JP7530594B2 - Dental clinic reservation device - Google Patents

Description

The present invention relates to a dental clinic reservation device.

In a dental appointment device, there is known technology for setting and changing appointments based on the treatment required for the patient and the treatment time required for the treatment (see, for example, Patent Document 1).

JP 2016-219016 A

Conventional technology sets and changes appointments based on the time required for treatment and the availability of appointments, so the patient's circumstances, such as the state of the disease and the degree of need for treatment depending on the state of the disease, are not taken into account, and only the convenience of the clinic is taken into consideration.

The present invention was made in consideration of the above problems, and aims to make it possible to arrange dental appointments while appropriately taking into account the patient's circumstances.

In order to solve the above problems, a dental clinic reservation device according to one embodiment of the present invention is a dental clinic reservation device that adjusts reservations for patient treatment based on the type and severity of oral disease estimated based on an image of the patient's oral cavity, and includes a determination unit that determines a priority level indicating the priority of the patient's treatment based on the type and severity of the oral disease, an extraction unit that refers to a reservation list that stores reservation users who are patients who have made reservations for treatment in association with the priority levels of the reservation users, and extracts reservation users who have a priority level at least equal to or lower than the priority level of the patient, and a reservation change unit that changes the reservation of the reservation user extracted by the extraction unit to a reservation for the patient.

According to the above embodiment, the priority level is determined according to the type and severity of the oral disease estimated based on the patient's oral cavity image, making it possible to compare the priorities of treatment. By comparing the priority levels of the patient and the person making the appointment, the person making the appointment who has a need for treatment at least equal to or less than the degree of the patient's need for treatment is extracted, and the appointment of the extracted person making the appointment is changed to the patient's appointment. Therefore, it is possible to adjust the appointment in a way that appropriately takes into account the need for treatment of the patient's disease.

According to one embodiment of the present invention, it is possible to arrange dental appointments while appropriately taking into account the patient's circumstances.

1 is a diagram showing the device configuration of a dental clinic reservation system according to an embodiment of the present invention. 1 is a block diagram showing a functional configuration of a dental clinic reservation device according to an embodiment of the present invention. FIG. 2 is a hardware block diagram of the dental clinic reservation device. 1A to 1C are diagrams illustrating an example of estimation of the type and degree of oral disease (dental caries, periodontal disease) and oral environment (oral dirt) based on an oral cavity image. 1A and 1B are diagrams illustrating an example of estimating the presence or absence of an oral disease (malignant tumor) based on an oral cavity image. 13A and 13B are diagrams illustrating an example of estimating the presence or absence of an oral disease (temporomandibular joint disorder) based on an oral cavity image. FIG. 13 is a diagram showing an example of a priority level table to be referred to for determining a priority level. FIG. 13 is a diagram showing an example of constructing a prediction model that outputs a degree of deterioration based on attribute information of a patient. FIG. 4 is a diagram showing an example of a reservation list stored in a reservation list storage unit; 11 is a flowchart showing the processing contents of a dental clinic reservation method in the dental clinic reservation device. 11 is a flowchart showing the process of extracting reservers in the flowchart shown in FIG. 10 . FIG. 13 is a diagram showing the configuration of a dental clinic reservation program.

An embodiment of a dental clinic reservation device according to the present invention will be described with reference to the drawings. Where possible, identical parts will be given the same reference numerals and duplicated explanations will be omitted.

Figure 1 is a diagram showing the device configuration of a dental clinic reservation system including a dental clinic reservation device according to this embodiment. As shown in Figure 1, the dental clinic reservation system 1 includes a dental clinic reservation device 10 and a terminal 20 that are configured to be able to communicate with each other via a network N.

The dental clinic reservation device 10 is a device that arranges treatment reservations for patients based on the type and severity of oral disease estimated based on oral images of patients who have been diagnosed with oral disease. In this embodiment, the information indicating the type and severity of oral disease may include information on the type and severity of matters related to the oral environment. Since a deterioration in the state of the oral environment, such as mouth dirt, may cause the occurrence of oral disease, information related to the oral environment is treated as information that may be included in information related to oral disease.

The terminals 20 may include a terminal 20A of a patient, a terminal 20B of a reservation holder who is a patient who has made a reservation for treatment, and a terminal 20C of an administrator of the dental clinic reservation device 10. Note that, although one each of the terminals 20A, 20B, and 20C is shown in FIG. 1, the number of each terminal 20 is not limited to the example shown.

Figure 2 is a diagram showing the functional configuration of the dental clinic reservation device 10 according to this embodiment. As shown in Figure 2, the dental clinic reservation device 10 functionally comprises an image acquisition unit 11, an estimation unit 12, a determination unit 13, a prediction unit 14, an extraction unit 15, a proposal unit 16, and a reservation change unit 17. Each of the functional units 11 to 17 of the dental clinic reservation device 10 is configured to be able to access a storage means such as a reservation list storage unit 19. Each of the functional units 11 to 17 of the dental clinic reservation device 10 may be configured in a single device, or may be distributed across multiple devices.

The block diagram shown in FIG. 2 shows functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and directly or indirectly connected (for example, using wires, wirelessly, etc.) and these multiple devices. The functional blocks may be realized by combining the one device or the multiple devices with software.

Functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regard, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the transmission function is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on the method of realization for either of these.

For example, the dental clinic reservation device 10 in one embodiment of the present invention may function as a computer. FIG. 3 is a diagram showing an example of the hardware configuration of the dental clinic reservation device 10 according to this embodiment. The dental clinic reservation device 10 may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

In the following description, the term "apparatus" can be interpreted as a circuit, device, unit, etc. The hardware configuration of the dental clinic reservation device 10 may be configured to include one or more of the devices shown in FIG. 3, or may be configured to exclude some of the devices.

Each function of the dental clinic reservation device 10 is realized by loading a specific software (program) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations and control communication via the communication device 1004 and the reading and/or writing of data in the memory 1002 and storage 1003.

The processor 1001, for example, runs an operating system to control the entire computer. The processor 1001 may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc. For example, each of the functional units 11 to 17 shown in FIG. 2 may be realized by the processor 1001.

The processor 1001 also reads out programs (program codes), software modules, and data from the storage 1003 and/or the communication device 1004 to the memory 1002, and executes various processes according to these. The programs used are those that cause a computer to execute at least some of the operations described in the above-mentioned embodiments. For example, the functional units 11 to 17 of the dental clinic reservation device 10 may be stored in the memory 1002 and implemented by a control program that runs on the processor 1001. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented in one or more chips. The programs may be transmitted from a network via a telecommunications line.

The memory 1002 is a computer-readable recording medium, and may be composed of at least one of, for example, a ROM (Read Only Memory), an EPROM (Erasable Programmable ROM), an EEPROM (Electrically Erasable Programmable ROM), a RAM (Random Access Memory), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a dental clinic reservation method according to one embodiment of the present invention.

Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, and the like. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including memory 1002 and/or storage 1003.

The communication device 1004 is hardware (transmitting/receiving device) for communicating between computers via a wired and/or wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc.

The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one configuration (e.g., a touch panel).

In addition, each device, such as the processor 1001 and memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured as a single bus, or may be configured as different buses between the devices.

The dental clinic reservation device 10 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented by at least one of these pieces of hardware.

Next, each functional unit of the dental clinic reservation device 10 will be described. The image acquisition unit 11 acquires an image of the oral cavity of the patient. Specifically, the image acquisition unit 11 acquires an image of the oral cavity captured by, for example, the terminal 20A. The oral cavity image may be a still image or a video. The oral cavity image may include areas such as the teeth, gums, and tongue. The oral cavity image may also be a video showing the mouth opening movement. If the terminal 20A is configured as a smartphone, the oral cavity image may be captured by a camera mounted on the smartphone. The terminal 20A may also provide the image acquisition unit 11 with an oral cavity image captured by a separate imaging device (for example, a single-lens reflex camera, etc.). Note that the device for capturing the oral cavity image is not limited to the above example.

The estimation unit 12 estimates the type and severity of oral disease based on the oral image. The method for estimating oral disease is not limited, but the estimation unit 12 may estimate oral disease using a known estimation technique. Specifically, the estimation unit 12 may estimate oral disease using a method that uses an estimation model constructed by machine learning. The estimation model may be constructed by machine learning using learning data consisting of images of teeth, gums, the entire oral cavity, etc., and correct labels indicating the type and severity of oral disease. The estimation unit 12 inputs an image of the entire oral cavity, or images of teeth and gums extracted from the oral image, etc., into the estimation model, and obtains information regarding the type and severity of oral disease output from the estimation model. The type of the estimation model is not limited as long as it is a model capable of estimating oral disease, but may be, for example, a model including a neural network.

Figure 4 is a diagram showing an example of estimating the type and degree of oral disease (dental caries, periodontal disease) and oral environment (oral stains) based on an oral cavity image. In the example shown in Figure 4, the estimation unit 12 inputs an oral cavity image p1 to an estimation model md1, and estimates caries, periodontal disease, and stains in the teeth and gums based on the output from the estimation model md1. The estimation model md1 is a trained object detection model by machine learning using training data consisting of pairs of oral cavity images and ground truth labels indicating teeth and gums with caries, periodontal disease, and oral stains.

Specifically, the estimation unit 12 inputs the oral cavity image p1 to the estimation model md1 and obtains an output image p11 showing the teeth with caries. Then, based on the output image p11, the estimation unit 12 obtains caries information ip indicating that the number of teeth with caries is p.

The estimation unit 12 also inputs the oral cavity image p1 into the estimation model md1 and obtains an output image p12 showing the part of the gums where periodontal disease is occurring. Then, based on the output image p12, the estimation unit 12 obtains periodontal disease information iq indicating that the number of gums where periodontal disease is occurring is q.

The estimation unit 12 also inputs the oral cavity image p1 into the estimation model md1 and obtains an output image p13 showing the area where mouth dirt has occurred. Then, based on the output image p13, the estimation unit 12 obtains mouth dirt information ir showing that the area where mouth dirt has occurred is r locations.

Figure 5 is a diagram showing an example of estimating the presence or absence of an oral disease (malignant tumor) based on an oral cavity image. In the example shown in Figure 5, the estimation unit 12 inputs an oral cavity image p21 representing the tongue region to an estimation model md2, and estimates the presence or absence of a malignant tumor based on the output from the estimation model md2. The estimation model md2 is a trained object detection model by machine learning using training data consisting of pairs of oral cavity images and ground truth labels indicating the areas where malignant tumors occur.

Specifically, the estimation unit 12 inputs the oral cavity image p21 into the estimation model md2 and obtains an output image p22 in which a malignant tumor is shown. Then, based on the output image p22, the estimation unit 12 obtains malignant tumor information is indicating that the area in which the malignant tumor is occurring is location s.

Figure 6 is a diagram showing an example of estimating the presence or absence of an oral disease (temporomandibular joint disorder) based on an oral cavity image. In the example shown in Figure 6, the image acquisition unit 11 acquires an oral cavity image p31, which is a video showing the mouth opening movement. The estimation unit 12 extracts feature points for acquiring the movement of the face, jaw, etc. from the oral cavity image p31 using a predetermined algorithm md31, and acquires mouth opening movement information p32 indicating the movement of the feature points. The method for extracting the feature points is not limited, but the algorithm md31 may be, for example, the well-known KAZE algorithm.

Next, the estimation unit 12 inputs the mouth opening movement information p32 to the estimation model md32, and estimates the presence or absence of temporomandibular joint disorder based on the output from the estimation model md32. The estimation model md32 is a trained model constructed by machine learning using training data consisting of pairs of mouth opening movement information indicating the movement of feature points and correct answer labels indicating the presence or absence of temporomandibular joint disorder.

Specifically, the estimation unit 12 inputs the mouth opening movement information p32 into the estimation model md32 and obtains temporomandibular joint disorder information it including information indicating the presence of temporomandibular joint disorder (t=1) or information indicating the absence of temporomandibular joint disorder (t=0).

Referring again to FIG. 2, the determination unit 13 determines a priority level indicating the priority of treatment for the examinee based on the type and severity of the oral disease. Specifically, the determination unit 13 determines the priority level of the examinee based on at least one of the pieces of information on various oral diseases estimated by the estimation unit 12.

The determination unit 13 may determine the priority level by tabulating the severity of each type of oral disease. The tabulation process may involve calculating a weighted sum or average of the numerical values representing the severity of each oral disease. An example of determining the priority level based on a weighted sum of the numerical values representing the severity of each oral disease is described below.

The judgment unit 13 calculates the severity information of the patient based on the information indicating the severity of each disease indicated in the caries information ip, periodontal disease information iq, oral dirt information ir, malignant tumor information is and temporomandibular joint disorder information it, using the following formula (1).
(Severity information) = ((α × p) + (β × q) + (γ × r) + (δ × s) + (ε × t)) / (α + β + γ + δ + ε) ... (1)
In the above formula (1), α, β, γ, δ, and ε are arbitrary values indicating weights preset for each disease.

The determination unit 13 determines the priority level based on the severity information. Specifically, the determination unit 13 may determine the priority level by, for example, referring to a priority level table that stores in advance an association between the range of severity information and the priority level.

Figure 7 is a diagram showing an example of a priority level table that is referenced to determine the priority level. In the example shown in Figure 7, the priority level table sets a range of severity information for each of priority levels 1 to 5. Variables a to f indicating the range of severity information are arbitrary values that are set in advance. For example, when the calculated severity information of a patient is equal to or greater than c and less than d, the determination unit 13 determines that the priority level of the patient is level 3.

In formula (1), by setting the values of α, β, γ, δ, and ε to 1, the determination unit 13 can calculate the average value of the numerical values representing the degree of each oral disease as the severity information. In addition, by setting any of the weighting values of α, β, γ, δ, and ε to 0, it is possible to set a priority level in which the oral disease corresponding to the weighting value set to 0 is not taken into consideration.

In addition to the information indicating the type and severity of the oral disease, the determination unit 13 may also use information indicating the oral environment, such as the state of the teeth alignment, whether or not orthodontic treatment has been performed, and the cleanliness of the teeth (whitening), to determine the priority level.

The prediction unit 14 predicts the degree of deterioration of a patient's disease based on attribute information associated with the patient. The degree of deterioration is an index value that indicates the likelihood of the disease worsening in the future. Therefore, the prediction unit 14 predicts the degree of deterioration of examinees, who are patients who have received a diagnosis, etc., and reservation recipients, who are patients who have already made a reservation for treatment and are registered on the reservation list.

Specifically, the prediction unit 14 inputs attribute information of a patient with an oral disease, inputs attribute information of the patient and the person with the appointment into a prediction model that outputs the degree of deterioration of the patient's disease, and obtains the degree of deterioration of the disease of each of the patient and the person with the appointment output from the prediction model.

Figure 8 is a diagram showing an example of constructing a prediction model that outputs the degree of deterioration based on the attribute information of a patient. As shown in Figure 8, the prediction model md4 is learned and constructed by machine learning using learning data ld consisting of the attribute information il of a patient and the degree of deterioration lb of the patient's disease. The attribute information il may be data that expresses lifestyle information, etc. of the patient at the time of disease onset and at the time of complete recovery, as a vector. The lifestyle information may be, for example, information indicating the presence or absence and the degree of habits, such as tooth brushing, drinking, and smoking, of the patient.

The prediction model md4 is not limited to a specific type as long as it can be configured to predict the degree of deterioration, but may be, for example, a model including a neural network, and more specifically, a multi-layer perceptron.

Referring again to FIG. 2, the extraction unit 15 refers to the appointment list and extracts appointments to be changed to the appointment of the patient based on a comparison between the priority level of the patient and the priority levels of appointments made by patients who have appointments made in the appointment list.

Figure 9 is a diagram showing an example of an appointment list stored in the appointment list storage unit 19. In the appointment list, reserving parties, who are patients who have made appointments for treatment, are stored in the order of appointments in association with the priority level of the reserving parties. In the example shown in Figure 9, patient A, who is a reserving party with priority level 4, is registered first in the appointment order. In addition, information on the treatment content "severe caries treatment" is associated with patient A. Similarly, the appointment list includes information on patient ID "B", treatment content "tartar removal", and priority level "2" second in the appointment order. Furthermore, the appointment list includes information on patient ID "C", treatment content "mild periodontal disease treatment", and priority level "3" third in the appointment order.

That is, the extraction unit 15 refers to the reservation list of dental clinics designated by the patient as the dental clinic where the patient will receive treatment, for example, starting from the top of the reservation order, and extracts reservation holders who have a priority level at least equal to or lower than the priority level of the patient.

Specifically, the extraction unit 15 extracts from the reservation list those with appointments who have a lower priority level than the priority level of the patient. For example, if the priority level of the patient is "3," the extraction unit 15 refers to the reservation list illustrated in FIG. 9 starting from the top of the reservation order and extracts Patient B, who has a priority level of "2," which is lower than the priority level of "3."

In addition, when there is no appointment with a priority level lower than that of the patient from the appointment list, the extraction unit 15 may acquire an appointment with the same priority level as the patient from the appointment list as an appointment candidate for extraction. For example, when the priority level of the patient is "2", there is no appointment with a priority level lower than the priority level "2" from the appointment list illustrated in FIG. 9, so the extraction unit 15 acquires patient B, who has the same priority level "2" as the patient. In this case, the extraction unit 15 may extract an appointment with a higher degree of deterioration of the patient than that of the appointment candidate for extraction. In the above example, the extraction unit 15 extracts patient B when the degree of deterioration of the patient is higher than that of patient B.

In addition, as described in detail later, when the suggestion unit 16 inquires about changing the reservation of the first reservation holder and receives a response indicating that the reservation change is not approved, the extraction unit 15 may re-extract a second reservation holder different from the first reservation holder.

The suggestion unit 16 sends an inquiry to the terminal 20B of the person making the appointment extracted by the extraction unit 15 or to the terminal 20C of the administrator of the dental clinic appointment device 10 (dental clinic appointment system 1) regarding changing the appointment of the person making the appointment to an appointment for a patient. The suggestion unit 16 then receives a response to the inquiry from the terminal 20B or terminal 20C. The received response is used to determine whether to change the appointment in the appointment change unit 17, as described below.

Specifically, when the extraction unit 15 extracts Patient B's appointment, the suggestion unit 16 sends an inquiry to Patient B's terminal 20B about changing Patient B's appointment to an appointment for a patient. Alternatively, when the decision to change the treatment appointment is left to the dental clinic related to the dental clinic reservation system 1 rather than to the patient whose appointment is to be changed, the suggestion unit 16 sends an inquiry to the terminal 20C of the administrator of the dental clinic reservation system 1 about changing Patient B's appointment to an appointment for a patient. The suggestion unit 16 then receives a response regarding whether or not the appointment can be changed from Patient B's terminal 20B or the administrator's terminal 20C.

The reservation change unit 17 changes the reservation of the reservation person extracted by the extraction unit 15 to a reservation of the patient. According to the example explanation with reference to the reservation list in Figure 9, since the reservation of patient B, who is second in the reservation order, was extracted by the extraction unit 15, the reservation change unit 17 changes the reservation for treatment of patient B to a reservation of the patient. In other words, the reservation of the patient is registered as second in the reservation order in the reservation list. In this case, the reservation of patient B may be moved down to third in the reservation order, or may be inserted in another order.

In addition, when the suggestion unit 16 sends an inquiry about changing the reservation and receives a response, the reservation change unit 17 changes the reservation of the reservation holder extracted by the extraction unit 15 to a reservation of the patient if the response agrees to the reservation change. According to the example explanation with reference to the reservation list in FIG. 9, when the suggestion unit 16 sends an inquiry about changing the reservation of patient B to a reservation of the patient to the terminal 20B of patient B or the terminal 20C of the administrator and receives a response, the reservation change unit 17 changes the reservation of patient B's treatment to a reservation of the patient if the response agrees to the reservation change.

Figure 10 is a flowchart showing the processing steps of the dental clinic reservation method in the dental clinic reservation device 10.

In step S1, the image acquisition unit 11 acquires an image of the patient's oral cavity. Specifically, the image acquisition unit acquires an image of the oral cavity captured by, for example, the terminal 20A. In step S2, the estimation unit 12 estimates the type and severity of the oral cavity disease based on the oral cavity image.

In step S3, the determination unit 13 determines a priority level indicating the priority of the patient's treatment based on the type and severity of the oral disease.

In step S4, the extraction unit 15 performs reservation extraction processing to extract reservation recipients. The reservation recipient extraction processing is processing to extract reservation recipients who are subject to reservation changes based on a comparison of the priority level of the patient and the priority levels of reservation recipients who have treatment reservations registered in the reservation list.

Figure 11 is a flowchart showing the process of extracting reservation users in step S4 of Figure 10.

In step S11, the extraction unit 15 refers to the dental clinic's reservation list and searches for reservation holders who have a lower priority level than the patient's priority level.

In step S12, the extraction unit 15 determines whether or not a reservation user has been extracted by the search in step S11. If a reservation user has been extracted, the reservation user extraction process ends. If a reservation user has not been extracted, the process proceeds to step S13.

In step S13, the extraction unit 15 refers to the dental clinic's reservation list and searches for reservation holders who have the same priority level as the patient's priority level as candidates for extraction.

In step S14, the extraction unit 15 determines whether or not a reservation candidate has been extracted by the search in step S13. If a reservation candidate has been extracted, the process proceeds to step S15. If a reservation candidate has not been extracted, the reservation candidate extraction process ends.

In step S15, the prediction unit 14 predicts the degree of deterioration of the disease of the patient and the candidate appointments.

In step S16, the extraction unit 15 determines whether the degree of deterioration of the patient is higher than the degree of deterioration of the appointment candidate who is a candidate for extraction. If it is determined that the degree of deterioration of the patient is higher than the degree of deterioration of the appointment candidate who is a candidate for extraction, the appointment candidate extraction process ends. If it is not determined that the degree of deterioration of the patient is higher than the degree of deterioration of the appointment candidate who is a candidate for extraction, the process proceeds to step S17.

In step S17, the extraction unit 15 extracts the reservation candidates as reservation holders to be changed.

Referring again to FIG. 10, in step S5, the extraction unit 15 determines whether or not a person with a reservation has been extracted in the reservation extraction process. If it is determined that a person with a reservation has been extracted in the reservation extraction process, the process proceeds to step S6. If it is not determined that a person with a reservation has been extracted in the reservation extraction process, the process ends. That is, in this case, no adjustment of the reservation is made that involves changing a reservation already registered in the reservation list, and the patient's reservation is inserted, for example, at the end of the reservation order in the reservation list.

In step S6, the suggestion unit 16 sends an inquiry about changing the appointment of the appointment holder extracted in step S4 to an appointment of a patient to the terminal 20B of the extracted appointment holder or the administrator's terminal 20C. The suggestion unit 16 then receives a response from terminal 20B or terminal 20C.

In step S7, the reservation change unit 17 determines whether or not the reservation change has been approved based on the received response. If it is determined that the reservation change has been approved, the process proceeds to step S8. If it is determined that the reservation change has not been approved, the process proceeds to step S4, and the extraction unit 15 re-extracts the reservation holder (second reservation holder).

In step S8, the reservation change unit 17 changes the reservation of the reservation person extracted by the extraction unit 15 in step S4 to a reservation of the patient.

Next, we will explain the dental clinic reservation program that causes a computer to function as the dental clinic reservation device 10 of this embodiment. Figure 12 is a diagram showing the configuration of the dental clinic reservation program.

The dental clinic reservation program P1 is composed of a main module m10 that controls the dental clinic reservation process in the dental clinic reservation device 10, an image acquisition module m11, an estimation module m12, a judgment module m13, a prediction module m14, an extraction module m15, a proposal module m16, and a reservation change module m17. Each of the modules m11 to m17 realizes the functions of the image acquisition unit 11, the estimation unit 12, the judgment unit 13, the prediction unit 14, the extraction unit 15, the proposal unit 16, and the reservation change unit 17.

The dental clinic reservation program P1 may be transmitted via a transmission medium such as a communication line, or may be stored in a recording medium M1 as shown in FIG. 12.

In the dental clinic reservation device 10, dental clinic reservation method, and dental clinic reservation program P1 of this embodiment described above, the priority level is determined according to the type and severity of oral disease estimated based on the patient's oral cavity image, making it possible to compare treatment priorities. By comparing the priority levels of the patient and the reservation holder, reservation holders who have a need for treatment at least equal to or less than the degree of the patient's need for treatment are extracted, and the reservation of the extracted reservation holder is changed to the reservation of the patient. Therefore, it is possible to adjust reservations in a way that appropriately takes into account the need for treatment for the patient's disease.

In another embodiment, the dental clinic reservation device further includes a prediction unit that predicts the degree of deterioration of the illness of each of the patient and the person who made the reservation based on attribute information associated with the patient and the person who made the reservation, and the extraction unit may extract the person who made the reservation based on a comparison of the degree of deterioration of the patient and the person who made the reservation, in addition to the comparison of the priority levels.

According to the above-mentioned embodiment, the degree of deterioration, which is the likelihood of the disease worsening in the future for each patient and person with a reservation, is predicted, and by comparing the predicted degree of deterioration with the priority level reflecting the current disease state, a reservation person for changing the reservation of the patient is extracted. It becomes possible to adjust the reservation taking into account not only the current but also the future disease state and the need for treatment.

In another embodiment of the dental clinic reservation device, the extraction unit may extract an appointment holder who has the same priority level as the patient as an appointment holder who is a candidate for extraction, and if the degree of deterioration of the patient is higher than the degree of deterioration of the appointment holder who is a candidate for extraction.

According to the above embodiment, even if the priority level of the patient and the priority level of the appointment candidate are the same and the priority of the treatment of both cannot be determined based on the priority level alone, it is possible to determine whether or not to extract the appointment candidate by comparing the degree of deterioration. Furthermore, a determination based on the degree of deterioration is made only when the priority level of the patient and the priority level of the appointment candidate are the same, thereby preventing an unnecessary increase in the processing load.

In another embodiment of the dental clinic reservation device, the prediction unit inputs attribute information of a patient with an oral disease and inputs attribute information of the patient and the person making the appointment into a prediction model that outputs the degree of deterioration of the patient's disease, and obtains the degree of deterioration of the disease of the patient and the person making the appointment that is output from the prediction model. The prediction model may be trained and constructed by machine learning using learning data consisting of the patient's attribute information and the degree of deterioration of the patient's disease.

According to the above embodiment, a prediction model constructed by machine learning using learning data consisting of the patient's attribute information and the degree of deterioration of the patient's disease is used to predict the degree of deterioration, so the degree of deterioration can be estimated with high accuracy.

In another embodiment, the dental clinic reservation device further includes a proposal unit that sends an inquiry to the reservation person extracted by the extraction unit or to a terminal of an administrator of the dental clinic reservation device about changing the reservation of the reservation person to a reservation of a patient, and receives a response to the inquiry, and the reservation change unit may change the reservation of the reservation person extracted by the extraction unit to a reservation of a patient when a response to the consent to the reservation change is received.

According to the above embodiment, the decision to change a reservation for a treatment that has already been registered in the reservation list can be made by the person who made the reservation or the administrator of the system. This prevents inappropriate changes to the reservation.

In another embodiment of the dental clinic reservation device, when the suggestion unit sends an inquiry about changing the reservation of a first reservation person to a reservation of a patient and receives a response indicating that the reservation change is not approved, the extraction unit may re-extract a second reservation person included in the reservation list who is a different reservation person from the first reservation person.

According to the above embodiment, even if the first reservation person does not agree to change the reservation to the patient's reservation, it is possible to attempt to change it to the reservation of the second reservation person. Therefore, the patient's reservation can be included in the reservation list with appropriate priority.

In another embodiment of the dental clinic reservation device, the determination unit determines the priority level by aggregating the severity of each type of oral disease, and the aggregating process may include calculating a weighted sum or average of the numerical values representing the severity of each oral disease.

According to the above embodiment, the priority level is determined by a predetermined aggregation process of the type and severity of oral disease, so that a priority level that appropriately reflects the priority of the examinee can be easily obtained.

Although the present embodiment has been described in detail above, it is clear to those skilled in the art that the present embodiment is not limited to the embodiment described in this specification. The present embodiment can be implemented in modified and altered forms without departing from the spirit and scope of the present invention as defined by the claims. Therefore, the description in this specification is intended as an illustrative example and does not have any restrictive meaning with respect to the present embodiment.

Each aspect/embodiment described herein may be applied to systems utilizing LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G, 5G, FRA (Future Radio Access), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth (registered trademark), or other suitable systems and/or next generation systems enhanced based thereon.

The steps, sequences, flow charts, etc. of each aspect/embodiment described herein may be reordered unless inconsistent. For example, the methods described herein present elements of various steps in an example order and are not limited to the particular order presented.

The input and output information may be stored in a specific location (e.g., memory) or may be managed in a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be sent to another device.

The determination may be based on a value represented by one bit (0 or 1), a Boolean (true or false) value, or a numerical comparison (e.g., with a predetermined value).

Each aspect/embodiment described in this specification may be used alone, in combination, or switched depending on the execution. In addition, notification of specific information (e.g., notification that "X is the case") is not limited to being done explicitly, but may be done implicitly (e.g., not notifying the specific information).

Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended as an illustrative example and does not have any limiting meaning on the present disclosure.

Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

Software, instructions, etc. may also be transmitted and received over a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies, such as coaxial cable, fiber optic cable, twisted pair, and digital subscriber line (DSL), and/or wireless technologies, such as infrared, radio, and microwave, these wired and/or wireless technologies are included within the definition of a transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

In addition, terms explained in this disclosure and/or terms necessary for understanding this specification may be replaced with terms having the same or similar meanings.

As used herein, the terms "system" and "network" are used interchangeably.

In addition, the information, parameters, etc. described in this specification may be expressed as absolute values, as relative values from a predetermined value, or as corresponding other information.

As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Additionally, "judgment" and "decision" can include considering resolving, selecting, choosing, establishing, comparing, etc., to have been "judged" or "decided." In other words, "judgment" and "decision" can include considering some action to have been "judged" or "decided." Additionally, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

When designations such as "first," "second," and the like are used herein, any reference to that element is not intended to generally limit the quantity or order of those elements. These designations may be used herein as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed therein or that the first element must precede the second element in some way.

To the extent that the terms "include," "including," and variations thereof are used herein in the specification or claims, these terms are intended to be inclusive, similar to the term "comprising." Further, the term "or" as used herein is not intended to be an exclusive or.

In this specification, multiple devices are included unless the context or technical basis clearly indicates that only one device exists.

Throughout this disclosure, the plural is intended to be included unless the context clearly indicates the singular.

1...dental clinic reservation system, 10...dental clinic reservation device, 11...image acquisition unit, 12...estimation unit, 13...judgment unit, 14...prediction unit, 15...extraction unit, 16...proposal unit, 17...reservation change unit, 19...reservation list storage unit, 20, 20A, 20B, 20C...terminal, M1...recording medium, m10...main module, m11...image acquisition module, m12...estimation module, m13...judgment module, m14...prediction module, m15...extraction module, m16...proposal module, m17...reservation change module, P1...dental clinic reservation program.

Claims (1)

A dental clinic reservation device that adjusts a treatment reservation for a patient based on a type and degree of oral disease estimated based on an oral cavity image of the patient,
A determination unit that determines a priority level indicating the priority of treatment for the patient based on the type and severity of the oral disease;
an extraction unit that refers to a reservation list that stores reservation persons, who are patients who have made a reservation for treatment, in association with the priority levels of the reservation persons, and extracts reservation persons having a priority level at least equal to or lower than the priority level of the patient;
a reservation change unit that changes the reservation of the reservation person extracted by the extraction unit to the reservation of the patient;
A dental clinic reservation device comprising:

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