and/or motion-based analysis of the video imaging data. Additionally, generating one or more waveforms based on the one more optical analysis techniques, wherein the one or more waveforms indicate physiological changes associated with each of the one or more body areas of the patient to represent vital signs of the patient. The measurements of the vital signs of the patient are determined based on the generated one or more waveforms.

“In one or more instances, this disclosure may relate to minority health issues. In at least one instance, this disclosure may relate to the use of remote monitoring, statistical models, behavioral therapies/analysis, and/or data analytics of various populations (including minority and/or other underserved populations). In at least on instance, this disclosure may relate to cardiovascular patients and the use of remote monitoring, statistical models, behavioral analysis, data analytics for prediction and diagnosis of anxiety and depression using an artificial intelligence-based model. More specifically, this may be used with regards to treatment and diagnoses of anxiety and depression in cardiovascular patients among other maladies and diseases.

“A variety of predictive analytics can be run using various statistical models from data collected from IoT devices (e.g., wearable devices, electronic tattoos, medical diagnostic devices, MRI, CT Scanners, Ultrasound, etc.). For example, while implantable cardiac sensors have been effective at reducing hospitalization for heart failure, wearable technology and noninvasive approaches including remote monitoring may be used with the predictive analytics system to predict heart failure rehospitalization. This system may utilize multi-sensor noninvasive remote monitoring for prediction of heart failure exacerbation. In one or more instances this novel remote monitoring system for diagnoses and predictive health outcomes can be used with various IoT devices (e.g., wearable devices, electronic tattoos, medical diagnostic devices, MRI, CT Scanners, Ultrasound, etc.) to predict mortality, readmission, and/or emergency department visits.

“In one or more instances this novel remote monitoring system for diagnoses and predictive health outcomes can be used with various IoT devices (e.g., wearable devices, electronic tattoos, medical diagnostic devices, MRI, CT Scanners, Ultrasound, etc.) to improve quality of care, quality of life, minimize unnecessary invasive surgeries, prevent complications, aid in falls prevention, treat life-threatening situations, provide for urgent care interventions for certain categories of chronic patients. In one or more instances this novel remote monitoring system for diagnoses and predictive health outcomes can be used with various IoT devices (e.g., wearable devices, electronic tattoos, medical diagnostic devices, MRI, CT Scanners, Ultrasound, etc.) to improve the quality of home care, nursing care treatment, provide various means for quality control of health professionals, provide either real-time and/or continuous monitoring of patients with chronic conditions.

“For the purposes of example and to give a detailed description for the following disclosure, the case of utilizing this unique system and method for prediction and diagnosis of diseases and maladies for patient populations using unique artificial intelligence-based models and neural network algorithms and will be demonstrated using the example cardiovascular disease (CVD) patients from a non-white or underserved, minority community. As such, what follows is a description of a system and method for prediction and diagnosis of depression and/or anxiety in non-white CVD patients using an artificial intelligence-based model including neural network algorithms.

“Cognitive behavioral therapy (CBT, known as iCBT when delivered remotely via the Internet) reduces anxiety and depression in CVD patients. Despite this evidence, the CBT is not generally incorporated into clinical management of the CVD patients with anxiety and/or depression. This gap is likely due to issues such as a fragmented health care system, lack of integrated technology solutions, lack of health insurance, and shortage of mental health professional capacity-particularly during the pandemic-that often disproportionately impacts access to mental health services for non-white, marginalized individuals.

“Remote patient monitoring (RPM)-also delivered via the internet-is a standard of care for the CVD patients with recent hospitalization. The RPM is a digital intervention that has shown to improve clinical management and outcomes for the CVD patients, and therefore represents a vehicle onto which can be layered additional interventions such as iCBT. The RPM may include activity from wearable devices. Virtual visits, relied on during the COVID-19 pandemic, remove care barriers for vulnerable patients and have been shown to be preferred by patients with depression and anxiety. Although racial disparities persist in the use and outcomes of telemedicine, research suggests telemedicine interventions designed with input from non-white patients can reduce racial disparities. To date, only one study examined the prevalence of depression and/or anxiety by race/ethnicity in CVD patients. None have examined feasibility of combining iCBT, RPM, activity from wearable devices (i.e., sleep; steps; temperature; respiration; heart rate variability; resting heart rate; floors climbed; heart rhythm assessment; oxygen saturation; stress management tools EDA sensor; heart rhythm assessment) and virtual visits into a single intervention to improve outcomes.

“As discussed above, persistent racial disparities remain for patients with the CVD and are exacerbated with co-occurring depression and/or anxiety. Little is known about racial differences with regard to prevalence of depression and anxiety among CVD patients. It is also not known whether a link between CVD and depression/anxiety is independent of race or comorbidity. Furthermore, it is unclear which effective treatments may improve outcomes for particular types of population. Currently, there is no method or system that can implement and disseminate an evidence-based intervention that improves outcomes for all CVD patients and reduces health disparities for non-white CVD patients with depression/anxiety.

“A diagnostics server may be connected over a network to a data server that may host medical data silos. The diagnostics server may be connected to remote users (such as doctors or patients) over the network. The diagnostics server may be connected to AI machine learning systems. The diagnostics server may provide training data from a local data source or form a blockchain ledger to train the models of the AI machine learning systems. The AI machine learning system(s) may be trained with the output of the data source, neural networks or the blockchain. One example embodiment provides a processor and memory of a diagnostics server, wherein the processor is configured to execute instructions to provide data to the AI system and to process the predicted diagnosis data. The present disclosure may provide an AI remote patient monitoring system configured to execute instructions to provide data to the AI system and to process the predicted diagnosis data. The present disclosure may provide an AI remote patient monitoring platform configured to execute instructions to provide data to the AI system and to process the predicted diagnosis data. The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in, the context of methods, techniques, and systems for providing AI assisted remote patient monitoring, data collection, data science and analysis. While the context of the present disclosure includes a focus on data collection and treatment of underrepresented minorities and non-white CVD patients, the applications of the technological functions described herein are not limited to that patient group, focus, or data set. There are many other applications including but not limited to economic and commercial applications of AI assisted remote consumer monitoring including data collection, data science and analysis.”

The claims supplied by the inventors are:

“1. (canceled)

“2. (canceled)

“3. (canceled)

“4. (canceled)

“5. (canceled)

“6. (canceled)

“7. (canceled)

“8. (canceled)

“9. (canceled)

“10. (canceled)

“11. (canceled)

“12. (canceled)

“13. (canceled)

“14. (canceled)

“15. (canceled)

“16. (canceled)

“17. (canceled)

“18. (canceled)

“19. (canceled)

“20. (canceled)

“21. A system, comprising: an Internet-based cognitive behavioral therapy (iCBT) system collecting patient data associated with assessment and screening of a patient; a remote patient monitoring system collecting patient data associated with remote and real-time monitoring of the patient; an artificial intelligent (AI) machine learning system collecting patient data associated with health diagnosis and predictions for the patient; and an integrated system receiving the patient from the iCBT system, the patient data from the remote patient monitoring system, and the patient data from the AI machine learning system, and analyzing the combination of the patient data to enable evidence-based interventions, diagnoses, and predictive health for the patient.

“22. The system of claim 21, wherein the remote patient monitoring system comprises: a plurality of communication points comprising one or more of: an emergency response service, biometric monitoring; video communication, interactive voice response (IVR), medical monitoring, clinical telecare, and Internet of Things (IoT) devices.

“23. The system of claim 21, wherein the patient is non-white and is associated with cardiovascular disease (CVD) and depression/anxiety.

“24. The system of claim 23, wherein the patient data associated with assessment and screening of the patient comprises digital screening of the patient for depression/anxiety using Generalized Anxiety Disorder 7 (GAD-7) and Patient Health Questionnaire 9 (PHQ-9) screening tools.

“25. The system of claim 24, wherein the iCBT system refers the patient to a full complement of functionalities of the remote patient monitoring system upon screening positive for depression/anxiety based on the GAD-7 and PHQ-9 screening tools.

“26. The system of claim 25, wherein the functionalities of the remote patient monitoring system comprise: video-based vital sign capture; diagnosis of health associated with the patient predictive health outcomes associated with the patient and employing one or more IoT devices.

“27. The system of claim 26, wherein employing the one or more IoT devices enables additional functionalities of the remote patient monitoring system, the additional functionalities comprising: improving quality of care; minimization of invasive surgeries; complication prevention; fall prevention; treating life-threatening situation prevention; urgent care interventions; improving quality of home care; improving nursing care treatment quality control of health professionals; real-time monitoring of chronic conditions associated with the patient and continuous monitoring of chronic conditions associated with the patient.

“28. The system of claim 21, wherein the integrated system generates treatment and diagnoses of disease and maladies of the patient using remote monitoring, data analytics, and therapies.

“29. The system of claim 28, wherein the data analytics is associated with various populations comprising one or more of: non-white populations; minority populations; and underserved populations.

“30. The system of claim 21, further comprising: a diagnostic server connected to the AI machine learning system via a network, the diagnostic server collecting data from one or more medical data silos; and remote users connected to the diagnostic server via the network, wherein the remote users comprise doctors and patients, wherein the AI machine learning system receives the collected data as training data, trains machine learning models using the training data to generate the patient data associated with health diagnosis and predictions for the patient, and provides AI assisted remote patient monitoring, data collection, and data analysis.

“31. The system of claim 30, wherein the diagnostic server collects training data from a ledger of a blockchain to train the machine learning models.

“32. The system of claim 31, wherein the collected data is stored in the blockchain based on a consensus mechanism ensuring that the collected data is verified and accurate.

“33. The system of claim 30, wherein the collected data comprises one or more of: patient medical data; historical data; patient parameters; race; and previous diagnosis.

“34. The system of claim 31, wherein the system comprises Internet of Things (IoT) devices writing records related to the patient directly to the blockchain.

“35. The system of claim 30, wherein the machine learning models predict or diagnose the health of the patient that is associated with one or more of: depression/anxiety; mortality; readmission; and emergency department visits.

“36. The system of claim 30, wherein the patient is a cardiovascular diseases (CVD) patient.

“37. A system comprising: an Internet-based cognitive behavioral therapy (iCBT) system collecting patient data associated with assessment and screening of a patient a remote patient monitoring system collecting patient data associated with remote and real-time monitoring of the patient, wherein the remote and real-time monitoring comprises vital sign measurements, and further wherein the remote patient monitoring system comprises: one or more data collection devices for obtaining vital sign measurements of the patient, wherein the one or more data collection devices comprises wearable devices; a mobile device for obtaining the vital sign measurements of the patient using a video-based vital sign capture; and a computer device communicatively connected to the one or more data collection devices and the mobile device to receive the obtained vital sign measurements of the patient, wherein the computer device enables the remote and real-time monitoring of the patient based on the received vital sign measurements of the patient an artificial intelligent (AI) machine learning system collecting patient data associated with health diagnosis and predictions for the patient and an integrated system receiving the patient from the iCBT system, the patient data from the remote patient monitoring system, and the patient data from the AI machine learning system, and analyzing the combination of the patient data to enable evidence-based interventions, diagnoses, and predictive health for the patient.

“38. The system of claim 37, wherein the mobile device comprises a digital camera capturing video imaging data of the body of the patient, and analyzes the video imaging data using one or more optical analysis techniques to obtain the vital sign measurements of the patient using the video-based vital sign capture.

“39. The system of claim 38, wherein the vital signs measurements of the patient obtained by the wearable devices and the mobile device comprise one or more of: heart rate; blood pressure; oxygen saturation (e.g., SpO₂); body temperature; pulse rate; respiration rate; and measurements of bodily functions monitored by medical professionals.

“40. A system, comprising: an Internet-based cognitive behavioral therapy (iCBT) system collecting patient data associated with assessment and screening of a patient a remote patient monitoring system collecting patient data associated with remote and real-time monitoring of the patient, wherein the remote and real-time monitoring comprises vital sign measurements, and further wherein the remote patient monitoring system comprises: one or more data collection devices for obtaining vital sign measurements of the patient, wherein the one or more data collection devices comprises wearable devices; a mobile device for obtaining the vital sign measurements of the patient using a video-based vital sign capture; and a computer device communicatively connected to the one or more data collection devices and the mobile device to receive the obtained vital sign measurements of the patient, wherein the computer device enables the remote and real-time monitoring of the patient based on the received vital sign measurements of the patient an artificial intelligent (AI) machine learning system collecting patient data associated with health diagnosis and predictions for the patient and providing AI assisted remote patient monitoring, data collection, and data analysis, the AI machine learning system comprising: a diagnostic server connected to the AI machine learning system via a network, the diagnostic server collecting data from one or more medical data silos; and remote users connected to the diagnostic server via the network, wherein the remote users comprise doctors and patients, and wherein the AI machine learning system receives the collected data as training data, and trains machine learning models using the training data to generate the patient data associated with health diagnosis and predictions for the patient and an integrated system receiving the patient from the iCBT system, the patient data from the remote patient monitoring system, and the patient data from the AI machine learning system, and analyzing the combination of the patient data to enable evidence-based interventions, diagnoses, and predictive health for the patient.”

For more information, see this patent application: Myers, Steven F. Treatment And Diagnoses Of Disease And Maladies Using Remote Monitoring, Data Analytics, And Therapies. U.S. Patent Application Number 20220400989, filed June 16, 2022 and posted December 22, 2022. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20220400989)&db=US-PGPUB&type=ids

(Our reports deliver fact-based news of research and discoveries from around the world.)