AU2021106870A4 - An intelligent patient emergency management system (ipems) based on a centrally based ai decision system (cbaids) - Google Patents
An intelligent patient emergency management system (ipems) based on a centrally based ai decision system (cbaids) Download PDFInfo
- Publication number
- AU2021106870A4 AU2021106870A4 AU2021106870A AU2021106870A AU2021106870A4 AU 2021106870 A4 AU2021106870 A4 AU 2021106870A4 AU 2021106870 A AU2021106870 A AU 2021106870A AU 2021106870 A AU2021106870 A AU 2021106870A AU 2021106870 A4 AU2021106870 A4 AU 2021106870A4
- Authority
- AU
- Australia
- Prior art keywords
- assist
- patient
- unit
- assist unit
- hospital
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Ceased
Links
- 230000007170 pathology Effects 0.000 claims abstract description 26
- 238000012360 testing method Methods 0.000 claims abstract description 15
- 229940079593 drug Drugs 0.000 claims abstract description 14
- 239000003814 drug Substances 0.000 claims abstract description 14
- 238000007726 management method Methods 0.000 claims description 25
- 238000012545 processing Methods 0.000 claims description 24
- 230000009471 action Effects 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 9
- 230000006855 networking Effects 0.000 claims description 9
- 238000004891 communication Methods 0.000 claims description 8
- 238000003745 diagnosis Methods 0.000 claims description 7
- 230000002093 peripheral effect Effects 0.000 claims description 6
- 208000003443 Unconsciousness Diseases 0.000 claims description 3
- 230000004913 activation Effects 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 claims description 3
- 238000007405 data analysis Methods 0.000 claims description 3
- 238000013500 data storage Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 230000007246 mechanism Effects 0.000 claims description 3
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000000034 method Methods 0.000 description 10
- 230000008901 benefit Effects 0.000 description 9
- 230000008569 process Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 238000010339 medical test Methods 0.000 description 2
- 206010020751 Hypersensitivity Diseases 0.000 description 1
- 230000007815 allergy Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 230000006996 mental state Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Biomedical Technology (AREA)
- General Business, Economics & Management (AREA)
- Epidemiology (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The present disclosure relates to an intelligent patient emergency management system
(IPEMS) based on a centrally based Al decision system (CBAIDS). The system comprises:
an ambulance assist unit wherein, the ambulance assist comprises of a virtual connection to a
RTO network; a doctor assist unit wherein, the doctor assist unit assesses a present condition
of a patient; a patient assist unit wherein, the patient assist unit is an automated voice
controlled device to interact with the patient; a hospital assist unit wherein, the hospital assist
unit alerts a hospital help desk to complete all formalities before an arrival of the ambulance;
a pathology assist unit wherein, the pathology assist unit defines a list of pathology tests to be
performed based on a previous medical history; and a pharmacy assist unit wherein, the
pharmacy assist unit defines a list of medicines to be given to the patient.
15
~I
4 C
I~nI .2 oI
O
LIIIJ
to
L
4-C
Iu->
u4- C)
4. C)
LAo
E
CC
0 +-' 0
.(t.
E .
Mcc<
r-iCN
Description
~I
4 C .2 oI I~nI
to L 4-C Iu-> u4- C)
LAo E 4. C) 0 +-' 0
.(t. CC E . Mcc< r-iCN
AN INTELLIGENT PATIENT EMERGENCY MANAGEMENT SYSTEM (IPEMS) BASED ON A CENTRALLY BASED Al DECISION SYSTEM (CBAIDS)
The present disclosure relates to an intelligent patient emergency management system (IPEMS) based on a centrally based Al decision system (CBAIDS) wherein, the IPEMS is an integrated real-time intelligent robotic automation system designed to monitor the patients' health and optimize the resources during the emergency situation based on artificial intelligence, machine learning, deep learning and IoT.
In many emergency cases the patient loses the crucial time due to many other factors such as un-timely diagnosis, drug supply and lack of appropriate medication. Due to the late addressing of these factors a patient may become disabled, or they may lose their life. During emergencies large volumes of patient related data has to be transferred within, doctors, medical assistants, nurses, pathologists etc. wherein, utilizing computer-based techniques should help in a faster transmission of data within its network. In an emergency case, time acts as a major element for helping a patient. Hence, a lot of work has been suggested to help make the emergency hospital measures more time efficient. Although none of these efforts are completely automated and most of them rely of manual techniques.
In order to make the existing solutions more efficient there is need to develop an intelligent patient emergency management system (IPEMS) based on a centrally based Al decision system (CBAIDS).
The present disclosure relates to an intelligent patient emergency management system (IPEMS) based on a centrally based Al decision system (CBAIDS). The disclosed Intelligent Patient Emergency Management System (IPEMS) is an integrated AI/ML/DL/IoT based automated system, which basically focuses on providing the best and most optimized medical support possible to the patient in need and at the earliest. This system mainly consists of a centrally based Al System which has multiple sub-units which work in sync with each other, also in accordance with the suggestions received from the central unit and further verified by the expert in-the-loop. The IPEMS helps in the quick detection and diagnosis of the problem, also suggesting the primary and secondary actions that can be performed enroute to save the crucial treatment time. All the assisting sub-units work together to perform all the tasks and keep the hospital staff and the treating doctor at the hospital updated about the current condition as well as the previous records (Blood Group, Allergies etc.) of the patient through the UID based information management services.
There are 2 on-board drones responsible for managing the supply of drugs or pathology samples collected enroute, from the nearest medical facility, if needed, thus reducing the diagnosis time at the hospital drastically. The automated voice-controlled device on-board will try to extract all details about the patients' mental state and assist the doctor to be prepared for the immediate treatment. The optimized usage of all these resources and advanced technologies will prove to be very beneficial for the doctor as well as for the patients' health.
In an embodiment, an intelligent patient emergency management system (IPEMS) 100 based on a centrally based Al decision system (CBAIDS), wherein, the said system comprises: an ambulance assist unit 102 wherein, the ambulance assist comprises of a virtual connection to a RTO network, to control and optimise a traffic while an ambulance is enroute; a doctor assist unit 104 wherein, the doctor assist unit assesses a present condition of a patient and suggests a primary tests; a patient assist unit 106 wherein, the patient assist unit is an automated voice-controlled device to interact with the patient to prevent the patient from being unconscious (if required), or talk to the patient to retrieve and record the patient's present state of mind; a hospital assist unit 108 wherein, the hospital assist unit alerts a hospital help desk to complete all formalities before an arrival of the ambulance; a pathology assist unit 110 wherein, the pathology assist unit defines a list of pathology tests to be performed based on a previous medical history and the current medical condition of the patient; and a pharmacy assist unit 112 wherein, the pharmacy assist unit defines a list of medicines to be given to the patient immediately based on an information received from the doctor assist unit and the patient assist unit.
To further clarify advantages and features of the present disclosure, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
Figure 1 illustrates an intelligent patient emergency management system (IPEMS) based on a centrally based Al decision system (CBAIDS) in accordance with an embodiment of the present disclosure.
Figure 2 illustrates (a) intelligent patient emergency management system (IPEMS) representative architecture; and (b) the functional block diagram of CBAIDS in accordance with an embodiment of the present disclosure.
Figure 3 illustrates the general architecture for all assist units in accordance with an embodiment of the present disclosure.
Further, skilled artisans will appreciate that elements in the drawings are illustrated for simplicity and may not have necessarily been drawn to scale. For example, the flow charts illustrate the method in terms of the most prominent steps involved to help to improve understanding of aspects of the present disclosure. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the drawings by conventional symbols, and the drawings may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the drawings with details that will be readily apparent to those of ordinary skill in the art having benefit of the description herein.
For the purpose of promoting an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the invention is thereby intended, such alterations and further modifications in the illustrated system, and such further applications of the principles of the invention as illustrated therein being contemplated as would normally occur to one skilled in the art to which the invention relates.
It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be restrictive thereof.
Reference throughout this specification to "an aspect", "another aspect" or similar language means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but do not necessarily, all refer to the same embodiment.
The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such process or method. Similarly, one or more devices or sub-systems or elements or structures or components proceeded by "comprises...a" does not, without more constraints, preclude the existence of other devices or other sub-systems or other elements or other structures or other components or additional devices or additional sub-systems or additional elements or additional structures or additional components.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The system, methods, and examples provided herein are illustrative only and not intended to be limiting.
Embodiments of the present disclosure will be described below in detail with reference to the accompanying drawings.
Referring to Figure 1 illustrates an intelligent patient emergency management system (IPEMS) based on a centrally based Al decision system (CBAIDS) in accordance with an embodiment of the present disclosure. The intelligent patient emergency management system (IPEMS) 100 based on a centrally based Al decision system (CBAIDS), wherein, the said system comprises: an ambulance assist unit 102 wherein, the ambulance assist comprises of a virtual connection to a RTO network, to control and optimize a traffic while an ambulance is enroute; a doctor assist unit 104 wherein, the doctor assist unit assesses a present condition of a patient and suggests a primary tests; a patient assist unit 106 wherein, the patient assist unit is an automated voice-controlled device to interact with the patient to prevent the patient from being unconscious (if required), or talk to the patient to retrieve and record the patient's present state of mind; a hospital assist unit 108 wherein, the hospital assist unit alerts a hospital help desk to complete all formalities before an arrival of the ambulance; a pathology assist unit 110 wherein, the pathology assist unit defines a list of pathology tests to be performed based on a previous medical history and the current medical condition of the patient; and a pharmacy assist unit 112 wherein, the pharmacy assist unit defines a list of medicines to be given to the patient immediately based on an information received from the doctor assist unit and the patient assist unit.
In an embodiment, the system, wherein, a general architecture of all the assist units comprises: a sensor hub wherein, the sensor hub acts as a common connection point for the data collected from a sensing elements from a devices peripheral in accordance with a physical systems wherein, the sensor hub then forwards a data to a processing unit through an input/output mechanism (Serial Peripheral Interface (SPI) or Inter-Integrated Circuit (12C)); a networking unit wherein, the networking unit acts as a medium which collects the data from the sensors and transfers it to a connected devices within the system through one or more of a plurality of means comprising Bluetooth, Wi-Fi, etc.; a processing unit wherein, the processing unit is a main data processing unit of the system which takes an inputs from the sensor hub, an external data sources or a local database wherein, it analyses a received data based on the AI/ML/DL algorithms and the patients' medical record to suggest a suitable action to the Control Unit or any of the other assist units wherein, the processing unit also updates the database to maintain a sync of all actions and provide an optimal solution; a control unit wherein, the control unit is an essential component which works very closely with the processing unit wherein, it ensures a proper sequencing and functioning of the system components wherein, it also generates the sync signals and an appropriate control signals to trigger any event or a device activation/de-activation operations; and a local database wherein, the local database is an on-board data storage unit that holds the local data associated with the assist units as well as the information regarding an operations/events performed by it wherein, it is used to by the processing unit for analysing the events and making appropriate decisions.
In another embodiment, the system, wherein, the CBAIDS uses an artificial intelligence, machine learning and deep learning algorithms to provide an intelligent decisions based on a situational analysis, a data from a hospital systems and a patients' medical history wherein, the CBAIDS establishes an integrated, intelligent, real time system architecture to communicate with various sub-systems, internal modules, assist units and functional departments of a hospital system wherein, it has access to a various databases locally and in a cloud environment wherein, it effectively manages and utilizes the data for arriving at intelligent decisions following appropriate analysis wherein, a data analysis, and the processing unit is an intelligent unit based on a pre-leamed ML/DL algorithms to help with a data driven advice and suggestions to a medical expert/staff and a various assist units in the system wherein, a Human-in the-loop operation provides a higher control on a machine and overrides the machine decisions if required.
In yet another embodiment, the system, wherein, the CBAIDS has a networking and a control unit that helps manage all in-bound and out-bound communication/system messages to be routed through an appropriate network protocols depending on a scenario wherein, various devices within the CBAIDS system and its periphery are managed by a device management module wherein, an application and a service management ensures the communication, alerts and data flow for various clinical services including a mobility solutions wherein, the various hospital departments and their operations are integrated into the CBAIDS and receives a commands/ directions in a form of various assist units interfaces wherein, the CBAIDS also comprises an integrated patient management and an accounting system to help with a registration, a billing and an insurance transactions wherein, various assist units are intelligent units in themselves and work in coordination with the CBAIDS guidance and direction and wherein, a data privacy and security is a critical element of the CBAIDS and is taken care by an information security module wherein, the ambulance assist unit helps the ambulance driver by providing a shortest and most suitable route to a hospital location and wherein, the ambulance assist system is in constant communication and control with an on-board drone system wherein, the on-board drones are responsible for managing a supply of drugs or a pathology samples collection from a nearest medical facility thus reducing the diagnosis time at the hospital drastically.
In yet another embodiment, the system, wherein, the doctor assist unit suggests a secondary tests to be performed to the Pathology Assist based on a diagnosis of the primary tests and the previous medical history and wherein, the hospital assist unit alerts a hospital staff from a designated ward wherein, it predicts a path to be followed by the patient inside the hospital in emergency conditions wherein, the pathology assist unit assists the drone on the ambulance to carry a samples to the designated pathology laboratory and wherein, the pharmacy assist unit assists the drone at the nearest certified pharmacy to deliver the necessary drugs at the system generated checkpoint as soon as possible and wherein, the intelligent patient emergency management system (IPEMS) is an integrated AI/ML/DL/IoT based automated system, wherein, the system focuses on providing a best and most optimised medical support possible to the patient in need and at the earliest and wherein, the system mainly comprises of a centrally based Al System which has multiple sub-units which work in sync with each other, also in accordance to a suggestions received from the central unit and further verified by the expert in-the-loop.
Figure 2 illustrates (a) intelligent patient emergency management system (IPEMS) representative architecture; and (b) the functional block diagram of CBAIDS in accordance with an embodiment of the present disclosure.
In an implementation, as shown in figure 2a, the working of the system comprises: receiving an emergency request for the ambulance by the Hospital Assist wherein, the hospital assist sends the received UID to the CBAIDS for further action wherein, the CBAIDS then retrieves the patients' location/address from the UID and sends the data to the Ambulance Assist unit, which further sends the start and stop GPS coordinates to the RTO
(Regional Transport Office) network to optimize its route for the easy movement. The patient is picked, and the Ambulance Assist sends a system generated voice message regarding the patient's current condition to the emergency contact numbers listed in the patients' UID, if necessary.
Enroute to the hospital the CBAIDS commands the Doctor Assist unit to check the patients' previous medical records and perform the primary medical test (Blood Pressure, Blood Sugar Level, SpO2 levels etc.). The Doctor Assist unit shares the results of the primary tests to the CBAIDS which uses the AIML/DL algorithms and necessary information from its database to predict the necessity of the secondary medical tests (Pathology Assist). If required the CBAIDS suggests the Pathology Assist to perform the secondary tests, where the doctor on-board can intervene and make required changes to the tests suggested by the CBAIDS. After the successful approval the Pathology Assist directs the Ambulance Assist to activate its Drone Control unit, while the doctor collects the patients' samples.
If the patient requires any medication in short time which is not available on-board the Pharmacy Assist can pass a request to the CBAIDS which analyses the availability of the medication at the various certified pharmacies and assign and provide the live location coordinates of the ambulance to a drone which will provide the required medication to the ambulance at the earliest. Meanwhile, the CBAIDS ascertains the estimated time of arrival of the ambulance to the hospital and directs the Ambulance Assist to send the drone to the specified pathology facility with the given GPS coordinates.
With regard to the test results and the inputs received from the Doctor Assist, the CBAIDS commands the Hospital Assist to complete all the formalities and also predicts the path to be followed by the patient inside the hospital on his/her arrival. To add on to this the Hospital Assist also assigns a doctor to the patient on-board according to his/her requirements. During this whole process the CBAIDS receives all the medical data from the various Assist Units and maintains it in the patients' database which is continuously shared with the assigned doctor at the hospital, to know about the patients' condition before the patient reaches and begins the treatment.
The CBAIDS is the core of the intelligent patient emergency management system (IPEMS) as shown in figure 2b, it establishes integrated, intelligent, real time system architecture to communicate with various sub-systems, internal modules, assist units and functional departments of the hospital system. It has access to various databases locally and in cloud environment. It effectively manages and utilizes the data for arriving at intelligent decisions following appropriate analysis. The data analysis and processing module is the intelligent unit based on pre-leamed ML/DL algorithms to help with data driven advice and suggestions to the medical expert/staff and various assist units in the system. The Human-in the-loop operation provides higher controls on the machine and overrides machine decisions if required. The CBAIDS has a networking and control unit that helps manage all in-bound and out-bound communication/system messages to be routed through appropriate network protocols depending on the scenario.
Various devices within the system and their periphery are managed by the device management module. App and service management ensures the communication, alerts and data flow for various clinical services including mobility solutions. Various hospital departments and their operations are integrated into this system and receives commands/ directions in the form of various assist units' interfaces. It also has patient management and accounting system integrated to help with the registration, billing, and insurance transactions. Various assist units are intelligent units in themselves and work in coordination with CBAIDS guidance and direction. The data privacy and security is a critical element of the CBAIDS and is taken care by the information security module.
Figure 3 illustrates the general architecture for all assist units in accordance with an embodiment of the present disclosure.
In an implementation, as shown in figure 3 the general architecture for all assist units comprises:
a sensor hub wherein, the sensor hub acts as a common connection point for the data collected from a sensing elements from a devices peripheral in accordance with a physical systems wherein, the sensor hub then forwards a data to a processing unit through an input/output mechanism (Serial Peripheral Interface (SPI) or Inter-Integrated Circuit (12C));
a networking unit wherein, the networking unit acts as a medium which collects the data from the sensors and transfers it to a connected devices within the system through one or more of a plurality of means comprising Bluetooth, Wi-Fi, etc.; a processing unit wherein, the processing unit is a main data processing unit of the system which takes an inputs from the sensor hub, an external data sources or a local database wherein, it analyses a received data based on the AI/ML/DL algorithms and the patients' medical record to suggest a suitable action to the Control Unit or any of the other assist units wherein, the processing unit also updates the database to maintain a sync of all actions and provide an optimal solution; a control unit wherein, the control unit is an essential component which works very closely with the processing unit wherein, it ensures a proper sequencing and functioning of the system components wherein, it also generates the sync signals and an appropriate control signals to trigger any event or a device activation/de-activation operations; and a local database wherein, the local database is an on-board data storage unit that holds the local data associated with the assist units as well as the information regarding an operations/events performed by it wherein, it is used to by the processing unit for analysing the events and making appropriate decisions.
The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims.
Benefits, other advantages, and solutions to problems have been described above with regard to specific embodiments. However, the benefits, advantages, solutions to problems, and any component(s) that may cause any benefit, advantage, or solution to occur or become more pronounced are not to be construed as a critical, required, or essential feature or component of any or all the claims.
Claims (10)
1. An intelligent patient emergency management system (IPEMS) based on a centrally based Al decision system (CBAIDS), wherein, the said system comprises:
an ambulance assist unit wherein, the ambulance assist comprises of a virtual connection to a RTO network, to control and optimise a traffic while an ambulance is enroute;
a doctor assist unit wherein, the doctor assist unit assesses a present condition of a patient and suggests a primary tests;
a patient assist unit wherein, the patient assist unit is an automated voice controlled device to interact with the patient to prevent the patient from being unconscious (if required), or talk to the patient to retrieve and record the patient's present state of mind;
a hospital assist unit wherein, the hospital assist unit alerts a hospital help desk to complete all formalities before an arrival of the ambulance;
a pathology assist unit wherein, the pathology assist unit defines a list of pathology tests to be performed based on a previous medical history and the current medical condition of the patient; and
a pharmacy assist unit wherein, the pharmacy assist unit defines a list of medicines to be given to the patient immediately based on an information received from the doctor assist unit and the patient assist unit.
2. The system as claimed in claim 1 wherein, a general architecture of all the assist units comprises:
a sensor hub wherein, the sensor hub acts as a common connection point for the data collected from a sensing elements from a devices peripheral in accordance with a physical systems wherein, the sensor hub then forwards a data to a processing unit through an input/output mechanism (Serial Peripheral Interface (SPI) or Inter Integrated Circuit (12C)); a networking unit wherein, the networking unit acts as a medium which collects the data from the sensors and transfers it to a connected devices within the system through one or more of a plurality of means comprising Bluetooth, Wi-Fi, etc.; a processing unit wherein, the processing unit is a main data processing unit of the system which takes an inputs from the sensor hub, an external data sources or a local database wherein, it analyses a received data based on the AI/ML/DL algorithms and the patients' medical record to suggest a suitable action to the Control Unit or any of the other assist units wherein, the processing unit also updates the database to maintain a sync of all actions and provide an optimal solution; a control unit wherein, the control unit is an essential component which works very closely with the processing unit wherein, it ensures a proper sequencing and functioning of the system components wherein, it also generates the sync signals and an appropriate control signals to trigger any event or a device activation/de-activation operations; and a local database wherein, the local database is an on-board data storage unit that holds the local data associated with the assist units as well as the information regarding an operations/events performed by it wherein, it is used to by the processing unit for analysing the events and making appropriate decisions.
3. The system as claimed in claim 1 wherein, the CBAIDS uses an artificial intelligence, machine learning and deep learning algorithms to provide an intelligent decisions based on a situational analysis, a data from a hospital systems and a patients' medical history.
4. The system as claimed in claim 3 wherein, the CBAIDS establishes an integrated, intelligent, real time system architecture to communicate with various sub-systems, internal modules, assist units and functional departments of a hospital system wherein, it has access to a various databases locally and in a cloud environment wherein, it effectively manages and utilizes the data for arriving at intelligent decisions following appropriate analysis wherein, a data analysis, and the processing unit is an intelligent unit based on a pre-learned ML/DL algorithms to help with a data driven advice and suggestions to a medical expert/staff and a various assist units in the system wherein, a Human-in the-loop operation provides a higher control on a machine and overrides the machine decisions if required.
5. The system as claimed in claim 3 wherein, the CBAIDS has a networking and a control unit that helps manage all in-bound and out-bound communication/system messages to be routed through an appropriate network protocols depending on a scenario wherein, various devices within the CBAIDS system and its periphery are managed by a device management module.
6. The system as claimed in claim 3 wherein, an application and a service management ensures the communication, alerts and data flow for various clinical services including a mobility solutions wherein, the various hospital departments and their operations are integrated into the CBAIDS and receives a commands/ directions in a form of various assist units interfaces wherein, the CBAIDS also comprises an integrated patient management and an accounting system to help with a registration, a billing and an insurance transactions wherein, various assist units are intelligent units in themselves and work in coordination with the CBAIDS guidance and direction and wherein, a data privacy and security is a critical element of the CBAIDS and is taken care by an information security module.
7. The system as claimed in claim 1 wherein, the ambulance assist unit helps the ambulance driver by providing a shortest and most suitable route to a hospital location and wherein, the ambulance assist system is in constant communication and control with an on-board drone system wherein, the on-board drones are responsible for managing a supply of drugs or a pathology samples collection from a nearest medical facility thus reducing the diagnosis time at the hospital drastically.
8. The system as claimed in claim 1 wherein, the doctor assist unit suggests a secondary tests to be performed to the Pathology Assist based on a diagnosis of the primary tests and the previous medical history and wherein, the hospital assist unit alerts a hospital staff from a designated ward wherein, it predicts a path to be followed by the patient inside the hospital in emergency conditions.
9. The system as claimed in claim 1 wherein, the pathology assist unit assists the drone on the ambulance to carry a samples to the designated pathology laboratory and wherein, the pharmacy assist unit assists the drone at the nearest certified pharmacy to deliver the necessary drugs at the system generated checkpoint as soon as possible.
10. The system as claimed in claim 1 wherein, the intelligent patient emergency management system (IPEMS) is an integrated AIML/DL/IoT based automated system, wherein, the system focuses on providing a best and most optimised medical support possible to the patient in need and at the earliest and wherein, the system mainly comprises of a centrally based AT System which has multiple sub-units which work in sync with each other, also in accordance to a suggestions received from the central unit and further verified by the expert in-the-loop.
Ambulance Doctor Patient Hospital Pathology Pharmacy Assist Unit Assist Unit Assist Unit Assist Unit Assist Unit Assist Unit 102 104 106 108 110 112
Figure 1 g
(a) (b)
Figure 2
Figure 3
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106870A AU2021106870A4 (en) | 2021-08-24 | 2021-08-24 | An intelligent patient emergency management system (ipems) based on a centrally based ai decision system (cbaids) |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| AU2021106870A AU2021106870A4 (en) | 2021-08-24 | 2021-08-24 | An intelligent patient emergency management system (ipems) based on a centrally based ai decision system (cbaids) |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| AU2021106870A4 true AU2021106870A4 (en) | 2021-12-16 |
Family
ID=78948957
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| AU2021106870A Ceased AU2021106870A4 (en) | 2021-08-24 | 2021-08-24 | An intelligent patient emergency management system (ipems) based on a centrally based ai decision system (cbaids) |
Country Status (1)
| Country | Link |
|---|---|
| AU (1) | AU2021106870A4 (en) |
-
2021
- 2021-08-24 AU AU2021106870A patent/AU2021106870A4/en not_active Ceased
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mohanta et al. | Healthcare 5.0: A paradigm shift in digital healthcare system using artificial intelligence, IOT and 5G communication | |
| US20220293285A1 (en) | Device and methods for machine learning-driven diagnostic testing | |
| US20090198733A1 (en) | Healthcare resource locator | |
| US20170235898A1 (en) | System and Method of Patient Flow and Treatment Management | |
| WO2020011244A1 (en) | Method and system for optimized matching of medical resources for emergency and severe disease rescue | |
| CN104978470A (en) | Mobile medical care information management system | |
| US20160012197A1 (en) | Health advising system | |
| WO2017108944A1 (en) | System, device and method for guiding a patient in a hospital setup | |
| US20120203564A1 (en) | Method and System for Real-Time Automatic Optimization of Emergency Room Resources Management | |
| WO2011084470A1 (en) | Health care device and systems and methods for using the same | |
| Moore et al. | Event detection: a clinical notification service on a health information exchange platform | |
| Al-Sakran | Framework architecture for improving healthcare information systems using agent technology | |
| US11838112B1 (en) | Systems and methods for real-time transmission of digital data using a plurality of channels | |
| US20200117195A1 (en) | Medical network system and external device | |
| AU2021106870A4 (en) | An intelligent patient emergency management system (ipems) based on a centrally based ai decision system (cbaids) | |
| CN117577257A (en) | Internet hospital data management system | |
| US20210020319A1 (en) | Integrated on-line and off-line interrogation system | |
| US11348695B2 (en) | Machine logic for recommending specialized first aid services | |
| US11482331B2 (en) | Assist system, assist method, and assist program | |
| Paramonov et al. | Communication between emergency medical system equipped with panic buttons and hospital information systems: use case and interfaces | |
| Singhal et al. | Context awareness for healthcare service delivery with intelligent sensors | |
| Domnori et al. | Ubimedic2: An agent-based approach in territorial emergency management | |
| Abdullah et al. | Pervasive healthcare computing: applications, challenges and solutions | |
| US11908573B1 (en) | Predictive resource management | |
| WO2023052832A1 (en) | A healthcare management system and a method to operate the same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| FGI | Letters patent sealed or granted (innovation patent) | ||
| MK22 | Patent ceased section 143a(d), or expired - non payment of renewal fee or expiry |