Management systems and management methods

Abstract

To provide a management system capable of properly managing object equipment.SOLUTION: A management system 1 manages multiple pieces of medical equipment ME as a management object. The management system 1 includes a function generation part 31 for generating an object function including a management cost of the management equipment ME which changes in accordance with the number of possessions of the medical equipment ME and a movement cost required to move the medical equipment ME from a standby place to a prescribed use place. The management system 1 includes an optimal operation part 32 for acquiring the optimal number of possessions of the medical equipment ME. The optimal operation part 32 acquires the optimal number of the medical equipment ME by executing optimization processing for minimizing the object function by using a mathematical programming method under a prescribed constraint condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] This disclosure relates to a management system and management method for managing multiple target devices. [Background technology]

[0002] Conventionally, systems have been proposed to manage and visualize the location and operational information of medical devices for the purpose of efficient management of medical devices (see, for example, Non-Patent Document 1). In addition, medical device management systems are known for clarifying owned medical devices, understanding the operational status of medical devices, analyzing the causes of failures and repairs, and understanding repair costs (see, for example, Non-Patent Document 2). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Kazuhito Kakutani (and one other), "Proposal and Implementation of a Method for Efficient Management of Medical Device Location Estimation and Operating Rate Management," Proceedings of the 83rd National Convention of the Information Processing Society of Japan, 2021, 4-641~4-642. [Non-Patent Document 2] Ai Nakaizumi (and 5 others), "On the Centralized Management Method of Medical Devices at Our Hospital," Journal of the Japanese Society for Rural Medicine, 2003, Vol. 52, No. 4, pp. 733-736. [Overview of the project] [Problems that the invention aims to solve]

[0004] The inventors of this invention have diligently examined the management of medical devices disclosed in Non-Patent Documents 1 and 2. Their examination revealed that there is still room for improvement regarding the management of medical devices.

[0005] For example, in medical practice, there is a tendency to possess an excessive number of medical devices. This means that no method has been established to evaluate whether the number of medical devices is appropriate. Note that such a problem may similarly arise when devices other than medical devices are targeted for management.

[0006] An object of the present disclosure is to provide a management system capable of appropriately managing target devices. [Means for Solving the Problems]

[0007] The invention according to Claim 1 is A management system for managing a plurality of target devices (ME), By possessing the target equipment, a function generation unit (31) that generates an objective function including, as costs, a management cost and a movement cost required for moving the target device from a standby location to a predetermined use location; Target devices and an optimization operation unit (32) that calculates an optimal number of possessed devices, The optimization operation unit calculates the optimal number by executing an optimization process of minimizing the objective function using mathematical programming under predetermined constraint conditions eye, Management costs and relocation costs, respectively, are costs that vary depending on the number held. The function generation unit is, By using equipment information, including the timing and location of use of the target equipment extracted from the location and operating status of the target equipment, the first term related to movement costs is generated by simulating the movement costs when the number of equipment owned is changed. Based on the purchase cost when acquiring the target equipment, the disposal cost when disposing of the target equipment, the maintenance cost when maintaining the target equipment, and the number of target equipment currently owned, a second item concerning management costs is generated. The objective function is generated by adding the first and second terms together.

[0008] Claim 7 The invention described in Using a computer A management method for managing a plurality of target devices (ME), By computer, Target device By possessing a generation step of generating an objective function including, as costs, a management cost and a movement cost required for moving the target device from a standby location to a predetermined use location; By computer, and an operation step of calculating an optimal number of possessed target devices, In the calculation process, an optimization process is performed using mathematical programming to minimize the objective function under predetermined constraints, thereby finding the optimal number. eye, Management costs and relocation costs, respectively, are costs that vary depending on the number held. In the production process, By using equipment information, including the timing and location of use of the target equipment extracted from the location and operating status of the target equipment, the first term related to movement costs is generated by simulating the movement costs when the number of equipment owned is changed. Based on the purchase cost when acquiring the target equipment, the disposal cost when disposing of the target equipment, the maintenance cost when maintaining the target equipment, and the number of target equipment currently owned, a second item concerning management costs is generated. The objective function is generated by adding the first and second terms together.

[0009] Thus, by determining the optimal number of target devices through optimization processing that takes into account the management and relocation costs of the target devices, it is possible to suppress the excessive ownership of target devices. In particular, since the optimal number of target devices is determined by considering relocation costs in addition to management costs, it is possible to ensure the convenience of the target devices while suppressing the excessive ownership of the target devices. Therefore, the management system of this disclosure enables the proper management of target devices. The reference numerals in parentheses attached to each component indicate an example of the correspondence between that component and the specific components described in the embodiments described later. [Brief explanation of the drawing]

[0010] [Figure 1] This is a schematic diagram of the management system according to the first embodiment. [Figure 2] This is an explanatory diagram illustrating how the position acquisition unit determines the location of the target device. [Figure 3] This is an explanatory diagram illustrating how the status identification unit identifies the operating status of the target equipment. [Figure 4] This is an explanatory diagram illustrating the relationship between the number of target devices owned and management costs. [Figure 5] This is an explanatory diagram illustrating the relationship between the number of target devices owned and the cost of relocation. [Figure 6] This is an explanatory diagram illustrating the objective function to be optimized in the management system according to the first embodiment. [Figure 7]This is an explanatory diagram illustrating the constraints when optimizing the objective function. [Figure 8] This flowchart shows an example of the detection process performed on the sensor device side. [Figure 9] This is an explanatory diagram illustrating an example of data acquired by the management device. [Figure 10] This flowchart shows an example of a management process performed on the management device side. [Figure 11] This is an explanatory diagram illustrating the formatting of acquired data in the management device. [Figure 12] This is an explanatory diagram illustrating the sorting process of formatted data in a data management system. [Figure 13] This is an explanatory diagram illustrating how to obtain route cost information. [Figure 14] This flowchart shows an example of a simulation performed on the management device side. [Figure 15] This is an explanatory diagram illustrating an example of a simulation result. [Figure 16] This is an explanatory diagram illustrating the relationship between the number of items owned and the cost of moving them. [Figure 17] This is a schematic diagram showing a part of the management system according to the second embodiment. [Figure 18] This is an explanatory diagram illustrating how to allocate the budget for each model. [Figure 19] This is an explanatory diagram to illustrate the weights used in budget allocation. [Figure 20] This is an explanatory diagram illustrating how to calculate equipment utilization. [Figure 21] This is an explanatory diagram illustrating the objective function to be optimized in the management system according to the second embodiment. [Figure 22] This is an explanatory diagram illustrating the constraints when optimizing the objective function. [Modes for carrying out the invention]

[0011] Embodiments of this disclosure will be described below with reference to the drawings. In the following embodiments, parts that are the same as or equivalent to those described in the prior embodiments will be denoted by the same reference numerals, and their descriptions may be omitted. Also, if only a part of a component is described in an embodiment, the components described in the prior embodiments can be applied to the other parts of that component. The following embodiments can be partially combined with each other, even if not explicitly stated, as long as it does not impede the combination.

[0012] (First Embodiment) This embodiment will be described with reference to Figures 1 to 16. In this embodiment, an example will be described in which the management system 1 and management method of this disclosure are applied to a medium to large-scale medical facility where many medical devices (MEs) are installed.

[0013] Management System 1 is a system installed in a medical facility that manages various medical devices (ME) used in the medical facility. Examples of medical devices (ME) include ventilators that support the patient's breathing, and hemodynamic monitors that monitor the patient's electrocardiogram, blood pressure, etc.

[0014] As shown in Figure 1, the management system 1 includes a sensor device 10 mounted on the medical device ME and a management device 20 that manages the medical device ME based on the acquired data obtained by the sensor device 10.

[0015] The sensor device 10 is installed in the medical device ME that is subject to management. In Figure 1, etc., for convenience, a diagram is shown in which the sensor device 10 is installed in one medical device ME, but the sensor device 10 is installed in all medical devices ME that are subject to management.

[0016] The sensor device 10 of this embodiment is composed of a wireless sensor device. Specifically, the sensor device 10 includes a position acquisition unit 12, a status identification unit 14, a data control unit 16, and a battery 18.

[0017] The position acquisition unit 12 acquires the position of the medical device ME and is installed on the medical device ME. The position acquisition unit 12 comprises a radio wave receiving unit 121 and a position identification unit 122. The radio wave receiving unit 121 receives radio waves emitted by radio transmitters such as Wi-Fi access points AP and BLE beacons BD installed in predetermined locations. The position identification unit 122 identifies the position of the medical device ME based on the signal level (e.g., RSSI) of the radio waves received by the radio wave receiving unit 121. For example, as shown in Figure 2, the position acquisition unit 12 identifies the position of the medical device ME based on the positions of multiple transmitters N0, N1, and N2 around the medical device ME and the signal levels of the radio waves emitted by each transmitter N0, N1, and N2. Note that the position identification unit 122 may be configured in the data control unit 16 of the sensor device 10 or in the management device 20, instead of the position acquisition unit 12.

[0018] The status identification unit 14 identifies the operating status of the medical device ME and is installed in the medical device ME. The status identification unit 14 detects changes in the power supplied to the medical device ME and identifies the operating status based on the changes in power.

[0019] The situation identification unit 14 includes a power strip 141 that supplies power to the medical device ME, and a current sensor 142 for detecting changes in the power supplied to the medical device ME. The current sensor 142 can be, for example, a high-precision sensor using the Hall effect. The situation identification unit 14 acquires raw data of the AC waveform of the current supplied to the medical device ME using, for example, the current sensor 142.

[0020] Here, the current value supplied to the medical device ME and the variance of the AC waveform of the current tend to be larger when the medical device ME is operating than when it is not operating. Utilizing this tendency, the status identification unit 14 identifies whether the medical device ME is operating or not based on the current value supplied to the medical device ME and the variance of the AC waveform of the current, for example, as shown in Figure 3. The threshold values ​​for the current value and the variance are set, for example, based on the measurement results when the change in the current value is measured when the operating status of the medical device ME is changed.

[0021] The status determination unit 14 determines, for example, that the medical device ME is operational if at least one of the current value supplied to the medical device ME and the variance value of the AC waveform of the current exceeds a predetermined threshold, and determines that it is not operational otherwise.

[0022] The data control unit 16 consists of a microcomputer including a processor and memory, and its peripheral circuits. The data control unit 16 includes a sensor control unit 161 that controls the position acquisition unit 12 and the status identification unit 14, and a communication device 162 that transmits various data acquired by the position acquisition unit 12 and the status identification unit 14 to the outside. In this embodiment, the data control unit 16 transmits various data acquired by the position acquisition unit 12 and the status identification unit 14 to a gateway GW installed in the medical facility using LPWA communication. Note that LPWA communication is a communication method conforming to the LPWA standard (for example, LoRaWAN), which enables lower power consumption, a wider coverage area, and lower costs compared to Wi-Fi, LTE, etc. Note that a communication method other than LPWA communication may be used between the sensor device 10 and the management device 20.

[0023] The battery 18 is provided to enable the detection of the location of the medical device ME, etc., even when the sensor device 10 is not connected to a power source. The battery 18 is charged when the sensor device 10 is connected to a power source.

[0024] The sensor device 10 configured in this way can communicate wirelessly with the management device 20 via a gateway GW. The gateway GW POSTs various acquired data received from the data control unit 16 of the sensor device 10 to the data server DB included in the management device 20. As a result, various acquired data are stored in the data server DB.

[0025] In medical settings, medical engineers often have to travel long distances to retrieve or search for medical equipment (ME). However, there is a tendency to possess an excessive amount of ME, and no method has been introduced to properly evaluate the number of devices owned. Currently, there are no clear indicators for optimizing the number or placement of ME, and this is left to the judgment of healthcare professionals working at each medical facility. In light of these considerations, the management system 1 in this embodiment includes a management device 20 that proposes an appropriate number of medical devices (MEs) to be held and optimizes their placement, thereby reducing travel time, search time, and the burden on medical personnel.

[0026] The management device 20 determines the appropriate number of medical devices (MEs) to possess by minimizing an objective function that includes management costs and transportation costs, which vary depending on the number of medical devices (MEs) possessed, under predetermined constraints. The management device 20 outputs the optimal number of medical devices (MEs) to an operating terminal (OE) operated by a medical professional.

[0027] The management device 20 of this embodiment includes a database DB and a management control unit 30. The database DB and the management control unit 30 may be installed in the medical facility or outside the medical facility.

[0028] The database DB is a large-capacity storage device that stores the location and operating status of the medical device ME collected by the sensor device 10. The database DB can communicate with the sensor device 10 and the management control unit 30, etc., via communication equipment (not shown).

[0029] The management control unit 30 consists of a microcomputer including a processor and memory, and its peripheral circuits. The management control unit 30 manages the medical device ME by referring to various data stored in the database DB.

[0030] The management control unit 30 includes a function generation unit 31, an optimal calculation unit 32, and an output unit 33 as functional configurations implemented on a microcomputer. Note that these functional configurations are merely conveniently set out to facilitate understanding of the contents of this disclosure. Therefore, even if these functional configurations are not actually implemented as subroutines or hardware, the requirements of this disclosure may be satisfied as long as the predetermined functions or processes of this disclosure are implemented.

[0031] The function generation unit 31 generates an objective function that includes the management cost of the medical devices ME, which changes according to the number of medical devices ME owned, and the movement cost required to move the medical devices ME from their standby location to a predetermined usage location, as costs. The “standby location” is a place where the target device can be kept in a usable state. The standby location is not limited to a separate room, but may also be a corner of a corridor.

[0032] Management costs are the costs (expenses) incurred by owning medical devices (MEs). As shown in Figure 4, management costs include purchase costs, disposal costs, and maintenance costs. Purchase costs are the expenses incurred when purchasing medical devices (MEs), and they increase as the number of devices owned increases. Disposal costs are the expenses incurred when disposing of or transferring medical devices (MEs) to other parties, and they increase as the number of devices owned decreases. Maintenance costs are the expenses required for daily and periodic inspections of medical devices (MEs), and they increase as the number of devices owned increases. Purchase costs, disposal costs, and maintenance costs may vary depending on the model of the medical device (ME). Therefore, it is desirable that purchase costs, disposal costs, and maintenance costs be set for each model of medical device (ME).

[0033] Movement costs are the costs incurred in operating medical equipment (ME). These costs (distance or time) are incurred when moving the ME from its standby location to a designated usage location such as a patient's room or ICU. As shown in Figure 5, movement costs decrease as the number of ME units increases.

[0034] The function generation unit 31 generates an objective function by adding a first term, which includes movement costs, and a second term, which includes management costs, as shown in Figure 6, for example. In this embodiment, the function generation unit 31 extracts device information, including the timing and location of use of medical devices ME, from data on the location and operating status of medical devices ME stored in the database DB. The function generation unit 31 then generates the first term of the objective function by simulating the movement costs when the number of medical devices ME changes using the device information.

[0035] Here, the medical facility has multiple standby locations set up for storing medical devices (MEs). The function generation unit 31 of this embodiment generates an objective function with the number of owned and the number of deployed units as variables by simulating the movement cost when the number of medical devices (MEs) placed in each standby location is changed, in addition to the number of owned units. Note that in the objective function shown in Figure 6, “x i " corresponds to the number held, and "x ij The " corresponds to the number of placements.

[0036] The optimal calculation unit 32 determines the optimal number of medical devices (MEs) to possess by performing an optimization process that minimizes the objective function shown in Figure 6 using mathematical programming under predetermined constraints. The optimal calculation unit 32 performs an optimization process that minimizes the objective function using linear programming.

[0037] The constraints, as shown in Figure 7, are: "Condition 1" that the number of placements is 0 or greater; "Condition 2" that the total number of placements is the number of possessions; and "Condition 3" that the number of possessions is the minimum required number D. i This includes the "third condition" that the above applies, and the "fourth condition" that the total management costs are less than or equal to the total budget. The total management costs are the sum of the purchase costs, disposal costs, and maintenance costs of the medical equipment (ME). Minimum required number D i This is the maximum number of simultaneous uses.

[0038] The optimal calculation unit 32 of this embodiment performs an optimization process to minimize the objective function shown in Figure 6 under the constraints shown in Figure 7, thereby determining the optimal number of medical devices (MEs) to be owned and the optimal number of each of the multiple standby locations.

[0039] The output unit 33 outputs the optimal number of medical devices (ME) to be held and the optimal number of devices to be placed in each of the multiple standby locations, as determined by the optimal calculation unit 32, to the medical professional's operating terminal (OE). The output unit 33 may also provide information to the medical professional via a web application provided on a web server.

[0040] The management system 1 configured in this way performs a detection process to detect the location of each medical device ME using sensor devices 10 installed on each medical device ME, and then uses the acquired data obtained in the detection process to perform a management process in the management device 20 to optimally manage the medical devices ME.

[0041] The following describes an example of the detection process performed on the sensor device 10, with reference to Figure 8. The detection process shown in Figure 8 is executed by the data control unit 16 of the sensor device 10, for example, when it receives a signal to start periodically or from the management device 20, etc.

[0042] As shown in Figure 8, in step S100, the sensor device 10 acquires the current waveform of the medical device ME using the current sensor 142 of the status identification unit 14. The sensor device 10 then calculates the current value and the variance value of the current waveform from the current waveform of the medical device ME.

[0043] Next, in step S110, the sensor device 10 identifies the operating status of the medical device ME based on the current value and variance value of the medical device ME. For example, the sensor device 10 compares the current value and variance value with predetermined thresholds and, based on the results of this comparison, identifies whether the medical device ME is operating or not.

[0044] Next, in step S120, the sensor device 10 determines whether or not the medical device ME is in operation. This determination process is performed based on the operating status of the medical device ME identified in step S110.

[0045] If the medical device ME is operational, the sensor device 10 receives radio waves emitted by radio transmitters such as Wi-Fi access point AP and BLE beacon BD with the radio wave receiving unit 121 in step S130.

[0046] Next, in step S140, the sensor device 10 uses the positioning unit 122 to determine the location of the medical device ME based on the signal level of the radio waves emitted by the radio wave transmitter. The sensor device 10 then transmits the date and time, the location of the medical device ME, and its operating status as acquired data to the gateway GW via LPWA communication. The gateway GW POSTs this data to the data server DB. As a result, the data server DB stores the acquired data, including the date and time, the operating status of the medical device ME, and the location of the medical device ME, for example, as shown in Figure 9.

[0047] On the other hand, if the medical device ME is not operational, the sensor device 10 determines in step S150 whether it is time to check the location of the inactive medical device ME. This check timing may include, for example, the timing when the medical device ME switches from operational to inactive, or the timing when a predetermined time has elapsed since switching to inactive.

[0048] If it is time to confirm the position, the sensor device 10 proceeds to step S130 to identify the position of the inactive medical device ME. If it is not time to confirm the position, it skips steps S130 and S140 and proceeds to step S160.

[0049] In step S160, the sensor device 10 determines whether a predetermined time (for example, 10 minutes) has elapsed since acquiring the current waveform of the medical device ME in step S100. If the predetermined time has elapsed since acquiring the current waveform of the medical device ME, the sensor device 10 returns to step S100 and acquires the current waveform of the medical device ME again.

[0050] On the other hand, if a predetermined time has not elapsed since acquiring the current waveform of the medical device ME, the sensor device 10 proceeds to step S170 to determine whether or not to stop the detection process. This determination is made, for example, based on a command from the management device 20. The sensor device 10 exits this process if it decides to stop the detection process, and returns to step S160 if it decides not to stop the detection process.

[0051] By performing this detection process, the sensor device 10 periodically detects the operating status and location of the medical device ME, and data including the operating status and location of the medical device ME is periodically stored in the database DB as acquired data. In this embodiment, the database DB constitutes the "storage unit".

[0052] Next, an example of management processing performed on the management device 20 will be explained with reference to Figure 9. The detection process shown in Figure 9 is executed by the management control unit 30 of the management device 20, for example, when it receives a signal instructing it to start periodically or from a medical professional's operating terminal OE. The management device 20 performs management processing when a certain amount of acquired data has been accumulated in the database DB.

[0053] In step S200, the management device 20 performs preprocessing. This preprocessing involves formatting the acquired data stored in the database DB. Based on the acquired data, the management device 20 generates formatted data, which includes, for example, an event ID (a sequential number), a type indicating the timing of use such as the start and stop of use of the medical device ME, the time of event occurrence, and the location of use of the medical device ME, as shown in Figure 11. Furthermore, the management device 20 sorts the formatted data chronologically, as shown in Figure 12.

[0054] Through this preprocessing, the management device 20 extracts device information, including the timing and location of use of the medical device ME, from acquired data, including the location and operating status of the medical device ME.

[0055] Next, in step S210, the management device 20 calculates the minimum cost of moving the medical device ME from the standby location to the usage location, and stores this calculation result in the database DB as route cost information. In this embodiment, the management device 20 finds the minimum cost using a search algorithm based on Dijkstra's algorithm. The database DB stores map information in the form of a graph representing multiple nodes set as the movement path of medical devices (MEs), connected by edges with predetermined weights. The management device 20 refers to the map information stored in the database DB to simulate the movement cost from the standby location to the usage location. The management device 20, for example as shown in Figure 13, applies Dijkstra's algorithm to an undirected graph represented by connecting each node associated with a waiting area, usage area, elevator (EV), and corridor with weighted edges to determine the minimum cost from a waiting area node to other nodes. Since travel by elevator (EV) tends to take longer than travel through corridors, etc., it is desirable to make the weight of the edges connecting nodes representing elevators (EV) larger than the others.

[0056] Next, in step S220, the management device 20 simulates the movement costs when the number of medical devices (MEs) owned and the number of devices in place are changed, thereby relating the movement costs to the number of owned and the number of devices in place, and generating an objective function with the number of owned and the number of devices in place as variables. Below, an example of the movement cost simulation will be explained with reference to Figure 14.

[0057] As shown in Figure 14, the management device 20 sets the maximum number of medical devices ME in step S300. The maximum number of medical devices ME is set to, for example, the number that is considered to be excessive in the facility being managed.

[0058] Next, in step S310, the management device 20 determines the number of units to be placed in each standby location. Initially, the management device 20 sets the number of units to be placed in each standby location to a predetermined number, and from the second round onward, it changes the number of units to be placed in each standby location to a different number. Then, in step S320, the management device 20 distributes the maximum number of medical devices ME to each standby location. For example, if the maximum number of units to be placed is "3" and there are three standby locations, there are multiple distribution patterns to the standby locations, such as [number of units in the first standby location, number of units in the second standby location, number of units in the third standby location] = [0, 0, 0], [0, 0, 1], ..., [3, 0, 0]. Therefore, the management device 20 selects one of these distribution patterns and distributes the medical devices ME to each standby location according to the selected pattern.

[0059] Next, in step S330, the management device 20 calculates the movement cost of the medical device ME using device information, including the timing and location of use. For example, the management device 20 determines the medical device ME and its location for which the movement cost is calculated, and identifies the medical device ME closest to the location of use among those in the standby location. Then, the management device 20 finds the minimum movement cost required to move the medical device ME from the identified standby location to the destination location by referring to map information stored in the database DB. The management device 20 repeats this process sequentially from the beginning of the acquired data sorted by time.

[0060] Here, the management device 20 stores the standby location from which the medical device ME was taken, decreases the number of medical devices ME in the standby location by one when use of the medical device ME begins, and increases it by one after use is finished. Furthermore, if all medical devices ME are out of service when it is time to start using the medical device ME, the number of medical devices ME held will be the minimum required number D. i Since the result falls below the specified threshold, the control device 20 terminates the simulation and returns to step S300. The result of the terminated simulation does not satisfy the "third condition" of the constraints shown in Figure 7, so it is desirable to discard it rather than use it to generate the objective function.

[0061] Next, in step S340, the management device 20 determines whether the calculation of travel costs has been completed for all patterns of distribution to each standby location for the medical device ME. If the calculation of travel costs has not been completed for all patterns, the management device 20 returns to step S310.

[0062] On the other hand, once the calculation of travel costs for all patterns is complete, the management device 20 outputs the sum of the travel costs for each medical device ME as the total travel cost in step S350 and exits this simulation.

[0063] The management device 20 outputs simulation results, such as in Figure 15, data relating movement costs, the number of units owned, and the number of units deployed, for example, when there are two standby locations, location A and location B. In the example shown in Figure 15, when the number of medical devices ME owned is "8", the movement cost is minimized when the number of units deployed at location A is "3" and the number of units deployed at location B is "5". The minimum movement cost then decreases as the number of medical devices ME owned increases, as shown in Figure 16.

[0064] Through such simulations, the management device 20 generates an objective function (the first term) that correlates movement costs with the number of units owned and the number of units deployed.

[0065] Once the simulation in step S220 of Figure 10 is completed, the management device 20 proceeds to step S230 and executes an optimization process to minimize the objective function. In this embodiment, the management device 20 executes an optimization process to minimize the objective function shown in Figure 6 under the constraints shown in Figure 7, thereby determining the optimal number of medical devices (MEs) to be owned and the optimal number of each of the multiple standby locations.

[0066] Next, in step S240, the management device 20 outputs the optimal number of medical devices ME to be held and the optimal number of devices to be placed in each of the multiple standby locations to the medical personnel's operating terminal OE, web server, etc.

[0067] By having the management device 20 perform such management processing, quantitative management information, including the optimal number of medical devices (MEs) to possess and the optimal number of units to be placed in each of the multiple standby locations, can be provided to healthcare professionals.

[0068] The management system 1 described above generates an objective function that includes the management cost and transportation cost of medical devices (MEs), which vary depending on the number of MEs owned, and performs an optimization process to minimize the objective function using mathematical programming under predetermined constraints. In other words, the above method for managing medical devices (MEs) includes a generation step of generating an objective function that includes the management cost and transportation cost, which vary depending on the number owned, and an calculation step of performing an optimization process that minimizes the objective function using mathematical programming under predetermined constraints. In this way, by determining the optimal number of medical devices (MEs) through an optimization process that takes into account the management cost and transportation cost of medical devices (MEs), it is possible to suppress the excessive ownership of medical devices (MEs). In particular, since the optimal number of medical devices (MEs) is determined by considering transportation costs in addition to management costs, the convenience of medical devices (MEs) can be ensured. Therefore, according to the management system 1 of this embodiment, medical devices (MEs) can be managed appropriately.

[0069] The management system 1 of this embodiment has the following features.

[0070] (1) The management system 1 includes a location acquisition unit 12 that acquires the location of the medical device ME, and a status identification unit 14 that identifies the operating status of the medical device ME. The management system 1 extracts device information, including the timing and location of use of the medical device ME, from the location and operating status of the medical device ME, and generates an objective function by simulating the movement costs when the number of devices owned is changed using this device information. In this way, by generating an objective function through simulation based on the actual location and operating status of the medical device ME, it becomes easier to find the optimal number of medical devices ME that are appropriate for the actual situation in the medical field.

[0071] (2) For example, in a medical facility, when using medical equipment (ME), it may be necessary to retrieve the equipment from a location far from the place of use, or to search for the equipment. This means that the management of the number of devices in the standby area is not adequate. Such problems can also arise when managing equipment other than medical equipment (ME).

[0072] In contrast, the function generation unit 31 of this embodiment generates an objective function that correlates movement costs with the number of units owned and the number of units placed in each of the multiple standby locations by simulating the movement costs when the number of units owned and the number of units placed in each of the multiple standby locations are changed. Then, the optimization calculation unit 32 performs optimization processing under predetermined constraints to find the optimal number of units placed in each of the multiple standby locations. This makes it possible to avoid having to go to a location far from the place of use to retrieve equipment or to search for equipment.

[0073] (3) The management system 1 includes a database DB in which map information is stored, which is a graph representing a map of multiple nodes set in the movement path of the medical device ME connected by edges having predetermined weights. The function generation unit 31 simulates the movement cost by referring to the above map information. In this way, by storing the map information in the database DB in advance, the amount of computation required when simulating the movement cost can be reduced.

[0074] (4) The travel cost is determined by a search algorithm using Dijkstra's algorithm. Users of medical devices (MEs), such as healthcare professionals, generally move the medical devices (MEs) along the shortest and most efficient route to minimize costs when using them. Therefore, by using the minimum cost determined by a search algorithm using Dijkstra's algorithm as the travel cost, it is possible to simulate the travel cost in a manner close to that of a medical setting.

[0075] (5) The constraint is that the total cost of managing multiple medical devices (MEs) is limited by a predetermined total budget B supincludes the following conditions. According to this, within the total budget B sup , the optimal number of medical devices ME can be determined.

[0076] (6) The mathematical programming method is a linear programming method. According to this, the optimal number of medical devices ME can be obtained by the optimization processing using the linear programming method.

[0077] (7) The position acquisition unit 12 includes a radio wave receiving unit 121 that receives radio waves emitted from radio wave transmitters installed at predetermined locations in a medical facility, and a position specifying unit 122 that specifies the position of the medical device ME based on the signal levels of the radio waves transmitted from the plurality of radio wave transmitters. According to this, the position of the medical device ME within the medical facility can be appropriately specified.

[0078] (8) The status specifying unit 14 detects a change in power supplied to the medical device ME, and specifies the operating status based on the change in power. According to this, the operating status of the medical device ME can be appropriately specified.

[0079] (Second Embodiment) Next, the second embodiment will be described with reference to Figures 17 to 21. In the present embodiment, portions different from the first embodiment will be mainly described.

[0080] The management system 1 of the present embodiment is configured to distribute the total budget B sup for each model, and determine the optimal number for each model. Specifically, as shown in Figure 17, the management control unit 30 of the management device 20 includes a budget distribution unit 34.

[0081] When medical devices ME of different models are included in the management targets, the budget distribution unit 34 distributes the total budget B based on the operating status of the medical devices ME for each model sup for each model. As shown in Figure 18, the budget distribution unit 34 of the present embodiment applies a weight w indicating the importance of management of the medical devices ME to the total budget B which is the upper limit of the management cost sup i ​By multiplying by this, the allocated budget B i We find the weight w. i Each of these values ​​will be in the range of 0 to 1, and all weights w i The sum is set to "1".

[0082] Here, weight w i For example, as shown in Figure 19, the reference weight w' i This is set using the following reference weight w'. i The usage rate of each medical device (ME) is r usei , original number of holdings X i , minimum required number D i This was determined based on the following factors. Note that the reference weight w' i is the usage rate r usei The larger the value, the larger the value set, and the usage rate r usei The smaller the value, the smaller the value set. Also, the reference weight w' i The original number held is X i The minimum number required is D i If it exceeds X, the number of holdings is considered sufficient and is set to a smaller value, and the original number of holdings X i The minimum number required is D i It is set to a larger value if it falls below a certain threshold.

[0083] Furthermore, the usage rate r mentioned above usei As shown in Figure 20, this can be calculated by dividing the total usage time and total inspection time (including the battery 18 charging time) by the data acquisition period. While the total usage time can generally be calculated based on the time the medical device (ME) is operational, some medical devices (MEs) need to be placed in the patient's room while not in operation. In such cases, it is desirable that the total usage time be calculated by also considering the time the medical device (ME) is located in the patient's room or other place of use.

[0084] Furthermore, the function generation unit 31 of this embodiment generates an objective function for each model of medical device ME, for example, by adding a first term including movement costs and a second term including management costs, as shown in Figure 21. The function generation unit 31 generates the first term of the objective function by, for example, using the device information for each model of medical device ME stored in the database DB, and simulating the movement costs for each model when the number of medical devices ME owned is changed.

[0085] The optimal calculation unit 32 of this embodiment determines the optimal number for each model of medical device ME by performing an optimization process that minimizes the objective function shown in Figure 21 using mathematical programming under predetermined constraints. The constraints include, as shown in Figure 22, the total management cost for each model, which is allocated by the budget allocation unit 34, and the allocated budget B. i The following conditions are included.

[0086] Other aspects are the same as in the first embodiment. The management system 1 of this embodiment can obtain the same effects as in the first embodiment, which are achieved from a configuration common to or equivalent to that of the first embodiment.

[0087] The management system 1 of this embodiment has the following features.

[0088] (1) When the management system 1 of this embodiment includes different types of medical devices (MEs) as targets for management, the total budget B is calculated based on the operating status of each type of medical device (ME). sup The system includes a budget distribution unit 34 that distributes the budget to each model. The constraints include the condition that the total management cost for each model is less than or equal to the allocated budget distributed by the budget distribution unit 34. The optimal calculation unit 32 then performs an optimization process under the predetermined constraints to find the optimal number for each model. This makes it possible to find the optimal number for each model of medical equipment (ME) with a small amount of computation.

[0089] (Modified version of the second embodiment) In the second embodiment, the usage rate of the medical device ME is r usei Based on this, total budget B supThe example given shows how the total budget B is distributed. sup The distribution method may be other than that of the second embodiment. For example, based on the most recent failure rate, the relationship between service life and years of use, the ownership rate of medical devices as a whole, the total budget B sup It may be arranged so that it is distributed.

[0090] (Other embodiments) While representative embodiments of this disclosure have been described above, this disclosure is not limited to the embodiments described above and can be modified in various ways, for example, as follows.

[0091] In the above embodiment, the objective function is generated by simulating the movement cost when the number of medical devices (MEs) is changed using device information including the timing and location of use. However, the objective function may be generated by other methods.

[0092] As described in the above embodiment, it is desirable to generate an objective function that associates the number of medical devices (MEs) owned, the number of medical devices (MEs) placed in each of the multiple standby locations, and the travel cost, but this is not required.

[0093] In the above-described embodiment, the simulation calculates the movement cost for all possible distribution patterns to each standby location of the medical device ME. However, this method leads to a combinatorial explosion when the number of medical devices ME becomes large. Therefore, when the number of medical devices ME exceeds a certain threshold, it is desirable to use mathematical optimization methods such as simulated annealing.

[0094] The minimum cost of moving the medical device (ME) from the standby location to the usage location may be pre-stored in a database (DB) as route cost information. Note that the minimum cost may be determined by a search algorithm other than Dijkstra's algorithm.

[0095] In the above embodiment, an example was described in which the optimal number of medical devices (MEs) is determined by an optimization process using linear programming. However, the embodiment is not limited to this, and the optimal number of medical devices (MEs) may be determined by mathematical programming methods other than linear programming.

[0096] In the above-described embodiment, an example was given in which the location of a medical device (ME) is determined based on the signal level of radio waves emitted from a radio transmitter installed at a predetermined location in a medical facility. However, the location of the medical device (ME) may be determined by other methods.

[0097] In the embodiments described above, the operating status of a medical device (ME) is identified by comparing the current value and variance value of the current waveform of the medical device (ME) with threshold values. However, the method for identifying the operating status is not limited to this. For example, the operating status may be identified using a model called GMM-HMM.

[0098] Here, GMM-HMM is a model that uses a Gaussian mixture distribution (GMM) for the output probability of a Hidden Markov Model (HMM). When using GMM-HMM, a model can be created using the current value and variance value of the medical device ME as training data, and the operating status can be identified using the generated model. Note that management system 1 may also be configured to identify the operating status of the medical device ME based on physical quantities other than power of the medical device ME.

[0099] In the embodiments described above, an example of applying the management system 1, etc., of this disclosure to a medium- to large-scale medical facility where many medical devices (MEs) are deployed was explained. However, the application of the management system 1 and management method of this disclosure is not limited to this. The management system 1 and management method can also be applied to logistics facilities, factory facilities, etc., where multiple manned transport vehicles such as forklifts or automated guided vehicles (AGVs) are deployed.

[0100] In the embodiments described above, it goes without saying that the elements constituting the embodiments are not necessarily essential, except in cases where they are explicitly stated to be essential or where they are clearly considered essential in principle.

[0101] In the embodiments described above, if numerical values ​​such as the number, numerical values, quantities, or ranges of the components of the embodiment are mentioned, the embodiment is not limited to those specific numbers unless explicitly stated as particularly essential or clearly limited to a specific number in principle.

[0102] In the embodiments described above, when referring to the shape, positional relationships, etc. of the components, the definition is not limited to those shapes, positional relationships, etc., unless otherwise specifically stated or when the definition is fundamentally limited to a particular shape, positional relationship, etc.

[0103] The control unit and its method of this disclosure may be implemented in a dedicated computer provided by configuring a processor and memory programmed to perform one or more functions embodied by a computer program. The control unit and its method of this disclosure may be implemented in a dedicated computer provided by configuring a processor by one or more dedicated hardware logic circuits. The control unit and its method of this disclosure may be implemented in one or more dedicated computers configured by a combination of a processor and memory programmed to perform one or more functions and a processor configured by one or more hardware logic circuits. The computer program may also be stored as instructions executed by the computer on a computer-readable non-transitional tangible recording medium. [Explanation of Symbols]

[0104] 1 Management System 31 Function generation section 32 Optimal Calculation Unit ME target equipment

Claims

1. A management system for managing multiple target devices (MEs), A function generation unit (31) generates an objective function that includes the management costs incurred by owning the aforementioned target equipment and the travel costs required to move the aforementioned target equipment from its standby location to a predetermined usage location as costs, The system includes an optimal calculation unit (32) that determines the optimal number of the aforementioned target devices to be owned, The optimization unit obtains the optimal number by performing an optimization process that minimizes the objective function using mathematical programming under predetermined constraints. The aforementioned management costs and the aforementioned transportation costs are costs that vary depending on the number held. The function generation unit, By using equipment information including the timing and location of use of the target equipment, extracted from the location and operating status of the target equipment, the first term relating to the movement cost is generated by simulating the movement cost when the number of owned equipment is changed. Based on the purchase cost when purchasing the aforementioned equipment, the disposal cost when disposing of the aforementioned equipment, the maintenance cost when maintaining the aforementioned equipment, and the number of aforementioned equipment units currently owned, a second term relating to the management cost is generated. A management system that generates the objective function by adding the first term and the second term.

2. A position acquisition unit (12) that acquires the position of the target device, The system includes a status identification unit (14) that identifies the operating status, The management system according to claim 1, wherein the function generation unit extracts equipment information from the position of the target equipment acquired by the position acquisition unit and the operating status identified by the status identification unit.

3. The management system according to claim 1 or 2, wherein the constraints include a condition that the total cost of managing the multiple target devices is less than or equal to a predetermined total budget.

4. The management system according to claim 1 or 2, wherein the mathematical programming method is a linear programming method.

5. The management system according to claim 2, wherein the position acquisition unit includes a radio wave receiving unit (121) installed on the target device and receiving radio waves emitted from a radio wave transmitter installed at a predetermined location, and a position identification unit (122) that identifies the position of the target device based on the signal levels of the radio waves emitted from a plurality of radio wave transmitters.

6. The management system according to claim 2, wherein the status identification unit detects changes in the power supplied to the target equipment and identifies the operating status based on the changes in power.

7. A management method for managing multiple target devices (MEs) using a computer, The computer generates an objective function that includes the management costs incurred by owning the target equipment and the travel costs required to move the target equipment from its standby location to a predetermined usage location as costs. The computer comprises a calculation step of determining the optimal number of the target devices to be owned, In the calculation step, the optimal number is obtained by performing an optimization process to minimize the objective function using mathematical programming under predetermined constraints. The aforementioned management costs and the aforementioned transportation costs are costs that vary depending on the number held. In the above generation process, By using equipment information including the timing and location of use of the target equipment, extracted from the location and operating status of the target equipment, the first term relating to the movement cost is generated by simulating the movement cost when the number of owned equipment is changed. Based on the purchase cost when purchasing the aforementioned equipment, the disposal cost when disposing of the aforementioned equipment, the maintenance cost when maintaining the aforementioned equipment, and the number of aforementioned equipment units currently owned, a second term relating to the management cost is generated. A management method that generates the objective function by adding the first term and the second term.

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