WO2022189880A1

WO2022189880A1 - System and method to optimise power consumption and computation while monitoring patients

Info

Publication number: WO2022189880A1
Application number: PCT/IB2022/051712
Authority: WO
Inventors: Manoj SANKER P R; Sabari PRABAAKER R
Original assignee: Nemocare Wellness Private Limited
Priority date: 2021-03-08
Filing date: 2022-02-26
Publication date: 2022-09-15

Abstract

A system and method for implementation of a connected gateway to optimise power consumption and computation while monitoring patients is disclosed. The disclosed system and method are based on devices associated with the patients to measure health parameters of the patients, and a first processing unit that receive a first set of data packets associated with the measured health parameters from each of the devices. On receipt of the first set of data packets, the first processing unit compare 10 values of the measured health parameters of each of the patients with respective predefined thresholds to determine a criticality score for each of the patients and prioritize bandwidth and dynamic compute memory allocation to the plurality of devices thereby optimising power consumption and computation capabilities in the devices based on the determined criticality score for each of the patients.

Description

SYSTEM AND METHOD TO OPTIMISE POWER CONSUMPTION AND COMPUTATION WHILE MONITORING PATIENTS

TECHNICAL FIELD

[0001] The present disclosure relates to a connected gateway for patient monitoring systems and clinical workflows optimization. In particular, the present disclosure relates to a system and method for a connected network implementation for optimisation of power consumption and computation capabilities related to monitoring vital signs of patients and clinical workflows in a Neonatal Intensive Care Unit (NICU), step down wards in hospitals and nursing homes.

BACKGROUND [0002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art. [0003] Infant patients, particularly neonates can experience relative rapid improvement or degradation in health conditions. The acuteness of the patient's condition has an effect on the monitoring and therapy action taken by a caregiver to address the needs of the infant patient. Generally, most operations in a neonatal intensive care unit (NICU) are carried out by the caregivers including nurses and doctors by looking at values from instruments/ medical devices, intuition, and paper based calculations. This method is prone to human error, dependent on duty shift timings of the caregiver and occupancy load of the patients at a given time in the NICU. The medical devices work in silos and do not share data with other systems, need manual calibrations from time to time and need inference by operators/ caregivers that is subjective. More than 70% of the time of the doctors is spent in documenting workflows and ensuring correctness of data. Decision making is very subjective and depends on expertise levels and training. The instrument/medical devices have power dependencies and cannot work for long on battery during outages. In addition, each instrument/medical needs to have a separate display which adds to cost and in resource constrained settings price sensitivity is a huge criteria for adoption and usage. Existing central monitoring systems using WLAN need an infrastructure in place, wiring and come at a huge premium as well. [0004] As is well known, the first 28 days of life are the most vulnerable time for infant’ survival. Globally, 2.6 million children died in the first month of life in 2016, approximately 7,000 new-borns deaths every day, most of which occurred in the first week, with about 1 million dying on the first day and close to 1 million dying within the next six days. In India, approximately 8 million clinically low birth weight babies are bom every year. These babies are susceptible to a variety of life threatening conditions like Apnoea, Hypothermia and Respiratory Distress, causing either death or some form of morbidity. Almost all these deaths are preventable with timely treatment. However, hospitals in the developing world are severely challenged by limited resources. These hospitals cannot afford the expensive equipment, which are also too bulky and unsuitable for continuous monitoring and they have a high rate of false alarms, forcing nurses to visually monitor the babies. In low resource settings where one nurse cares for about 40 new-boms, she may not be able to give equal attention to every baby and thereby most of the distress conditions often go unnoticed, causing irreversible injury to the new-bom and sometimes even lead to the death. In the current situation if the child has to be monitored, the child is kept in a separate room and hooked on to wires, which hinders breastfeeding and kangaroo mother care which in turn hinders the growth and recovery of the child.

[0005] The economic burden due to prematurity in a country like the United States is close to $26.2bn each year. The economic burden of prematurity in India may be also of the same order, drastically impacting the economic development of the country. There is an urgent need for a solution to enable these overloaded healthcare workers to monitor and diagnose the above-mentioned conditions of the new-boms more accurately and provide timely treatment, optimize workflows in the NICU so that they spend their time on treatment and not documenting values and readings.

[0006] There is, therefore, a need to provide an easy to use, efficient, accurate, and reliable system and method which can overcome the above-mentioned challenges. OBJECTS OF THE INVENTION

[0007] A general object of the present disclosure is to provide an efficient and cost-effective solution of the above-mentioned problems.

[0008] An object of the present disclosure is to provide a system and method to enable healthcare workers to monitor and diagnose critical conditions of new-borns more accurately and provide timely treatment, optimize workflows in a NICU so that they spend their time on treatment and not documenting values and readings. [0009] Another object of the present disclosure is to provide a system and method for monitoring vital signs of patients in NICUs through a medical device gateway implementation thereby saving lives of new-borns across economic strata without barrier.

[0010] Another object of the present disclosure is to provide a system and method to optimise workflow in NICUs and step down wards.

[0011] Yet another object of the present disclosure is to provide an easy to implement and a reliable system and method which decrease energy consumption at medical device level and drastically decrease cost by enabling a many to one node to hub to cloud architecture with a use case in timely distress management and resource optimization in NICUs and step down wards in hospitals and nursing homes.

[0012] Still another object of the present disclosure is to provide a cost effective and efficient system and method, which optimize time of doctor and caregivers towards care and not documentation and reduces stress on them by removing burden of staying alert throughout their duty time.

SUMMARY

[0013] Aspects of the present disclosure relate to a connected gateway for patient monitoring systems and clinical workflows optimization. In particular, the present disclosure relates to a system and method for a connected network implementation for optimisation of power consumption and computation capabilities related to monitoring vital signs of patients and workflows in NICUs and step down wards in hospitals and nursing homes. [0014] In an aspect, the present disclosure provides a system for implementation of a connected gateway to optimise power consumption and computation for monitoring patients. The system can include a plurality of devices/ medical device associated with the patients. Each of the plurality of devices is configured to measure one or more health parameters of the corresponding patient. The system further includes a first processing unit communicatively coupled to the plurality of devices. The first processing unit is configured to receive a first set of data packets associated with the measured one or more health parameters from each of the plurality of devices. Each of the measured one or more health parameters being associated with a predefined weight.

[0015] In an embodiment, in response to the received first set of data packets, the first processing unit can compare values of the measured one or more health parameters of each of the patients with respective predefined thresholds to determine a criticality score for each of the patients.

[0016] In an embodiment, the first processing unit can prioritize bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients. By default the plurality of devices are operated on low sampling rates to optimise battery life by reducing power consumption used for acquiring data from sensors of the devices, processing raw values to denoised signal with useful information and transmitting them wirelessly at lower sampling frequency operation. The first processing unit uses this data and derive the criticality score to decide if sampling frequency has to be increased, more bandwidth to be allocated to receive and compute on the incoming first set of data packets. When prioritised based on the criticality scores, the device operates at a higher sampling frequency, more incoming data and processing and larger allocated bandwidth and compute memory at first processing unit/gateway accommodates this. The received data at the first processing unit can be pushed to a second processing unit that is communicatively coupled to the first processing unit, the second processing unit has more resources to perform these actions thereby freeing the medical device of compute functionalities and ensuring resource optimisation. [0017] In an embodiment, the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices enable higher sampling rates of the first set of data packets from the plurality of devices by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the plurality of devices. Whenever a medical device is given high priority, a large network bandwidth is allocated along with more compute resources on the first processing unit/gateway. The other medical devices continue to operate on low sampling frequency operation mode and continue to be power and resource efficient. By this method of dynamic sampling allocation the medical devices are prevented to function at a higher default sampling frequency thereby optimising power consumption and resource allocation.

[0018] In an embodiment, the first processing unit can be configured to determine a ranking of each of the plurality of devices based on the predefined weight of the measured one or more health parameters. If the calculated criticality score for two or more of the patients are same, the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices can be based on the determined ranking of each of the plurality of devices.

[0019] In an embodiment, the system can include one or more display devices communicatively coupled to the plurality of devices and the first processing unit. The first processing unit can transmit the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to the one or more display devices associated with one or more caregivers.

[0020] In an embodiment, the first processing unit can transmit alerts signals to the one or more display devices based on the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to alert the associated one or more caregivers.

[0021] In an embodiment, the first processing unit can transmit the measured one or more health parameters of each of the patients, and the determined criticality score for each of the patients to one or more computing devices located at remote locations. The first processing unit can also transmit alerts signals to the to the one or more computing devices based on the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to alert users of the one or more computing devices.

[0022] In an embodiment, the plurality of devices can include a group of medical devices comprising, but not limited to, wearable multi parameter monitoring units, infusion pumps, ventilators, phototherapy units, radiant warmers, blood pressure (BP) cuffs, glucometers, Electrocardiography (ECG), Respiration rate monitors , wired multi parameter monitoring units, blood gas analysers, nebulizers, feeder, food pump and other pumps, dialysis machines, Electronic bed, ventilator, CPAP, anaesthesia unit, drip machine, phototherapy units, weighing scales , infant warmer and other legacy and modern wireless devices

[0023] In an embodiment, the measured one or more health parameters can include any or combination of, but not limited to, a body temperature measured at various sites, a pulse rate, a respiration rate, a weight, oxygen saturation, heart rate, perfusion index, pleth variability index, heart rate variability, transcutaneous bilirubin, arterial blood gas compositions, blood glucose levels, urine output and a blood pressure.

[0024] In an embodiment, the second processing unit can be configured to receive a second set of signals associated with the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients. The second processing unit can be configured to store and execute another set of instmctions.

[0025] In another aspect, the present disclosure provides a method for implementation of a connected network implementation related to monitoring vital signs of patients and clinical workflows. The method can include steps of measuring, using a plurality of devices, one or more health parameters of the patients; receiving, using a processing unit, a first set of data packets associated with the measured one or more health parameters from each of the plurality of devices, each of the measured one or more health parameters being associated with a predefined weight; in response to the received first set of data packets, comparing, using the processing unit, values of the measured one or more health parameters of each of the patients with respective predefined thresholds; determining, using the processing unit, a criticality score for each of the patients based on the comparison; and prioritizing, using the processing unit, bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients.

[0026] 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

[0027] 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. The diagrams are for illustration only, which thus is not a limitation of the present disclosure.

[0028] 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.

[0029] FIG. 1 illustrates a network implementation of the proposed system, in accordance with an embodiment of the present disclosure.

[0030] FIG. 2 illustrates exemplary functional components of a first processing unit of the proposed system, in accordance with an embodiment of the present disclosure.

[0031] FIGs. 3 A and 3B illustrate exemplary implementation of the proposed system, in accordance with embodiments of the present disclosure.

[0032] FIG. 4 illustrates an exemplary flow diagram of the proposed method, in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] 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. [0034] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.

[0035] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, firmware and/or by human operators.

[0036] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The machine-readable medium may include, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).

[0037] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.

[0038] The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention. [0039] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).

[0040] Embodiment explained herein relate to a connected gateway for patient monitoring systems and workflows optimization. In particular, the present disclosure relates to an improved system and method for a connected network implementation for optimisation of power consumption and computation capabilities related to monitoring vital signs of patients and clinical workflows in a Neonatal Intensive Care Unit (NICU), step down wards in hospitals and nursing homes. [0041] In an embodiment, the prosed system provides a connected medical device gateway that may decrease energy consumption at medical device node level, i.e., of single or plurality of independent medical devices, and drastically decrease cost by connecting many medical devices to a hub to a cloud architecture with a use case in timely distress management and resource optimization in NICUs and step down wards in hospitals and nursing homes. The system is capable of optimal offline edge computation for localised alerting. The system prioritizes bandwidth sharing with medical devices based on criticality risk scoring of patients such as new-boms in the NICU and enables dynamic switching and sampling rates of data received from the medical devices to optimise power consumption at the medical devices. The system is able to save lives of new-borns across economic strata without barriers. The time of doctor and caregivers in the NICUs and step down wards can be optimised, by the system, towards care and not documentation and reduces stress on them by removing burden of staying alert throughout their duty time with no aid in the same.

[0042] FIG. 1 illustrates a network implementation of the proposed system 100 for monitoring patients, in accordance with an embodiment of the present disclosure. The proposed system can include a plurality of medical devices 102-1, 102-2, 102-3... 102-N (collectively referred to as devices 102 and individually referred to as device 102). The devices 102 are associated with patients 104-1, 104- 2, 104-3.. . 104-N (collectively referred to as patients 104 and individually referred to as patient 104) as shown in FIG. 1. In an exemplary embodiment, the patients 104 are infant patients in a NICU. For example, each of the plurality of devices 102 can be selected a group of medical devices including, but not limited to, wearable multi parameter monitoring units, infusion pumps, ventilators, phototherapy units, radiant warmers, blood pressure (BP) cuffs, glucometers, Electrocardiography (ECG), weighing scales , Respiration rate monitors , wired multi parameter monitoring units, blood gas analysers, nebulizers, feeder, food pump and other pumps, dialysis machines, Electronic bed, ventilator, CPAP, anaesthesia unit, drip machine, phototherapy units, infant warmer , other legacy and modern wireless devices etc. In an exemplary embodiment, the device 102 can be a sensor communicatively connected with medical devices.

[0043] In an embodiment, each of the device 102 can include a power unit comprising of batteries and AC Dc convertors and connectivity modules including both wired and wireless connectivity modalities that can include Bluetooth, Bluetooth Low Energy, WiFi, Lora WAN, NBIoT and other similar protocols. [0044] Each of the devices 102 can be configured to measure one or more health parameters of the corresponding patient 104. In an embodiment, the measured one or more health parameters can include any or combination of, but not limited to, a body temperature, a pulse rate, a respiration rate, a weight, and a blood pressure. [0045] The each of the plurality of devices 102 can be connected to a first processing unit 106 of the system 100 through a first network 108. In an embodiment, the first network 108 can be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, Bluetooth, Bluetooth Low Energy, Lora WAN, Narrowband Internet of Things (NBIoT) and the like. Further, the first network 108 can either be a dedicated network or a shared network. The shared network can represent an association of the different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Intemet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.

[0046] In an embodiment, each of the plurality of devices 102 transmits a first set of data packets associated with the measured one or more health parameters to the first processing unit 106. The first processing unit 106 comprising one or more processors coupled with a memory. The memory stores instructions executable by the one or more processors. In an exemplary embodiment, the processing unit can include predefined set of instructions for decision making locally. In an exemplary embodiment, the first processing unit 106 can include a router and/or a server. [0047] In an embodiment, the first processing unit 106 can be in communication with a second processing unit 110. The second processing unit 110 can be a cloud and/or server. The second processing unit 110 can be located at local, remote location and/or cloud.

[0048] In an embodiment, the first processing unit 106 can be communicatively coupled with one or more user/entity devices 112-1, 112-2... 112-N (individually referred to as the entity device 112, and collectively referred to as the entity devices 112, hereinafter) through a second network 114. The one or more entity devices 112 are connected to the living subjects/ users /entities 116-1, 116-2... 116N (individually referred to as the entity 116 and collectively referred to as the entities 116, hereinafter). Example of the entities 116 can include, but not limited to, a doctors, a nurse, any relative and/or guardian of the patients 104 such as parents and the like. A few of the entity devices 112 can be located locally and other entity devices can be located at the remote locations.

[0049] The entity devices 112 can include a variety of computing systems, including but not limited to, a laptop computer, a desktop computer, a notebook, a workstation, a portable computer, a personal digital assistant, a handheld device, a tablet, and a mobile device.

[0050] In an embodiment, the system 100 can be implemented using any or a combination of hardware components and software components such as a cloud, a server, a computing system, a computing device, a network device and the like. [0051] In an embodiment, on receipt of the received first set of data packets from each of the devices 102, the first processing unit 106 can compare values of the measured one or more health parameters of each of the patients 104 with respective predefined thresholds to determine a criticality score for each of the patients 104. The predefined thresholds can be stored in a database. The criticality score of the patients may indicate critical condition of the patients 104.

[0052] In an embodiment, the first processing unit 106 can prioritize bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients 116. By default each of the medical devices 102 is operated on low sampling rates to optimise battery life by reducing power consumption used for acquiring data from sensors of the medical devices, processing raw values to denoised signal with useful information and transmitting them wirelessly at lower sampling frequency operation. The processing unit 106 will use this data and derive a criticality score to decide if sampling frequency has to be increased, more bandwidth to be allocated to receive and compute on the incoming data. When prioritised based on the criticality scores, one of the devices 102 operates at a higher sampling frequency, more incoming data and processing and larger allocated bandwidth and compute memory at the first processing unit gatewayl06 accommodates this. The received data at the first processing unit can be is also pushed to the second processing unit 110 which has more resources to perform these actions thereby freeing the devices 102 of compute functionalities and ensuring resource optimisation.

[0053] In an embodiment, the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices 102 enable higher sampling rates of the first set of data packets from the medical devices 102 by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the devices 102 Whenever a medical device from 102 is given high priority, a large network bandwidth is allocated along with more compute resources on the processing unit 106. The other medical devices from plurality of devices 102 continue to operate on low sampling frequency operation mode and continue to be power and resource efficient. By this method of dynamic sampling allocation we are avoiding the plurality of devices 102 to function at a higher default sampling frequency thereby optimising power consumption and resource allocation.

[0054] For example, if the calculated risk score of the patient 104-1 is 20 and the calculated risk score of the patient 104-2 is 40, then the first processing unit 106 give higher priority to the device 102-2 and the data packets received from the device 102-2 are analysed first at first processing the unit 106. Based on the calculated risk score of the patients 104, operation of the associated devices 102 can be controlled by the first processing unit. For example, when the calculated risk score of the patient 104-3 is within a normal range, the device 102 associated with the patient 104-3 may be switched Off for a predefined time interval.

[0055] In an embodiment, each of the measured one or more health parameters can be associated with a predefined weight. Based on the predefined weight of the measured one or more health parameters, the first processing unit 106 can be configured to determine a ranking of each of the devices 102. For example, the first processing unit can calculate a sum of the predefined weights of the measured health parameters for each of patients 104. The ranking of the devices 102 can be determined in a descending order bases the sum values of the predefined weights of the measured health parameters of the patients. While calculating sum of the predefined weight of the measured health parameters, the values of those health parameters which are within a predefined threshold range may not be considered, only the values of the health parameters which do not fall in the predefined threshold range may be considered. In case if the calculated criticality score for two or more of the patients 104 are same, the first processing unit 106 can perform the prioritization of bandwidth and dynamic compute memory allocation to the devices 102 based on the determined ranking of each of the devices 102.

[0056] In an embodiment, the first processing unit 106 can transmit the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to the entity devices 112.

[0057] In an embodiment, the first processing unit 106 can transmit alerts signals to the entity devices 112 based on the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to alert the associated entities 116. For example, if one or more values of the measured health parameters of the patient 104-1 is more than the predefined threshold values or the calculated critical score exceeds the normal valve, the first processing unit 106 transmits the alert signal to the entity devices 112 to inform the associated entities 116 such as caregivers about the critical condition of the patient 104-1 and the patient 104-1 needs special care, treatment and/or observation. [0058] In an exemplary embodiment, the alert signals can include any or combination of audio signals, video signals, vibration signals and the like.

[0059] In an embodiment, the first processing unit 106 can be configured to transmit a second set of signals the second processing unit 110. The second set of signals can be associated with any or more of the measured one or more health parameters of the patients 104, the determined criticality score for each of the patients and the calculated ranking of the devices 102. The second processing unit can be configured to store and execute another set of instructions. If required, the second processing unit 110 can also calculate the criticality score for each of the patients 104 and the calculated ranking of the devices 102. The second processing unit 110 can include processors and memory that stores the another set of instructions. The second processing unit 110 can store the received data.

[0060] The entity devices 112 can interact with the first processing unit 106 and the second processing unit 110 through a website or an application that can reside in the entity devices 112. In an implementation, the first processing unit 106 and the second processing unit 110 can be accessed by website or application that can be configured with any operating system, including but not limited to, AndroidTM, iOSTM, and the like.

[0061] In an embodiment, the entity devices 112 can be connected to the second processing unit 110 through the second network 114. The second network 114 can be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, a cellular network and the like. Further, the second network 114 can either be a dedicated network or a shared network. The shared network can represent an association of the different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.

[0062] In another embodiment, the system 100 can further include antennas for reception and/or transmission of long, midrange and short range signals, peripheral pins for connecting one or more sensors, audio visual alerting units, haptic feedback units, authenticator using RFID mechanism etc.

[0063] The system helps to prioritize which patient needs more attention and when test samples to be sent for reports etc. Based on the measure health parameters and the determined criticality score, the system 100 helps in allocation of resources like nursing staff, available medical devices like ventilatory, phototherapy units, warmer and other similar instmments to aid survival and recovery of the patients. The system helps in providing better quality care and regulate use of medicines of patients.

[0064] Although in various embodiments, the implementation of the system 100 is explained with regard to the first processing unit 106 and the second processing unit 110, those skilled in the art would appreciate that, the system 100 can be fully or partially be implemented in other computing devices, such as entity devices that are operatively coupled with network with minor modifications, without departing from the scope of the present disclosure.

[0065] FIG. 2 illustrates exemplary functional components of the first processing unit 106 of the proposed system, in accordance with an embodiment of the present disclosure. The first processing unit 106 may comprise one or more processor(s) 202. The one or more processor(s) 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 204 of the first processing unit 106. The memory 204 may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 may comprise any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.

[0066] The first processing unit 106 may also comprise 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 of the first processing unit 106 with various devices coupled to the first processing unit 106 such as an input unit and an output unit. The interface(s) 206 may also provide a communication pathway for one or more components of the computing device. Examples of such components include, but are not limited to, processing engine(s) 208 and database 210. [0067] The processing engine(s) 208 may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor executable instructions stored on a non- transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may comprise 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 processing engine(s) 208. In such examples, the computing device may comprise 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 first processing unit 106 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry. The database 210 may comprise data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208. The database 210 can include any or more of, but not limited to, pre-stored data, threshold data, reference data, set of trained data, set of instructions and any other required data. It would be appreciated that the database 210 can be configured at a remote location. The database 210 can be a server, a computing device etc.

[0068] In an exemplary embodiment, the processing engine(s) 208 may comprise a criticality score computing unit 212, a prioritization unit 214, an alert unit 216, and other units(s) 218. The other unit(s) 218 can implement functionalities that supplement applications or functions performed by the first processing unit 106 or the processing engine(s) 208.

[0069] In an embodiment, the criticality score computing unit 212 can receive the first set of data packets associated with the measured one or more health parameters from each of the devices 102. Each of the measured one or more health parameters can be associated with a predefined weight. [0070] In an embodiment, the criticality score computing unit 212 can compare values of the measured one or more health parameters of each of the patients with respective predefined thresholds which are stored in the database 210 to determine a criticality score for each of the patients 104.

[0071] In an embodiment, the prioritization unit 214 can prioritize bandwidth and dynamic compute memory allocation to the devices 102 based on the determine criticality score for each of the patients 104.

[0072] In an embodiment, the prioritization of bandwidth and dynamic compute memory allocation to the devices 102 enable higher sampling rates of the first set of data packets from the plurality of devices by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the plurality of devices.

[0073] In an embodiment, the criticality score computing unit 212 can determine a ranking of each of the devices 102 based on the predefined weight of the measured one or more health parameters. If the calculated criticality score for two or more of the patients are same, the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices can be based on the determined ranking of each of the devices 102.

[0074] After calculating the criticality scores, the criticality score computing unit 212 can transmit the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to the devices 112. The entity devices 112 can be located at remote locations. This allows monitoring of the patients 102 by the entities 116 associated with the devices 112 from remote locations.

[0075] In an embodiment, the alert unit 216 can transmit alerts signals to the entity devices 112 based on the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients 104 to alert the associated entities 116. The determined criticality score indicates heath condition of the corresponding patient. For example, the determined criticality scores help in prioritizing which patient needs more attention, estimating when test samples of the patients to be sent for reports, etc. The criticality scores also help in allocation of resources like nursing staff, available medical devices like ventilatory, phototherapy units, warmer and other similar instruments to aid survival and recovery of the patient and to provide quality care and regulate their medicines such as antibiotics.

[0076] In an embodiment, the criticality score computing unit 212 can transmit a second set of signals the second processing unit 110. The second set of signals can be associated with any or more of the measured one or more health parameters of the patients 104, the determined criticality score for each of the patients and the calculated ranking of the devices 102. The second processing unit can be configured to execute another set of instructions. If required, the second processing unit 110 can also calculate the criticality score for each of the patients 104 and the calculated ranking of the devices 102. The second processing unit 110 can include processors and memory that stores the another set of instructions. The second processing unit 110 can store the received and computed data.

[0077] FIGs. 3A and 3B illustrate exemplary implementation of the proposed system, in accordance with embodiments of the present disclosure. As shown in figure 3A, a plurality of devices 102 are associated with a plurality of patients to measure one or more health parameters of the patients. Each of the devices 102 sends a first set of data packets associated with the measured one or more health parameters to the first processing unit 106. The first processing unit can be a hub or router. The first processing unit 106 can transmit a second set of data packets to a second processing unit 110 which can be cloud.

[0078] In an embodiment, the first processing unit 106 can compare values of the measured one or more health parameters of each of the patients with respective predefined thresholds to determine a criticality score for each of the patients. The first processing unit 106 can prioritize bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients. The prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices enable higher sampling rates of the first set of data packets from the plurality of devices by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the plurality of devices.

[0079] The second processing unit 110 can store the received data. The second processing unit 110 can process the received data to determine a criticality score for each of the patients. Any of the first processing unit 106 and the second processing unit 110 can transmit alert signals to the computing devices associated with doctors or nurses to alert them based on the determine a criticality score for each of the patients or the measure heath parameter.

[0080] As shown in FIG. 3B, one or more medical devices 102 can be connected with a patient 104 to measure multiple health parameters. The measured heath parameters can be transmitted to the tablet/display device 304 which can be associated with the nurse or doctor to inform the nurse or doctor, in real-time, about the health condition of the patient 104.

[0081] In an exemplary embodiment, the system 100 can be implemented in a NICU in hospitals to monitor patients/newboms health comprehensively, 24x7 in a completely non-invasive manner. The system 100 can alert the caregiver including nursing staff/health worker, mother etc. at a right time based on measure health parameters of the newborns and enables the nursing staff in the hospital to monitor and manage all the newborns under their care efficiently and accurately. The system 100 can work as an intelligent platform can detects health conditions of the patients/newboms accurately in real-time. The devices 102 can sense the health parameters from multiple points on a body of the patient, which leads to high accuracy levels in measuring health parameters of the patient/newbom.

[0082] In an embodiment, the devices 102 can include medical devices such as wearable multi parameter monitoring, infusion pumps, ventilators, phototherapy units, and radiant warmers. The devices 102 can be Bluetooth low energy (BLE) enabled to connect with the first processing unit/hub connected to the second processing unit, which can be used for central monitoring from remote location. [0083] In an embodiment, the system can be implemented in a NICU, a step down ward, a small clinic, an ambulance, a home, and the like. The system can provide biofeedback mechanism and establishes a full stack Internet of Medical Things.

[0084] In an embodiment, as the proposed system facilitates wireless central monitoring of newborns, the system can obviate the existing inadequacy in the nurse to new-born ratio (1:40). By using proposed system, multiple newborns’ health status and distress alarms can be monitored through a single interface on a display device 112. For instance, up to 15 devices/medical devices 102 can be efficiently connect to the first processing unit/single bluetooth hub/gateway 106 and data from devices can be displayed comfortable on one device 116. For example, in country like India, a given UNICEF Special newborn care centre (SNCU) typically houses 20 - 50 newborns. The proposes system can be configured in such a way that for every 15 newborns there is one tablet/computer/display device 304 and for all 20-50 newborns there is a desktop screen 302, which can be located at the nursing station and displays all of the health statuses and alarms and trends. [0085] In an exemplary embodiment, when a newborn distress is detected, the audio-visual alarm also goes off (along with an alarm on the device) at the central monitor 302, which sits at the nursing station, thereby alerting the nurse on time. This affordable central monitoring system enables the nurse or doctor to monitor the newborn patients continuously and remotely in the hospital, this reduces their response time drastically. The data displayed on the central screen can be prioritized based on the criticality sore of the newborns’ health condition.

[0086] In an exemplary embodiment, the computing devices 304 and/or central monitors 304 placed at the nursing station are contend to the first processing unit and/or second processing unit, by using computing devices and/or central monitors the nurse and doctor can access the data and trends through a dedicated application in the computing devices and/or central monitors whenever required to monitor the babies centrally and remotely. Hence the proposed system helps to collect, store, visualize and analyze the data generated.

[0087] In an exemplary embodiment, the system stores a comprehensive compilation of all the vital data required to analyse. The data may be used in real time to provide early warning scores or other predictive indicators for other conditions like cerebral palsy, and accurate identification of shock in already hospitalized newborns and used offline to develop new predictive algorithms. This may be a great decision making tool for the doctors to augment other clinical examination parameters and help them allocate their resources better and provide quality care and regulate their use of antibiotics.

[0088] In an exemplary embodiment, the system can be be used as a tool for policymakers who can stored data of the system on neonatal health and disease burden.

[0089] FIG. 4 illustrates a flow diagram for the proposed method 400, in accordance with an embodiment of the present disclosure. The method may be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method may be considered to be implemented in the above-described system.

[0090] In an embodiment, the method 400 can include, at block 402, measuring one or more health parameters of the patients using a plurality of devices, and at block 404, receiving a first set of data packets associated with the measured one or more health parameters from each of the plurality of devices a processing unit. Each of the measured one or more health parameters being associated with a predefined weight.

[0091] In an embodiment, the method 400 can include, at block 406, comparing values of the measured one or more health parameters of each of the patients with respective predefined thresholds using the processing unit, and at at block 408, determining a criticality score for each of the patients based on the comparison using the processing unit.

[0092] In an embodiment, the method 400 can include at at block 410, prioritizing bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients using the processing unit.

[0093] In an embodiment, the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices enable higher sampling rates of the first set of data packets from the plurality of devices by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the plurality of devices.

[0094] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention 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 invention when combined with information and knowledge available to the person having ordinary skill in the art.

Claims

We Claim:

1. A system to optimise power consumption and computation while monitoring patients, the system comprising: a plurality of devices associated with the patients, each of the plurality of devices being configured to measure one or more health parameters of the corresponding patient; a first processing unit communicatively coupled to the plurality of devices, the first processing unit comprising one or more processors coupled with a memory, the memory storing instmctions which, when executed by the one or more processors cause the first processing unit: receive a first set of data packets associated with the measured one or more health parameters from each of the plurality of devices, each of the measured one or more health parameters being associated with a predefined weight; in response to the received first set of data packets, compare values of the measured one or more health parameters of each of the patients with respective predefined thresholds; determine a criticality score for each of the patients based on the comparison; and prioritize bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients.

2. The system as claimed in claim 1, wherein the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices enable higher sampling rates of the first set of data packets from the plurality of devices by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the plurality of devices.

The system as claimed in claim 1, wherein the first processing unit is configured to determine a ranking of each of the plurality of devices based on the predefined weight of the measured one or more health parameters; and wherein if the calculated criticality score for two or more of the patients are same, the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices are based on the determined ranking of each of the plurality of devices.

The system as claimed in claim 1, wherein the system comprises one or more display devices communicatively coupled to the plurality of devices and the first processing unit, wherein the first processing unit transmits the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to the one or more display devices associated with one or more caregivers; and wherein the first processing unit transmits alerts signals to the one or more display devices based on the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to alert the associated one or more caregivers.

The system as claimed in claim 1, wherein the first processing unit transmits the measured one or more health parameters of each of the patients, and the determined criticality score for each of the patients to one or more computing devices located at remote locations; and wherein the first processing unit transmits alerts signals to the one or more computing devices based on the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients to alert users of the one or more computing devices.

The system as claimed in claim 1, wherein the plurality of devices can include a group of medical devices comprising wearable multi parameter monitoring units, infusion pumps, ventilators, phototherapy units, radiant warmers, blood pressure (BP) cuffs, glucometers, Electrocardiography (ECG), Respiration rate monitors , wired multi parameter monitoring units, blood gas analysers, nebulizers, feeder, food pump and other pumps, dialysis machines, Electronic bed, ventilator, CPAP, anaesthesia unit, drip machine, phototherapy units, weighing scales , infant warmer and other legacy and modern wireless devices.

7. The system as claimed in claim 1, wherein the measured one or more health parameters comprise any or combination of a body temperature, a pulse rate, a respiration rate, a weight, oxygen saturation, heart rate, perfusion index, pleth variability index, Heart rate variability, transcutaneous bilirubin, arterial blood gas compositions, blood glucose levels, urine output and a blood pressure.

8. The system as claimed in claim 1, wherein the system comprises a second processing unit communicatively coupled to the first processing unit, the second processing unit being configured to receive a second set of signals associated with the measured one or more health parameters of each of the patients and the determined criticality score for each of the patients, and wherein the second processing unit is configured to store and execute another set of instructions.

9. A method to optimise power consumption and computation while monitoring patients, the method comprising: measuring, using a plurality of devices, one or more health parameters of the patients; receiving, using a processing unit, a first set of data packets associated with the measured one or more health parameters from each of the plurality of devices, each of the measured one or more health parameters being associated with a predefined weight; in response to the received first set of data packets, comparing, using the processing unit, values of the measured one or more health parameters of each of the patients with respective predefined thresholds; determining, using the processing unit, a criticality score for each of the patients based on the comparison; and prioritizing, using the processing unit, bandwidth and dynamic compute memory allocation to the plurality of devices based on the determine criticality score for each of the patients.

10. The method as claimed in claim 9, wherein the prioritization of bandwidth and dynamic compute memory allocation to the plurality of devices enable higher sampling rates of the first set of data packets from the plurality of devices by dynamic switching of connectivity and allocating higher resources, thereby decreasing energy consumption at the plurality of devices.