Automated in-patient medical emergency management system

Abstract

The present invention discloses a patient monitoring system (102) for monitoring health of hospitalized non-ICU patients. This system (102) includes a set of monitoring devices (104) for displaying a comprehensive set of vital parameters related to patients. Additionally, a set of cameras (106) is integrated into the system, continuously capturing images of each monitoring device (104). The system is powered by processors (202) that execute instructions for image analysis using an AI engine. Through seamless communication with computing devices, the processors (202) facilitate swift transmission of alert signals to healthcare professionals, offering real-time updates upon detection of critical patient conditions. These alert signals include essential information about the identified vital parameter, enabling healthcare professionals to promptly intervene and address the needs of hospitalized non-ICU patients.

Description

AUTOMATED IN-PATIENT MEDICAL EMERGENCY MANAGEMENT SYSTEMTECHNICAL FIELD

[0001] The present invention relates to the field of patient monitoring. In particular, it relates to a system and method for monitoring health of hospitalized non-ICU patients and alerting healthcare professionals upon detection of abnormal variations in health parameters to avoid critical medical conditions.BACKGROUND

[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.

[0003] In today's world, a significant number of patients face adverse health outcomes in hospital settings outside of the Intensive Care Unit (ICU). In many instances, these unfortunate cases result from critical health conditions or emergency situations that go unnoticed until they reach a severe stage. The non-ICU environments, while essential for various medical procedures and patient care, may lack the constant monitoring necessary to promptly identify and respond to emergent health issues. This gap in continuous surveillance poses a substantial challenge in healthcare, potentially leading to adverse outcomes, prolonged hospital stays, increased healthcare costs and increased fatality.

[0004] The prevailing issue underscores the critical need for advanced patient monitoring systems that can seamlessly operate in non-ICU environments. While current monitoring practices are adequate for detecting subtle changes in vital parameters, they fall short in predicting upcoming health crises due to the absence of a proper framework comprising solid medical, technical, and procedural elements. The lack of real-time, comprehensive monitoring mechanisms leaves healthcare professionals with limited tools to identify deteriorating health conditions before they escalate. Bridging this gap requires innovative solutions that not only automate vital parameter monitoring but also incorporate adaptive learning systems to enhance the efficiency of healthcare delivery.

[0005] Moreover, the silent nature of certain critical health conditions further complicates the situation. Patients may not exhibit overt symptoms until their condition becomes severe, making early detection and intervention even more crucial. This underscores the importance of a proactive approach to healthcare that leverages technological and process advancementsto provide continuous and comprehensive patient monitoring. Addressing this issue is not only pivotal for minimizing adverse outcomes but also for optimizing healthcare resources, reducing hospitalization durations, and ultimately improving the quality of patient care in non-ICU settings.

[0006] Therefore, there is a need for a reliable, and robust patient monitoring system with adaptive learning capabilities to emerge as a promising avenue to revolutionize healthcare practices, helping in reducing fatalities and enhancing patient outcomes.OBJECTS OF THE PRESENT DISCLOSURE

[0007] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0008] It is an object of the present disclosure to provide a system and method that is used in non-intensive care unit environments within the hospital.

[0009] It is an object of the present disclosure to provide a system and method that continuously monitors vital parameters for early detection of health issues, enabling timely intervention and potentially preventing more serious conditions.

[0010] It is an object of the present disclosure to provide a system and method that ensures healthcare professionals are promptly notified when vital parameters deviate beyond predefined thresholds, facilitating quick and effective responses to critical situations.

[0011] It is an object of the present disclosure to provide a system and method that enhances the quality of patient care, allowing healthcare professionals to make well-informed decisions.

[0012] It is an object of the present disclosure to provide a system and method that automates vital parameter monitoring and analysis to reduce burden on healthcare professionals, allowing them to focus on critical tasks and improving operational efficiency.

[0013] It is an object of the present disclosure to provide a system and method with an adaptive and learning system that refining thresholds over time, reflects its ability to learn and improve, providing a dynamic and evolving solution for patient monitoring.SUMMARY

[0014] The present invention relates to the field of health monitoring. In particular, it relates to an automated in-patient medical emergency management system (AIMEMS) and method for monitoring health of hospitalized non-ICU patients and alerting healthcare professionals upon detection of abnormal variations in health parameters to prevent critical medicalconditions and reduce fatalities. The system automates vital parameter monitoring, and its adaptive learning system alleviates burdens on healthcare professionals, enhancing operational efficiency and providing a dynamic, evolving solution for patient monitoring.

[0015] An aspect of the present disclosure pertains to a patient monitoring system that includes monitoring devices attached to individual patients, displaying their vital parameters. Additionally, cameras continuously capture images of each monitoring device, enabling a comprehensive view of patient status. This information is processed by communicatively coupled processors and analyzed through an Artificial Intelligence (Al) engine. The system is configured to receive images, extract vital parameters from these images, and identify deviations beyond pre-defined thresholds. Upon detection, an alert signal is promptly transmitted to computing devices associated with healthcare professionals, ensuring immediate attention to critical situations and enhancing patient care in near real-time.

[0016] In an aspect, the set of vital parameters includes any or a combination of heart rate, blood pressure, oxygen saturation, body temperature, electrical activity of heart, and electrical activity of the brain.

[0017] In an aspect, the alert signal pertains to information regarding the identified vital parameter exceeding the pre-defined threshold.

[0018] In an aspect, the processors compare the received set of vital parameters with a database storing vital parameters of a plurality of entities to identify whether any of the received set of vital parameters exceeds the pre-defined threshold.

[0019] Another aspect of the present disclosure pertains to a method for patient monitoring, the method includes displaying of a set of vital parameters on monitoring devices, with each device attached to an individual patient. Images of each monitoring device are acquired by a set of cameras, and one or more processors extract vital parameters from these images. The extracted set of vital parameters is then analyzed by the processors using an Artificial Intelligence (Al) engine to identify if any parameter exceeds a pre-defined threshold. Upon detection of such an occurrence, the processors transmit an alert signal to a computing device associated with a healthcare professional. This ensures timely intervention and effective responses to critical situations, enhancing patient care in real-time healthcare scenarios.

[0020] Various objects, features, aspects, and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings are included to provide a further understanding of the present disclosure and are incorporated in, and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure, and together with the description, serve to explain the principles of the present disclosure.

[0022] In the figures, similar components, and / or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

[0023] FIG. 1 illustrates an exemplary network architecture of the proposed system for patient monitoring, in accordance with some embodiments of the present disclosure.

[0024] FIG. 2 illustrates an exemplary architecture of the proposed system for patient monitoring, in accordance with some embodiments of the present disclosure.

[0025] FIG. 3 illustrates an exemplary flow chart to illustrate working of proposed system, in accordance with an embodiment of the present disclosure.

[0026] FIG. 4 illustrates an exemplary view of a flow diagram of the proposed method for patient monitoring, in accordance with an embodiment of the present disclosure.DETAILED DESCRIPTION

[0027] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

[0028] The ensuing description provides exemplary embodiments only and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth.

[0029] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0030] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0031] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, any aspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive — in a manner similar to the term “comprising” as an open transition word — without precluding any additional or other elements.

[0032] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0033] Embodiments of present disclosure pertain to an automated in-patient medical emergency management system (AIMEMS) system and method for monitoring health of hospitalized non-ICU patients and alerting healthcare professionals upon detection of abnormal variations in health parameters to prevent critical medical conditions and reduce fatalities.

[0034] An embodiment of the present disclosure pertains to a patient monitoring system that includes monitoring devices attached to individual patients, displaying their vital parameters. Additionally, cameras continuously capture images of each monitoring device, enabling a comprehensive view of patient status. This information is processed by communicatively coupled processors and analyzed through an Artificial Intelligence (Al) engine. The system is configured to receive images, extract vital parameters from these images, and identify deviations beyond pre-defined thresholds. Upon detection, an alert signal is promptly transmitted to computing devices associated with healthcare professionals, ensuring immediate attention to critical situations and enhancing patient care in real-time.

[0035] In an embodiment, the set of vital parameters includes any or a combination of heart rate, blood pressure, oxygen saturation, body temperature, electrical activity of heart, and electrical activity of the brain.

[0036] In an embodiment, the alert signal pertains to information regarding the identified vital parameter exceeding the pre-defined threshold.

[0037] In an embodiment, the processors compare the received set of vital parameters with a database storing vital parameters of a plurality of entities to identify whether any of the received set of vital parameters exceeds the pre-defined threshold.

[0038] Another embodiment of the present disclosure pertains to a method for patient monitoring, the method includes displaying of a set of vital parameters on monitoring devices, with each device attached to an individual patient. Images of each monitoring device are acquired by a set of cameras, and one or more processors extract vital parameters from these images. The extracted set of vital parameters is then analyzed by the processors using an Artificial Intelligence (Al) engine to identify if any parameter exceeds a pre-defined threshold. Upon detection of such an occurrence, the processors transmit an alert signal to a computing device associated with a healthcare professional. Thus, the proposed method ensures timely notifications, allowing healthcare professionals to respond promptly to critical health situations and thereby enhancing patient care in real-time healthcare scenarios.

[0039] FIG. 1 illustrates an exemplary network architecture (100) of an automated in-patient medical emergency management system (AIMEMS) (102) (interchangeably referred to as asystem, hereinafter) for patient monitoring, in accordance with some embodiments of the present disclosure.

[0040] In an embodiment, the system (102) includes a set of monitoring devices (104) (collectively referred to as monitoring devices (104), and individually referred to as monitoring device (104), hereinafter) attached to single or multiple wards in a hospital to acquire a set of vital parameters patients in the wards, specifically outside the intensive care unit (non-intensive care unit areas). For one example, the system (102) can be used in areas such as homes, or other designated places where patient monitoring is deemed necessary. Each monitoring device (104) can be attached to an individual patient to acquire the set of vital parameters that may include but not limited to heart rate, blood pressure, oxygen saturation, body temperature, electrical activity of heart, and electrical activity of the brain.

[0041] In an exemplary embodiment, the monitoring device (104) is a medical device to collects and displays various vital parameters and health-related information of a patient while they are in a hospital bed. These devices are typically positioned near the patient's bed for convenient and continuous monitoring. The key function of a bedside monitoring device is to track essential physiological parameters that provide insights into the patient's health status.

[0042] Common vital parameters monitored by bedside devices include:

[0043] Heart Rate: The number of heartbeats per minute, indicating the heart's rhythm and function.

[0044] Blood Pressure: The force exerted by circulating blood against the walls of the arteries.

[0045] Oxygen Saturation: The percentage of hemoglobin in the blood that is saturated with oxygen.

[0046] Body Temperature: The degree of heat in the body, which can be indicative of various health conditions.

[0047] Electrocardiogram (ECG or EKG): A graphical representation of the electrical activity of the heart.

[0048] Respiratory Rate: The number of breaths taken per minute.

[0049] This monitoring device often have a visual display that provides real-time data, allowing healthcare professionals to monitor a patient's condition closely.

[0050] The system (102) includes a set of cameras (106) (interchangeably referred to as camera (106), hereinafter) positioned in the wards to continuously capture images of the monitoring devices (106). Each camera (106) is strategically placed to acquire images of anindividual monitoring device (104) corresponding to a specific patient. The type of camera (106) can vary based on the specific requirements and characteristics of the designated area. Different types of cameras may be suitable for different environments and applications. For instance:

[0051] a) Fixed Cameras: These cameras are stationary and provide a constant view of a specific area. They are suitable for monitoring a fixed location within a hospital room or designated home space.

[0052] b) Pan-Tilt-Zoom (PTZ) Cameras: These cameras offer the flexibility to pan, tilt, and zoom to capture a broader range of views. They are useful for monitoring larger areas or for tracking movement within a space.

[0053] c) Infrared Cameras: Particularly effective in low-light conditions, infrared cameras can capture images in the absence of visible light. This can be valuable for night time monitoring or in areas with limited illumination.

[0054] d) Wireless Cameras: Cameras with wireless capabilities allow for more flexible placement and installation. They can be beneficial in home settings or areas where wiring may be challenging.

[0055] e) 360-Degree Cameras: These cameras provide a panoramic view, capturing a full 360-degree field of vision. They are suitable for monitoring large spaces or areas where a comprehensive view is essential.

[0056] The system (102) will be connected to a centralized server (108), and the centralized server (108) is further to connected to one or more computing devices (112-1, 112-2, ... 112- N (collectively referred to as computing device (112), herein) of one or more healthcare professionals (114-1, 114-2, ... 114-N) through a network (110). The healthcare professionals (114) can encompass a range of individuals with expertise in various fields of healthcare, including physician, nurse, emergency medical technician, specialized technician, telehealth provider, healthcare administrator, hospital administrator, or the like. The computing device (112) may be personal computers, laptops, tablets, wristwatches or any custom-built computing device integrated within a modem diagnostic machine that can connect to a network as an loT (Internet of Things) device. The computing device (112) may also be referred to as User Equipment (UE). Accordingly, the terms “computing device” and “User Equipment” may be used interchangeably throughout the disclosure.

[0057] In an exemplary embodiment, the network (110) may include, but not be limited to, a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, aninfrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof.

[0058] Further, the centralized server (108) acts as a centralized repository or database where vital parameters of multiple entities are stored. This could include a variety of health-related data such as heart rate, blood pressure, oxygen saturation, body temperature, and other relevant parameters obtained through the patient monitoring system. Also, the centralized server (108) is responsible for efficiently organizing and managing the vast amounts of data generated by monitoring multiple entities. It ensures data integrity, security, and accessibility for authorized users.

[0059] In an exemplary embodiment, healthcare professionals or authorized users can access the centralized server (108) in real-time to retrieve vital parameters and other health-related information. This enables timely decision-making and intervention based on the latest available data. Additionally, the centralized server (108) maintains a historical record of vital parameters, allowing healthcare professionals to track changes over time. This historical data can be valuable for trend analysis, identifying patterns, and assessing the effectiveness of interventions. Moreover, the centralized server (108) handles vital parameters from multiple entities, the server is scalable to accommodate the growing volume of data as the number of monitored entities increases. This ensures the system's adaptability to varying healthcare environments.

[0060] Although FIG. 1 shows exemplary components of the network architecture (100), in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in FIG. 1. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0061] FIG. 2 illustrates an exemplary architecture of the proposed system for patient monitoring, in accordance with some embodiments of the present disclosure.

[0062] In an aspect, the system (102) may include one or more processor(s) (202) (interchangeably referred to as processor (202), hereinafter) stored on a centralized server (108). The processor (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, edge or fog microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions. Among other capabilities, the processor (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system(102). The memory (204) may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as Random Access Memory (RAM), or non-volatile memory such as Erasable Programmable Read-Only Memory (EPROM), flash memory, and the like.

[0063] Referring to FIG. 2, the system (102) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output devices, referred to as I / O devices, storage devices, and the like. The interface(s) (206) may facilitate communication to / from the system (102). The interface(s) (206) may also provide a communication pathway for one or more components of the system (102).

[0064] In an embodiment, an Artificial Intelligence (Al) engine (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the Al engine. The Al engine (208) is stored on the centralized server (108), the Al engine (208) contributes advanced computational capabilities to identify and assess vital parameters. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the Al engine (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the Al engine (208) may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the Al engine. In such examples, the system (102) may include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine- readable storage medium may be separate but accessible to the system (102) and the processing resource. In other examples, the Al engine may be implemented by electronic circuitry.

[0065] In an embodiment, the system includes a database (210) that may include data that may be either stored or generated as a result of functionalities implemented by any of the components of the processor (202) or the Al engine (208). In an embodiment, the database (210) may be separate from the system 102.

[0066] In an exemplary embodiment, the Al engine may include one or more engines selected from any of a data acquisition module (212), an extraction module (214), an analysis module (216), and other modules (218) having functions that may include but are not limitedto testing, storage, and peripheral functions, such as wireless communication unit for remote operation, audio unit for alerts and the like. The data acquisition module (212) may be configured to receive data including images of at least one patient in a ward, from a camera (106). The reference to "at least one patient" emphasizes that the system can monitor multiple individuals by associated monitoring devices (104) and cameras (106). These images provide a visual representation of a screen of a monitoring device (104) attached to the patient in the ward. Additionally, these images can be transmitted to associated computing devices (112), allowing healthcare professionals to access patient information.

[0067] In an embodiment, the extraction module (214) extracts a set of vital parameters from the received images by employing image processing algorithms to analyze the images captured by the cameras (104). Additionally, the extraction module (214) may leverage machine learning and artificial intelligence (Al )models to learn and recognize patterns over time. These models can be trained on a dataset that associates vital parameters. As the system encounters more data, the Al models can improve their accuracy in extracting relevant health information from images.

[0068] In some embodiments, the analysis module (216) analyses the received set of vital parameters to identify whether any of the received set of vital parameters exceeds a predefined threshold. The analysis module (216) operates based on pre-defined thresholds set for each vital parameter. These thresholds serve as benchmarks to determine normal and abnormal ranges for health metrics. The thresholds are established through calibration and can be adjusted based on specific patient characteristics or clinical requirements. Additionally, using machine learning algorithms and pattern recognition techniques, the Al engine analyzes the received data. It assesses the interplay between vital parameters and visual attributes to identify patterns, trends, or anomalies that may indicate deviations from normal health conditions.

[0069] Further, the analysis module (216) may exhibit dynamic adaptation, continuously learning and refining its analysis over time. This adaptability allows it to account for variations in individual patient characteristics, environmental factors, and changing health conditions. In some embodiments, the analysis module (216) identifies whether any of the vital parameters and attributes exceed the pre-defined thresholds. To identify whether any of the received set of vital parameters exceeds the pre-defined threshold, the analysis module (216) compares the received set of vital parameters with the database (210) storing vital parameters of a plurality of entities (i.e. health and unhealthy persons). If deviation beyond these thresholds is detected, it signifies a potential health concern or abnormality. Uponidentifying vital parameter deviations beyond the pre-defined thresholds, the Al engine triggers the generation of alert signals. The alert signals include information regarding the identified vital parameter beyond the pre-defined threshold and information of the associated patient. These alerts serve as notifications to healthcare professionals (114), signalling the need for attention and potential intervention on associated computing devices (112). Furthermore, upon receiving an alert from the patient monitoring system, healthcare professionals initiate a series of actions to address the detected vital parameter deviations and ensure the well-being of the monitored entities.

[0070] The analysis by the Al engine occurs in near real-time, providing prompt decision support to healthcare professionals. This enables timely responses to critical situations and enhances the system's effectiveness in patient monitoring.

[0071] FIG. 3 illustrates an exemplary flow chart (300) to illustrate working of proposed system, in accordance with an embodiment of the present disclosure. Hospitals can install monitoring devices (104) and cameras (106) in general wards where patients are recovering from surgeries, undergoing treatments, or managing chronic conditions.

[0072] An Al engine (208) stored on a centralized server (108) analyzes the received data, looking for patterns and potential deviations from normal vital parameters. The Al engine (208) adapts and learns over time, improving its ability to identify subtle changes. If the Al engine (208) detects any vital parameter beyond predefined thresholds or identifies concerning patterns, it triggers an alert signal. Moreover, healthcare professionals, including nurses and doctors, receive immediate alerts on their computing devices. The alert includes details about the patient and the specific vital parameter deviation. The alerted healthcare professionals immediately go to the patient's bedside for a clinical assessment. The healthcare professional reviews the patient's medical history, assess the current condition, and may perform additional tests or examinations.

[0073] Furthermore, based on the information provided by the patient monitoring system and their clinical assessment, healthcare professionals make informed decisions about the next steps in patient care. This could include adjusting medications, initiating interventions, or consulting with specialists.

[0074] By the proposed system (102) patient's vital signs and condition are continuously monitored. Any interventions or changes in the treatment plan are documented in the patient's electronic health record.

[0075] The early detection and prompt response facilitated by the patient monitoring system contribute to improved patient outcomes, reduced complications, and enhanced healthcare quality in a non-ICU hospital setting.

[0076] FIGS. 4 illustrates an exemplary view of a flow diagram of proposed method (400) for patient monitoring, in accordance with an embodiment of the present disclosure.

[0077] The process begins at step (402), where a set of monitoring devices (104) in a ward or multiple wards of a hospital displays a set of vital parameters of associated patients These vital parameters may encompass crucial health indicators such as heart rate, blood pressure, oxygen saturation, body temperature, and the electrical activity of both the heart and brain.

[0078] At (404) a set of cameras (106) are attached in the wards near bed of the patients acquiring images of each monitoring device (104). Each individual camera (106) is affixed in the wards in a manner that ensures it acquires images specifically from at least one of the monitoring devices (104) in use. This setup allows for precise and targeted image acquisition, contributing to the effectiveness of the patient monitoring system by focusing on the relevant monitoring devices associated with each patient.

[0079] At step (406) the processor (202) extracts values of the set of vital parameters from the received images, including vital parameters like heart rate, blood pressure, oxygen saturation, body temperature, and electrical activity of the heart and brain. The extraction process involves application of image analysis techniques by the processor (202). This indicates that the method (400) utilizes advanced computational methods to interpret the visual information captured by the cameras (106), enabling it to derive specific numerical values representing the patient's vital signs from the images.

[0080] Subsequently, at step (408), the method includes analysis of values of the received set of vital parameters by an Artificial Intelligence (Al) engine (208) and identifies instances where any vital parameter exceeds a pre-defined threshold, indicative of potential health concerns. Furthermore, the method (400) includes an additional step of comparing the received set of vital parameters with a database (210) that stores vital parameters of a plurality of entities, which may include historical data or a broader dataset. This comparison aims to determine whether any of the received vital parameters exceed the pre-defined threshold. By referencing a database of vital parameters from various individuals, the method gains a broader context for assessing the significance of deviations in the patient's vital signs.

[0081] Furthermore, at step (410), upon detecting such deviations, the processor (202) triggers the transmission of an alert signal. This alert signal is promptly sent to a computing device (112) that is connected to the processor (202) through a network (110). The computingdevice (112) is specifically associated with a healthcare professional (114) present in the hospital. The purpose of this alert is to ensure that healthcare professionals receive timely notifications about critical health situations, allowing them to respond swiftly and effectively to the identified issues, thereby enhancing the level of patient care in the hospital environment.

[0082] Accordingly, the present disclosure provides AIMEMS that ensures prompt notifications to healthcare professionals when vital parameters deviate, enabling quick and effective responses to critical situations, and ultimately enhancing quality of patient care.

[0083] Moreover, in interpreting the specification, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a nonexclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C....and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

[0084] While the foregoing describes various embodiments of the disclosure, other and further embodiments of the disclosure may be devised without departing from the basic scope thereof. The scope of the disclosure is determined by the claims that follow. The disclosure is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the disclosure when combined with information and knowledge available to the person having ordinary skill in the art.ADVANTAGES OF THE PRESENT DISCLOSURE

[0085] The present disclosure provides a system and method that monitors vital parameters, enabling the early detection of health issues for timely intervention and potential prevention of more severe conditions.

[0086] The present disclosure provides a system and method that ensures healthcare professionals receive prompt notifications when vital parameters deviate beyond predetermined thresholds, thereby facilitating swift and effective responses to critical situations.

[0087] The present disclosure provides a system and method that elevates the quality of patient care, empowering healthcare professionals to make well-informed decisions.

[0088] The present disclosure provides a system and method that automates the monitoring and analysis of vital parameters, alleviating the workload on healthcare professionals, enabling them to concentrate on crucial tasks, and enhancing operational efficiency.

[0089] The present disclosure provides a system and method featuring an adaptive and learning system that, by refining thresholds over time, reflects its capacity to learn and improve, presenting a dynamic and evolving solution for patient monitoring.

Claims

I Claim:

1. A patient monitoring system (102) comprises: a set of monitoring devices (104) configured to display a set of vital parameters of patients, wherein each monitoring device is attached to an individual patient; a set of cameras (106) configured to continuously acquire images of each monitoring device, wherein each camera is attached to acquire the images of at least one of the monitoring devices from the set of monitoring devices; one or more processors (202) communicatively coupled to the set of monitoring devices and the set of cameras; and a memory (204) coupled to the one or more processors (202), wherein said memory (204) stores instructions which when executed by the one or more processors (202) cause the system (102) to: receive the images from the set of cameras (104); extract a set of vital parameters from the received images by an Artificial Intelligence (Al) engine; and identify whether any of the received set of vital parameters exceeds a predefined threshold, wherein upon detection, an alert signal is transmitted to a computing device associated to a healthcare professional.

2. The patient monitoring system (102) as claimed in claim 1, wherein the set of vital parameters comprise any or a combination of heart rate, blood pressure, oxygen saturation, body temperature, electrical activity of heart, and electrical activity of the brain.

3. The patient monitoring system (102) as claimed in claim 1, wherein the one or more processors (202) are communicatively coupled to the computing device through a network.

4. The patient monitoring system (102) as claimed in claim 1, wherein the alert signal comprises information regarding the identified vital parameter exceeds the pre-defined threshold.

5. The patient monitoring system (102) as claimed in claim 1, wherein to identify whether any of the received set of vital parameters exceeds the pre-defined threshold, the one ormore processors (202) compare the received set of vital parameters with a database storing vital parameters of a plurality of entities.

6. A method (400) for patient monitoring in an area comprising: displaying (402) a set of vital parameters of patients on a set of monitoring devices, wherein each monitoring device is attached to an individual patient; acquiring (404) images of each monitoring device by a set of cameras, wherein each camera is attached to acquire the images of at least one of the monitoring devices from the set of monitoring devices; extracting (406), by one or more processors a set of vital parameters from the received images; analyzing (408), by the one or more processors, the extracted set of vital parameters using an Artificial Intelligence (Al) engine for identifying whether any of the received set of vital parameters exceeds a pre-defined threshold; transmitting (410), by the one or more processors, an alert signal to a computing device associated to a healthcare professional, upon detection of at least one of the received set of vital parameters exceeding the pre-defined threshold.

7. The method as claimed in claim 6, wherein the set of vital parameters comprise any or a combination of heart rate, blood pressure, oxygen saturation, body temperature, electrical activity of heart, and electrical activity of the brain.

8. The method as claimed in claim 6, wherein the one or more processors are communicatively coupled to the computing device through a network.

9. The method as claimed in claim 6, wherein the alert signal comprises information regarding the identified vital parameter exceeds the pre-defined threshold.

10. The method as claimed in claim 6, further comprises the step of comparing the received set of vital parameters with a database storing vital parameters of a plurality of entities to identify whether any of the received set of vital parameters exceeds the pre-defined threshold.

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