WO2023111628A1 - Cadre d'apprentissage multimodal pour prédiction d'empreinte carbone d'activités d'acquisition de soins de santé et de gestion de déchets, système, procédé et produit programme informatique - Google Patents

Cadre d'apprentissage multimodal pour prédiction d'empreinte carbone d'activités d'acquisition de soins de santé et de gestion de déchets, système, procédé et produit programme informatique Download PDF

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Publication number
WO2023111628A1
WO2023111628A1 PCT/IB2021/061666 IB2021061666W WO2023111628A1 WO 2023111628 A1 WO2023111628 A1 WO 2023111628A1 IB 2021061666 W IB2021061666 W IB 2021061666W WO 2023111628 A1 WO2023111628 A1 WO 2023111628A1
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Prior art keywords
engine
data
healthcare
inventory
information processing
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PCT/IB2021/061666
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English (en)
Inventor
Mohanasankar SIVAPRAKASAM
Keerthi Ram S.S
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Indian Institute Of Technology Madras
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Priority to PCT/IB2021/061666 priority Critical patent/WO2023111628A1/fr
Publication of WO2023111628A1 publication Critical patent/WO2023111628A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/087—Inventory or stock management, e.g. order filling, procurement or balancing against orders
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • 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
    • G16H40/20—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 for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Definitions

  • the present disclosure relates to information processing and more particularly to information processing of administrative data records and emission data from sensors in a healthcare provider setup for predictive analytics.
  • the Sustainable Procurement Index for Health is a globally established, recognized, and adaptable measurement tool for policymakers, manufacturers, suppliers, procurers, and healthcare facilities end users. It is designed to provide an incentive for entities to improve their environmental and social sustainability record.
  • the aim of the index is to facilitate sustainable procurement in the health sector by supporting the decision making of buyers and certainty for suppliers, to provide a robust and transparent method that communicates supply chain performance and to provide clear pathways for stakeholders to improve their performance.
  • the objective of SPIH is to develop standards for sustainable manufacturing, distribution and content of products procured by the health sector, strengthen capacity for sustainable procurement, sustainable production, supply and disposal of healthcare products, and application of appropriate indicators and monitoring and evaluation processes that help promote accountability for sustainable procurement.
  • the goal is to aggregate the demand and move the global supply chain toward greater sustainability, improving health outcomes and the global environment, to leverage the healthcare sector’s purchasing power to drive policies and markets towards ethically produced, non-toxic, and sustainable products and services, to enable healthcare organizations in making responsible purchasing decisions and move towards value-based procurement, reducing the overall cost of care whilst guaranteeing both human and environmental health throughout the supply chain.
  • any low-carbon health sector should adhere to certain broad principles. These include the use of appropriate low-carbon technology for care; low-carbon building design and construction; investment in renewable energy and energy efficiency; sustainable waste, water, and transport management; use of telemedicine; minimizing use of high greenhouse gas (GHG)- emitting anesthetic gases; procurement policies for low-carbon supply chains; promoting sustainable, healthy diets; and resilient strategies for withstanding extreme weather events.
  • GOG greenhouse gas
  • Carbon footprints are increasing day by day and there is no prediction and managing methods for healthcare system covering procurement activities, which combines minimizing transport, maximizing inventory capability, and handling real-time trade-offs arising while maintaining inventory. Carbon footprints must be realized at the consumer end, since most are indirect emissions, which require distributed data analysis at multi-facility level, capturing location-related factors, tracking facility level requirements through administrative data, for pattern mining and predictive analytics.
  • a multimodal learning system (100) for computing resource usage pattern is provided.
  • the system (100) includes a data repository engine (104) that receives input data from a plurality of healthcare setups (102), an information processing engine (106) that communicates with the data repository engine (104) to collect the input data that is collected from the plurality of healthcare setups (102), a live emission recording engine (110) that communicates with the information processing engine (106) to record the set of input data from the plurality of healthcare setups (102) in real time and maintains information on current utilization and occupancy across the plurality of healthcare setups (102), an inventory engine (112) that communicates with the information processing engine (106) to store historical data, current levels of stocks, past orders, and ratings of various suppliers, a pattern prediction engine (108) that communicates with the information processing engine (106) and predicts the demand in an anticipated time interval, an evaluation engine (116) that evaluates the supplier based on the sustainability practices followed, quality of product delivered, past orders, delays in delivery of orders and optimizes ordering sustainably, and a procurement engine (114) that communicates with the supplier (118) to place the order based on the data
  • the information processing engine (106) computes and maintains a real-time inventory of greenhouse gas (GHG) emissions at the entity level using the live emissions and utilization data repository. In yet another aspect of the invention, the information processing engine (106) performs computations that result in accounting of GHG, combining administrative and consumption information at a level of individual admitted patients, individual procedures performed, and any clinical or surgical or palliative services rendered.
  • GHG greenhouse gas
  • the pattern prediction engine (108) accesses the inventory engine (112) to derive patterns of ordering, and combining forecasted demand and current inventory levels, provides a facility level time and size recommendation for orders. In yet another aspect of the invention, the value predicted by the pattern prediction engine (108) is verified against the inventory engine (112).
  • the evaluation engine (116) automatically evaluates the suppliers (118) based on a collected data of shipments.
  • the evaluation engine (116) includes incentives for optimizing sustainability by selecting suppliers, time, and size of orders, with a configurable trade-off parameter between holding inventory at a site and deferring orders until predicted or demanded, or anticipated ordering for upcoming demand, while avoiding over-stocking and risking expiry or damage to inventory.
  • a method (200) for prediction of activity - based carbon footprint for healthcare setups is provided.
  • the method (200) includes collecting (202) input data from a plurality of healthcare facilities (102) by a data repository engine (104), receiving (204) the input data from the data repository engine (104) in an information processing engine (106) that communicates with a live emission recording engine (114) and maintains past resource utilization information across said plurality of health care facilities (102), forecasting a consumption pattern by pattern prediction engine (108) based on an integrated data received from information processing engine (106) and inventory engine (112) that contains information related to current resource consumption, analyzing a forecasted data received from pattern prediction engine and evaluation engine (116) that evaluates a supplier (118) based on the ratings and past records, and contacting the supplier (118) by the procurement engine (114) based on the evaluated data and forecasted data.
  • Figure 1 illustrates a block diagram of a multimodal system (100), according to an aspect herein;
  • Figure 2 illustrates a flowchart (200) that depicts working of the multimodal system (100) of Figure 1, according to an aspect herein;
  • Figure 3 illustrates an example computer system as may be used in a plurality of the devices illustrated in Figure 1, according to an aspect herein.
  • like reference numerals have been used, where possible to designate like elements common to the figures.
  • the embodiment herein overcomes the limitation by providing a system to compute a resource usage pattern and forecast a demand in order to minimize the wastage and pave the way of sustainable environment.
  • multimodal system (100) and “system (100)” and other such terms indicate a system for predicting a resource usage pattern and are used interchangeably.
  • plality of healthcare system (102) “plurality of healthcare centers (102)”, “plurality of healthcare setups (102)” and “networked hospitals (102)” are interchangeably used across the context.
  • healthcare facility and “healthcare system” refer to healthcare setups and are interchangeably used across the context.
  • Figure 1 illustrates a block diagram of a multimodal system (100), according to an embodiment herein.
  • the multimodal system (100) for computing a resource usage pattern is provided.
  • the multimodal system (100) consists of a plurality of healthcare setups (102), a data repository engine (104), an information processing engine (106), a pattern prediction engine (108), a live emission recording engine (110), an inventory engine (112), a procurement engine (114), an evaluation engine (116), and a supplier (118).
  • the data repository engine (104) receives a set of input data from the plurality of healthcare setups (102).
  • the plurality of healthcare setups (102) includes, but not limited to, a primary healthcare setup, a secondary healthcare setup, a tertiary healthcare setup, or a quaternary healthcare setup.
  • a set of inputs contains, not limited to, a patient’s transaction data, a clinician’s transaction data, resource availability data, emission data, co-ordinate care data.
  • the patient’s transaction data includes, not limited to, patient’s transaction details, billing details, medication history, hospitalized history, pharmacy history, surgery history, diagnosis history, treatment history, consumables and drugs, services rendered by healthcare providers, clinical, diagnostic, therapeutic, prognostic, palliative care, interventions, health history or fitness history.
  • the patient’s transaction details are collected by wearable and non- wearable electric devices.
  • the clinician’s transaction data includes, not limited to, patient’s database, patient’s profile or drug database.
  • resource availability data includes, not limited to, hospital resource data, patient’s database, Clinician’s database, treatment database, diagnosis database, surgery database, medication database, pharmacy database, or other management database.
  • the emission data includes, not limited to, greenhouse gas (GHG) emission of a patient, emission of a clinician, emission data of a treatment, emission data of a diagnosis, emission data of a medication, emission data of a medical service, emission data of an emergency vehicle, emission of a respective department, emission of a floor, emission data of a building, emission data of a healthcare system data, emission data of a city, emission data of a territory or emission data of a country.
  • the data repository engine (104) is a storage device.
  • the storage is a cloud storage. In another embodiment, the storage is a flash storage. In another embodiment, the storage is a disk storage. In another embodiment, the storage device is next-gen DNA based storage, RNA based storage, phage-based storage, or cell -based storage.
  • the information processing engine (106) communicates with the data repository engine (104) to collect an information that is collected from the plurality of healthcare system (102).
  • the information processing unit (106) interacts with an operational data store of equipment to track preventive maintenance schedule, efficiency of appliances and device, energy ratings and frequency of utilization.
  • the information processing engine (106) computes and maintains a real-time inventory of greenhouse gas (GHG) emissions at the entity level using a live emissions and utilization data repository.
  • GHG greenhouse gas
  • information processing engine (106) performs computations that result in accounting of GHG, combining administrative and consumption information at a level of individual admitted patients, individual procedures performed, and any clinical or surgical or palliative services rendered.
  • the information processing engine (106) also communicates with the live emission recording engine (110) which records set of inputs from the plurality of healthcare system (102) in real time that maintains information on current utilization and occupancy across the plurality of healthcare setups (102).
  • the live emission recording engine (110) optimizes resource utilization at individual sites, understand the extent and duration of admitted patients in real-time, anticipated needs, and carbon footprints of processes, equipment, and operational efficiency.
  • the live emission recording engine (110) is a cloud storage.
  • the live emission recording engine (110) is a flash storage.
  • the live emission recording engine (110) is a disk storage.
  • the storage device that acts a live emission recording engine (110) is next-gen DNA based storage device, RNA based storage device, phage-based storage device, or cell-based storage device.
  • the information processing engine (106) communicates with the inventory engine (112) that stores historical data, current levels of stocks, past orders, and ratings across various suppliers.
  • historical data contains, not limited to, past patient’s transaction data, past clinician’s transaction data, past resource availability data, past GHG emission data, past co-ordinate care data.
  • the past patient’s transaction data includes, not limited to, patient’s transaction details, billing details, medication history, hospitalization history, pharmacy history, surgery history, diagnosis history, treatment history, consumables and drugs, services rendered by healthcare providers, clinical, diagnostic, therapeutic, prognostic, palliative care, interventions, health history, fitness history or a combination thereof.
  • the patient’s transaction details are collected by a wearable or non-wearable electric device.
  • the past clinician’s transaction data includes, not limited to, patient’s database, patient’s profile or drug database.
  • resource availability data includes, not limited to, healthcare system resource data, patient’s database, clinician’s database, treatment database, diagnosis database, surgery database, medication database, pharmacy database, or other management database.
  • the past emission data includes, not limited to, GHG emission of a patient, GHG emission of a clinician, GHG emission data of a treatment, GHG emission data of a diagnosis, GHG emission data of a medication, GHG emission data of a medical service, GHG emission data of an emergency vehicle, GHG emission of a respective department, GHG emission of a floor, GHG emission data of a building, GHG emission data of a healthcare system data storage, GHG emission data of a healthcare system, GHG emission data of a city, GHG emission data of a territory or GHG emission data of a country.
  • the pattern prediction engine (108) communicates with the information processing engine (106) and predicts the demand in an anticipated time interval.
  • the anticipated time is a week.
  • the anticipated time is a month.
  • the anticipated time is a quarter of a year.
  • the anticipated time is a year.
  • the pattern prediction engine (108) performs predictive analysis for forecasting demand in an anticipated time interval such as next one week, next one month, next year, next five years, next ten years and so on.
  • the predictive value predicted by the pattern prediction engine (108) is verified against the inventory engine (112), that maintains current levels of stock of various consumables, drugs, therapeutic agents, and other components.
  • the inventory engine (112) maintains information of past orders, shipment timings, delays and variations in batches, packaging size, quality, pricing, and carbon footprint and sustainable practices information across various suppliers.
  • the inventory engine (112) also maintains information furnished by the suppliers, regarding a process efficiency and emission footprint.
  • the inventory engine (112) periodically refreshes an information based on supplier surveys and audits.
  • the inventory engine (112) further communicates with the evaluation engine (116) that automatically evaluates the supplier (118) based on a collected data of shipments.
  • the evaluation engine (116) allocates ratings to the suppliers (118).
  • the evaluation engine (116) includes incentives for optimizing sustainability by selecting suppliers, time, and size of orders, with a configurable trade-off parameter between holding inventory at a site and deferring orders until predicted or demanded, or anticipated ordering for upcoming demand, while avoiding over- stocking and risking expiry or damage to inventory.
  • the pattern prediction engine (108) accesses the inventory engine (112) to derive patterns of ordering, and combining forecasted demand and current inventory levels, provides a facility level time and size recommendation for orders.
  • the procurement engine (114) receives a predicted data from the pattern prediction engine (112) that contains the forecasted data and communicates with the supplier based on the ratings and capability data received from the evaluation engine (116)
  • system (100) also generates a patient’s journey and predicted analysis of anticipated events.
  • the system 100 accumulate the data on a per-day basis to provide on-demand predictive analysis.
  • the system (100) forecast a demand and minimize unused operation of storage equipment such as coolers, while also not under-stating a demand and creating procurement-related delays which have potential impact on patient care.
  • the demand forecast is aggregated across sites, that allows shipments to receive or forward directly to the supplier (118) or an entity.
  • Figure 2 illustrates a flowchart that depicts working of the system (100) of Figure 1, according to an embodiment herein.
  • the method (200) for providing an optimization strategies for greenhouse gas reduction is provided.
  • the input data contains, not limited to, patient's transaction data, clinician's transaction data, resource availability and emission data by data repository engine (104).
  • the plurality of healthcare system (102) contains, not limited to, a primary healthcare setup, a secondary healthcare setup, a tertiary healthcare setup, or a quaternary healthcare setup.
  • a set of inputs contains, not limited to, a patient’s transaction data, a clinician’s transaction data, a resource availability data, an emission data, a co-ordinate care data.
  • the patient’s transaction data includes, not limited to, patient’s transaction details, billing details, medication history, hospitalized history, pharmacy history, surgery history, diagnosis history, treatment history, consumables and drugs, service rendered, health history or fitness history.
  • the patient’s transaction details are collected by a wearable or non-wearable electric device.
  • the clinician’s transaction data includes, not limited to, patient’s database, patient’s profile or drug database.
  • resource availability data includes, not limited to, healthcare system resource data, patient’s database, Clinician’s database, treatment database, diagnosis database, surgery database, medication database, pharmacy database, or other management database.
  • the emission data includes, not limited to, emission of a patient, emission of a clinician, emission data of a treatment, emission data of a diagnosis, emission data of a medication, emission data of a medical service, emission data of an emergency vehicle, emission of a respective department, emission of a floor, emission data of a building, emission data of a healthcare system data, emission data of a city, emission data of a territory or emission data of a country.
  • the data repository engine (104) is a storage device.
  • the storage is a cloud storage.
  • the storage is a flash storage.
  • the storage is a disk storage.
  • the storage is next-gen DNA based storage, RNA based storage, phage-based storage, or cell-based storage.
  • an information processing engine (106) communicates with a live emission recording engine (114) that maintains record on real-time emission and an inventory engine (112) that maintains past resource utilization information across the plurality of health care facilities (102).
  • the inventory engine (112) is a storage device.
  • the data repository engine (104) is a storage device.
  • the storage is a cloud storage.
  • the storage is a flash storage.
  • the storage is a disk storage.
  • the storage is next-gen DNA based storage, RNA based storage, phage-based storage, or cell-based storage.
  • step (206) forecasting a consumption pattern by a pattern prediction engine 108 based on an integrated data received from information processing engine 106 and the inventory engine (112) that contains information related to current resource consumption.
  • step (208) analyzing a forecasted data received from pattern prediction engine and evaluation engine (116) that evaluates a supplier (118) based on the ratings and past records.
  • figure 3 depicts a schematic illustration of an example communications and/or computing system 300 implemented according to an exemplary embodiment herein.
  • the system 300 includes at least one processing element 310.
  • the processing element is a central processing unit (CPU).
  • the CPU is coupled by way of a bus 305 to a memory 320.
  • the memory 320 includes, in an exemplary embodiment, a memory portion 322 that can contain instructions that when executed by the processing element 310 can perform the methods described in more detail herein.
  • the memory 320 may be further used, according to an exemplary embodiment, as a temporary storage element for the processing element 310, and/or other uses, as the case may be.
  • the memory may comprise, in an exemplary embodiment, volatile memory such as, e.g., but not limited to, a random-access memory (RAM), and/or a non-volatile memory (NVM), such as, e.g., but not limited to, Flash memory, etc., according to an exemplary embodiment.
  • Memory 320 may further include, in an exemplary embodiment, a memory portion 324 containing an application program and/or application data, etc., according to an exemplary embodiment.
  • the processing element 310 may be coupled to an input 350, in one exemplary embodiment.
  • the processing element 310 may be further coupled with a database 330 and/or other storage device 330, according to an exemplary embodiment.
  • Database system and/or storage device 330 in an example embodiment, can be used for the purpose of holding a copy of the method executed in accordance with the disclosed technique, according to an exemplary embodiment.
  • Database 330 may further include, e.g., but may not be limited to, a storage portion 334, which may include and/or contain sub-portions of an application, and/or data referenced by the application, in an exemplary embodiment.
  • the promotion system can be configured to execute the methods described herein with respect of the remaining figures, according to an exemplary embodiment.
  • the exemplary method, system, and/or computer program products may be hardwired or, presented as a series of programmable instructions to be executed by the processing element 310.
  • the principles disclosed herein can be implemented as hardware, firmware, software, or any combination thereof.
  • the software can be implemented as an application program tangibly embodied on a program storage unit or computer readable medium.
  • the application program may be uploaded to, and/or be executed by, a machine comprising any suitable architecture, according to an exemplary embodiment.
  • the machine may be implemented on a computer platform 300 having hardware such as, e.g., but not limited to, a processing unit (“CPU”) 310, a memory 320, and/or input interfaces 350, output interfaces (not shown), as well as other components not shown for simplicity, but as would be well known to those skilled in the relevant art, according to an exemplary embodiment.
  • the computer platform may also include, in an exemplary embodiment, an operating system and/or microinstruction code.
  • the various processes and/or functions described herein may be either part of the microinstruction code and/or part of the application program, and/or any combination thereof, which may be executed by a CPU 310, whether or not such computer and/or processor is explicitly shown, according to an exemplary embodiment.
  • peripheral units may be connected, and/or coupled, to the computer platform such as, e.g., but not limited to, an additional memory unit 326 and/or removable memory unit 326, an additional data storage unit 336 and/or removable storage unit 336, and a printing unit, and/or display unit, and/or other input 350, output 360, communication 370 and/or networking components 370, etc., according to an exemplary embodiment.
  • references to “one embodiment,” “an embodiment,” “example embodiment,” “various embodiments,” “exemplary embodiment,” “exemplary embodiments,” etc., may indicate that the embodiment(s) so described may include a particular feature, structure, or characteristic, but not every embodiment necessarily includes the particular feature, structure, or characteristic. Further, repeated use of the phrase “in one embodiment,” or “in an exemplary embodiment,” do not necessarily refer to the same embodiment, although they may.
  • Coupled may mean that two or more elements are in direct physical or electrical contact, according to an exemplary embodiment. However, “coupled” may also mean that two or more elements are not in direct contact with each other, and yet still co-operate or interact with each other, according to an exemplary embodiment.
  • An algorithm is here, and generally, considered to be a self-consistent sequence of acts or operations leading to a desired result, according to an exemplary embodiment.
  • These include physical manipulations of physical quantities.
  • these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated, according to an exemplary embodiment. It has proven convenient at times, principally for reasons of common usage, to refer to these non-transitory signals as bits, values, elements, symbols, characters, terms, numbers or the like, according to an exemplary embodiment. It should be understood, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities, according to an exemplary embodiment.
  • processor can refer to any device or portion of a device that processes electronic data from registers and/or memory to transform that electronic data into other electronic data that can be stored in registers and/or memory, according to an exemplary embodiment.
  • a “computing platform” can comprise one or more processors, according to an exemplary embodiment.
  • a processor can include an embedded processor, and/or another subsystem processor, and/or a system on a chip (SOC), device, according to an exemplary embodiment.
  • SOC system on a chip
  • Embodiments may include apparatuses for performing the operations herein, according to an exemplary embodiment.
  • An apparatus may be specially constructed for the desired purposes, or it may comprise a general purpose and/or special purpose device selectively activated or reconfigured by a program stored in the device, according to an exemplary embodiment.
  • Computer programs may include computer application programs, and can include object-oriented computer programs, and can be stored in memory 320, and/or secondary memory, such as, e.g., storage 320, 322, 324, 326, 330, 334, 336 and/or removable memory and/or storage units 326, 336, also called computer program products, according to an exemplary aspect.
  • Such computer programs when executed, may enable the computer system 300 to perform the features as discussed herein.
  • the computer programs when executed, may enable the processor 310 to provide various functionality to the system 300 so as perform certain functions, according to an exemplary embodiment. Accordingly, such computer programs may represent controllers of the computer system 300, according to an exemplary embodiment.
  • the methods may be directed to a computer program product comprising a computer readable medium having control logic (computer software) stored therein.
  • the control logic when executed by the processor 310, may cause the processor 310 to perform features as described herein, according to an exemplary embodiment.
  • the software can be stored in a computer program product 336, 326, and can be loaded into computer system 300 using, e.g., but not limited to, the storage 330, the removable memory and/or storage device 326, 336, respectively, hard drive and/or communications and/or network interface 370, and/or router, etc.
  • the control logic when executed by the processor 310, can cause the processor 310 to perform the functions as described herein, according to an exemplary embodiment.
  • the computer software can run as a standalone software application program running atop an operating system (OS), or may be integrated into the operating system and/or application program, and/ or may be executed as an applet, or networked and/or client- server, and/or browserbased and/or other process as is well known, according to an exemplary embodiment.
  • OS operating system
  • applet or networked and/or client- server, and/or browserbased and/or other process as is well known, according to an exemplary embodiment.
  • implementation may be primarily in hardware using, for example, but not limited to, hardware components such as, e.g., but not limited to, application specific integrated circuits (ASICs), or one or more state machines, etc., according to an exemplary embodiment.
  • ASICs application specific integrated circuits
  • implementation of the hardware state machine to perform the functions described herein will be apparent to persons skilled in the relevant art(s), according to an exemplary embodiment.
  • implementation can be primarily in firmware.
  • implementation can combine any of, e.g., but not limited to, hardware, firmware, and software, etc.
  • Exemplary embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by a computing platform to perform the methods described herein.
  • a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer).
  • a machine-readable medium can include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices; electrical, optical, acoustical, or other form of non-transitory propagated signals (e.g., carrier waves, infrared signals, digital signals, etc.), memory 320, storage 330, and others, according to an exemplary embodiment.
  • Wired networks can include any of a wide variety of well- known means, or configuration to, for coupling voice and data communications devices together, according to an exemplary embodiment.
  • any of various exemplary wireless network technologies may be used to implement the embodiments discussed, according to an exemplary embodiment.
  • Specific details of wireless and/or wired communications networks are well known and are not included, as will be apparent to those of ordinary skill in the relevant art, according to an exemplary embodiment.
  • the computer-based data processing system and method described above is for purposes of example only and may be implemented in any type of computer system or programming or processing environment, or in a computer program, alone or in conjunction with hardware.
  • the present invention may also be implemented in software stored on a computer-readable medium and executed as a computer program on a general purpose or special purpose computer, according to an exemplary embodiment.
  • a general purpose or special purpose computer for clarity, only those aspects of the system germane to the invention are described, and product details well known in the art are omitted.
  • the computer hardware is not described in further detail. It should thus be understood that the invention is not limited to any specific computer language, program, communications and/or computing device, and/or computer, etc.
  • the present invention may be run on a stand-alone computer system, according to an exemplary embodiment, and/or may be run from a server computer system that can be accessed by a plurality of client computer systems interconnected over a network, according to an exemplary embodiment, such as, e.g., but not limited to, an intranet network, internet network, etc., and/or that is accessible to clients over the global Internet, etc..
  • a server computer system that can be accessed by a plurality of client computer systems interconnected over a network, according to an exemplary embodiment, such as, e.g., but not limited to, an intranet network, internet network, etc., and/or that is accessible to clients over the global Internet, etc.
  • many exemplary embodiments of the present invention may have application to a wide range of industries, according to an exemplary embodiment.
  • the present application discloses a system, the method implemented on a system, as well as a computer program product, such as, e.g., but not limited to, software instructions stored on a computer-readable/accessible non-transitory storage medium and executed on an electronic computer processor as a computer program to perform various steps of the method on a special purpose computer and/or in communication with other communication network devices including distributed mobile devices over one or more communication networks which may include wireless communication networks, etc., are within the scope of the present invention, according to an exemplary embodiment. Further, to the extent the present application discloses a method, a system of apparatuses configured to implement the method are within the scope of the present invention, according to an exemplary embodiment.

Abstract

La présente divulgation concerne un système multimodal (100) comprenant une pluralité de centres de soins de santé (102), un moteur de référentiel de données (104), un moteur de traitement d'informations (106), un moteur de prédiction de motif (108), un moteur d'enregistrement d'émission en direct (110), un moteur d'inventaire, un moteur d'acquisition (114), un moteur d'évaluation (116) et un fournisseur (118), ainsi qu'un procédé (200) associé.
PCT/IB2021/061666 2021-12-13 2021-12-13 Cadre d'apprentissage multimodal pour prédiction d'empreinte carbone d'activités d'acquisition de soins de santé et de gestion de déchets, système, procédé et produit programme informatique WO2023111628A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130325200A1 (en) * 2008-06-06 2013-12-05 Saudi Arabian Oil Company Methods For Planning and Retrofit of Energy Efficient Eco-Industrial Parks Through Inter-Time-Inter-Systems Energy Integration
US20180247022A1 (en) * 2017-02-24 2018-08-30 International Business Machines Corporation Medical treatment system
WO2019231466A1 (fr) * 2018-06-01 2019-12-05 Johnson Controls Technology Company Plateforme d'entreprise pour améliorer les performances opérationnelles
WO2021092263A1 (fr) * 2019-11-05 2021-05-14 Strong Force Vcn Portfolio 2019, Llc Tour de commande et plateforme de gestion d'entreprise pour réseaux à chaîne de valeurs

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130325200A1 (en) * 2008-06-06 2013-12-05 Saudi Arabian Oil Company Methods For Planning and Retrofit of Energy Efficient Eco-Industrial Parks Through Inter-Time-Inter-Systems Energy Integration
US20180247022A1 (en) * 2017-02-24 2018-08-30 International Business Machines Corporation Medical treatment system
WO2019231466A1 (fr) * 2018-06-01 2019-12-05 Johnson Controls Technology Company Plateforme d'entreprise pour améliorer les performances opérationnelles
WO2021092263A1 (fr) * 2019-11-05 2021-05-14 Strong Force Vcn Portfolio 2019, Llc Tour de commande et plateforme de gestion d'entreprise pour réseaux à chaîne de valeurs

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