Device management apparatus, device management system, and device management method

According to one embodiment, a device management apparatus includes processing circuitry. The processing circuitry acquires information indicating occurrence of a first failure prediction of a first device. The processing circuitry adjusts detection sensitivity of a second failure prediction of a second device relating to the information based on relevance between the first device and the second device. The second device is different from the first device.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based upon and claims the benefit of priority from the prior Japanese Patent Application No. 2019-221460, filed Dec. 6, 2019, the entire contents of which are incorporated herein by reference.

FIELD

Embodiments described herein relate generally to a device management apparatus, a device management system, and a device management method.

BACKGROUND

There is always a potential risk that a failure caused by a design specification or a manufacturing defect in parts included in a device will occur similarly in other devices including the same parts.

A device only detects a failure prediction for its own device. Although the risk of failure can be grasped for devices detecting a failure prediction, the risk of failure cannot be grasped for devices not detecting the failure prediction. Therefore, it is difficult to grasp similar potential risks among a plurality of devices at an early stage.

DETAILED DESCRIPTION

In general, according to one embodiment, a device management apparatus includes processing circuitry. The processing circuitry acquires information indicating occurrence of a first failure prediction of a first device. The processing circuitry adjusts detection sensitivity of a second failure prediction of a second device relating to the information based on relevance between the first device and the second device. The second device is different from the first device.

First Embodiment

A first embodiment will be explained with reference to the drawings.

In the first embodiment, based on a medical device issuing a failure prediction, an adjustment of detection sensitivity of the failure prediction is forwarded to a medical device not issuing the failure prediction. For example, the adjustment of detection sensitivity of the failure prediction is an adjustment for the failure prediction to be detected easier. For example, the adjustment for the failure prediction to be detected easier is achieved by increasing the detection sensitivity of the failure prediction. The first embodiment is an example of a medical device issuing a failure prediction and a medical device not issuing the failure prediction existing in the same hospital. A failure prediction is an indicator indicating that, although a failure has not yet occurred in a medical device, there is a high probability that a failure or an abnormality may occur to one of the portions in the medical device in the future. A portion is a part of the medical device, and may be in units of parts configuring the medical device, or may be in units of modules assembling a plurality of parts. Hereinafter, the medical device issuing the failure prediction will also be referred to as a source device. The source device may also be referred to as a first device. A medical device that has not issued the failure prediction and is to be an adjustment target of the detection sensitivity of the failure prediction will be referred to as a destination device. The destination device may also be referred to as a second device.

FIG.1is a diagram showing a configuration of an in-hospital system10according to the first embodiment. As shown inFIG.1, the in-hospital system10is a system configured by a plurality of apparatuses in a hospital H1. The in-hospital system10is an example of a device management system.

The in-hospital system10includes a plurality of medical devices1-1to1-m(m is an integer equal to or larger than two), a section server2, and a display apparatus3. The plurality of medical devices1-1to1-m, the section server2, and the display apparatus3are connected to each other via an intra-hospital network such as a local area network (LAN) in a communicatory manner. The network connection may be either wired or wireless.

The medical devices1-1to1-minclude medical devices of a variety of uses for acquiring data used for diagnosing a patient. Here, the medical device1-1and the medical device1-2are assumed to be computed tomography (CT) apparatuses.1-mis assumed to be a magnetic resonance imaging (MRI) apparatus.

The section server2is a server used by a system managing information on test appointments, etc. at a medical care division. For example, the section server2is a server used by a radiology information system (RIS) managing information on test appointments, etc. at a radiology division which is one of the divisions of the medical care division. InFIG.1, an example of a case in which one section server2is used is shown. However, the number of servers is not limited thereto. A plurality of section servers2may be provided as needed. The section server2is an example of a device management apparatus. The device management system is not limited to the section server2in the in-hospital system10. The device management system may include other servers in the in-hospital system10, a server in a system different from the in-hospital system10in the hospital H1, or a server existing outside the hospital H1, etc.

The display apparatus3is an apparatus for displaying various kinds of information. For example, the display apparatus3includes a liquid crystal display (LCD) and an organic electro-luminescence display (OELD), etc. For example, the display apparatus3displays at least one of information of the destination device or the source device. The display apparatus3is an example of an output apparatus.

The medical device1-1includes at least diagnosis processing circuitry11, processing circuitry12, a memory13, and a communication interface14.

The diagnosis processing circuitry11is circuitry for executing processing relating to a diagnosis in the medical device1-1. For example, in the CT apparatus, the diagnosis processing circuitry11is circuitry for executing processing relating to imaging.

The processing circuitry12includes processors such as a central processing unit (CPU) and a graphics processing unit (GPU). By activating a program installed in the memory13, etc., the processor executes a system control function121, a detection function122, a specifying function123, a transmission function124, a reception function125, and a reflecting function126, etc. Each of the functions121to126is not limited to the case of being realized by a single processing circuitry. The processing circuitry may be configured by combining a plurality of independent processors, and may realize each of the functions121to126by having each of the processors execute the program. Each of the functions121to126will be described later.

The memory13is a storage apparatus, such as a read-only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a solid-state drive (SSD), and an integrated-circuit storage apparatus, which stores various kinds of information. Other than the above storage apparatus, the memory13may be a portable storage medium, such as a compact disc (CD), a digital versatile disc (DVD), and a flash memory, or a driving apparatus that reads and writes various kinds of information between semiconductor memory devices, etc.

The memory13stores characteristic data of the medical device1-1. For example, the characteristic data of the medical device1-1includes device information, measurement data relating to an operation status, and data specifying a detection algorithm of the failure prediction.

The device information is information specifying the medical device1-1. The device information may include a list of portions configuring the device, and a type, a model number, and a production lot of the device, etc. For example, the portion may be a monitor. For example, the type of device may be a CT apparatus. For example, the model number may be a number specifying the model of the device. For example, the production lot may be a production number.

The measurement data relating to an operation status is data obtained by measuring the status of operation of the medical device1-1. The measurement data relating to an operation status includes measurement data relating to an operation and measurement data relating to usage, etc.

The measurement data relating to an operation is data obtained by measuring an event that has occurred corresponding to the operation of the medical device1-1. For example, the measurement data relating to an operation includes data relating to an operation environment such as vibration data, temperature data, and audio data, and current value data and voltage value data.

The vibration data is data relating to vibration of the medical device1-1. The vibration data is measured by a sensor (not shown) attached to the medical device1-1to supervise its safety. The vibration data may be measured by a sensor attached to a plurality of portions of the medical device1-1.

The temperature data is data relating to an environmental temperature to which the medical device1-1is subjected. The temperature data is measured by a sensor (not shown) attached to the medical device1-1to supervise its safety. The temperature data may be measured by a sensor attached to a plurality of portions on the medical device1-1.

The audio data is data relating to a sound emitted by the medical device1-1. The audio data may be data relating to a frequency. The data is measured by a sensor (not shown) attached to the medical device1-1. The audio data may be measured by a sensor attached to a plurality of portions on the medical device1-1.

The current value data and the voltage value data are data relating a current value and a voltage value at the medical device1-1. The current value data and the voltage value data are measured by a sensor (not shown) attached to the medical device1-1. The current value data and the voltage value data may be measured for each portion configuring the medical device1-1.

The measurement data relating to usage is data obtained by measuring matters that change in accordance with the usage of the measurement data1-1. The measurement data relating to usage includes data relating to frequency of usage and data relating to used hours, etc. For example, the data relating to frequency of usage includes the number of persons using the medical device1-1per day and the number of times of using an application per day. For example, the data relating to used hours includes an average of used hours per day and a total of used hours from installation to the present. The measurement data relating to usage is measured by the processing circuitry12in accordance with an on/off of the power of the medical device1-1and activation of the application.

The data specifying a detection algorithm is data for specifying a detection algorithm used for detecting a failure prediction and specifying a failure estimation portion. The failure estimation portion is a portion at which a failure has not yet occurred, but is estimated to occur with a high probability in the future. For example, in a case where the detection algorithm includes a later-described failure estimation model generated by machine learning, the data specifying the detection algorithm is version information of the failure estimation model.

The failure estimation model is, for example, a trained model to which the measurement data relating to an operation of the medical device is input, and from which a presence or absence of the failure prediction can be output. The failure estimation model may also be trained to have the measurement data relating to the operation input, and to be able to output the failure estimation portion.

The failure estimation model is generated by the processing circuitry12by machine learning using training data in which measurement data relating to the operation of a medical device serves as input data and an occurrence of a failure prediction is used as supervisory data. At least one piece of measurement data among the pieces of measurement data relating to the operation indicates a different trend between a case in which the failure prediction has occurred and a case in which the failure prediction has not occurred. Therefore, there is a certain correlation between the measurement data relating to the operation and the presence or absence of the failure prediction.

Furthermore, the failure estimation model may be generated by machine learning using training data in which the measurement data relating to the operation of the medical device serves as the input data and the failure estimation portion at which the failure prediction has occurred is used as the supervisory data. At least one piece of measurement data among the pieces of measurement data relating to the operation indicates a different trend between a case in which the failure prediction has occurred and a case in which the failure prediction has not occurred. At least one piece of measurement data among the pieces of measurement data relating to the operation indicates a different trend for each failure estimation portion. Therefore, there is a certain correlation between the measurement data relating to the operation and the failure estimation portion.

The failure estimation model may also be designed to be able to output a cause of the occurrence of the failure prediction. For example, when predetermined data is input to the failure estimation model, at least one feature amount that is different from the last output is output from the failure estimation model, and a cause relating to the output feature amount is estimated.

The communication interface14is an interface for performing data communications with other computers. For example, the communication interface14performs various data communications with the section server2via an intra-hospital network in conformity with a known preset standard.

Each of the functions121to126will be explained.

The system control function121controls each unit in the medical device1-1based on input information that is input.

The detection function122detects a failure prediction of its own medical device1-1. For example, by the detection algorithm, the detection function122detects the presence or absence of the failure prediction of the medical device1-1by using, for example, the above-mentioned failure estimation model. The detection function122detects the presence or absence of the failure prediction of the medical device1-1by executing the detection algorithm at an appropriate timing, such as prior to the opening time of the hospital H1, after the closing time of the hospital H1, and when the operation of the medical device1-1is suspended.

By the detection algorithm, the specifying function123specifies the failure estimation portion of the medical device1-1by using, for example, the above-mentioned failure estimation model. By the detection algorithm, the specifying function123may estimate a failure estimation cause of the failure estimation portion using the above-mentioned failure estimation model.

The transmission function124transmits maintenance data to the section server2via the communication interface14based on detection of the failure prediction by the detection function122. For example, the maintenance data includes a failure prediction analysis result and characteristic data of the medical device1-1. For example, the failure prediction analysis result includes information indicating occurrence of the failure prediction. In the case where the failure estimation portion is specified by the specifying function123, the failure prediction analysis result includes information indicating the failure estimation portion. The failure prediction analysis result may also include information indicating a failure estimation cause of the failure estimation portion. For example, the maintenance data is used for extracting a destination device by the section server2. Furthermore, the transmission function124transmits the characteristic data to the section server2at, for example, a predetermined cycle via the communication interface14.

The reception function125receives adjustment information of detection sensitivity of the failure prediction (hereinafter, simply referred to as “adjustment information”) adjusted by the section server2described later via the communication interface14. The adjustment information includes information indicating adjustment details of the detection algorithm for adjusting the detection sensitivity of the failure prediction and adjustment instructions of the detection algorithm. For example, in a case where the detection algorithm includes the failure estimation model, the adjustment information includes training data for further training the failure estimation model as information indicating the adjustment details of the detection algorithm. Furthermore, for example, the adjustment information may also include information relating to execution frequency.

The reflecting function126reflects the adjustment information on a detection algorithm stored in the memory13. “Reflecting” includes the meaning of updating, applying, adjusting, changing, and relearning, etc., regardless of whether it is temporarily or continuously. In this manner, the detection function122and the specifying function123use a detection algorithm on which the algorithm for adjusting the detection sensitivity of the failure prediction is reflected. In accordance with the reception of the adjustment information, the reflecting function126may automatically reflect the adjustment information on the detection algorithm. Instead of this, the reflecting function126may reflect the adjustment information on the detection algorithm based on a direct or a remote input of a reflecting instruction by a manager of the in-hospital system10.

The medical devices1-2to1-mmay be configured in the same manner as the medical device1-1described above. Therefore, the explanations thereof will be omitted.

The section server2also has a function of forwarding the adjustment of the detection sensitivity of the failure prediction to the destination device based on the source device.

The section server2includes at least processing circuitry21, a memory22, and a communication interface23.

In the same manner as the processing circuitry12, the processing circuitry21includes a processor such as a CPU or a GPU. By activating a program installed in the memory13, etc., the processor executes a reception function211, an acquisition function212, a setting function213, an extraction function214, an adjustment function215, a transmission function216, and an output function217, etc. Each of the functions211to217will be described later.

Similar to the memory13, the memory22is a storage apparatus such as a ROM, a RAM, an HDD, an SSD, and an integrated circuit storage apparatus configured to store various kinds of information. For example, the memory22stores characteristic data of each of the medical devices1-1to1-m. For example, the section server2acquires the characteristic data of the medical device at an appropriate timing or on a regular basis from the medical devices1-1to1-m, and stores it in the memory22. The characteristic data of each of the medical devices1-1to1-mis used to extract a destination device.

The memory22stores an extraction item management table. The extraction item management table is a look up table (LUT) or a database associating the failure estimation portion with one or more extraction items. The extraction item is an item indicating a condition for extracting the destination device. The extraction item is used to configure the extraction condition for extracting the destination device. The extraction item is an item that can be collated with the characteristic data of each of the medical devices1-1to1-m. The extraction condition is a condition for extracting a medical device that corresponds to all of the extraction items configuring the extraction condition as the destination device. The extraction condition is a condition for extracting the destination device. A configuration example of the extraction item management table will be described later.

The memory22stores a forwarding management table. In one example, the forwarding management table is an LUT or a database in which the failure estimation portion is associated with the necessity of forwarding. The necessity of forwarding indicates the necessity of adjustment of the detection sensitivity of the failure prediction with respect to the destination device. In another example, the forwarding management table is an LUT or a database in which the failure estimation cause of the failure estimation portion is associated with the necessity of forwarding. A configuration example of the forwarding management table will be described later.

The communication interface23is an interface for performing data communications with other computers. For example, the communication interface23performs various data communications with each of the medical devices1-1to1-mand the display apparatus3via the intra-hospital network in conformity with a known preset standard.

Each of the functions211to217will be described.

The reception function211receives the maintenance data from one of the source devices among the medical devices1-1to1-mvia the communication interface23. For example, the reception function211can receive the maintenance data as a response to a request for the maintenance data transmitted from the section server2to the medical devices1-1to1-m. For example, the reception function211can receive the maintenance data autonomously transmitted by the source device. The reception function211receives the characteristic data from one of the medical devices1-1to1-mvia the communication interface23. For example, the reception function211can receive the characteristic data as a response to a request for the characteristic data transmitted from the section server2to the medical devices1-1to1-m. For example, the reception function211can receive the maintenance data autonomously transmitted by the source device on a regular basis.

The acquisition function212includes a function for acquiring information indicating the occurrence of the failure prediction of the source device. For example, the acquisition function212acquires information indicating the occurrence of the failure prediction of the source device from the maintenance data received by the reception function211. Furthermore, the acquisition function212includes a function for acquiring information indicating the failure estimation portion of the source device. For example, the acquisition function212acquires the information indicating the failure estimation portion of the source device from the maintenance data received by the reception function211.

The setting function213sets the extraction condition. For example, in a case where the acquisition function212acquires the information indicating the occurrence of the failure prediction of the source device and does not acquire the information indicating the failure estimation portion of the source device, the setting function213sets the device, the model number, or the production lot corresponding to the source device as the extraction condition in accordance with a predetermined rule. For example, in a case where the acquisition function212acquires the information indicating the failure estimation portion of the source device, the setting function213sets the extraction condition based on the failure estimation portion of the source device acquired by the acquisition function212. In this example, the setting function213uses the extraction item management table to acquire one or more extraction items associated with the failure estimation portion of the source device acquired by the acquisition function212. The setting function213sets a condition corresponding to all of the one or more extraction items as the extraction condition. The extraction condition is an example of relevance between the source device and one or more destination devices.

The extraction function214extracts one or more destination devices from among a plurality of medical devices under the extraction condition set by the setting function213. For example, the extraction function214collates the characteristic data of each of the medical devices1-1to1-mstored in the memory22with all of the extraction items configuring the extraction condition. The extraction function214extracts one or more medical devices that correspond to all of the extraction items configuring the extraction condition as the destination device. The destination device included in the extraction condition may be considered to be a device that is highly relevant and similar to the source device.

The adjustment function215adjusts the detection sensitivity of the failure prediction of the destination device relating to at least the information indicating the occurrence of the failure prediction based on the relevance between the source device and one or more destination devices. For example, in a case where the acquisition function212acquires the information indicating the occurrence of the failure prediction of the source device and does not acquire the information indicating the failure estimation portion of the source device, the adjustment function215adjusts the detection sensitivity of the failure prediction of the source device. For example, in a case where the acquisition function212acquires the information indicating the failure estimation portion of the source device, the adjustment function215adjusts the detection sensitivity of the failure prediction at a portion corresponding to the failure estimation portion for the destination device. An example of adjusting the detection sensitivity of the failure prediction will be described later.

The transmission function216transmits the adjustment information based on the adjustment by the adjustment function215to the destination device via the communication interface23.

The output function217outputs at least one of the information of the source device or the information of the destination device to the display apparatus3. For example, the information of the source device includes information specifying the source device. The information of the source device may also include information relating to the failure prediction. The information relating to the failure prediction includes the information indicating the occurrence of the failure prediction. The information relating to the failure prediction may also include information specifying the failure estimation portion. The information of the source device is based on the maintenance data. The information specifying the source device is information indicating a CT apparatus, etc. The information specifying the failure estimation portion is information indicating an X-ray tube, etc. The information of the destination device includes information specifying the destination device and the adjustment information of the detection sensitivity. The information of the destination device is based on the extraction of the destination device by the extraction function214. The adjustment information of the detection sensitivity is based on the adjustment of the detection sensitivity by the adjustment function215.

The output function217may output at least the information relating to the prediction of a failure that occurred with respect to the source device to the display apparatus3.

The extraction condition will be explained.

FIG.2is a diagram showing an example of a range of the extraction condition. The range of the extraction condition corresponds to the number of medical devices included in the extraction condition. For example, extraction items include a portion, a device, a model number, a production lot, and an operation status.

In a case where the portion is the extraction item, the extraction item is an item indicating extraction of a device including the same portion as that of the source device. In a case where the device is the extraction item, the extraction item is an item indicating extraction of a device that is the same as the source device. The extraction condition under which the device is the extraction item is narrower than the extraction condition under which the portion is the extraction item, and is included in the extraction condition under which the portion is the extraction item. In a case where the model number is the extraction item, the extraction item is an item indicating extraction of a device with the same model number as that of the source device. The extraction condition under which the model number is the extraction item is narrower than the extraction condition under which the device is the extraction item, and is included in the extraction condition under which the device is the extraction item. In a case where the production lot is the extraction item, the extraction item is an item indicating extraction of a device with the same production lot as that of the source device. The extraction condition under which the production lot is the extraction item is narrower than the extraction condition under which the model number is the extraction item, and is included in the extraction condition under which the model number is the extraction item.

In a case where the operation status is the extraction item, the extraction item is an item indicating extraction of the same operation status as that of the source device. For example, the operation status indicates a status of a device that is subjected to an environmental temperature equal to or more than 30 degrees Celsius, a device that is used by X or more users per day, and a device on which an application is used Y or more times per day. The operation status does not indicate the characteristic of the device itself. Therefore, the extraction condition under which the operation status is the extraction item may be narrower or wider than the extraction condition under which the production lot, the model number, or the device is the extraction item.

A configuration example of the extraction item management table will be explained.

FIG.3is a diagram showing an example of the extraction item management table stored in the memory22of the section server2.

The extraction item management table associates the failure estimation portion with one or more extraction items. The “failure estimation portion” of the extraction item management table comprehensively indicates various kinds of failure estimation portions. The “extraction item” of the extraction item management table indicates one or more extraction items for configuring the extraction condition.

The “extraction item” includes one of the portion, the device, the model number, or the production lot for each of the failure estimation portions. The “extraction item” includes one of the portion, the device, the model number, or the production lot for each of the failure estimation portions to extract a device that is highly relevant and similar to the source device as the destination device. The extraction condition under which the portion is the extraction item is a condition for extracting a medical device including a portion that corresponds to the failure estimation portion. Since the extraction condition under which the device is the extraction item is included in the extraction condition under which the portion is the extraction item, it is a condition for extracting all of or a part of the medical device including a portion that corresponds to the failure estimation portion. Since the extraction condition under which the model number is the extraction item is included in the extraction condition under which the portion is the extraction item, it is a condition for extracting all of or a part of the medical device including a portion that corresponds to the failure estimation portion. Since the extraction condition under which the production lot is the extraction item is included in the extraction condition under which the portion is the extraction item, it is a condition for extracting all of or a part of the medical device including a portion that corresponds to the failure estimation portion. Therefore, the extraction condition that is associated with the failure estimation portion is a condition for extracting a medical device including a portion that corresponds to at least the failure estimation portion as the destination device.

The “extraction item” may include one or more operation statuses in addition to one of the portion, the device, the model number, or the production lot. In a case where the “extraction item” includes one or more operation statuses that may have an effect on the failure of the failure estimation portion, the extraction condition is a condition obtained by considering also the operation status of the source device. That is, the extraction condition is a condition for extracting a device that includes a portion corresponding to the failure estimation portion and corresponds to the operation status of the source device. A device corresponding to the operation status of the source device is a device in which the trend of the operation status is similar to that of the source device. The range of similarity may be set as appropriate. The extraction condition is a condition for extracting a device by narrowing it down to a device highly relevant and similar to the source device.

In the example shown inFIG.3, the “extraction item” associated with an X-ray tube is a tube current, a filament current, and a gantry temperature. The tube current, the filament current, and the gantry temperature may have an effect on the failure of the X-ray tube. The extraction condition is a condition for extracting a device of the same production lot as the source device, and a device corresponding to the tube current, the filament current, and the gantry temperature of the source device.

One or more extraction items included in the “extraction item” may be set as appropriate. Which of the items of the portion, the device, the model number, or the production lot to include in the “extraction item” may differ depending on the type of the failure estimation portion. Whether or not to include one or more operation statuses in the “extraction item” may differ depending on the type of the failure estimation portion. For example, for a failure estimation portion which is subjected to degradation over time, one or more operation statuses may be included in the “extraction item”. The contents of one or more operation statuses included in the “extraction item” may differ depending on the type of the failure estimation portion. Therefore, the extraction condition differs in accordance with the type of the failure estimation portion.

A configuration example of the forwarding management table will be explained.

FIG.4is a diagram showing an example of the forwarding management table stored in the memory22of the section server2.

The forwarding management table associates the failure estimation portion with the necessity of forwarding.

The “failure estimation portion” of the forwarding management table comprehensively indicates various kinds of failure estimation portions. For example, in the case where the “failure estimation portion” is portion B, the “necessity of forwarding” in the forwarding management table is “necessary”. When “necessity of forwarding” is “necessary”, it indicates that it is necessary to adjust the detection sensitivity of the failure prediction at the destination device. In the case where “necessity of forwarding” is “necessary”, the processing circuitry21determines that it is necessary to adjust the detection sensitivity of the failure prediction at the destination device. On the other hand, when “necessity of forwarding” is “unnecessary”, it indicates that it is not necessary to adjust the detection sensitivity of the failure prediction at the destination device. In the case where “necessity of forwarding” is “unnecessary”, the processing circuitry21determines that it is unnecessary to adjust the detection sensitivity of the failure prediction at the destination device.

The reason for changing the necessity of adjusting the detection sensitivity of the failure prediction at the destination device in accordance with the type of failure estimation portion is as follows. The adjustment of the detection sensitivity of the failure prediction results in increasing the processing load of the destination device. By narrowing down the adjustment of the detection sensitivity of the failure prediction to a case in which the detection probability of the failure prediction is to be increased, the processing circuitry21prevents the processing load of the destination device from increasing unnecessarily.

Whether or not the “necessity of forwarding” is “necessary” or “unnecessary” depends on whether the importance of the portion is high or low. The importance is determined depending on the extent to which the detection probability of the failure prediction is to be increased. For example, the importance is determined based on the length of time for the destination device to recover from the failure caused by the failed portion. In a case where the length of time from the failure to the recovery of the destination device is equal to or longer than a certain time, the importance of the portion is high. For the portion with high importance, it is preferable to have the failure prediction detected reliably at the destination device. On the other hand, in a case where the length of time from the failure to the recovery of the destination device is shorter than a certain time, the importance of the portion is low. For the portion with low importance, even if the failure prediction is not detected reliably at the destination device, there is less effect.

For example, a hospital may not have a spare for an expensive portion such as an X-ray tube. In a case where the failed portion is an expensive portion, the time for the destination device to recover from the failure caused by such a portion would be long. Therefore, the importance of an expensive portion is high. For example, in a case where the failed portion can only be replaced by a certain person, the time for the destination device to recover from the failure caused by such a portion would be long. Therefore, the importance is high for a portion that can only be replace by a certain person. For example, when a certain portion fails, other portions may also fail in a linked manner. In a case where the failed portion is a portion that has an effect on other portions in a linked manner, the time for the destination device to recover from the failure caused by such a portion would be long. Therefore, the importance is high for a portion that has an effect on other portions in a linked manner. On the other hand, for example, the importance is low for a portion that is sufficiently stocked at a hospital, and that can be easily replaced by anyone. For example, the importance is low for a portion that has less effect on the performance of the device, when it fails.

Whether or not the “necessity of forwarding” is “necessary” or “unnecessary” may be set in advance, or may be changed as appropriate. For example, the setting of “necessary” or “unnecessary” for the “necessity of forwarding” may be changed for each hospital. For a hospital on, such as, an isolated island, time is required to supply parts. Therefore, the “necessity of forwarding” may mostly be set to “necessary”.

It should be noted that the forwarding management table may associate the failure estimation portion with points instead of the necessity of forwarding. In this example, the points are allocated for each portion in accordance with the importance of the portion. In this example, in a case where the points are equal to or higher than a threshold value, the processing circuitry21determines that it is necessary to adjust the detection sensitivity of the failure prediction at the destination device. On the other hand, in a case where the points are lower than the threshold value, the processing circuitry21determines that it is unnecessary to adjust the detection sensitivity of the failure prediction at the destination device. The threshold value may be set in advance, or may be changed as appropriate. The threshold value may be changed for each hospital.

FIG.5is a diagram showing another example of the forwarding management table stored in the memory22of the section server2.FIG.5shows an example of the forwarding management table in which an ultrasound probe is the failure estimation portion. The memory22stores the forwarding management table for each failure estimation portion.

The forwarding management table associates a failure estimation cause of the failure estimation portion with the necessity of forwarding.

The “estimation cause of failure estimation portion” of the forwarding management table comprehensively indicates various kinds of failure estimation causes. For example, in a case where the “estimation cause of failure estimation portion” is a heated ultrasound probe, the “necessity of forwarding” in the forwarding management table is “necessary”. When the “necessity of forwarding” is “necessary”, it indicates that it is necessary to adjust the detection sensitivity of the failure prediction at the destination device. On the other hand, when the “necessity of forwarding” is “unnecessary”, it indicates that it is not necessary to adjust the detection sensitivity of the failure prediction at the destination device. The reason for changing the necessity of adjusting the detection sensitivity of the failure prediction at the destination device in accordance with the failure estimation cause is as follows. The adjustment of the detection sensitivity of the failure prediction results in increasing the processing load of the destination device. By narrowing down the adjustment of the detection sensitivity of the failure prediction to a case in which the detection probability of the failure prediction is to be increased, the processing circuitry21prevents the processing load of the destination device from increasing unnecessarily.

It is determined whether the “necessity of forwarding” is “necessary” or “unnecessary” depending on whether the degree of risk of the failure estimation cause is high or low. The degree of risk is determined depending on the extent to which the probability of detecting the failure prediction is to be increased. For example, the degree of risk is determined based on the probability of erroneous diagnosis caused by the failed portion to occur or the probability of patient injury caused by the failed portion to occur. In the case where the probability of erroneous diagnosis caused by the failed portion to occur or the probability of patient injury caused by the failed portion to occur is high, the degree of risk of the failure estimation cause is high. For a portion with a high degree of risk, it is preferable to have the failure prediction detected reliably at the destination device. On the other hand, in the case where the probability of erroneous diagnosis caused by the failed portion or the probability of patient injury caused by the failed portion is low, the degree of risk is low. For a portion with a low degree of risk, even if the failure prediction is not detected reliably at the destination device, there is less effect.

For example, there is a possibility that a heated ultrasound probe may burn a patient. Therefore, the degree of risk of the heated ultrasound probe is high. For example, there is a high probability that a defective element of the ultrasound probe may cause erroneous diagnosis. Therefore, the degree of risk of the defective element of an ultrasound probe is high. For example, for a cable breakage of the ultrasound probe, there is a high probability that a manager himself/herself of the in-hospital system10may notice a malfunction. Therefore, the degree of risk of the cable breakage of the ultrasound probe is low.

The setting of whether the “necessity of forwarding” is “necessary” or “unnecessary” may be determined in advance, or may be changed as appropriate. For example, the setting of “necessary” or “unnecessary” for the “necessity of forwarding” may be changed for each hospital.

It should be noted that the forwarding management table may associate the failure estimation cause with points instead of associating the failure estimation portion with the necessity of forwarding. In this example, the points are allocated for each estimation cause in accordance with the degree of risk of the failure estimation cause. In this example, in a case where the points are equal to or higher than a threshold value, the processing circuitry21determines that it is necessary to adjust the detection sensitivity of the failure prediction at the destination device. On the other hand, in a case where the points are lower than the threshold value, the processing circuitry21determines that it is unnecessary to adjust the detection sensitivity of the failure prediction at the destination device. The threshold value may be determined in advance, or may be changed as appropriate. The threshold value may be changed for each hospital.

Hereinafter, processing of the section server2will be explained in detail.

FIG.6is a diagram showing an example of a flow of forwarding processing performed by the processing circuitry21of the section server2. The order of processing may be changed, and a part of the processing may be omitted, as appropriate.

Here, the medical device1-1is assumed to be the source device. The medical device1-2is assumed to be the destination device.

By the reception function211, the processing circuitry21receives maintenance data from the medical device1-1, which is the source device, via the communication interface23(step S11).

By the acquisition function212, the processing circuitry21acquires, for example, information indicating occurrence of the failure prediction of the medical device1-1from the maintenance data (step S12). Furthermore, in step S12, by the acquisition function212, the processing circuitry21acquires, for example, information indicating a failure estimation portion of the medical device1-1.

By the setting function213, the processing circuitry21sets an extraction condition (step S13). In step S13, for example, in a case where the processing circuitry21acquires the information indicating the occurrence of the failure prediction of the source device and does not acquire the information indicating the failure estimation portion of the source device, the processing circuitry21sets the device, the model number, or the production lot corresponding to the source device as the extraction condition in accordance with a predetermined rule. Furthermore, for example, in a case where the processing circuitry21acquires the information indicating the failure estimation portion of the source device, the processing circuitry21sets the extraction condition based on the failure estimation portion with reference to the above-mentioned extraction item management table.

In step S13, for example, the processing circuitry21may refer to the above-mentioned extraction item management table and set a different extraction condition for each of the acquired failure estimation portions. For example, the processing circuitry21changes the range of the extraction condition. In this manner, the processing circuitry21is able to set a suitable extraction condition in accordance with the type of failure estimation portion. As a result, the processing circuitry21is able to prevent the processing load of the destination device from increasing unnecessarily.

In step S13, for example, the processing circuitry21may refer to the above-mentioned extraction item management table and set an extraction condition in consideration of the operation status of the source device. That is, the processing circuitry21sets the extraction condition to a condition for extracting a device that includes a portion corresponding to the failure estimation portion and corresponds to the operation status of the source device. In this manner, the processing circuitry21is able to set an extraction condition for extracting a device by narrowing it down to a device highly relevant and similar to the source device. As a result, the processing circuitry21is able to prevent the processing load of the destination device from increasing unnecessarily.

By the extraction function214, the processing circuitry21extracts, for example, the medical device1-2as the destination device from among the medical devices1-1to1-mincluded in the in-hospital system10under the extraction condition (step S14). The destination device extracted in step S14is a device relating to information indicating at least the occurrence of failure prediction extracted based on the extraction condition.

By the adjustment function215, the processing circuitry21determines whether or not to adjust the detection sensitivity of the failure prediction of the medical device1-2(step S15). In step S15, the processing circuitry21may refer to the above-mentioned forwarding management table and determine the necessity of adjusting the detection sensitivity of the failure prediction of the medical device1-2.

In step S15, in one example, the processing circuitry21refers to the above-mentioned forwarding management table and determines the necessity of adjusting the detection sensitivity of the failure prediction for each of the failure estimation portions. In this manner, the processing circuitry21is able to adjust the detection sensitivity of the failure prediction by narrowing the target down to the failure estimation portion for which the probability of detecting the failure prediction is to be increased. As a result, the processing circuitry21is able to prevent the processing load of the destination device from increasing unnecessarily.

In step S15, in another example, the processing circuitry21refers to the above-mentioned forwarding management table and determines the necessity of adjusting the detection sensitivity of the failure prediction based on the estimation cause of the failure estimation portion. In this manner, the processing circuitry21is able to adjust the detection sensitivity of the failure prediction by narrowing down the target to the estimation cause for which the probability of detecting the failure prediction is to be increased. As a result, the processing circuitry21is able to prevent the processing load of the destination device from increasing unnecessarily.

In the case where the processing circuitry21determines that it is unnecessary to adjust the detection sensitivity of the failure prediction (step S15, NO), the forwarding processing ends without adjusting the detection sensitivity of the failure prediction at the medical device1-2.

In the case where the processing circuitry21determines that it is necessary to adjust the detection sensitivity of the failure prediction (step S15, YES), the detection sensitivity of the failure prediction is adjusted at the medical device1-2(step S16). In step S16, for example, in a case where the processing circuitry21acquires the information indicating the occurrence of the failure prediction of the source device and does not acquire the information indicating the failure estimation portion of the source device, the detection sensitivity of the failure prediction of the destination device is adjusted. For example, in a case where the processing circuitry21acquires the information indicating the failure estimation portion of the source device, the detection sensitivity of the failure prediction at a portion corresponding to the failure estimation portion is adjusted for the destination device.

In step S16, the adjustment function215is able to adjust the detection sensitivity of the failure prediction in the manner exemplified below.

The adjustment function215is able to improve the quality of supervisory data of the failure estimation model. The adjustment function215may apply data for specifying the failure estimation portion at the source device to the destination device as the training data. For example, the data for specifying the failure estimation portion at the source device includes data obtained by associating measurement data relating to the operation of the source device contributing to the specification of the failure estimation portion with whether or not the failure estimation portion is specified.

The reason for applying the data for specifying the failure estimation portion at the source device to the destination device as the training data is as follows. A plurality of medical devices use the same failure estimation model upon shipping. The medical devices each add data acquired at its own device thereto as supervisory data, and update the failure estimation model by machine learning. The failure estimation model used at a certain device may become a model different from the failure estimation model used at other devices with the passage of time. Therefore, even if the devices start being used at around the same time, some devices may and some devices may not be able to detect the failure prediction. The destination device is able to use data for specifying the failure estimation portion at the source device as the training data and update the failure estimation model. As a result, the destination device is able to increase the probability of detecting the failure prediction of portion corresponding to the failure estimation portion.

By the transmission function216, the processing circuitry21determines whether or not to transmit adjustment information based on the adjustment by the adjustment function215to the medical device1-2(step S17). In step S17, the processing circuitry21may determine the necessity of transmitting the adjustment information in accordance with whether or not a transmission instruction is input by the manager of the in-hospital system10. In the case where the processing circuitry21determines that it is unnecessary to transmit the adjustment information (step S17, NO), the forwarding processing ends without transmitting the adjustment information to the medical device1-2. In the case where the processing circuitry21determines that it is necessary to transmit the adjustment information (step S17, YES), the adjustment information is transmitted to the medical device1-2via the communication interface23(step S18).

By the output function217, the processing circuitry21outputs at least one of the information of the medical device1-1or the information of the medical device1-2to the display apparatus3(step S19). In this manner, the display apparatus3displays at least one of the information of the medical device1-1or the information of the medical device1-2. In step S19, by the output function217, the processing circuitry21may output at least the information relating to the prediction of a failure that occurred with respect to the medical device1-1to the display apparatus3. In this manner, the display apparatus3displays at least the information relating to the prediction of a failure that occurred with respect to the medical device1-1.

It should be noted that, in step S19, the processing circuitry21may output the information to an output apparatus other than the display apparatus3. For example, the output apparatus may be a printer. In this manner, the manager of the in-hospital system10is able to confirm at least one of the information of the medical device1-1or the information of the medical device1-2. Alternatively, the manager of the in-hospital system10is able to confirm at least the information relating to the prediction of a failure that occurred with respect to the medical device1-1.

According to the first embodiment, by adjusting the detection sensitivity of the failure prediction at the destination device, the section server2is able to grasp a potential failure prediction risk at the destination device at an early stage.

Various modified examples will be explained below.

In the above, an example of each of the medical devices1-1to1-mdetecting the failure prediction at its own device and specifying the failure estimation portion was explained. However, it is not limited thereto. The section server2may detect the failure prediction of each of the medical devices1-1to1-mand specify the failure estimation portion.

In this example, the section server2includes the detection function122, the specifying function123, and the reflecting function126. The section server2stores the detection algorithm of each of the medical devices1-1to1-min the memory22. The section server2intermittently acquires the measurement data relating to the operation from each of the medical devices1-1to1-m. The detection function122detects the failure prediction of each of the medical devices by the measurement data relating to the operation and the detection algorithm. The specifying function123specifies the failure estimation portion by the measurement data relating to the operation and the detection algorithm. The reflecting function126reflects the adjustment information on the detection algorithm stored in the memory22.

In the above, an example of the source device and the destination device being medical devices managed by the section server2was explained. However, it is not limited thereto. The source device and the destination device may also be medical devices that are respectively managed by different section servers. In this example, a server (not shown) connected to each section server is prepared, and each of the functions211to217is implemented on this server.

In the above, an example of the source device and the destination device being medical devices was explained. However, it is not limited thereto. The source device and the destination device may also be various kinds of devices, such as servers. The “device” is assumed to include facilities.

In the above, the failure estimation model was used to explain an example of the detection algorithm. However, it is not limited thereto. The detection algorithm may be analysis processing of measurement data relating to an operation. In this example, the detection algorithm may include a threshold value or a reference that is used for detecting the failure prediction, specifying the failure estimation portion, and specifying the failure estimation cause. The detection algorithm may include a sampling interval of the measurement data relating to the operation. The detection algorithm may include frequency of detecting the failure prediction, specifying the failure estimation portion, and specifying the failure estimation cause. The detection algorithm may include a quality of the measurement data relating to the operation used for detecting the failure prediction, specifying the failure estimation portion, and specifying the failure estimation cause. For example, the quality of the measurement data is filtered data or non-filtered raw data.

The adjustment function215may alleviate the threshold value or the reference. The adjustment function215may alleviate the threshold value or the reference by narrowing down the target to the measurement data relating to an operation relating to a portion corresponding to the failure estimation portion. In this example, the adjustment information includes a threshold value or a reference which is alleviated as information indicating the adjustment content of the detection algorithm, and includes an update instruction of the threshold value or the reference as an adjustment instruction of the detection algorithm. The adjustment function215may shorten the sampling interval of the measurement data relating to the operation. In this example, the adjustment information includes a shortened sampling interval as information indicating the adjustment content of the detection algorithm, and includes an update instruction of the sampling interval as an adjustment instruction of the detection algorithm. The adjustment function215may increase the frequency of detecting the failure prediction, specifying the failure estimation portion, and specifying the failure estimation cause. In this example, the adjustment information includes the increased frequency as information indicating the adjustment content of the detection algorithm, and includes an update instruction of the frequency as an adjustment instruction of the detection algorithm. The adjustment function215may improve the quality of data so that the raw data of the measurement data relating to the operation may be used for detecting the failure prediction, specifying the failure estimation portion, and specifying the failure estimation cause. The adjustment function215may improve the quality by narrowing down the target to the measurement data relating to an operation relating to a portion corresponding to the failure estimation portion. In this example, the adjustment information includes types of data to be used as information indicating the adjustment content of the detection algorithm, and includes an update instruction of the data quality as an adjustment instruction of the detection algorithm. It should be noted that the adjustment function215may also narrow down the granularity of the target for adjusting the detection sensitivity of the failure prediction to per part, and not per module. In this manner, the destination device is able to detect the failure prediction in a more precise unit.

Second Embodiment

Hereinafter, a second embodiment will be explained with reference to the drawings.

In the same manner as the first embodiment, the second embodiment forwards an adjustment of a detection sensitivity of a failure prediction to a destination device based on a source device. The second embodiment is different from the first embodiment in that it is an example of the source device and the destination device being in different hospitals.

For configurations that are the same as those of the first embodiment, the same symbols will be used, and the explanations thereof will be omitted. In the second embodiment, portions different from those of the first embodiment will be mainly explained.

FIG.7is a diagram showing a configuration of an information management system100according to a second embodiment.

The information management system100includes an in-hospital system10in a hospital H1, a cloud system20in the hospital H1, an in-hospital system30in a hospital H2, a cloud system40in the hospital H2, a server50, and a display apparatus60. The in-hospital system10and the cloud system20are connected by, for example, a dedicated line. The in-hospital system30and the cloud system40are connected by, for example, a dedicated line. The server50is connected to the cloud system20and the cloud system40via a network in a manner that enables data communications. The network may be either wired or wireless.

The information management system100is a network system that complies with guidelines used when storing medical information in a public cloud, etc., such as three guidelines issued by the three ministers of the Ministry of Health, Labour and Welfare, the Ministry of Economy, Trade and Industry, and the Ministry of Public Management, Home Affairs, Posts and Telecommunications, i.e., three guidelines of three ministries. That is, the information management system100is a network system that complies with “Guidelines for safety management of medical information systems” specified by the Ministry of Health, Labour and Welfare, “Safety management guide for information processing companies that manage medical information on a contract basis” specified by the Ministry of Economy, Trade and Industry, and “Guidelines for safety management when cloud service providers handle medical information” specified by the Ministry of Public Management, Home Affairs, Posts and Telecommunications.

The in-hospital system10includes a relay apparatus4in addition to the plurality of medical devices1-1to1-mand the section server2mentioned above. It should be noted that the section server2omits executing each of the functions211to217explained in the first embodiment.

The relay apparatus4is connected to the cloud system20by a dedicated line, and is connected to the section server2via a network in a manner that enables data communications. The relay apparatus4receives various types of data from the section server2. For example, the relay apparatus4receives various types of data the section server2has acquired from the medical devices1-1to1-m. The relay apparatus4transmits the various types of data received from the section server2to the cloud system20via the dedicated line.

The cloud system20receives the various types of data from the in-hospital system10, converts the data into a common standardized form set in advance, accumulates it, and transmits it to the server50. The cloud system20is, for example, a cloud computing system that shares a predetermined function among a plurality of apparatuses via a network and realizes the function in cooperation therewith.

The in-hospital system30includes a plurality of medical devices31-1to31-n(n is an integer equal to or larger than two), a section server32, and a relay apparatus33. The plurality of medical devices31-1to31-nand the section server32are connected to each other via an intra-hospital network in a communicatory manner. The medical devices31-1to31-nmay be configured in the same manner as the medical device1-1described above. Therefore, the explanations thereof will be omitted. The section server32may be configured in the same manner as the section server2described above. Therefore, the explanations thereof will be omitted.

The relay apparatus33is connected to the cloud system40by a dedicated line, and is connected to the section server32via a network in a manner that enables data communications. The relay apparatus33receives various types of data from the section server32. For example, the relay apparatus33receives various types of data the section server32has acquired from the medical devices31-1to31-n. The relay apparatus33transmits the various types of data received from the section server32to the cloud system40via the dedicated line.

The cloud system40receives the various types of data from the in-hospital system30, converts the data into a common standardized form set in advance, accumulates it, and transmits it to the server50. The cloud system40is, for example, a cloud computing system that shares a predetermined function among a plurality of apparatuses via a network and realizes the function in cooperation therewith.

The server50includes processing circuitry51, a memory52, and a communication interface53. The server50is an example of a device management apparatus.

In the same manner as the processing circuitry12, the processing circuitry51includes a processor such as a CPU or a GPU. By activating a program installed in the memory52, etc., the processor executes a reception function511, an acquisition function512, a setting function513, an extraction function514, an adjustment function515, a transmission function516, and an output function517, etc. Each of the functions511to517will be described later.

Similar to the memory13, the memory52is a storage apparatus such as a ROM, a RAM, an HDD, an SSD, and an integrated circuit storage apparatus configured to store various kinds of information. For example, the memory52stores various types of data transmitted from the in-hospital system10via the cloud system20. The memory52stores various types of data transmitted from the in-hospital system30via the cloud system40.

The communication interface53is an interface for performing data communications with other computers. For example, the communication interface53performs various data communications with the cloud system20and the cloud system40via the network in conformity with a known preset standard.

Each of the functions511to517will be explained.

Each of the functions511to517are the same as each of the functions211to217explained in the first embodiment. It should be noted that the extraction function514is able to extract a destination device belonging to a different hospital from a hospital to which a source device belongs. The output function517is able to output at least one of information of the source device or information of the destination device to the display apparatus60. Alternatively, the output function517is able to output at least information relating to the prediction of a failure that occurred with respect to the source device to the display apparatus60. The display apparatus60is the same as the display apparatus3. The display apparatus60is an example of an output apparatus. The information management system100including the server50and the display apparatus60is an example of a device management system.

According to the second embodiment, the server50is able to adjust the detection sensitivity of the failure prediction with respect to a destination device belonging to a different hospital from a hospital to which the source device belongs.

Third Embodiment

Hereinafter, a third embodiment will be explained with reference to the drawings.

The third embodiment collates measurement data relating to an operation of a source device with measurement data relating to an operation of a destination device. The third embodiment is an example of the source device and the destination device being in the same hospital.

For configurations that are the same as those of the first embodiment, the same symbols will be used, and the explanations thereof will be omitted. In the third embodiment, portions different from those of the first embodiment will be mainly explained.

FIG.8is a diagram showing a configuration of an in-hospital system10according to the third embodiment.

Processing circuitry21of a section server2includes a collation function218and an output function219instead of the adjustment function215, the transmission function216, and the output function217explained in the first embodiment.

The collation function218collates measurement data relating to an operation of a destination device with measurement data relating to an operation of a source device. Hereinafter, the measurement data relating to the operation of the source device will be referred to as first data. The measurement data relating to the operation of the destination device will be referred to as second data. In a first collation example, the collation function218collates the first data at a timing of occurrence of the failure prediction of the source device with the second data at a timing of occurrence of the failure prediction of the destination device. The timing may be at a certain point in time or may be a period. The first collation example will be described later. In a second collation example, the collation function218searches for the second data that is similar to the first data at a timing prior to the timing of occurrence of the failure prediction of the destination device. Hereinafter, the timing prior to the timing of occurrence of the failure prediction of the source device will be referred to as a reference timing. The collation function218determines the presence or absence of the failure prediction at the destination device in accordance with the search result. The second collation example will be described later.

The output function219outputs a collation result obtained by the collation function218. The collation result will be described later.

The first collation example will be explained.

FIG.9is a diagram showing an example of collation processing performed by the collation function218.

It is assumed that, by an extraction function214, the processing circuitry21has extracted the destination device based on the acquisition of information indicating a failure estimation portion of the source device. The source device is assumed to have actually failed after the occurrence of the failure prediction. It is assumed that the destination device has not failed at the timing at which the source device has failed. The destination device and the source device are assumed to have started being used at the same timing; however, they may also start being used at different timings.

For example, after the source device has failed, the collation function218collates the first data at a timing of occurrence of the failure prediction of the source device with the second data at a timing of occurrence of the failure prediction of the source device.

A difference between the first data at a timing of occurrence of the failure prediction of the failure estimation portion and the second data at a timing of occurrence of the failure prediction of the failure estimation portion may become a hint to a specific cause of failure of the source device. One of the reasons is that the destination device is a device that is highly relevant and similar to the source device.

The collation function218may determine data indicating the difference between the first data and the second data as the collation result. The collation function218may estimate a specific cause of failure of the source device based on the difference between the first data and the second data, and determine the estimated cause of failure as the collation result.

In the first collation example, the processing circuitry21may transmit a request for the first data to the source device, and acquire the first data at a timing of occurrence of the failure prediction from the source device. Instead of this, the processing circuitry21may be configured to store the first data intermittently transmitted from the source device in a memory22, and acquire the first data at a timing of occurrence of the failure prediction from the memory22. Similarly, the processing circuitry21may transmit a request for the second data to the destination device, and acquire the second data at a timing of occurrence of the failure prediction of the source device from the destination device. Instead of this, the processing circuitry21may store the second data intermittently transmitted from the destination device in the memory22, and acquire the second data at a timing of occurrence of the failure prediction of the source device from the memory22.

According to the first collation example, the section server2is able to support the estimation of the cause of failure of the destination device. Furthermore, the section server2is able to reduce the processing load by narrowing down the device to be compared with the source device to the destination device.

The second collation example will be explained.

FIG.10is a diagram showing an example of the collation processing performed by the collation function218.

It is assumed that, by the extraction function214, the processing circuitry21has extracted the destination device based on acquisition of information indicating the failure estimation portion of the source device. The source device is assumed to have actually failed after five days of the occurrence of the failure prediction. The destination device and the source device are assumed to have started being used at the same timing; however, they may also start being used at different timings.

For example, after the occurrence of the failure prediction of the source device, the collation function218collates the first data at a reference timing with the second data at a timing after the occurrence of the failure prediction of the source device. The collation function218searches for the second data that is similar to the first data at the reference timing. The range of similarity may be set as appropriate. Here, the reference timing is a timing five days prior to the timing of occurrence of the failure prediction of the source device. The reference timing may be changed as appropriate. One of the reasons for conducting the search is because there is a high probability that, if a trend similar to the first data is found in the second data at the reference timing, the destination device may fail in the same manner as the source device in the future. By searching for the second data that is similar to the first data at the reference timing, the collation function218is able to detect the failure prediction earlier than the destination device detects the failure prediction.

The collation function218determines the presence or absence of the failure prediction at the destination device in accordance with the search result. In a case where the second data that is similar to the first data cannot be found at the reference timing, the collation function218determines that there is no failure prediction for the destination device. The collation function218continuously searches for the second data that is similar to the first data at the reference timing in accordance with the lapse of time. On the other hand, in a case where the second data that is similar to the first data is found at the reference timing, the collation function218determines that there is a failure prediction for the destination device.

The collation function218is assumed to have searched for the second data that is similar to the first data at a timing five days prior to the timing of occurrence of the failure prediction of the source device. The collation function218is able to detect the failure prediction of the destination device five days prior to the day on which it is highly possible that the destination device would detect a similar failure prediction as the source device. That is, the collation function218is able to detect the failure prediction of the destination device10days prior to the day on which there is a high probability that the destination device may actually fail.

The collation function218may determine the presence or absence of the failure prediction at the destination device as the collation result. The collation function218may also determine the day on which the failure prediction is detected at the destination device as the collation result.

In the second collation example, the processing circuitry21may transmit a request for the first data to the source device, and acquire the first data at the reference timing. Instead of this, the processing circuitry21may store the first data intermittently transmitted from the source device in the memory22, and acquire the first data at the reference timing from the memory22. Similarly, the processing circuitry21may transmit a request for the second data to the destination device, and acquire the second data for a necessary period. Instead of this, the processing circuitry21may store the second data intermittently transmitted from the destination device in the memory22, and acquire the second data of the necessary period from the memory22.

According to the second collation example, the section server2is able to improve the probability of detecting the failure prediction of the destination device at an early stage. Furthermore, the section server2is able to reduce the processing load by narrowing down the device to be compared with the source device to the destination device.

Hereinafter, processing of the section server2will be explained in detail.

FIG.11is a diagram showing an example of a flow of the collation processing performed by the processing circuitry21of the section server2. The order of processing may be changed, and a part of the processing may be omitted, as appropriate.

Here, the medical device1-1is assumed to be the source device. The medical device1-2is assumed to be the destination device.

By a reception function211, the processing circuitry21receives maintenance data from the medical device1-1, which is the source device, via the communication interface23(step S21).

By an acquisition function212, the processing circuitry21acquires information indicating a failure estimation portion of the medical device1-1from the maintenance data (step S22).

By a setting function213, the processing circuitry21sets an extraction condition based on the failure estimation portion of the medical device1-1(step S23). In step S23, the processing circuitry21may refer to the above-mentioned extraction item management table and set the extraction condition.

By an extraction function214, the processing circuitry21extracts the medical device1-2as the destination device under the extraction condition (step S24).

By the collation function218, the processing circuitry21collates the first data with the second data (step S25). In step S25, in one example, in the manner mentioned above, the processing circuitry21collates the first data at a timing of occurrence of the failure prediction of the source device with the second data at a timing of occurrence of the failure prediction of the source device. In another example, the processing circuitry21searches for the second data that is similar to the first data at the reference timing.

By the output function219, the processing circuitry21outputs a collation result obtained by the collation function218to a display apparatus3(step S26). In this manner, the display apparatus3displays the collation result. In step S26, the processing circuitry21may output the collation result to an output apparatus other than the display apparatus3, such as a printer.

According to the third embodiment, the section server2is able to analyze the state of the source device or the destination device by collating the first data of the source device and the second data of the destination device.

Fourth Embodiment

Hereinafter, a fourth embodiment will be explained with reference to the drawings.

The fourth embodiment collates measurement data relating to an operation of a source device with measurement data relating to an operation of a destination device. The fourth embodiment is different from the third embodiment in that it is an example of the source device and the destination device being in different hospitals.

For configurations that are the same as those of the second embodiment, the same symbols will be used, and the explanations thereof will be omitted. In the fourth embodiment, portions different from those of the first embodiment will be mainly explained.

FIG.12is a diagram showing a configuration of an in-hospital system10according to the fourth embodiment.

Processing circuitry51of a section server50includes a collation function518and an output function519instead of the adjustment function515, the transmission function516, and the output function517explained in the second embodiment.

The collation function518is the same as the collation function218. The collation function518is able to collate measurement data relating to an operation of a source device with measurement data relating to an operation of a destination device belonging to a hospital different from a hospital to which the source device belongs.

The output function519is the same as the output function219. The output function519is able to output a collation result to a display apparatus60.

According to the fourth embodiment, the server50is able to analyze the state of the source device or the destination device by collating first data of the source device with second data of the destination device belonging to a different hospital from the hospital to which the source device belongs.

The third embodiment and the fourth embodiment may be configured in the following manner.

[1] A device management apparatus comprising processing circuitry configured to acquire information indicating a failure estimation portion of a first device, set an extraction condition for extracting one or more second devices based on the failure estimation portion, extract the second device by the set extraction condition, collate first data relating to an operation of the first device with second data relating to an operation of the second device, and output a collation result.

[2] The device management apparatus according to [1], wherein the processing circuitry collates the first data with the second data at a timing of occurrence of a failure prediction of the first device.

[3] The device management apparatus according to [1] or [2], wherein the processing circuitry searches for the second data that is similar to the first data at a timing prior to a timing of occurrence of a failure prediction of the first device, and determines the presence or absence of a failure prediction at the second device in accordance with a search result.

The term “processor” used in the above explanation indicates, for example, a circuit, such as a CPU, a GPU, or an application specific integrated circuit (ASIC), and a programmable logic device (for example, a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)). The processor reads and executes a program stored in storage circuitry to realize the functions thereof. Instead of storing a program in the storage circuitry, the program may be directly incorporated into a circuit of the processor. In this case, the processor reads and executes the program integrated into the circuit, thereby realizing its functions. The functions corresponding to the program may be realized by a combination of logic circuits, and not by executing the program. Each processor of the present embodiment is not limited to a configuration as a single circuit; a plurality of independent circuits may be combined into one processor to realize the functions of the processor.