Infection spread minimization using route management

Abstract

A system includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the system to: obtain real-time data of potential routes for transporting a patient with an infectious disease within a hospital; apply a prediction model to the real-time data to minimize the potential for spreading the infectious disease while transporting the patient; and suggest a route among the potential routes with a minimized potential for spreading the infectious disease.

Description

INFECTION SPREAD MINIMIZATION USING ROUTE MANAGEMENTBACKGROUND

[0001] Infectious diseases are diseases capable of being transmitted from person to person. Some infectious disease may cause life threatening illnesses. As a whole, infectious diseases present a serious public health hazard. Medical transportation and handling of patients with infectious diseases contribute to increased risks to others including healthcare professionals. Staff in a medical facility involved with transport and treatment are exposed to increased risk of transmission, and the risk further increases if proper equipment is not used and / or proper training, policies and / or procedures are not followed. Planning for, and monitoring and tracking of, viral / bacterial infection spread during transportation is crucial for controlling disease transmission.

[0002] For medical facilities, there are many different contexts for which transmission threat escalates, such as in connection with the absence of personal protective equipment, the transfer from one team to another, or lack of appropriate cleaning policy. Infectious patients should typically not be moved or transported unless highly necessary, and they should only be moved in a way that limits contact with other people. Restricting contact with other people can be challenging insofar as most infected patients have to be in contact with multiple health care professionals for providing care and treatment. Moreover, in many cases, emergency services are likely to be the first in contact with the patients as they offer care even before being able to identify the infectious pathogen.

[0003] Currently, there is no real-time sensor-driven automated path planning, nor separate protocols for such patient transportation and staff assignment.SUMMARY

[0004] According to an aspect of the present disclosure, a system includes a memory that stores instructions; and a processor that executes the instructions. When executed by the processor, the instructions cause the system to: obtain real-time data of potential routes for transporting a patient with an infectious disease within a hospital; apply a prediction model to the real-time data to minimize the potential for spreading the infectious disease while transporting the patient; andsuggest a route among the potential routes with a minimized potential for spreading the infectious disease.

[0005] According to another aspect of the present disclosure, a method for suggesting a route for transporting a patient includes obtaining, by a system comprising a memory that stores instructions and a processor that executes the instructions, real-time data of potential routes for transporting a patient with an infectious disease within a hospital; applying, by the system, a prediction model to the real-time data to minimize the potential for spreading the infectious disease while transporting the patient; and suggesting, by the system, a route among the potential routes with a minimized potential for spreading the infectious disease.

[0006] According to another aspect of the present disclosure, a tangible, non-transitory computer-readable medium stores instructions. When executed by a processor, the instructions cause the processor to obtain real-time data of potential routes for transporting a patient with an infectious disease within a hospital; apply a prediction model to the real-time data to minimize the potential for spreading the infectious disease while transporting the patient; and suggest a route among the potential routes with a minimized potential for spreading the infectious disease.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The example embodiments are best understood from the following detailed description when read with the accompanying drawing figures. It is emphasized that the various features are not necessarily drawn to scale. In fact, the dimensions may be arbitrarily increased or decreased for clarity of discussion. Wherever applicable and practical, like reference numerals refer to like elements.

[0008] FIG. 1 illustrates a system for infection spread minimization using route management, in accordance with a representative embodiment.

[0009] FIG. 2 illustrates a method for infection spread minimization using route management, in accordance with a representative embodiment.

[0010] FIG. 3 illustrates a set of potential routes for infection spread minimization using route management, in accordance with a representative embodiment.

[0011] FIG. 4. illustrates a hybrid system and flow for infection spread minimization using route management, in accordance with a representative embodiment.

[0012] FIG. 5 illustrates a computer system, on which a method for infection spread minimization using route management is implemented, in accordance with another representative embodiment.DETAILED DESCRIPTION

[0013] In the following detailed description, for the purposes of explanation and not limitation, representative embodiments disclosing specific details are set forth in order to provide a thorough understanding of embodiments according to the present teachings. However, other embodiments consistent with the present disclosure that depart from specific details disclosed herein remain within the scope of the appended claims. Descriptions of known systems, devices, materials, methods of operation and methods of manufacture may be omitted so as to avoid obscuring the description of the representative embodiments. Nonetheless, systems, devices, materials and methods that are within the purview of one of ordinary skill in the art are within the scope of the present teachings and may be used in accordance with the representative embodiments. It is to be understood that the terminology used herein is for purposes of describing particular embodiments only and is not intended to be limiting. Definitions and explanations for terms herein are in addition to the technical and scientific meanings of the terms as commonly understood and accepted in the technical field of the present teachings.

[0014] It will be understood that, although the terms first, second, third etc. may be used herein to describe various elements or components, these elements or components should not be limited by these terms. These terms are only used to distinguish one element or component from another element or component. Thus, a first element or component discussed below could be termed a second element or component without departing from the teachings of the inventive concept.

[0015] As used in the specification and appended claims, the singular forms of terms ‘a’, ‘an’ and ‘the’ are intended to include both singular and plural forms, unless the context clearly dictates otherwise. Additionally, the terms "comprises", and / or "comprising," and / or similar terms when used in this specification, specify the presence of stated features, elements, and / or components, but do not preclude the presence or addition of one or more other features, elements, components, and / or groups thereof. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0016] Unless otherwise noted, when an element or component is said to be “connected to”, “coupled to”, or “adjacent to” another element or component, it will be understood that the element or component can be directly connected or coupled to the other element or component, or intervening elements or components may be present. That is, these and similar terms encompass cases where one or more intermediate elements or components may be employed to connect two elements or components. However, when an element or component is said to be “directly connected” to another element or component, this encompasses only cases where the two elements or components are connected to each other without any intermediate or intervening elements or components.

[0017] The present disclosure, through one or more of its various aspects, embodiments and / or specific features or sub-components, is thus intended to bring out one or more of the advantages as specifically noted below.

[0018] As described herein, a real-time locating system may be used to provide inputs to a machine learning model that optimizes transportation route and / or other optimizable functions and characteristics such as medical staff assignment when moving patients. The machine learning model optimizes the transportation route and / or other functions and characteristics associated with moving infectious patients to minimize the chances of pathogen transmission to the medical staff, other patients and any other persons potentially present when moving a patient. The optimal route may be selected from multiple potential routes, and may be selected from route options that vary in two dimensions or three dimensions, that vary in time, that vary in medical staff assigned to move the patient, and that vary potentially in other ways.

[0019] FIG. 1 illustrates a system 100 for infection spread minimization using route management, in accordance with a representative embodiment.

[0020] The system 100 in FIG. l is a system for infection spread minimization using route management and includes components that may be provided together or that may be distributed. The system 100 includes a first location sensor 111, a second location sensor 112, a third location sensor 113, a first monitoring camera 116, a second monitoring camera 117, a third monitoring camera 118, a network 101, a computer 120, and a display 180. The computer 120 includes a first interface 121, a second interface 122, and a controller 150. The controller 150 includes at least a memory 151 that stores instructions and a processor 152 that executes theinstructions. A computer that can be used to implement the controller 150 is depicted in FIG. 5, though a computer that can be used to implement the controller 150 may include more or fewer elements than depicted in FIG. 1 or FIG. 5. The display 180 includes a graphical user interface 181.

[0021] The first location sensor 111, the second location sensor 112 and the third location sensor 113 are representative of sensors such as global positioning satellite (GPS) devices or smartphones with applications for location tracking installed thereon. The first location sensor 111, the second location sensor 112 and the third location sensor 113 may be used to determine locations using methodologies other than GPS, such as based on differences in characteristics (e.g., frequency and / or phase) of signals from local transmitters at predetermined locations in the vicinity of a hospital in which these sensors are used. The first monitoring camera 116, the second monitoring camera 117 and the third monitoring camera 118 are representative of security cameras used to monitor passages and rooms in facilities such as hospitals and government buildings. These monitoring cameras may be placed at known and fixed locations, and may provide images from known viewpoints and of known views of passages and rooms, so that the images can be analyzed to determine information relevant to the instructions executed by the processor 152 of the controller 150.

[0022] Although three location sensors and three monitoring cameras are shown in FIG. 1 , a system for infection spread minimization using route management may include fewer or more than three such location sensors and / or monitoring cameras. In some embodiments, a medical facility such as a hospital may have dozens, hundreds or even more than one thousand such location sensors and monitoring cameras.

[0023] The first interface 121 connects the computer 120 to the various location sensors and monitoring cameras via the network 101. The second interface 122 connects the computer 120 to the display 180. The computer 120 may include additional interfaces. One or more of the interfaces of the computer 120 may include ports, disk drives, wireless antennas, or other types of receiver circuitry that connect the computer 120 directly or indirectly to other electronic elements. One or more of the interfaces may also include user interfaces such as buttons, keys, a mouse, a microphone, a speaker, a display separate from the display 180, or other elements that users can use to interact with the computer 120 such as to enter instructions and receive output.

[0024] The controller 150 may perform some of the operations described herein directly and may implement other operations described herein indirectly. For example, the controller 150 may indirectly control operations such as by generating and transmitting content to be displayed on the display 180. The controller 150 may directly control other operations such as logical operations performed by the processor 152 executing instructions from the memory 151 based on input received from electronic elements and / or users via the interfaces. Accordingly, the processes implemented by the controller 150 when the processor 152 executes instructions from the memory 151 may include steps not directly performed by the controller 150. The instructions may include one or more computer programs that include three or more software modules as set forth below with respect to FIG. 4. Three software modules that may be implemented by a computer or network of computers may include (i) a transportation route planning module, (ii) a staff assignment module, and (iii) a transmission tracking module.

[0025] The display 180 may be local to the controller 150 or may be remotely connected to the controller 150. The display 180 may be connected to the controller 150 via a local wired interface such as an Ethernet cable or via a local wireless interface such as a Wi-Fi connection. The display 180 may be interfaced with other user input devices by which users can input instructions, including mouses, keyboards, thumbwheels and so on. The display 180 may be a monitor such as a computer monitor, a display on a mobile device, an augmented reality display, a television, an electronic whiteboard, or another screen configured to display electronic imagery. The display 180 may also include one or more input interface(s) such as those noted above that may connect to other elements or components, as well as an interactive touch screen configured to display prompts to users and collect touch input from users.

[0026] As an example, the computer 120 may comprise a server or other type of computer in a hospital complex or another type of large medical facility. The computer 120 may also comprise more than one computer, such as a networked arrangement of computers. The operations described herein are largely attributable to a computer or networked arrangement of computers such as the computer 120. In some embodiments, however, the computer 120 may be implemented remote from the various location sensors and monitoring cameras, such as in a separate building in a complex, or even in the cloud.

[0027] The system 100 may help track infection spread during transportation using a dynamicprediction-based model with real-time mobility data. The system 100 may minimize infection spread by controlled route planning and staff assignment. Factors such as length and crowdedness of potential routes, and presence of other vulnerable people in the route may be considered to suggest a controlled route limiting contact with other patients. The system may assign staff for transfer based on their past association with infectious transfer, incubation period, and clinical history. Special measures such as isolated path assignment, appropriate staff allocation, and hygiene of medical devices may be considered while transporting vulnerable patients between departments. The system 100 increases operational readiness to transport, minimize threat of transmission during transfer, ensure minimum transfer time for critical vulnerable patients, and support real-time transportation monitoring with RTLS support. Operational readiness to transport may be increased by the system 100, for example, via assigning staff, supporting equipment, and inter-departmental route selection.

[0028] FIG. 2 illustrates a method for infection spread minimization using route management, in accordance with a representative embodiment.

[0029] The method of FIG. 2 may be performed by the system 100 including the controller 150.

[0030] At S210, real-time data of potential routes is obtained. Real-time data may be obtained by the computer 120 from the various location sensors and monitoring cameras in FIG. 1. For example, the real-time data may be obtained from sensors including at least one of cameras or global positioning system sensors or other location sensors. The real-time data is of potential routes for transporting a patient with an infectious disease within a hospital. The real-time data may include data of locations of patients and medical personnel, crowdedness levels of passages along passages in potential routes, and other real-time data relevant to identifying an optimal route among multiple potential routes. The real-time data may include at least one of real-time location data for people in a hospital, real-time crowdedness of each of the potential routes, presence of vulnerable persons proximate to each route among the people in the hospital, and more.

[0031] At S220, static data is obtained. Static data may comprise data that does not vary over time, or at least over a few minutes or even an hour or two hours. For example, static data may include layouts of medical facilities, including hallways, stairways, elevators, doorways including whether the doorways are one-way controlled entry points, open spaces such aswaiting rooms, and more. Static data may also include medical data such as priority and other characteristics pertaining to different types of infections that may be spread, such as whether a particular type of infection can be spread over the air. The static data may include at least one of length of at least one corridor in at least one of the potential routes; demographic information for staff who may be assigned to transporting the patient; past assignments of staff to transfers of infectious patients, incubation period of the infectious disease; contagiousness of the infectious disease; clinical history of the patient with the infectious disease; hygiene of medical devices used in transporting the patient. The static data may also include a means of transmission of the infectious disease determined to be most likely. The most likely means of transmission may be identified from public data, such as announcement by public health officials.

[0032] Although S220 is shown after S210 in FIG. 2, static data may be obtained before realtime data is obtained at S210. For example, static data may be collected once before S210, and real-time data may be repeatedly obtained at S210 each time an infected patient is to be moved.

[0033] At S230, a prediction model is applied. The prediction model is dynamically applied to the real-time data to minimize the potential for spreading an infectious disease while transporting a patient. The model may comprise a trained machine learning model. For example, the model may comprise a constrained multi-resource generalization model which optimizes assignment of people for transporting different patients among the plurality of patients. The prediction model may comprise the software modules as set forth below with respect to FIG. 4, and may be applied to predict an optimal transportation route, an optional transportation time, and optimal staff to assign to transportation. The prediction model or an affiliated software program may also be used to track transmission of one or more infectious pathogens, particularly for patients and staff at a medical facility, but also known and confirmable visitors to the medical facility. The prediction model may take information from each of the software modules described with respect to FIG. 4 as input, and output guidance to each of the software modules.

[0034] At S240, a score is generated for a probability. The score may be for probability of infection from the infectious disease. The score may be provided for each person participating in transporting the patient. In some embodiments, the score may be provided for other personnel such as other hospital patients or visitors, or other medical personnel in proximity of a potential route. For example, the probability score may reflect demographic information and medicalhistory for a patient or staff, closest proximity in space and time to a known infected patient and / or potentially or likely infected patient(s) or staff, time-gap from the last associated infectious transfer, the type of infectious disease, and incubation period for the specific disease.

[0035] At S250, a route is suggested. The suggested route is selected and suggested from among potential routes, and is selected based on having a minimized potential for spreading an infectious disease. The selected route may be selected to, among other concerns, minimize time required for transporting the patient, minimize contact with vulnerable persons along the route, minimize the number of persons along the route potentially exposed to the patient with the infection, or other concerns.

[0036] At S260, staff are assigned. The staff are assigned for transporting the patient based on applying the prediction model to the real-time data. Staff may be assigned, for example, according to their health status / pre-clinical condition, past associated transfer, type and incubation period of the infectious disease transfer in order to minimize transmission to more people around.

[0037] At S270, the selected route is tagged. The computer 120 may tag the route suggested by the system after transporting the patient until the selected route is cleaned. By tagging a path for following safety and cleaning protocol after the transportation of infectious patients, the likelihood of transmission may be reduced,

[0038] At S280, an alert is provided. By alerting a path for following safety and cleaning protocol after the transportation of infectious patients, the likelihood of transmission may be reduced. The alert may be provided for people along the route suggested by the system. The alert may warn people of a pending transfer along the route, and / or may warn people that the transfer recently took place and the route has not yet been cleaned.

[0039] FIG. 3 illustrates a set of potential routes for infection spread minimization using route management, in accordance with a representative embodiment.

[0040] In FIG. 3, the layout of potential routes each start and end at the same points. A first route at the top is labelled “A”. A second route in the middle is labelled “B”. A third route at the bottom is labelled “C”. However, as noted throughout this application, routes may vary in more than two dimensions, such as by the use of stairways and elevators. Routes may also vary in time and length. The routes in FIG. 3 do not cross each other, but in practice potential routes maycross in some circumstances such as when the proposed routes are at different times.

[0041] FIG. 4. illustrates a hybrid system and flow for infection spread minimization using route management, in accordance with a representative embodiment.

[0042] In FIG. 4, a transmission monitoring and control system 420 may include at least a computer such as the computer 120 in FIG. 1. A monitoring camera 416 is representative of one or more monitoring cameras.

[0043] The transmission monitoring and control system 420 primarily implements three software modules: (i) a transportation route planning module, (ii) a staff assignment module, and (iii) a transmission tracking module. The three modules may be implemented by one or more computer programs stored in a memory and executed by a processor such as in the computer 120 in FIG. 1.

[0044] The first software module comprises a transportation route planning module: The transportation route planning module generates a controlled path for the transportation of infected patients by considering different aspects such as contagiousness of a pathogen, crowdedness of each of a set of potential paths, the layout of each potential path, time and support required for transfer, and part of the day selected or at least available for transfers. The transportation route planning module may generate multiple potential routes and times, including differences in locations, time and staff used or usable for moving an infected patient. The first software module may comprise part of the prediction model applied at S230 in FIG. 2. The transportation planning module considers these factors and proposes a regulated path and associated policy for the transfer.

[0045] The transportation route planning module relies on categorization of patients based on an understanding and labelling of the type and transmissibility of pathogens. Contagiousness of the disease is taken into account when generating an optimal controlled path. For example, a “novel” virus that is still not well-understood may require isolation so that patients suspected or known to have the novel virus may only be moved through empty passages by staff members wearing or otherwise using protective equipment. On the other hand, a seasonal influenza that is well understood may allow for less isolation so that patients suspected or know to have the seasonal influenza may be moved through populated passages by staff members. The transportation route planning module may also estimate real-time crowdedness of the path. Crowdedness may be calculated from data collected from the various location sensors and optical monitoring camerasover different alternative paths. The transportation route planning module may also rely on an understanding of specialized medical support required for the transfer of the patient, such as equipment. The transportation route planning module minimizes threat of transmission of the infectious pathogen and maximizes safety of the associated medical staffs by designing appropriate measures and strategies for successful transfer of patients with minimal risks of transmission and further spread of the infectious disease. As an example, the transportation route planning module may propose relevant cleaning guidelines depending on the grade of infection, so that an optimal route is cleaned before and / or after the transport. One or more other viable strategy or strategies may include covering the patient with an air-tight oxygen cover, providing HVAC controls for pressure, ventilation, and / or temperature of the transfer route, including, for example, temperature modification of the transfer route.

[0046] The second software module comprises a staff assignment module: The staff assignment module is responsible for assigning medical staff including nurses, porters, and supporting medical technicians for the transfer of patients infected with the contagious pathogen. The staff assignment module may also comprise part of the prediction model applied at S230 in FIG. 2. The staff assignment module is also responsible for tagging the staff associated with the transfer and maintaining close surveillance and monitoring of their health status before assigning them new non-infectious patients.

[0047] Staff may be classified based on demographic information such as age and / or gender, medical history, drug allergies, medical conditions, current medications, and family medical history. The demographic information may include information obtained at S220 in FIG. 2. A constrained multi-resource assignment approach may be used by a trained machine learning model for associating relevant staff with each vulnerable transfer. The staff assignment module may also associate proper safety accessories required specific to the infectious pathogen to safeguard the staff from getting contaminated. Proper safety accessories may include, for example, personal protective equipment kits or multi-layered masks. The staff assignment module may also tag and monitor health conditions of staff associated with a transfer of patient infected with contagious disease.

[0048] The third software module comprises a transmission tracking and control module. The transmission tracking and control module may be implemented as instructions stored in thememory 151 in FIG. 1, and may comprise a program separate from the prediction model applied at S230 in FIG. 2. The transmission tracking and control module tracks the probable means of transmission via staff or medical accessories such as devices, beds, or equipment that were in use for the transfer of the infectious patient. The transmission tracking and control module may calculate a probability infection score for the staff assisting in transfer and assist in proposing effective protocols for assuring wellness and safety of medical staff and other patients. A dynamic prediction model may be applied to calculate a probability infection score for the staff or other patients using their collective mobility data, personal demographic data, and other relevant static data.

[0049] The transmission tracking and control module may generate insight from historical transfers made for infectious patients may be used to generate insight for the transmission monitoring and control system 420. The historical data may include path of transfer, time of transfer, transport support used, staff and their clinical conditions / history associated with transfers. The transmission tracking and control module may also suggest additional protocols on top of existing protocols for further improvement of safety of medical staff and patients with the aim of minimizing the threat of being infected with the contagious pathogen associated with the patient.

[0050] In FIG. 4, planning, monitoring, and tracking of viral / bacterial contagious infection spread during transportation of the patients is performed to control disease transmission. The transmission monitoring and control system considers factors such as length and crowdedness of route, and presence of other vulnerable people in the route as established from real-time location sensors. Measures that may be implemented by the transmission monitoring and control system 420 include isolating assigned paths, appropriate staff allocation, and hygiene of medical devices.

[0051] Inputs and outputs for the three modules are described next along with hardware support, and specific types of machine learning models that may be used.

[0052] The transportation route planning module uses hardware including the various optical monitoring cameras described herein. The optical monitoring cameras are set-up in various pathways of the hospital. The transportation route planning module takes as input static information such as a hospital path map to understand the pathways within the hospital to plan anoptimal route for the transfer. The transportation route planning module also inputs real-time images of inter-departmental paths to understand the crowdedness of each potential path. The transportation route planning module may also input the cleanliness status of a path. Real-time status of cleanliness for each internal inter-department path may be determined based on records maintained by the hospital. Once a path used for transferring an infectious patient, the path may be flagged as red until the path is cleaned and marked green for a safe use and transfer. The categorization of patients based on grade of infection may be used to tag the type and transmission capability of pathogen, and for determining appropriate safety protocol for transfer and arrangements for relevant cleaning after the transfer. The real-time estimation of the crowdedness may be used to assign a relative score for each path depending on the length and crowdedness score of the path.

[0053] In some embodiments, potential routes may be ranked. An optimal route may be selected, in part, based on historical data with identification of the time of day when the route is less crowded. Images of the path may be analyzed for crowdedness, and used along with factors such as length of path, crowdedness of path, cleanliness status of path, time required for transfer, part of day selected. A machine learning model may be trained using these types of data as weighted inputs. The trained machine learning model may generate and output a ranked list of interdepartmental paths for a safe transfer.

[0054] In some embodiments, equipment may be assigned for a transfer. Equipment may include an air-tight oxygen cover for covering the patient, and use of personal protective equipment for staff. In some embodiments, heating ventilation and air conditioning (HVAC) control may be implemented for a route, so as to lower or raise pressure, activate and / or deactivate ventilation, and control temperature. A real-time alert may be output while a path is selected for transfer with the path marked as red until the path is marked safe again with green signal. In some embodiments, a cleaning alert may be output with estimated time and intensity of the required cleaning, depending on the grade of infection.

[0055] The staff assignment module is supported by the location sensors described herein which track staff mobility including patterns. The staff assignment module may also input demographic information such as gender, age or comorbidities, along with clinical and / or genetic history of any disease associated with staff which might prove them more vulnerable to the infectiouspathogen. Clinical features of staff included in the input to the staff assignment module may include blood pressure, blood sugar level, ions level, oxygen level, pulse rate, lipid-profile, liverfunctionality, kidney-functionality, heart conditions.

[0056] Multi-resource generalization assignment may be used to assign staff. The staff assignment may be formulated as a constrained multi-resource generalization assignment that examines a set of tasks to be assigned to a set of agents subjected to availability of multiple resources. The tasks may include transportation of different inter-departmental transfers of patients. The agents may include different staff with various vulnerabilities. The availability of resources may include staff experience, special training, and / or transportation history. Safety protocols may be associated with a task, including use of personal protection equipment (PPE) kits, multi-layered masks, and proper sanitization specific to the infectious pathogen. Staff associated with a vulnerable transfer may be tagged and their health condition monitored for a period such as a complete incubation period of the pathogen.

[0057] The transmission tracking and control module is supported by location devices. Location devices may be carried by staff, attached to transport beds and supporting medical devices such as oxygen supply, ventilator and attached monitors. The transmission tracking and control module inputs historical data for the record of infectious patient transfer. The historical data may include paths taken, times of transfer, cleaning status of the paths and more. The transmission tracking and control module also inputs the tracking data for staff, patients and tagged beds and other medical devices associated with the transfer of infectious patients. The transmission tracking and control module also inputs or otherwise monitors the number of days passed after a staff member was associated with a transfer.

[0058] The transmission tracking and control module may be trained using historical records of transfers made for the infectious patients, including path of transfer, time of transfer, transport support used, and staff associated with transfers. A trained machine learning prediction model may be designed to calculate the probability infection score for all staff and patients based on collective mobility pattern, historic record of association with contagious patient, time duration after the transfer, cleaning status of the tagged bed and equipment used for infectious pathogen. The trained machine learning model may generate insights for the associated probability score. An example of a generated insight may be that staff ‘X’ has higher probability infection score ashe was in the team which supported a transfer of patient infected with COVID. Constructive augmentation may be provided to the existing protocol and policies of the hospital system for the transfer of infectious patients. The transmission tracking and control module outputs probability of infection score for the in-hospital medical staffs, nurses, and practitioners, and may also output suggestions such as safe duration between two infectious transfer, different levels of cleaning for different pathogen, incubation period of the staff before associating staff with another transfer.

[0059] FIG. 5 illustrates a computer system, on which a method for infection spread minimization using route management is implemented, in accordance with another representative embodiment.

[0060] FIG. 5 illustrates a computer system, on which a method for infection spread minimization using route management is implemented, in accordance with another representative embodiment.

[0061] Referring to FIG. 5, the computer system 500 includes a set of software instructions that can be executed to cause the computer system 500 to perform any of the methods or computer- based functions disclosed herein. The computer system 500 may operate as a standalone device or may be connected, for example, using a network 501, to other computer systems or peripheral devices. In embodiments, a computer system 500 performs logical processing based on digital signals received via an analog-to-digital converter.

[0062] In a networked deployment, the computer system 500 operates in the capacity of a server or as a client user computer in a server-client user network environment, or as a peer computer system in a peer-to-peer (or distributed) network environment. The computer system 500 can also be implemented as or incorporated into various devices, such as a workstation that includes a controller, a stationary computer, a mobile computer, a personal computer (PC), a laptop computer, a tablet computer, or any other machine capable of executing a set of software instructions (sequential or otherwise) that specify actions to be taken by that machine. The computer system 500 can be incorporated as or in a device that in turn is in an integrated system that includes additional devices. In an embodiment, the computer system 500 can be implemented using electronic devices that provide voice, video or data communication. Further, while the computer system 500 is illustrated in the singular, the term “system” shall also be takento include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of software instructions to perform one or more computer functions.

[0063] As illustrated in FIG. 5, the computer system 500 includes a processor 510. The processor 510 may be considered a representative example of a processor of a controller and executes instructions to implement some or all aspects of methods and processes described herein. The processor 510 is tangible and non-transitory. As used herein, the term “non- transitory” is to be interpreted not as an eternal characteristic of a state, but as a characteristic of a state that will last for a period. The term “non-transitory” specifically disavows fleeting characteristics such as characteristics of a carrier wave or signal or other forms that exist only transitorily in any place at any time. The processor 510 is an article of manufacture and / or a machine component. The processor 510 is configured to execute software instructions to perform functions as described in the various embodiments herein. The processor 510 may be a general- purpose processor or may be part of an application specific integrated circuit (ASIC). The processor 510 may also be a microprocessor, a microcomputer, a processor chip, a controller, a microcontroller, a digital signal processor (DSP), a state machine, or a programmable logic device. The processor 510 may also be a logical circuit, including a programmable gate array (PGA), such as a field programmable gate array (FPGA), or another type of circuit that includes discrete gate and / or transistor logic. The processor 510 may be a central processing unit (CPU), a graphics processing unit (GPU), or both. Additionally, any processor described herein may include multiple processors, parallel processors, or both. Multiple processors may be included in, or coupled to, a single device or multiple devices.

[0064] The term “processor” as used herein encompasses an electronic component able to execute a program or machine executable instruction. References to a computing device comprising “a processor” should be interpreted to include more than one processor or processing core, as in a multi-core processor. A processor may also refer to a collection of processors within a single computer system or distributed among multiple computer systems. The term computing device should also be interpreted to include a collection or network of computing devices each including a processor or processors. Programs have software instructions performed by one or multiple processors that may be within the same computing device or which may be distributed across multiple computing devices.

[0065] The computer system 500 further includes a main memory 520 and a static memory 530, where memories in the computer system 500 communicate with each other and the processor 510 via a bus 508. Either or both of the main memory 520 and the static memory 530 may be considered representative examples of a memory of a controller, and store instructions used to implement some or all aspects of methods and processes described herein. Memories described herein are tangible storage mediums for storing data and executable software instructions and are non-transitory during the time software instructions are stored therein. As used herein, the term “non-transitory” is to be interpreted not as an eternal characteristic of a state, but as a characteristic of a state that will last for a period. The term “non-transitory” specifically disavows fleeting characteristics such as characteristics of a carrier wave or signal or other forms that exist only transitorily in any place at any time. The main memory 520 and the static memory 530 are articles of manufacture and / or machine components. The main memory 520 and the static memory 530 are computer-readable mediums from which data and executable software instructions can be read by a computer (e.g., the processor 510). Each of the main memory 520 and the static memory 530 may be implemented as one or more of random access memory (RAM), read only memory (ROM), flash memory, electrically programmable read only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, a hard disk, a removable disk, tape, compact disk read only memory (CD-ROM), digital versatile disk (DVD), floppy disk, blu-ray disk, or any other form of storage medium known in the art. The memories may be volatile or non-volatile, secure and / or encrypted, unsecure and / or unencrypted.

[0066] “Memory” is an example of a computer-readable storage medium. Computer memory is any memory which is directly accessible to a processor. Examples of computer memory include, but are not limited to RAM memory, registers, and register files. References to “computer memory” or “memory” should be interpreted as possibly being multiple memories. The memory may for instance be multiple memories within the same computer system. The memory may also be multiple memories distributed amongst multiple computer systems or computing devices.

[0067] As shown, the computer system 500 further includes a video display unit 550, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid-state display, or a cathode ray tube (CRT), for example. Additionally, the computer system 500 includes an input device 560, such as a keyboard / virtual keyboard or touch-sensitive inputscreen or speech input with speech recognition, and a cursor control device 570, such as a mouse or touch-sensitive input screen or pad. The computer system 500 also optionally includes a disk drive unit 580, a signal generation device 590, such as a speaker or remote control, and / or a network interface device 540.

[0068] In an embodiment, as depicted in FIG. 5, the disk drive unit 580 includes a computer- readable medium 582 in which one or more sets of software instructions 584 (software) are embedded. The sets of software instructions 584 are read from the computer-readable medium 582 to be executed by the processor 510. Further, the software instructions 584, when executed by the processor 510, perform one or more steps of the methods and processes as described herein. In an embodiment, the software instructions 584 reside all or in part within the main memory 520, the static memory 530 and / or the processor 510 during execution by the computer system 500. Further, the computer-readable medium 582 may include software instructions 584 or receive and execute software instructions 584 responsive to a propagated signal, so that a device connected to a network 501 communicates voice, video or data over the network 501. The software instructions 584 may be transmitted or received over the network 501 via the network interface device 540.

[0069] In an embodiment, dedicated hardware implementations, such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), programmable logic arrays and other hardware components, are constructed to implement one or more of the methods described herein. One or more embodiments described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules. Accordingly, the present disclosure encompasses software, firmware, and hardware implementations. Nothing in the present application should be interpreted as being implemented or implementable solely with software and not hardware such as a tangible non-transitory processor and / or memory.

[0070] In accordance with various embodiments of the present disclosure, the methods described herein may be implemented using a hardware computer system that executes software programs. Further, in an exemplary, non-limited embodiment, implementations can include distributed processing, component / object distributed processing, and parallel processing. Virtual computer system processing may implement one or more of the methods or functionalities as describedherein, and a processor described herein may be used to support a virtual processing environment.

[0071] Accordingly, infection spread minimization using route management enables optimization of transportation routs using a real-time locating system that provides inputs to a machine learning model that optimizes transportation route and / or other optimizable functions and characteristics such as medical staff assignment when moving patients. The optimal route may be selected from multiple potential routes, and may be selected from route options that vary in two dimensions or three dimensions, that vary in time, that vary in medical staff assigned to move the patient, and that vary potentially in other ways.

[0072] Although infection spread minimization using route management has been described with reference to several exemplary embodiments, it is understood that the words that have been used are words of description and illustration, rather than words of limitation. Changes may be made within the purview of the appended claims, as presently stated and as amended, without departing from the scope and spirit of infection spread minimization using route management in its aspects. Although infection spread minimization using route management has been described with reference to particular means, materials and embodiments, infection spread minimization using route management is not intended to be limited to the particulars disclosed; rather infection spread minimization using route management extends to all functionally equivalent structures, methods, and uses such as are within the scope of the appended claims.

[0073] The illustrations of the embodiments described herein are intended to provide a general understanding of the structure of the various embodiments. The illustrations are not intended to serve as a complete description of all of the elements and features of the disclosure described herein. Many other embodiments may be apparent to those of skill in the art upon reviewing the disclosure. Other embodiments may be utilized and derived from the disclosure, such that structural and logical substitutions and changes may be made without departing from the scope of the disclosure. Additionally, the illustrations are merely representational and may not be drawn to scale. Certain proportions within the illustrations may be exaggerated, while other proportions may be minimized. Accordingly, the disclosure and the figures are to be regarded as illustrative rather than restrictive.

[0074] One or more embodiments of the disclosure may be referred to herein, individuallyand / or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any particular invention or inventive concept. Moreover, although specific embodiments have been illustrated and described herein, it should be appreciated that any subsequent arrangement designed to achieve the same or similar purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all subsequent adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the description.

[0075] The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. In addition, in the foregoing Detailed Description, various features may be grouped together or described in a single embodiment for the purpose of streamlining the disclosure. This disclosure is not to be interpreted as reflecting an intention that the claimed embodiments require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter may be directed to less than all of the features of any of the disclosed embodiments. Thus, the following claims are incorporated into the Detailed Description, with each claim standing on its own as defining separately claimed subject matter.

[0076] The preceding description of the disclosed embodiments is provided to enable any person skilled in the art to practice the concepts described in the present disclosure. As such, the above disclosed subject matter is to be considered illustrative, and not restrictive, and the appended claims are intended to cover all such modifications, enhancements, and other embodiments which fall within the true spirit and scope of the present disclosure. Thus, to the maximum extent allowed by law, the scope of the present disclosure is to be determined by the broadest permissible interpretation of the following claims and their equivalents and shall not be restricted or limited by the foregoing detailed description.

Claims

CLAIMS:

1. A system, comprising: a memory that stores instructions; and a processor that executes the instructions, wherein, when executed by the processor, the instructions cause the system to: obtain real-time data of potential routes for transporting a patient with an infectious disease within a hospital; apply a prediction model to the real-time data to minimize potential for spreading the infectious disease while transporting the patient; and suggest a route among the potential routes with a minimized potential for spreading the infectious disease.

2. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: assign staff for transporting the patient based on applying the prediction model to the real-time data.

3. The system of claim 1, wherein the real-time data includes at least one of real-time location data for people in the hospital; real-time crowdedness of each of the potential routes, or presence of vulnerable persons proximate to each route among the people in the hospital.

4. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: obtain static data; and apply the prediction model to the static data to minimize the potential for spreading the infectious disease while transporting the patient.

5. The system of claim 4, wherein the static data includes at least one of length of at least one corridor in at least one of the potential routes; demographic information for staff who may beassigned to transporting the patient; past assignments of staff to transfers of infectious patients, incubation period of the infectious disease; contagiousness of the infectious disease; clinical history of the patient with the infectious disease; or hygiene of medical devices used in transporting the patient.

6. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: minimize time required for transporting the patient.

7. The system of claim 1 , wherein the real-time data is obtained from sensors including at least one of cameras or global positioning system sensors.

8. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: tag the route suggested by the system after transporting the patient until the route is cleaned; and provide an alert for people along the route suggested by the system.

9. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: generate a score for a probability of infection from the infectious disease for each person participating in transporting the patient.

10. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: identify a means of transmission of the infectious disease determined to be most likely.

11. The system of claim 1, further comprising: sensors used to generate the real-time data.

12. The system of claim 11, wherein the sensors comprise at least one of cameras and global positioning system sensors.

13. The system of claim 1, wherein, when executed by the processor, the instructions further cause the system to: suggest routes for transporting a plurality of patients with infectious diseases using the prediction model, wherein the prediction model comprises a constrained multi-resource generalization model which optimizes assignment of people for transporting different patients among the plurality of patients.

14. A method for suggesting a route for transporting a patient, comprising: a memory that stores instructions; and obtaining, by a system comprising a memory that stores instructions and a processor that executes the instructions, real-time data of potential routes for transporting a patient with an infectious disease within a hospital; applying, by the system, a prediction model to the real-time data to minimize potential for spreading the infectious disease while transporting the patient; and suggesting, by the system, a route among the potential routes with a minimized potential for spreading the infectious disease.

15. The method of claim 14, further comprising: assigning staff for transporting the patient based on applying the prediction model to the real-time data.

16. The method of claim 14, wherein the real-time data includes at least one of real-time location data for people in the hospital; real-time crowdedness of each of the potential routes, or presence of vulnerable persons proximate to each route among the people in the hospital.

17. The method of claim 14, further comprising: obtaining static data; andapplying the prediction model to the static data to minimize the potential for spreading the infectious disease while transporting the patient.

18. The method of claim 14, further comprising: minimizing time required for transporting the patient.

19. The method of claim 14, further comprising: tagging the route suggested by the system after transporting the patient until the route is cleaned; and providing an alert for people along the route suggested by the system.

20. A tangible, non-transitory computer-readable medium that stores instructions, which when executed by a processor, cause the processor to: obtain real-time data of potential routes for transporting a patient with an infectious disease within a hospital; apply a prediction model to the real-time data to minimize potential for spreading the infectious disease while transporting the patient; and suggest a route among the potential routes with a minimized potential for spreading the infectious disease.

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