WO2025181760A1

WO2025181760A1 - System and method for monitoring health of user

Info

Publication number: WO2025181760A1
Application number: PCT/IB2025/052207
Authority: WO
Inventors: Chindu KABIR; Marlene LOBO
Original assignee: Post OP Ltd
Current assignee: Post OP Ltd
Priority date: 2024-03-01
Filing date: 2025-02-28
Publication date: 2025-09-04
Anticipated expiration: 2026-09-01
Also published as: GB202403004D0

Abstract

Disclosed is a system (100) for monitoring health of a user. The system comprises a software application comprising a user interface (104) and a processing arrangement (106). The processing arrangement is configured to: receive a physiological data from a wearable device (108), communicably coupled to the processing arrangement, and a comprehensive health data from the user interface; combine the physiological data and the comprehensive health data into a unified dataset; analyse the unified dataset using an artificial intelligence (AI) algorithm; generate a predictive health report based on the analysis by the AI algorithm, the predictive health report comprising predictive health insights; and transmit, via the user interface, the predictive health report in real time. Moreover, the system continuously updates and refines the predictive health insights based on the received physiological data and the comprehensive health data.

Description

SYSTEM AND METHOD FOR. MONITORING HEALTH OF USER

TECHNICAL FIELD

The present disclosure relates to systems for monitoring health of users. Moreover, the present disclosure relates to methods for monitoring health of users. Furthermore, the present disclosure relates to non-transitory computer-readable storage mediums having computer-readable instructions stored thereon, that being executable by a computerized device comprising processing hardware to execute the aforementioned methods.

BACKGROUND

Digital health and predictive analytics have gained significant attention in recent years as tools for improving patient care, particularly in remote and home-based health monitoring. Traditional healthcare models primarily rely on periodic clinical visits and subjective self-reporting, often missing critical early signs of health deterioration.

Existing remote monitoring solutions have attempted to address this challenge through various approaches, including standalone Electronic patient recorded outcome measures (ePROM) platforms, wearable device-based tracking systems, and Al-driven predictive models. Studies such as the PRE-MACE initiative have demonstrated the feasibility of integrating biometrics, biomarkers, and patient-reported data for health event prediction. Similarly, advances in digital medicine have enabled continuous monitoring using wearable biosensors and mobile applications. However, these solutions often face critical limitations including limited predictive accuracy due to small sample sizes, and incomplete clinical profiling. Additionally, many current systems lack real-time analytics and early warning capabilities, reducing their effectiveness in pre-emptive health intervention.

Therefore, in light of the foregoing discussion, there exists a need to overcome the aforementioned drawbacks.

SUMMARY

The aim of the present disclosure is to provide a system and a method for integrated health monitoring that leverages subjective electronic patient-reported outcome measures (ePROMs) data combined with objective data from wearable devices, to facilitate a comprehensive view of a patient's health status. The aim of the present disclosure is achieved by a system and a method for monitoring health of a user as defined in the appended independent claims to which reference is made to. Advantageous features are set out in the appended dependent claims.

Throughout the description and claims of this specification, the words "comprise" , "include", "have", and "contain" and variations of these words, for example "comprising" and "comprises" , mean "including but not limited to", and do not exclude other components, items, integers or steps not explicitly disclosed also to be present. Moreover, the singular encompasses the plural unless the context otherwise requires. In particular, where the indefinite article is used, the specification is to be understood as contemplating plurality as well as singularity, unless the context requires otherwise.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a system for monitoring health of a user, in accordance with an embodiment of the present disclosure; and FIG. 2 is an illustration of a flowchart depicting steps of a method for monitoring health of a user, in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognize that other embodiments for carrying out or practising the present disclosure are also possible.

In a first aspect, the present disclosure provides a system for monitoring health of a user, the system comprising a software application comprising a user interface and a processing arrangement, the processing arrangement configured to: receive a physiological data from a wearable device, communicably coupled to the processing arrangement, and a comprehensive health data from the user interface; combine the physiological data and the comprehensive health data into a unified dataset; analyse the unified dataset using an artificial intelligence (Al) algorithm; generate a predictive health report based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights; and transmit, via the user interface, the predictive health report in real time, wherein the system continuously updates and refines the predictive health insights based on the received physiological data and the comprehensive health data. In a second aspect, the present disclosure provides a method for monitoring health of a user, the method comprising: receiving a physiological data from a wearable device, communicably coupled to the processing arrangement, and a comprehensive health data from a user interface associated with the software application run on a user device; combining the physiological data and the comprehensive health data into a unified dataset; analysing the unified dataset using an artificial intelligence (Al) algorithm; generating a predictive health report based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights; and transmitting, via the user interface, the predictive health report in real time, wherein the method comprises continuously updating and refining of the predictive health insights based on the received physiological data and the comprehensive health data.

The disclosed system and method provide a significant advancement in remote health monitoring by effectively integrating physiological data from a wearable device with comprehensive health data through a software application into a unified dataset, to provide more comprehensive and accurate assessment of a user's health status. Specifically, by utilizing an artificial intelligence (Al) algorithm, the system enhances predictive capabilities, overcoming the challenge of limited sample sizes and incomplete clinical profiling in prior solutions. The Al-driven analysis continuously updates and refines predictive health insights in real time, ensuring that the generated predictive health reports reflect the most current patient data. This dynamic approach significantly improves early detection of potential health issues, thereby enabling timely intervention.

The real-time transmission of predictive health reports through the user interface further ensures immediate access to critical health insights. This feature addresses integration challenges faced by prior solutions, as it allows for seamless communication between users and healthcare providers. Additionally, by maintaining continuous data refinement, the system enhances the reliability and accuracy of health predictions. Hence, the disclosed system and method provides a robust, Al-driven predictive health monitoring system that overcomes the limitations of fragmented data analysis, enhances predictive accuracy, and ensures real-time, actionable insights— ultimately improving patient outcomes and reducing healthcare burdens.

In a third aspect, the present disclosure provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a computerized device comprising processing hardware to execute the aforementioned method of the second aspect.

Throughout the present disclosure, the phrase "system for monitoring health" or "system" refers to a comprehensive arrangement designed to facilitate the assessment and management of health of a person or a patient over a predefined period of time. The system integrates various components, including a user device, a wearable device, a software application, and a processing arrangement, to capture, analyse, and report health-related data. The system is configured to utilize artificial intelligence (Al) algorithms to process health-related data obtained from distinct sources, namely the objective physiological data and the subjective comprehensive health data, and generate the predictive health report. The system is further capable of transmitting the predictive health report to relevant stakeholders, such as healthcare professionals, for subsequent analysis and decision-making. The healthcare professional refers to the medical practitioner who reviews the predictive health report and provides treatment or care recommendations. The system supports healthcare professionals by providing accurate and timely health assessments.

Herein, the term "user" refers to an individual whose health is being monitored by the system. The user also wears the wearable device, which collects physiological data related to the user's health. However, the user may be a caregiver of the patient, in case the patient is incapacitated. However, in such cases the patient is required to wear the wearable device, and the caregiver is authorized to provide, via the user interface, the information on behalf of the patient or by carefully monitoring the patient (such as the incapacitated patient).

The term "health" refers to the physiological and psychological state of a user, which is monitored and assessed by the system. Health is represented by measurable physiological data, such as heart rate, blood pressure, oxygen saturation, activity levels, sleep patterns, ECG, and blood glucose concentration, as well as subjective comprehensive health data, such as symptoms and signs associated with a medical condition. Additionally, health is represented by additional factors such as clinical data associated with the patient, such as Radiographs (X-rays), diagnostic scans (including ultrasound , CT, MRI, PET scans). Moreover, in addition to the clinical data, patient demographic data, environmental data (surrounding temperature, altitude, etc) also contribute to the overall evaluation of health of a patient or a group of people in a society.

Health is the primary focus of the system for monitoring health, and the system is designed to provide insights and recommendations to improve or maintain the health of the user. Herein, the health is generally associated with a patient or a subject complaining of a certain symptom associated with a medical potential condition for which he/she is not yet diagnosed. Optionally, health may be associated with an overall well-being of an individual, and comprise physical health (physiological state of the body), mental health (psychological and emotional well-being), emotional health (feelings), social health (meaningful relationships and interacting effectively within a community), post-surgical or recovery health (healing of a wound, infections, and so on), chronic disease health (long-term conditions such as diabetes, hypertension, or cardiovascular diseases), preventive health (potential health risks before they develop into serious conditions), lifestyle health (daily habits, such as diet, exercise, and sleep), environmental health (external factors, such as living conditions and exposure to pollutants), and community health (well-being of groups or populations rather than individuals).

The system comprises the software application comprising the user interface and the processing arrangement. The term "software application" is a digital platform within the system that facilitates the interaction between the user and the system for monitoring the health of the user or patient. Moreover, the software application also enables the synchronization of physiological data from the wearable device with the system.

In an embodiment, the system further comprises a user device configured to run the software application thereon, wherein the user interacts with the system via the user interface hosted on the software application. Typically, the software application runs on a user device that facilitates interaction between the user and the system for monitoring health. The user interface is a component of the software application that facilitates interaction between the user and the system for monitoring health of the user. The user interface is configured to receive inputs and transmit output. In this regard, the user interface receives user inputs, comprehensive health data, such as symptoms, signs, and multimedia indicative of a medical condition. The user interface ensures that the system is user-friendly and accessible to both patients and healthcare professionals.

In an embodiment, the system further comprises the wearable device that is configured to be worn by the user to measure physiological data of the user, wherein the wearable device comprises a sensor arrangement configured to measure the physiological data selected from a group comprising at least one of: heart rate, blood pressure, oxygen saturation, activity levels, sleep patterns, ECG, blood glucose concentration. The term "wearable device" refers to a device that is equipped with a sensor arrangement to collect physiological data of the patient. The wearable device is communicably coupled to the user device running the software application, enabling the transmission of physiological data to the processing arrangement. The term "physiological data" refers to objective health-related metrics collected by the wearable device. Physiological data includes measurable parameters such as heart rate, blood pressure, oxygen saturation, activity levels, sleep patterns, ECG, and blood glucose concentration.

The processing arrangement is a computational component of the software application that is configured to analyse the input received via the user interface as well as the wearable device using an artificial intelligence algorithm. The term "processing arrangement" refers to programmable and/or non-programmable components configured to execute one or more software applications for storing, processing, sharing data and/or set of instructions. It will be appreciated that the "processing arrangement" refers to "one processing arrangement" in some implementations, and "a plurality of processing arrangements" in other implementations. In cases where the processing arrangement is a plurality of processing arrangements, the plurality of processing arrangements is communicably coupled to each other via a communication network. Optionally, the processing arrangement is a set of one or more hardware components or a multi-processor system, depending on a particular implementation. More optionally, the processing arrangement includes, for example, a component included within an electronic communications network. Additionally, the processing arrangement includes one or more data processing facilities for storing, processing, sharing data and/or a set of instructions. Optionally, the processing arrangement includes functional components, for example, a processor, a memory, a network adapter and so forth.

Throughout the present disclosure, the term "communication network" refers to a network of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, such as the wearable device, user device, multimedia unit and the processing arrangement or software application, whether available or known at the time of filing or as later developed. Furthermore, the communication network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations. Furthermore, the processing arrangement facilitates communication between the multimedia unit or user device and the software application over the communication network, likely to execute tasks related to the system's function.

The processing arrangement is configured to receive the physiological data from the wearable device, communicably coupled to the processing arrangement, and the comprehensive health data from the user interface. The term "comprehensive health data" refers to subjective health-related information obtained through the user interface. Herein, said subjective health-related information comprises structured and unstructured data types that contribute to a holistic understanding of a patient's health. Typically, the comprehensive health data comprises clinical data and health data. The health data includes patient demographic data, environmental data, and other contextual information that may influence health outcomes. Typically, the comprehensive health data may be provided by the user, received from a storage unit, or both. In an example, the user may submit their self-assessments, namely, user-assessment data, via ePROM. In another example, the system may integrate with patient health databases (such as electronic health records (EHRs)) to obtain a wide range of clinical data.

Optionally, the clinical data comprises medical history, diagnoses, medications, allergies, immunization records, laboratory results, radiology/diagnostic images (e.g., X-rays, CT scans, MRIs, PET scans, ultrasound), vital signs, clinical notes, pathological data (e.g., blood tests, pathology reports), and so on. Beneficially, the clinical data provides a detailed medical background and context for the patient's current health status. It is essential for understanding chronic conditions, past treatments, and ongoing medical needs.

Optionally, the health data comprises demographics (age, gender, etc.), environmental data and contextual data that may influence health outcomes. Herein, the environmental data includes information about the patient's surroundings, such as temperature, altitude, air quality, and other environmental factors. In an example, altitude can affect oxygen saturation levels, and air quality can influence respiratory conditions. Optionally, the environmental data may be automatically gathered from the user's smartphone sensors (e.g., GPS for altitude) or external APIs (e.g., weather data). Optionally, data from smartphone may also include accelerometer readings, step counts, and other activity metrics, similar to the wearable device data. Moreover, data from smartphones may also provide metadata such as timestamps, geolocation, and device usage patterns. Beneficially, metadata helps in tracking trends and patterns over time, ensuring that the algorithm has a complete picture of the patient's health and activity levels.

The contextual data may include subjective information, namely, user-assessment data, provided by the patient or their caregiver (collectively, user), that includes symptoms, signs, and responses to electronic patient-reported outcome measures (ePROMs). Optionally, the contextual data may also include multimedia inputs like images, videos, or audio recordings indicative of a medical condition, as well as responses to electronic patient-reported outcome measures (ePROMs).

In other words, data collected from EHRs (which can include demographics, medical history, diagnoses, medications, allergies, immunisation records, laboratory results, radiology images, vital signs, clinical notes) and any records with regards to their health collected from care homes if they are in care homes, along with the ePROMS, metadata, and data collected via wearable devices, or objective information submitted by patient or a carer/ca regiver thereof such as their Blood pressure, heart rate etc, if they do not have a wearable device and may need to manually input it into the software application, are used to capture the overall health status of the patient.

Herein, both the wearable device and the user device are communicably coupled to the processing arrangement. This connection enables the transmission of physiological data and comprehensive health data to the processing arrangement for analysis.

Moreover, the processing arrangement is configured to combine the physiological data and the comprehensive health data into a unified dataset. In this regard, the processing arrangement merges the objective physiological data and the subjective comprehensive health data into a single dataset for analysis. This integration ensures that the subjective and objective aspects of a user's health are considered together, providing a more comprehensive and accurate representation of the user's overall health status. It may be appreciated that by combining the datasets, the system can identify patterns that might be missed when the data is analysed separately, leading to more accurate and reliable predictive health insights. For example, a sudden increase in heart rate (objective data) combined with a user-reported symptom of chest pain (subjective data) can provide a stronger indication of a potential cardiac issue than analysing either dataset independently.

Moreover, the unified dataset allows the Al algorithm to learn complex relationships between the subjective and objective health indicators, improving the quality of the predictive health insights and early warnings. On the contrary, two distinct datasets require independent analysis, which may lead to inefficiencies and a lack of synergy in the insights generated. Furthermore, the unified dataset simplifies the data processing workflow by eliminating the need for separate analysis pipelines for the objective and subjective data, thereby, reducing computational overhead and ensuring that the system operates efficiently in real time.

Furthermore, the processing arrangement is configured to analyse the unified dataset using the artificial intelligence (Al) algorithm, and generate the predictive health report based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights. The unified dataset serves as the input for the Al algorithm to generate predictive health insights. Beneficially, the unified dataset is processed as a single entity, allowing the Al algorithm to analyse correlations, patterns, and trends across both data types simultaneously. The Al algorithm analyses the unified dataset to generate predictive health insights, which are used to provide recommendations, guidance, and alert notifications. Optionally, the Al algorithm analyses the unified dataset using advanced techniques such as stochastic regression analysis and deep learning.

The term "predictive health report" refers to a comprehensive document generated as an actionable output by the Al algorithm, which includes actionable insights and recommendations for managing the user's health. The predictive health report provides users and healthcare providers with valuable information for decision-making. The predictive health report is designed to be transmitted in real time to relevant stakeholders, such as the user, healthcare providers, or telehealth platforms, enabling timely intervention and proactive health management.

Herein, the predictive health insights are actionable insights derived from the analysis of the unified dataset, which provide information about the user's current health status and potential future health risks. For example, early warnings about potential complications, such as infection, abnormal physiological trends, or mental health decline. In other words, the predictive health insights form the basis of the predictive health report. Moreover, the predictive health report includes identifying patterns that indicate deteriorating health conditions, such as a combination of elevated heart rate and user-reported chest pain. Furthermore, the predictive health report includes recommendations or suggestions for managing symptoms, improving lifestyle, or seeking medical attention based on the predictive health insights.

Beneficially, the predictive health report provides a holistic view of the user's health; and allows identifying early signs of health deterioration, enabling timely intervention. Moreover, the predictive health insights are tailored to the user's specific health condition, symptoms, and physiological data, making the recommendations more relevant and actionable. Furthermore, the processing arrangement is further configured to transmit, via the user interface, the predictive health report in real time. It may be appreciated that the predictive health report is transmitted over the communication network existing between the various components of the system, namely, processing arrangement, user interface, and user device. Optionally, the predictive health report is transmitted over a secure cloud server infrastructure. Beneficially, the transmission capability of the system is essential for enabling remote monitoring and telehealth applications. The term "real time" refers to the immediate transmission of the predictive health report, after the analysis of the unified dataset. The predictive health report is presented, via the user interface, on a display of the user device in a clear and intuitive format, such as graphs, charts, or dashboards, to help users understand trends and insights. Moreover, the predictive health report can be accessed on various implementations of the user devices, such as smartphones, tablets, or computers, ensuring convenience and flexibility.

Beneficially, the real time transmission of the predictive health report ensures that users and healthcare providers receive the most up-to-date information about the user's health. Moreover, real time updates empower users to take proactive steps in managing their health, such as adjusting their lifestyle or seeking medical advice. In an example, the real time transmission of the predictive health report enables a patient to receive a notification on their smartphone about an elevated risk of infection based on their reported symptoms and wearable data, and at the same time, a healthcare provider might view a detailed dashboard on their computer, showing the patient's health trends and Al-generated insights. Moreover, the real time transmission of the predictive health report allows timely intervention, consequently preventing serious complications, and hospital admission, effectively reducing hospital costs, and load on emergency medical services. In an embodiment, the user interface is configured to: receive inputs associated with at least one of: multimedia indicative of the medical condition of the user, comprehensive health data comprising at least one of: symptoms and signs associated with a medical condition of the user experienced by the user, physical condition, and customisation of one or more analysis parameters based on at least one of: the medical condition, user needs, healthcare provider needs; and transmit at least one of: recommendations for symptoms management, diet and lifestyle, and/or physical activities, guidance for capturing and uploading multimedia, and alert notifications for potential complications.

The user interface serves as the central platform for collecting inputs (e.g., multimedia, comprehensive health data) and delivering outputs (e.g., recommendations, guidance, alerts). In this regard, the user interface is configured to allow the user to upload multimedia (e.g., images, videos, or audio) that visually or audibly represent the user's medical condition.

For example, images of photos of wounds, rashes, or other visible symptoms, videos or short clips showing the progression of a physical condition, such as swelling or restricted movement, or voice recordings describing symptoms or breathing sounds for respiratory conditions. Beneficially, the multimedia inputs are useful for tracking physical conditions, such as wound healing or skin conditions, and can complement other data types (e.g., physiological data and comprehensive health data). Moreover, the user interface is configured to collect comprehensive health data, which includes subjective inputs provided by the user. Herein, the user can report symptoms (e.g., pain, fatigue, nausea) and observable signs (e.g., swelling, redness) that they are experiencing. Moreover, the user can provide information about their overall physical state, such as mobility, energy levels, or recovery progress. Furthermore, the user interface allows customization of the system's analysis parameters based on the medical condition, for example, tailoring the analysis for post-surgical recovery, chronic disease management, or mental health monitoring. Alternatively, optionally, customization of the system's analysis parameters may be based on the user needs to adjust the system to focus on specific health concerns or preferences, such as prioritizing mental health over physical activity. Alternatively, optionally, customization of the system's analysis parameters may be aligned based on the healthcare provider's needs, such as focusing on specific metrics or generating detailed reports.

Besides receiving inputs, the user interface is configured to transmit actionable recommendations to the user based on the analysis performed by the processing arrangement of the system. Optionally, the recommendations include symptom management such as taking prescribed medications, applying topical treatments, or seeking medical attention. Optionally, the recommendations include personalized advice on dietary changes, exercise routines, or sleep habits to improve the user's health condition. Optionally, the recommendations include recommendations for specific exercises or physical therapy routines tailored to the user's condition and recovery progress. It may be appreciated that the recommendations are generated based on the predictive health insights derived from the unified dataset and are designed to empower the user to take proactive steps in managing their health.

Moreover, besides the recommendations, the user interface is configured to provide guidance to the user on how to capture and upload multimedia effectively. For example, the guidance may include instructions for: (i) capturing images under varying lighting conditions to ensure clarity, (ii) recording videos that focus on specific symptoms or conditions, or (iii) providing audio recordings, such as breathing sounds, in a quiet environment. Beneficially, the guidance for capturing and uploading multimedia ensures that the multimedia inputs are of sufficient quality suitable for analysis, enhancing the accuracy of the system's insights.

Optionally, the guidance may include, but not limited to, imaging using Al-guided positioning instructions relative to the wound, lighting recommendations (such as using natural light or the smartphone's flashlight, to avoid shadows or glare that could obscure details), focus and stability tips (to capture clear and sharp images), angle and perspective (capturing images from multiple angles or perspectives to provide a comprehensive view of the target), and uploading instructions (ensuring that the images are securely transmitted for analysis). Beneficially, guidance ensures that users, regardless of their technical proficiency, can easily capture and upload images that meet the system's requirements, enabling accurate and reliable assessments.

Furthermore, the user interface is configured to provide alert notifications to the user in real time for potential complications detected by the system. Optionally, the alerts may include warnings about deteriorating health conditions, such as signs of infection, abnormal physiological data, or worsening symptoms. Moreover, the alerts may include notifications to seek immediate medical attention if critical thresholds are exceeded (e.g., dangerously low oxygen saturation or high blood glucose levels). Alternatively, optionally, the alerts may include reminders to follow up with healthcare providers or schedule appointments. The alert notifications are transmitted in real time to ensure timely intervention and management of potential complications.

In an embodiment, the user device further comprises a multimedia unit for capturing the multimedia, over a predefined period of time, and wherein the multimedia comprises at least one of: an audio, an image, a video. The "multimedia unit" refers to the hardware and software components of the user device (e.g., smartphone, tablet, or wearable device) that enable capturing of multimedia data. The multimedia unit works in conjunction with the software application to ensure that the captured images, video or audio are suitable for analysis. The multimedia unit is essential for providing the raw data required for the processing arrangement to perform Al-driven analysis. Herein, the audio refers to sounds that may indicate a health condition, such as breathing sounds for respiratory issues, voice recordings for speech or mental health analysis, or heart sounds for cardiovascular monitoring. The images of photos provide static information of physical conditions, such as wounds, rashes, or swelling, to monitor healing or detect complications over a predefined period of time. Short video clips provide dynamic information about a condition, such as movement limitations, gait abnormalities, or visible symptoms that change over time. Beneficially, inclusion of different multimedia types ensures that the system can capture a wide range of health-related data, both static (e.g., images) and dynamic (e.g., videos and audio).

The multimedia unit may be implemented as an imaging unit, that is configured to record or upload images, or videos related to a patient's health condition. Alternatively, or additionally, the multimedia unit may be implemented as an audio unit, that is configured to record or upload audio related to a patient's health condition. It may be appreciated that such recording of the audio, image or video is repeated over a predefined period of time, namely at specific intervals or over a set duration, ensuring consistent and relevant data collection for analysis. In an example, a patient recovering from surgery might use the multimedia unit to take daily photos of a surgical wound to track healing progress. In another example, a user with a respiratory condition might record audio of their breathing sounds over a week to monitor changes.

In an embodiment, the multimedia unit is associated with any of: a mobile device, the software application, and wherein the multimedia unit is implemented as an Al-aided multimedia unit. Herein, the multimedia unit could be integrated into a mobile device, such as a smartphone or tablet, or it could be a standalone device that works in conjunction with the software application. The multimedia unit may include a mobile device camera or any other Al-aided multimedia unit capable of capturing high-resolution images, video or audio from the target area. The "mobile device" refers to the hardware platform, such as a smartphone or tablet, that hosts the software application and serves as the multimedia unit. The mobile device enables the system for monitoring health to be portable and accessible. Optionally, the mobile device is any device associated with a user that comprises an imaging unit or camera, namely, devices with a camera, and an audio or voice recorder. Herein, it may be appreciated that the mobile device is not just restricted to a smartphone or a tablet, as mentioned above.

Typically, the Al-aided multimedia unit incorporates artificial intelligence capabilities to enhance the multimedia capture process. In case of the Al-aided multimedia unit implemented as Al-aided imaging unit, such Al capabilities may include, but not limited to, features such as automatic focus adjustment, positional corrections, optimization of lighting conditions, or real-time preprocessing of the captured images to ensure high-quality and consistent results. Similarly, in case of the Al-aided multimedia unit implemented as Al-aided audio unit, such Al capabilities may include, but not limited to, features such as noise reduction and filtering, automatic gain control, positional and environmental corrections, real time preprocessing, patter detection and anomaly detection, speech and sound analysis, adaptive learning. Beneficially, said implementation ensures that the multimedia unit can adapt to varying conditions, such as different lighting or noise environments or user proficiency, to produce images, video or audios suitable for analysis. In an embodiment, the system further comprises an image processing module for maintaining image quality in varying lighting conditions, wherein the image processing module is implemented as an Al-aided image processing module. The term "image processing module" refers to a software component that is designed to enhance and maintain the quality of multimedia images or videos captured by the multimedia unit. The image processing module is responsible for correcting issues such as poor lighting, shadows, glare, or noise that may arise during image or video capture. The image processing module ensures that the multimedia images and/or videos are clear, consistent, and suitable for further analysis by the system's processing arrangement. Specifically, the Al-aided image processing modules utilize artificial intelligence techniques to enhance and optimize the captured multimedia images or videos. In this regard, the image processing module functions by analysing the captured multimedia images or videos, and applying Al-driven adjustments to correct issues such as poor lighting, shadows, glare, positioning object(s) of interest, or other inconsistencies that may arise due to environmental factors or user error during image or video capture. This ensures that the images or videos are of sufficient quality for accurate analysis by the system's processing arrangement and artificial intelligence algorithm. Beneficially, the Al-aided image processing module allows the system to dynamically adapt to different lighting conditions and user environments. For example, the Al-aided image processing modules can automatically adjust brightness, contrast, and colour balance or remove noise from the images, thus ensuring that the captured images or videos remain consistent and reliable, regardless of the conditions in which they are captured.

Furthermore, the system continuously updates and refines the predictive health insights based on the received physiological data and the comprehensive health data. Herein, continuous updating and refinement of the predictive health insights refers to the iterative process by which the Al algorithm updates the predictive health insights based on new data received from the wearable device and the user interface, ensuring that the insights remain accurate and relevant over time.

In an embodiment, the Al algorithm is trained on a dataset of medical conditions and a dataset of symptoms and signs associated with corresponding medical conditions used by deep learning techniques employed by the Al algorithm to compare the received physiological data and comprehensive health data. The Al algorithm is trained on a dataset of medical conditions and a dataset of symptoms and signs to analyse the received physiological data and comprehensive health data. The dataset of medical conditions and a dataset of symptoms and signs are the training data used by the artificial intelligence algorithm. These datasets enable the Al algorithm to learn and improve its ability to analyse the incoming multimedia and determine health condition of an individual.

The dataset of medical conditions comprises a wide range of medical conditions, such as cardiovascular diseases, respiratory illnesses, post-surgical complications, and chronic diseases like diabetes or hypertension. Each condition is associated with specific physiological patterns and symptoms. Moreover, the dataset of symptoms and signs maps symptoms (subjective experiences reported by patients, such as pain or fatigue) and signs (objective observations, such as swelling or fever) to their corresponding medical conditions. Optionally, the dataset of microbial species may also include metadata, such as the environmental conditions under which the symptoms and signs are likely to vary, to improve the algorithm's ability to identify probable causes and symptoms of a given medical condition or comorbidities associated therewith. It may be appreciated that the metadata is collected irrespective of the symptom or signs reported out. As mentioned above, the metadata provides additional data about the specific symptoms and signs associated with the medical conditions. Optionally, such metadata is obtained from published data.

Optionally, the Al algorithm is trained using deep learning techniques, which involve feeding these datasets with labelled data into neural networks so it can identify and classify specific characteristics in new, unseen data. With regards to training, the physiological data and the user assessment data are pre-processed to ensure consistency and quality. The datasets are used to recognize patterns and correlations between physiological data, symptoms, and medical conditions. Such patterns are used to train/learn how specific combinations of data points (e.g., elevated heart rate, low oxygen saturation, and user-reported fatigue) correspond to particular health conditions.

Optionally, the trained Al algorithm is validated and tested on separate datasets to evaluate its accuracy, precision, and ability to generalize to new data. By training on a dataset of medical conditions, the Al algorithm can accurately assess or predict a certain medical condition, detect complications, and provide actionable insights. For example, it can identify whether a wound is healing normally or if there are signs of delayed healing or infection. Additionally, training on a dataset of symptoms and signs enables the Al algorithm to detect and identify medical conditions and/or comorbidities associated therewith. This is particularly important for early intervention, as different symptoms may be associated with different stages of a medical condition. This holistic approach improves the accuracy and reliability of the health assessment, and provides comprehensive assessments, detect medical conditions early, and support personalized health care management.

In an embodiment, the Al algorithm employs deep learning techniques to compare the received data against the dataset of medical conditions and the dataset of symptoms and signs. The Al algorithm is developed using machine learning and deep learning techniques, or multimodal techniques, which require large datasets to "learn" patterns, features, and relationships. The deep learning techniques refer to the advanced machine learning methods employed by the artificial intelligence algorithm to analyse the incoming data. These techniques enable the Al algorithm to compare the received physiological data and the comprehensive health data against the datasets and identify signs and symptoms associated with medical conditions, and categorize such medical conditions based on their severity. Deep learning techniques, such as convolutional neural networks (CNNs), may be used. Deep learning techniques enable accurate, automated assessments and early detection of medical conditions, significantly improving the quality and efficiency of health care management.

In an embodiment, the software application is further configured to provide an interactive platform for multiple users to connect for advice and support in health monitoring and management, and wherein the interactive platform comprises at least one of: a clinician dashboard, an administrator dashboard, a hospital dashboard, a nursing dashboard, a superuser dashboard. The term "interactive platform" is a feature of the software application that enables multiple users to connect and share advice and support in health monitoring and management. The interactive platform fosters community engagement and knowledge sharing, enhancing the overall user experience. Herein, the multiple users may include, the user (patient and/or caregiver), healthcare professional (nurse or physician), superuser (administrator or a third party). Via the interactive platform, the patients can connect with healthcare professionals to receive personalized advice and recommendations. Moreover, patients can share their experiences, challenges, and tips with others who are going through similar situations, creating a sense of community and emotional support (peer support group). Furthermore, healthcare providers can exchange insights, discuss complex cases, and collaborate on best practices for health monitoring that may be accessible to all the registered stakeholders of the interactive platform. Optionally, the interactive platform leverages communication tools such as chat functionality (to engage in real-time text-based conversations with healthcare providers or other patients), video/audio calls (for consultations, enabling remote discussions about health monitoring), discussion forums (to share experiences and learn from others), and so on. Moreover, the interactive platform provides access to educational materials, such as articles, videos, and tutorials on best practices for health monitoring and maintaining; FAQs addressing common concerns about health monitoring and management; and guidelines for capturing and uploading multimedia, to registered users and other stakeholders. It may be appreciated that users and other stakeholders are required to create profiles with relevant information, such as their role (patient, caregiver, or healthcare provider), medical condition, and care needs. Additionally, healthcare providers may include their credentials and areas of expertise. Moreover, healthcare providers can invite patients to join the platform for ongoing monitoring and advice. Furthermore, healthcare providers can host virtual Q&A sessions or webinars on health care, monitoring and management topics. Moreover, healthcare providers can collaborate with colleagues to refine treatment plans or address challenging cases.

Amongst the aforementioned role-specific dashboards, the clinician dashboard is designed for doctors and healthcare providers to monitor patient health, review Al-generated insights, and make informed decisions about care. Moreover, the interactive platform for clinician dashboard provides tools for communication with patients (e.g., messaging, video consultations) as well as flag high-risk patients for immediate attention. Similarly, the administrator dashboard is designed for healthcare administrators to manage system operations, user access, and resource allocation. The administrator dashboard provides tools for (i) scheduling and resource planning (e.g., appointment booking, staff allocation), and (ii) reporting and analytics for operational efficiency. Moreover, the hospital dashboard provides a high-level overview of patient health and system performance for hospital managers and decision-makers. Such a dashboard provides aggregated data on patient outcomes, recovery rates, and system usage, as well as insights into hospital-wide trends, such as infection rates or readmission rates. Moreover, such a dashboard provides tools for managing hospital resources, such as bed availability and staff assignments and integration with hospital electronic health record (EHR.) systems for seamless data sharing. The nursing dashboard is tailored for nursing staff to monitor and manage patient care on a day-to-day basis, for real-time alerts for patients requiring immediate attention, access to patient health data and recovery progress, tools for documenting observations and updating patient records, and communication tools for coordinating with doctors and other healthcare providers. Superuser dashboard is designed for advanced users, such as system administrators or senior healthcare professionals, who require full access to the platform's features and data. Such full access to all patient data, dashboards, and system settings allows for customizing the platform (e.g., adding new features, modifying workflows), and managing and troubleshooting technical issues. The superuser dashboard provides advanced analytics and reporting tools for system-wide insights.

In an embodiment, the software application is further configured to be operated in multiple languages, and wherein the predictive health report is accessible in multiple languages. By supporting multiple languages, the software application ensures that both the user interface (III) and the predictive health report can be understood and utilized effectively by users who speak different languages and ensures accessibility for diverse users. This includes translating all aspects of the user interface and functionality into multiple languages. In this regard, users can select their preferred language during the initial setup of the application or change it later in the settings. Alternatively, the software application may automatically detect the user's language based on their device settings or location and provide a default option. In this regard, the software application leverages a GPS or related functionality. Additionally, all elements of the user interface, such as menus, buttons, instructions, and notifications, are translated into the selected language. For example, instructions for capturing multimedia (e.g., "Hold your phone steady and ensure good lighting") are displayed in the user's chosen language. The predictive health report, which is generated by the Al algorithm, is also accessible in multiple languages. This ensures that both patients and healthcare providers can understand the predictive health report, regardless of their linguistic background. For example, if the predictive health report includes a recommendation like "Signs of infection detected. Consult a healthcare provider immediately," it will be translated into the user's preferred language. Similarly, healthcare providers who speak different languages can access the report in their preferred language, ensuring clear communication and understanding of the health condition. This is particularly useful in multilingual healthcare settings or when patients and healthcare providers speak different languages.

In this regard, advanced language processing technologies and translation frameworks are employed by the software application. The multilingual translation process involves employing a database of translations for all III elements, instructions, and report content in multiple languages. This database is regularly updated to ensure accuracy and consistency. Moreover, the software application employs dynamic translation modules to dynamically translate content based on the user's selected language. Furthermore, the software application employs integration with Al-powered translation tools, such as neural machine translation (NMT) systems, to provide high-quality translations for complex or context-specific content. Furthermore, the software application leverages customization for regional variations in language. For example, English may be customized for US, UK, or Australian users. Spanish may be adapted for users in Spain, Mexico, or Latin America. Beneficially, by allowing users to operate the application and access predictive health reports in their preferred language, this feature enhances patient engagement, improves communication in multilingual healthcare settings, and promotes inclusivity.

In an embodiment, the processing arrangement is further configured to integrate with at least one of: electronic health records (EHR.), telehealth platforms, for transmitting the predictive health report with a healthcare professional for subsequent analysis. The processing arrangement generates the predictive health report based on the analysis and transmits the predictive health report to the user interface or integrated platforms, such as electronic health records or telehealth platforms, for review by healthcare professionals, facilitating further analysis, decision-making, and treatment planning.

Electronic Health Records (EHR) are digital systems used by healthcare providers to store and manage patient medical information, including medical history, diagnoses, treatments, and test results. Integration with EHR allows the predictive health report to be automatically uploaded to the patient's medical record. Optionally, the integration may use standardized protocols such as HL7 (Health Level Seven) or FHIR (Fast Healthcare Interoperability Resources) to ensure compatibility with various EHR systems.

Telehealth platforms enable remote communication between patients and healthcare providers through video calls, messaging, and data sharing. Integration with telehealth platforms allows the predictive health report to be shared with healthcare professionals during remote consultations. The integration may include real-time data sharing, allowing the provider to review it during a telehealth session. Moreover, integration may include a two-way communication, wherein the patient uploads multimedia, and the healthcare providers can send feedback, additional instructions, or treatment recommendations to the patient through the integrated platform.

In this regard, the processing arrangement uses secure communication protocols (e.g., HTTPS, TLS) to transmit the predictive health report to EHR. or telehealth systems. Optionally, data in the predictive health report is encrypted to ensure patient privacy and compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) or GDPR (General Data Protection Regulation). Notably, the system sends the predictive health report to relevant stakeholders, such as healthcare professionals, through telehealth platforms or electronic health records (EHR) systems.

It may be appreciated that the EHRs and the telehealth platforms allow sending information such as clinical data to the processing arrangement for analysis, as discussed above. In this regard, the data is transferred over a secure communication network.

Beneficially, by automating data transmission and supporting remote monitoring, the integration improves patient outcomes, enhances provider efficiency, and reduces healthcare costs.

Moreover, the processing arrangement may leverage image preprocessing techniques, namely, image normalization and colour correction techniques, to standardize the appearance of images before analysis. Furthermore, the processing arrangement may leverage advanced feature extraction methods to identify specific characteristics, such as edges and contours, changes in texture or surface irregularities, colour gradients, spikes and dips in a graph, and so on. Furthermore, the system undergoes thorough validation and testing to ensure its accuracy across different conditions, namely medical conditions and the symptoms and signs associated therewith under different environmental conditions. Such validation and testing include comparative studies to evaluate performance of the user in different environments, receiving feedback from healthcare professionals to refine the algorithm, and so on.

Beneficially, by leveraging diverse training datasets, advanced Al techniques, and image preprocessing methods, the system eliminates bias and promotes equitable healthcare outcomes.

In an embodiment, the system further comprises a memory unit to store at least one of: the captured multimedia, the predictive health report, the recommendations for health monitoring and management. The memory unit serves as a data storage component that enables the system to store and manage critical information related to monitoring the health of the user (or patient). The memory unit ensures that the system can retain and manage essential information, such as multimedia, the predictive health report, and care recommendations for health monitoring and management, for future reference, analysis, and sharing.

Optionally, the memory unit can be implemented using various storage technologies, depending on the system's design and requirements. For example, the memory unit can be implemented as a local storage (data stored directly on the users device (e.g., smartphone or tablet) or on a dedicated hardware component within the system); cloud storage (data is uploaded to a secure cloud server, enabling remote access and scalability); or hybrid storage (combination of local and cloud storage, where critical data is stored locally for quick access, and backups are maintained in the cloud). Optionally, the memory unit organizes data into structured categories, such as patient profiles, chronological records of multimedia and reports, or recommendations and alerts, to ensure efficient retrieval and management of data.

Optionally, the data stored in the memory unit is encrypted to prevent unauthorized access. The memory unit is provided with access control to restrict its access only to authorized users, such as the patient and their healthcare provider. The memory unit is compliant with data privacy regulations, such as HIPAA (Health Insurance Portability and Accountability Act) or GDPR (General Data Protection Regulation). Optionally, the system includes mechanisms for regular data backups to prevent loss due to hardware failure or other issues. Recovery protocols ensure that data can be restored in case of accidental deletion or corruption.

The present disclosure also relates to the method as described above. Various embodiments and variants disclosed above, with respect to the aforementioned system, apply mutatis mutandis to the method.

In an embodiment, the method further comprises: receiving inputs associated with at least one of: multimedia indicative of the medical condition of the user, comprehensive health data comprising at least one of: symptoms associated with a medical condition of the user experienced by the user, physical condition, and customisation of one or more analysis parameters based on at least one of: the medical condition, user needs, healthcare provider needs; and transmitting at least one of: recommendations for symptoms management, diet and lifestyle, and/or physical activities, guidance for capturing and uploading multimedia, and alert notifications for potential complications.

In an embodiment, the method further comprises capturing, via a multimedia unit of the user device, the multimedia, over a predefined period of time, and wherein the multimedia comprises at least one of: an audio, an images, a video, and wherein the multimedia unit is associated with any of: a mobile device, the software application, and wherein the multimedia unit is implemented as an Al-aided multimedia unit.

In an embodiment, the method further comprises providing an interactive platform for multiple users to connect for advice and support in health monitoring and management, and wherein the interactive platform comprises at least one of: a clinician dashboard, an administrator dashboard, a hospital dashboard, a nursing dashboard, a superuser dashboard.

In an embodiment, the method further comprises training the Al algorithm on a dataset of medical conditions and a dataset of symptoms associated with corresponding medical conditions used by deep learning techniques employed by the Al algorithm to compare the received physiological data and comprehensive health data.

In an embodiment, the method further comprises transmitting the predictive health report with a healthcare professional for subsequent analysis by integrating with at least one of: electronic health records (EHR.), telehealth platforms.

In an embodiment, the method further comprises storing in a memory unit at least one of: the captured multimedia, the predictive health report, the recommendations for health monitoring and management.

The present disclosure also relates to the non-transitory computer-readable storage medium as described above. Various embodiments and variants disclosed above, with respect to the aforementioned system and the aforementioned method, apply mutatis mutandis to the non-transitory computer-readable storage medium.

EXPERIMENTAL PART

The disclosed system and method was versatile and applicable in various settings, including hospitals for post-surgical monitoring, outpatient clinics, and at-home patient care. It was particularly beneficial for patients who have limited mobility or live in remote areas. The system was also crucial in situations like the COVID-19 pandemic, where minimizing in-person contact was vital. It enabled healthcare providers to remotely monitor wound healing, promptly identify any complications, and adjust treatment plans accordingly.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to FIG. 1, illustrated is a system 100 for monitoring health of a user, in accordance with an embodiment of the present disclosure. The system 100 comprises a software application comprising a user interface 102 and a processing arrangement 104. The processing arrangement 100 is configured to receive a physiological data from a wearable device 106, communicably coupled to the processing arrangement 104, and a comprehensive health data (comprising clinical data (electronic patient records (EHRs), clinical notes, blood test results, pathological reports, and radiographic/diagnostic scan results) and health data (comprising patient demographic data, environmental data, and other contextual information that may influence health outcomes)) from the user interface 102. Moreover, the processing arrangement 104 is configured to combine the physiological data and the comprehensive health data into a unified dataset; and analyse the unified dataset using an artificial intelligence (Al) algorithm. Furthermore, the processing arrangement 104 is configured to generate a predictive health report based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights; and transmit the predictive health report in real time. Herein, the system 100 continuously updates and refines the predictive health insights based on the received physiological data and the comprehensive health data.

The system 100 further comprises a user device 108 configured to run the software application thereon, wherein the user interacts with the system 100 via the user interface 102 hosted on the software application. As shown, the user interface 102 is hosted on a user device implemented as a mobile device. The user device 108 further comprises a multimedia unit 110 for capturing the multimedia, over a predefined period of time, and wherein the multimedia comprises at least one of: an audio, an image, a video.

The system 100 further comprises a memory unit 112 to store at least one of: the captured multimedia, the predictive health report, the recommendations for health monitoring and management.

As shown, the processing arrangement 104 is communicably coupled to the multimedia unit 110, the user interface 102, and the memory unit 112 over a communication network 114.

Referring to FIG. 2, illustrated is a method for monitoring health of a user, in accordance with an embodiment of the present disclosure. At step 202, a physiological data is received from a wearable device, communicably coupled to the processing arrangement, and a comprehensive health data (comprising clinical data (electronic patient records (EHRs), clinical notes, blood test results, pathological reports, and radiographic/diagnostic scan results) and health data (comprising patient demographic data, environmental data, and other contextual information that may influence health outcomes)) is received from a user interface associated with the software application run on a user device. At step 204, the physiological data and the comprehensive health data are combined into a unified dataset. At step 206, the unified dataset is analysed using an artificial intelligence (Al) algorithm. At step 208, a predictive health report is generated based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights. At step 210, the predictive health report is transmitted, via the user interface, in real time. Optionally, at step 212, predictive health insights are continuously updated and refined based on the received physiological data and the comprehensive health data.

Claims

1. A system for monitoring health of a user, the system comprising a software application comprising a user interface and a processing arrangement, the processing arrangement configured to: receive a physiological data from a wearable device, communicably coupled to the processing arrangement, and a comprehensive health data from the user interface; combine the physiological data and the comprehensive health data into a unified dataset; analyse the unified dataset using an artificial intelligence (Al) algorithm; generate a predictive health report based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights; and transmit, via the user interface, the predictive health report in real time, wherein the system continuously updates and refines the predictive health insights based on the received physiological data and the comprehensive health data.

2. A system of claim 1, further comprising the wearable device that is configured to be worn by the user to measure physiological data of the user, wherein the wearable device comprises a sensor arrangement configured to measure the physiological data selected from a group comprising at least one of: heart rate, blood pressure, oxygen saturation, activity levels, sleep patterns, ECG, blood glucose concentration.

3. A system of Claim 1 or 2, further comprising a user device configured to run the software application thereon, wherein the user interacts with the system via the user interface hosted on the software application.

4. A system of claim any of the preceding claims, wherein the user interface is configured to: receive inputs associated with at least one of: multimedia indicative of the medical state of the user, comprehensive health data comprising at least one of: symptoms and signs associated with a medical condition of the user experienced by the user, physical condition, and customisation of one or more analysis parameters based on at least one of: the medical condition, user needs, healthcare provider needs; and transmit at least one of: recommendations for symptoms management, diet and lifestyle, and/or physical activities, guidance for capturing and uploading multimedia, and alert notifications for potential complications.

5. A system of claim 4, wherein the user device further comprises a multimedia unit for capturing the multimedia, over a predefined period of time, and wherein the multimedia comprises at least one of: an audio, an image, a video.

6. A system of claim 4 or 5, wherein the multimedia unit is associated with any of: a mobile device, the software application, and wherein the multimedia unit is implemented as an Al-aided multimedia unit.

7. A system of any of the preceding claims, wherein the software application is further configured to provide an interactive platform for multiple users to connect for advice and support in health monitoring and management, and wherein the interactive platform comprises at least one of: a clinician dashboard, an administrator dashboard, a hospital dashboard, a nursing dashboard, a superuser dashboard.

8. A system of any of the preceding claims, wherein the software application is further configured to be operated in multiple languages, and wherein the predictive health report is accessible in multiple languages.

9. A system of any of the preceding claims, further comprising an image processing module for maintaining image quality in varying lighting conditions, wherein the image processing module is implemented as an Al-aided image processing module.

10. A system of any of the preceding claims, wherein the Al algorithm is trained on a dataset of medical conditions and a dataset of symptoms and signs associated with corresponding medical conditions used by deep learning techniques employed by the Al algorithm to compare the received physiological data and comprehensive health data.

11. A system of any of the preceding claims, wherein the processing arrangement is further configured to integrate with at least one of: electronic health records (EHR.), telehealth platforms, for transmitting the predictive health report with a healthcare professional for subsequent analysis.

12. A system of claim 4-11, further comprising a memory unit to store at least one of: the captured multimedia, the predictive health report, the recommendations for health monitoring and management.

13. A method for monitoring health of a user, the method comprising: receiving a physiological data from a wearable device, communicably coupled to the processing arrangement, and a comprehensive health data from a user interface associated with the software application run on a user device; combining the physiological data and the comprehensive health data into a unified dataset; analysing the unified dataset using an artificial intelligence (Al) algorithm; generating a predictive health report based on the analysis by the Al algorithm, the predictive health report comprising predictive health insights; and transmitting, via the user interface, the predictive health report in real time, wherein the method comprises continuously updating and refining of the predictive health insights based on the received physiological data and the comprehensive health data.

14. A method of claim 13, further comprising: receiving inputs associated with at least one of: multimedia indicative of the medical condition of the user, comprehensive health data comprising at least one of: symptoms associated with a medical condition of the user experienced by the user, physical condition, and customisation of one or more analysis parameters based on at least one of: the medical condition, user needs, healthcare provider needs; and transmitting at least one of: recommendations for symptoms management, diet and lifestyle, and/or physical activities, guidance for capturing and uploading multimedia, and alert notifications for potential complications.

15. A method of claim 14, further comprising capturing, via a multimedia unit of the user device, the multimedia, over a predefined period of time, and wherein the multimedia comprises at least one of: an audio, an images, a video, and wherein the multimedia unit is associated with any of: a mobile device, the software application, and wherein the multimedia unit is implemented as an Al-aided multimedia unit

16. A method of claims 13-15, further comprising providing an interactive platform for multiple users to connect for advice and support in health monitoring and management, and wherein the interactive platform comprises at least one of: a clinician dashboard, an administrator dashboard, a hospital dashboard, a nursing dashboard, a superuser dashboard.

17. A method of claims 13-16, further comprising training the Al algorithm on a dataset of medical conditions and a dataset of symptoms associated with corresponding medical conditions used by deep learning techniques employed by the Al algorithm to compare the received physiological data and comprehensive health data.

18. A method of claims 13-17, further comprising transmitting the predictive health report with a healthcare professional for subsequent analysis by integrating with at least one of: electronic health records (EHR.), telehealth platforms.

19. A method of claims 13-18, further comprising storing in a memory unit at least one of: the captured multimedia, the predictive health report, the recommendations for health monitoring and management.

20. A non-transitory computer-readable storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a computerized device comprising processing hardware to execute a method as claimed in any one of claims 13-19.