Time management apparatus

Abstract

The present disclosure refers to a method for generating a plan-view image of a location based on an image of an object at the location, comprising determining a first coordinate in a first image, the first coordinate associated with a first pair of components detected in the first image capturing the object at a first position at the location, wherein a component of the first pair of components is positioned at a first side of the object and a further component of the first pair of components is positioned at a second side of the object that is opposite the first side, determining a second coordinate in a second image, the second coordinate associated with the first pair of components detected in the second image capturing the object at a second position at the location, the first image and the second image being captured via a sensor device that is disposed to capture a non-plan view image of the location, and generating, by the processor, the plan-view image of the location based on a comparison between the first coordinate and the second coordinate.

Description

TIME MANAGEMENT APPARATUS

[0001] The present disclosure relates to a time management apparatus.

[0002] A technique to manage a time of a patient at a hospital is known. Examples of the technique to manage a time of a patient include a system disclosed in Patent Literature

[0003] PTL 1: USP 8,700,425

[0004] The system disclosed in Patent Literature 1 ascertains an inflow amount of patients by carrying out weighting according to triage levels of patients and manages wait times of patients by measuring a wait time for each triage level. However, in the technique of Patent Literature 1, it is difficult to predict a treatment start time of a patient before a visit of the patient. If it is possible to predict a treatment start time of a patient before a visit of the patient, such a technique is beneficial for both hospitals and patients and contributes, for example, to improvement of management at hospitals.

[0005] The present disclosure is accomplished in view of the above problem, and an example object thereof is to provide a technique which makes it possible to predict a treatment start time of a patient before a visit of the patient.

[0006] A time management apparatus in accordance with an example aspect of the present disclosure includes: a reception unit for receiving a self-triage result by a patient and an estimated arrival time at which the patient will arrive at a hospital, the self-triage result including an estimated triage level that corresponds to a disease condition of the patient; and a prediction unit for predicting, on the basis of the self-triage result and the estimated arrival time, a treatment start time at which treatment to the patient at the hospital will start.

[0007] An example aspect of the present disclosure brings about an example advantage of providing a technique which makes it possible to predict a treatment start time of a patient before a visit of the patient.

[0008] Fig. 1 is a block diagram illustrating a configuration of a triage system in accordance with the present disclosure.Fig. 2 is a flowchart illustrating a flow of processes in a time management method in accordance with the present disclosure.Fig. 3 is a block diagram illustrating a configuration of a triage system in accordance with the present disclosure.Fig. 4 is a flowchart illustrating a flow of processes in a time management method in accordance with the present disclosure.Fig. 5 is a diagram illustrating an integration range in convolutional integration for wait time calculation in accordance with the present disclosure.Fig. 6 is a diagram illustrating an estimated treatment start time which is presented in a triage system in accordance with the present disclosure.Fig. 7 is a block diagram illustrating a configuration of a triage system in accordance with the present disclosure.Fig. 8 is a block diagram illustrating a configuration of a computer which functions as the time management apparatus in accordance with the present disclosure.

[0009] The following description will discuss example embodiments of the present invention. The present invention is not limited to the example embodiments below, but may be altered in various ways by a skilled person within the scope of the claims. For example, the present invention can also encompass, in its scope, any example embodiment derived by appropriately combining techniques (part of or all of products or methods) employed in the example embodiments described below. Alternatively, the present invention also encompasses, in its scope, any example embodiment derived by appropriately omitting part of techniques employed in the example embodiments described below. The advantages described in each of the example embodiments below are example advantages expected in that example embodiment, and do not define an extension of the present invention. That is, the present invention also encompasses, in its scope, any example embodiment that does not bring about the example advantages described in the example embodiments below.

[0010] <First example embodiment> 　　The following description will discuss details of a first example embodiment, which is an example of an embodiment of the present invention, with reference to the drawings. The present example embodiment is a basic form of example embodiments described later. Note that an application scope of techniques which are employed in the present example embodiment is not limited to the present example embodiment. That is, techniques employed in the present example embodiment can be employed also in the other example embodiments included in the present disclosure, within a range in which no particular technical problem occurs. Moreover, techniques indicated in the drawings referred to for describing the present example embodiment can be employed also in the other example embodiments included in the present disclosure, within a range in which no particular technical problem occurs.

[0011] (Configuration of triage system 1) 　　The following description will discuss a configuration of a triage system 1, with reference to Fig. 1. Fig. 1 is a block diagram illustrating the configuration of the triage system 1. The triage system 1 includes n terminal apparatuses 10(1) through 10(n) (n is an integer of 1 or more), a network NW, and a time management apparatus 20.

[0012] The terminal apparatuses 10(1) through 10(n) (hereinafter referred to also as a terminal apparatus 10(i) representing the n terminal apparatuses 10, i is an integer of 1 or more and n or less) are each a terminal (e.g., a computer, a mobile phone, or a mobile terminal) which can be used by each of patients PA(1) through PA(n) (hereinafter referred to also as a patient PA(i) representing n patients PA). The network NW is a network via which information can be transmitted in the form of electrical signals, radio waves, optical signals, or the like.

[0013] In a case where a patient PA(i) feels ill in a health condition thereof, the patient PA(i) can carry out self-triage prior to a visit to a hospital H. The term "self-triage" means that the patient PA(i) himself / herself determines a triage level corresponding to a disease condition thereof without relying on a medical specialist such as a doctor or a nurse.

[0014] The self-triage may be carried out in facilities or buildings for self-triage or may be carried out at a residence of the patient PA(i) as appropriate (e.g., a house, a work place, or a travel destination of the patient PA(i)). In this case, the patient PA(i) can carry out self-triage using the terminal apparatus 10(i) at the residence.

[0015] In self-triage, the patient PA(i) can use the terminal apparatus 10(i). For example, the patient PA(i) inputs a disease condition of the patient PA(i) to the terminal apparatus 10(i) in which an application for self-triage has been installed, and thus the patient PA(i) can determine a triage level. In this case, the terminal apparatus 10(i) functions as an apparatus for carrying out self-triage. It is possible that the application is not installed in the terminal apparatus 10(i), and self-triage can be carried out by connecting the terminal apparatus 10(i) to a predetermined site (cloud).

[0016] In the self-triage, for example, pieces of information as below are input or determined by the application. Hereinafter, a self-triage result may include the pieces of information (identification data and disease condition data indicated below) in addition to the triage level.

[0017] - Identification data for identifying a patient PA(i): e.g., ID, a name, an address, and a gender of the patient PA(i) - Disease condition data indicating a disease condition of a patient PA(i): a disease condition determined by self-triage (a diseased site, a type of illness, and severity thereof) - An estimated triage level of a patient PA(i): which is determined from a disease condition of the patient PA(i), and indicates urgency of treatment to the patient PA(i)

[0018] Note that components (e.g., ID, name) of the identification data may be separately input or may be input at a time. For example, by automatically identifying a user with use of a biometric authentication technique (e.g., face authentication), the components of the identification data can be easily input at a time.

[0019] The estimated triage level may be indicated by, for example, numerical values of levels 1 to 4, and a smaller numerical value may indicate higher urgency. It should be noted that, hereinafter, a phrase "a triage level is high" means that "urgency is high", and does not mean the size of the numerical value itself. For example, in a case where the level 1 is compared with the level 2, the level 2 is greater as a numerical value. However, the level 1 indicates higher urgency, and therefore the level 1 is determined to be higher than the level 2.

[0020] The estimated triage level may be indicated by a representative value which specifies a level. For example, the estimated triage level may be a "level 3". Meanwhile, the estimated triage level does not need to be specified as one level, and a plurality of levels may be indicated with probabilities that the levels hold true. For example, the estimated triage level may be indicated as follows: "level 1: 0%, level 2: 10%, level 3: 70%, level 4: 20%". That is, the self-triage result may include information indicating a probability that the disease condition of the patient PA(i) corresponds to the estimated triage level.

[0021] In addition to the above described self-triage result, the terminal apparatus 10(i) holds an estimated arrival time ti at which the patient PA(i) will arrive at the hospital H.

[0022] The estimated arrival time ti at which the patient PA(i) will arrive at the hospital H can be obtained by an appropriate method. For example, the estimated arrival time ti can be calculated from a time at which the patient PA(i) leaves the residence and a time (travel time) which is taken to travel from the residence to the hospital H. It is possible to use various methods (application, website) for calculating the travel time. Fig. 1 illustrates estimated arrival times t1 through tn at which the respective patients PA(1) through PA(n) will arrive at the hospital H.

[0023] Thus, the self-triage result and the estimated arrival time ti are input to the terminal apparatus 10(i) or generated and output by the application or the like. The self-triage result and the estimated arrival time ti are then transmitted to the time management apparatus 20 via the network NW, and received by a reception unit 21 of the time management apparatus 20.

[0024] (Configuration of time management apparatus 20) 　　As illustrated in Fig. 1, the time management apparatus 20 includes a reception unit 21 and a prediction unit 22, and predicts a treatment start time at which treatment to the patient PA(i) is started at the hospital H.

[0025] The reception unit 21 receives a self-triage result by the patient PA(i) and an estimated arrival time ti at which the patient PA(i) will arrive at the hospital H. The self-triage result includes an estimated triage level corresponding to a disease condition of the patient PA(i). The patient PA(i) is a patient to whom a treatment start time (described later) is applied (i.e., a patient for whom treatment is started at the treatment start time), and is hereinafter sometimes referred to as a subject patient PA(i).

[0026] The prediction unit 22 predicts, on the basis of the self-triage result and the estimated arrival time ti, a treatment start time (hereinafter sometimes referred to as an "estimated treatment start time") at which treatment to the subject patient PA(i) will start at the hospital H.

[0027] The prediction unit 22 can predict the treatment start time on the basis of wait states of patients at the hospital H. The prediction unit 22 predicts wait states of patients at the hospital H at the estimated arrival time ti, and calculates, on the basis of the predicted wait states, a wait time of the subject patient PA(i) at the hospital H. Then, the prediction unit 22 adds the calculated wait time to the estimated arrival time ti, and thus can calculate a treatment start time of the subject patient PA(i).

[0028] At this time, the prediction unit 22 obtains the wait time of the subject patient PA(i) while taking into consideration the estimated triage level of the subject patient PA(i). That is, among patients before start of treatment, a treatment time of a patient who has a triage level lower (i.e., urgency is lower) than the estimated triage level of the patient PA(i) is excluded from the wait time of the patient PA(i). This is because a patient having a higher triage level (i.e., urgency is higher) receives a higher priority for treatment.

[0029] (Example advantage of time management apparatus 20) 　　As described above, the time management apparatus 20 employs the configuration in which a treatment start time at which treatment to the subject patient PA(i) will start at the hospital H is predicted on the basis of a self-triage result including an estimated triage level and an estimated arrival time ti. Therefore, according to the time management apparatus 20, it is possible to bring about an example advantage of predicting a treatment start time at the hospital H while taking into consideration an estimated triage level of the subject patient PA(i) prior to a visit of the subject patient PA(i) to the hospital H.

[0030] (Flow of time management method) 　　The following description will discuss a flow of a time management method S1, with reference to Fig. 2. Fig. 2 is a flowchart illustrating a flow of the time management method S1. As illustrated in Fig. 2, the time management method S1 includes a reception process S11 and a prediction process S12.

[0031] The reception process S11 is a process of receiving a self-triage result by the subject patient PA(i) and an estimated arrival time at which the subject patient PA(i) will arrive at the hospital H, the self-triage result including an estimated triage level corresponding to a disease condition of the subject patient PA(i). The prediction process S12 is a process of predicting, on the basis of the self-triage result and the estimated arrival time, a treatment start time at which treatment to the subject patient PA(i) will start at the hospital H.

[0032] (Example advantage of time management method) 　　As described above, the time management method S1 employs the configuration in which a treatment start time at which treatment to the subject patient PA(i) will start at the hospital H is predicted on the basis of a self-triage result and an estimated arrival time. Therefore, according to the time management method S1, it is possible to bring about an example advantage of predicting a treatment start time at the hospital H while taking into consideration the estimated triage level of the subject patient PA(i) prior to a visit of the subject patient PA(i) to the hospital H.

[0033] <Second example embodiment> 　　The following description will discuss details of a second example embodiment, which is an example of an embodiment of the present invention, with reference to the drawings. The same reference numerals are given to constituent elements having the same functions as those described in the foregoing example embodiment, and descriptions of such constituent elements are omitted as appropriate. Note that an application scope of techniques which are employed in the present example embodiment is not limited to the present example embodiment. That is, techniques employed in the present example embodiment can be employed also in the other example embodiments included in the present disclosure, within a range in which no particular technical problem occurs. Moreover, techniques indicated in the drawings referred to for describing the present example embodiment can be employed also in the other example embodiments included in the present disclosure, within a range in which no particular technical problem occurs.

[0034] (Configuration of triage system 1A) 　　The following description will discuss a configuration of a triage system 1A, with reference to Fig. 3. Fig. 3 is a block diagram illustrating the configuration of the triage system 1A. The triage system 1A includes terminal apparatuses 10(1) through 10(n), a network NW, and a time management apparatus 20A. A configuration of the time management apparatus 20A is different from that of the time management apparatus 20 in accordance with the first example embodiment.

[0035] (Configuration of time management apparatus 20A) 　　The following description will discuss a configuration of the time management apparatus 20A, with reference to Fig. 3. Fig. 3 is a block diagram illustrating the configuration of the time management apparatus 20A. The time management apparatus 20A includes an information accumulation unit 23 and an information presentation unit 24, in addition to the reception unit 21 and the prediction unit 22 which are included in the time management apparatus 20.

[0036] The reception unit 21 of the time management apparatus 20A receives self-triage results of a plurality of patients PA(i) including a subject patient PA(i) and estimated arrival times of the plurality of patients PA(i).

[0037] The information accumulation unit 23 accumulates pieces of wait state information of patients PA(i) at the hospital H in association with self-triage results by the plurality of patients PA(i) including the subject patient PA(i) and estimated arrival times of the plurality of patients PA(i). The accumulation of the pieces of wait state information may be automatically carried out. The wait state information may be input or changed, as appropriate, in accordance with progress of treatment (e.g., reception, start of treatment, end of treatment) to a patient PA at the hospital H.

[0038] Wait state information of a patient PA(i) at the hospital H may mean information of a patient PA(i) who has arrived at the hospital H and treatment to whom has not been finished yet. Wait state information of a patient PA(i) can be pieces of information as follows.

[0039] - Identification data for identifying a patient PA(i): e.g., ID, a name, an address, a gender of the patient PA(i). Note that, as described above, components (e.g., ID, name) of the identification data may be separately input or may be input at a time. - Data indicating a status of a patient PA(i): before a visit (before reception), after a visit (after reception) and before start of treatment (before medical examination), during treatment, end of treatment. - Disease condition data indicating a disease condition of a patient PA(i) which can basically be a disease condition determined by self-triage. Note that, after start of treatment, the disease condition data may be changed based on medical examination by a doctor, a nurse, a paramedic, or the like. - A triage level of a patient PA(i) which can basically be a triage level determined by self-triage. Note that, after start of treatment, the triage level may be changed based on medical examination by a doctor, a nurse, a paramedic, or the like.

[0040] The prediction unit 22 predicts a treatment start time of a subject patient PA(i) on the basis of pieces of wait state information which are accumulated in the information accumulation unit 23.

[0041] Fig. 4 is a flowchart illustrating a flow of processes which are carried out by the time management apparatus 20A. The time management apparatus 20A carries out a reception process S11, a first prediction process S12a, a second prediction process S12b, a third prediction process S12c, and an updating process S13. The reception process S11 is similar to that of the first example embodiment, and therefore a description thereof is omitted here. The prediction unit 22 carries out the first through third prediction processes S12a through S12c, which are divisions of the prediction process S12 of the first example embodiment.

[0042] The first prediction process S12a is a process of predicting, on the basis of pieces of wait state information, wait states of patients PA(i) at the hospital H at an estimated arrival time of the subject patient PA(i). The prediction unit 22 predicts a change in wait state according to progress of treatment, on the basis of wait states (i.e., the number, current statuses, disease conditions, triage levels, and the like of patients PA(i)) at the time point of prediction.

[0043] The second prediction process S12b is a process of predicting, on the basis of wait states of patients PA(i) at the hospital H which have been predicted in the first prediction process S12a, a wait time of the subject patient PA(i) caused due to a patient PA(i) having an estimated triage level equal to or higher than the estimated triage level of the subject patient PA(i).

[0044] That is, the wait time is calculated while taking into consideration a patient PA(i) having a triage level equal to or higher than the triage level of the subject patient PA(i) among patients PA(i) for whom treatment is predicted to have not been started (i.e., in a wait state) at the estimated arrival time of the subject patient PA(i).

[0045] The self-triage result includes information indicating a probability that the disease condition of the subject patient PA(i) corresponds to the estimated triage level. In the second prediction process, the prediction unit 22 may predict a wait time of the subject patient PA(i) on the basis of the probability. Examples of the probability include a probability that a disease condition of a patient PA(i) corresponds to an estimated triage level.

[0046] The wait time can be probabilistically calculated based not only on a probability that the disease condition of the patient PA(i) corresponds to the estimated triage level but also on a variety of elements. Examples of the wait time include an average wait time T indicated in the following formula (1).

[0047] The formula (1) indicates a method for calculating an expected value T of a wait time of the subject patient PA(i) in a situation in which N patients PA(i) are waiting who have estimated triage levels equal to or higher than the estimated triage level of the subject patient PA(i). A product of a probability density p(t) that the wait time of the subject patient PA(i) is t and a time t is integrated from 0 to ∞, and thus an average wait time T of the subject patient PA(i) is calculated.

[0048] Here, the probability density p(t) can be calculated with use of probability densities pi(t) of treatment times t of the respective N patients PA(i) each having an estimated triage level equal to or higher than the estimated triage level of the subject patient PA(i). 　　pi(t): A probability density that a treatment time of a patient PA(i) is t

[0049] In the formula (1), a wait time T is defined by integration of a product of probability densities pi"p1(τ1)・p2(τ2)・...・pN(τN)" for the respective N patients. 　　τ: Time

[0050] The calculation of p(t) in the formula (1) is a kind of convolutional integration in an integration range which is indicated by the following formula (2). A condition in the integration is that a sum of treatment times τiof the respective patients is t, and consequently τNis a value obtained by subtracting a sum of τ1to τN-1from t. 　　For example, in a case where N is 4, as illustrated in Fig. 5, the integration range means integration at times τ1through τ3in a range of 0 to t, where "τ1+τ2+τ3" is not greater than t.

[0051] As an example of the probability density pi(t), it is possible to use the following formula (3) (negative exponential distribution) or formula (4) (Erlang distribution), in accordance with the queueing theory.

[0052] μi: Service rate 　　　　　　The service rate μiis a reciprocal number of a wait time (treatment time) caused due to an i-th patient PA(i), and is a quantity determined based on a triage level and a type of illness.

[0053] In the above description, a queueing theory for a case of one window is applied to calculate the wait time. In a case where there are a plurality of windows, the wait time can be calculated by applying a queueing theory for a case of a plurality of windows. In such a case, the windows may be classified according to types of illness. In that case, a wait time of a patient can be calculated by applying a queueing theory individually for a type of illness of that patient.

[0054] In the formulae (1) through (4) above, the average wait time T is analytically calculated based on the queueing theory. Note, however, that the average wait time T may be calculated with use of a sum of average treatment times of respective patients. For example, the average wait time T may be calculated according to the following formula (5).

[0055] Here, E(τi) represents an average value of a treatment time τiof an i-th patient PA(i). In a case of a negative exponential distribution indicated by the formula (3), the average value E(τi) is "1 / μi". In a case of an Erlang distribution indicated by the formula (4), the average value E(τi) is "k / μi".

[0056] In the above description, the wait time is analytically calculated using the mathematical formulae. Meanwhile, the wait time may be obtained by simulation such as Monte Carlo simulation, or may be obtained by a statistical distribution of treatment times at the hospital H.

[0057] That is, the information accumulation unit 23 may accumulate a statistical distribution of treatment times at the hospital H, and the prediction unit 22 may predict, in the second prediction process, a wait time of the subject patient PA(i) on the basis of the statistical distribution. The statistical distribution is, for example, a statistical distribution indicating a distribution of treatment times taken for patients PA with respect to disease conditions and triage levels of the patients PA.

[0058] The third prediction process S12c is a process of predicting a treatment start time of the subject patient PA(i) on the basis of a wait time of the subject patient PA(i) which has been predicted in the second prediction process. For example, the treatment start time of the subject patient PA(i) is calculated by adding the predicted wait time to a hospital arrival time.

[0059] The updating process S13 is a process of updating the estimated treatment start time which has been predicted in the third prediction process S12c. For example, the prediction unit 22 may update the treatment start time by predicting wait states of patients PA including a certain patient PA (a patient who will visit the hospital later but will be treated earlier than the subject patient PA(i)). The certain patient PA has an estimated arrival time which is later than an estimated arrival time ti of the subject patient PA(i) and earlier than a treatment start time of the subject patient PA(i), and has an estimated triage level which is higher than an estimated triage level of the subject patient PA(i) (hereinafter referred to as "condition A").

[0060] There is a possibility that certain patients PA who satisfy the condition A appear in turn, and the treatment start time is shifted (updated) in turn as indicated below. (1) In a case where the treatment start time has not been decided yet, the treatment start time is decided without taking into consideration the presence of certain patients PA who satisfy the condition A. (2) Next, in a case where there is a certain patient PA who satisfies the condition A at the decided treatment start time, a treatment time of the certain patient PA is added to the wait time of the subject patient PA(i), and the treatment start time of the subject patient PA(i) is delayed accordingly. (3) In a case where there is a certain patient PA who satisfies the condition A at the delayed treatment start time, the treatment start time of the subject patient PA(i) is further delayed.

[0061] As described above, the treatment start time is updated in turn and, in a case where there is no certain patient PA who satisfies the condition A at the updated treatment start time, the update of the treatment start time ends (i.e., the treatment start time is fixed).

[0062] For example, an amount of increase in wait time of the subject patient PA(i) caused due to a patient who will visit later but will be treated earlier than the subject patient PA(i) is obtained, and the increase amount is added to an estimated treatment start time which has been predicted in the third prediction process S12c, and thus the treatment start time can be updated. The increase in wait time can be calculated, for example, by convolutional integration using the foregoing formula (1).

[0063] Thus, by taking into consideration a situation in which a patient PA having an estimated triage level higher than that of the subject patient PA(i) will visit after a visit of the subject patient PA(i) and before start of treatment, it is possible to more accurately predict the treatment start time. This is because the patient PA(i) who visits later but has a higher triage level receives a higher priority for treatment than the subject patient PA(i).

[0064] For example, at a time point 10 minutes before a visit of a subject patient PA(a), it is assumed that a patient PA(b) has not come to the hospital yet and a patient PA(c) is already in the hospital. In this case, (1) if an estimated triage level of the patient PA(c) is equal to or higher than an estimated triage level of the subject patient PA(a), a wait time of the subject patient PA(a) may be calculated while including a treatment time of the patient PA(c). (2) If an estimated triage level of the patient PA(b) is higher than the estimated triage level of the subject patient PA(a) and it is estimated that, at an estimated arrival time (20 minutes later) of the patient PA(b), the subject patient PA(a) is already in the hospital but treatment has not started yet (i.e., in a wait state), the wait time of the subject patient PA(a) may be calculated while including a treatment time of the patient PA(b).

[0065] The prediction unit 22 may predict a treatment end time at which treatment to the subject patient will end. The prediction can be carried out, for example, with use of a table in which disease conditions and estimated triage levels of patients are associated with treatment times which are taken for treatment of the patients.

[0066] The information accumulation unit 23 is, for example, a display apparatus such as a liquid crystal panel. For example, the information accumulation unit 23 is disposed in a waiting room of the hospital H and presents a treatment start time to a patient PA(i).

[0067] Fig. 6 is a diagram illustrating an estimated treatment start time which is presented in the triage system 1A in accordance with the present disclosure. As illustrated in H1 of Fig. 6, the information presentation unit 24 may present, together with a treatment start time, a probability that a disease condition of the subject patient PA(i) corresponds to an estimated triage level. Here, for each estimated triage level, a probability thereof and an estimated treatment start time are indicated. As the estimated triage level gets higher, the estimated treatment start time comes earlier because a higher priority for treatment is given. As illustrated in H2 of Fig. 6, the information presentation unit 24 may present, as a representative value (e.g., average value, mode, median), one estimated triage level and one estimated treatment start time.

[0068] In Fig. 6, as an example, the estimated treatment start time and the like are presented in a table form. The presentation may be in an appropriate form, for example, a graph form.

[0069] Here, the estimated treatment start time and the like are presented to a patient PA(i) who has come to the hospital. However, the presentation may be carried out before a visit of the patient PA(i) via, for example, a terminal apparatus 10(i).

[0070] As described above, the time management apparatus 20A employs the configuration in which a treatment start time of the subject patient PA(i) is predicted on the basis of pieces of wait state information of patients PA(i) at the hospital H associated with self-triage results by a plurality of patients PA(i) including the subject patient PA(i) and estimated arrival times ti at which the plurality of patients PA(i) will arrive at the hospital H. Thus, according to the time management apparatus 20A, it is possible to bring about an example advantage of predicting a treatment start time with higher accuracy while taking into consideration an estimated triage level prior to a visit of a patient PA(i).

[0071] Generally, a wait time at the hospital H tends to be unclear. For example, for emergency visit, even persons who are not in emergency visit the hospital H, resulting in a crowded condition. Therefore, it is not easy to predict a treatment start time even at the hospital H. As a result, it is often unclear when treatment will be started, even if a visited patient PA(i) inquires of the hospital H. According to the present disclosure, it is possible to notify the patient PA(i) of a treatment start time with high accuracy.

[0072] Moreover, by presenting a treatment start time to a patient PA(i) before a visit, the following example advantages can be obtained. In a case where a treatment start time is late, a patient PA(i) may cancel a visit. That is, in a case where the hospital H is crowded, selection of the other hospital H by the patient PA(i) is prompted. As a result, the crowded conditions of the hospitals H are leveled. Thus, presentation of a treatment start time to a patient PA(i) brings about a reduction of a wait time of the patient PA(i) and alleviation of crowdedness of the hospital H, which are beneficial to both the patient PA(i) and the hospital H.

[0073] <Third example embodiment> 　　The following description will discuss details of a third example embodiment, which is an example of an embodiment of the present invention, with reference to the drawings. The same reference numerals are given to constituent elements having the same functions as those described in the foregoing example embodiments, and descriptions of such constituent elements are omitted as appropriate. Note that an application scope of techniques which are employed in the present example embodiment is not limited to the present example embodiment. That is, techniques employed in the present example embodiment can be employed also in the other example embodiments included in the present disclosure, within a range in which no particular technical problem occurs. Moreover, techniques indicated in the drawings referred to for describing the present example embodiment can be employed also in the other example embodiments included in the present disclosure, within a range in which no particular technical problem occurs.

[0074] (Configuration of triage system 1B) 　　The following description will discuss a configuration of a triage system 1B, with reference to Fig. 7. Fig. 7 is a block diagram illustrating the configuration of the triage system 1B. The triage system 1B includes terminal apparatuses 10(1) through 10(n), a network NW, and a time management apparatus 20B. A configuration of the time management apparatus 20B is different from those of the time management apparatuses 20 and 20A in accordance with the first and second example embodiments.

[0075] Here, there are a plurality of hospitals H(1) through H(m), and patients PA(1) through PA(n) can select a hospital H(j) to visit from among the hospitals H(1) through H(m). The time management apparatus 20B includes prediction units 22(1) through 22(m) and information presentation units 24(1) through 24(m), which correspond to the hospitals H(1) through H(m), respectively. Hereinafter, the prediction units 22(1) through 22(m) may be referred to as a prediction unit 22(j) as a representative, and the information presentation units 24(1) through 24(m) may be referred to as an information presentation unit 24(j) as a representative.

[0076] A patient PA(i) transmits, via a terminal apparatus 10(i), estimated arrival times tij to the respective hospitals H(1) through H(m) together with a self-triage result. The patient PA(i) may select one or more hospitals H(j) from the hospitals H(1) through H(m) as candidates to visit, and transmit estimated arrival times tij for the selected hospitals H(j). In this case, a plurality of estimated arrival times at which the patient PA(i) will arrive at a plurality of hospitals H(j), where the patient PA(i) can visit, are to be transmitted. Then, the transmitted information is to substantially (implicitly) include information indicating whether the patient PA(i) will visit each of the hospitals H(1) through H(m). This is because it is considered that the patient PA(i) does not intend to visit hospitals H(j) which are not included in the transmitted information. Note that the transmitted information may explicitly include information indicating whether the patient PA(i) will visit.

[0077] The reception unit 21 receives triage results by a plurality of patients PA(i) including a subject patient PA(i) and one or more estimated arrival times at which the plurality of patients PA(i) will arrive at one or more hospitals H(j) where the plurality of patients PA(i) can visit. Fig. 7 illustrates estimated arrival times t11 through t1m at which a patient PA(1) will arrive at the hospitals H(1) through H(m), ..., and estimated arrival times tn1 through tnm at which a patient PA(n) will arrive at the hospitals H(1) through H(m).

[0078] The information accumulation unit 23 accumulates pieces of wait state information of patients PA(i) at each of the hospitals H(j) in association with self-triage results by the plurality of patients PA(i) and estimated arrival times of the plurality of patients PA(i) at each of the hospitals H(j). Here, the single information accumulation unit 23 accumulates estimated arrival times and pieces of wait state information at a plurality of hospitals. However, information accumulation units 23 may be provided for the respective hospitals.

[0079] The prediction units 22(1) through 22(m) (hereinafter sometimes referred to as a prediction unit 22(j)) may predict treatment start times of the subject patient PA(i) at one or more hospitals H(j) on the basis of self-triage results by a plurality of patients PA(i) and one or more estimated arrival times.

[0080] As with the time management apparatus 20A, the prediction unit 22(j) may carry out a first prediction process S12a, a second prediction process S12b, a third prediction process S12c, and an updating process S13 for the hospital H(j) to predict a treatment start time of the subject patient PA(i) at the hospital H(j). In this case, the prediction unit 22(j) can predict treatment start times of the subject patient PA(i) at one or more hospitals H(j) on the basis of probabilities that the subject patient PA(i) will visit the one or more hospitals H(j).

[0081] That is, the prediction units 22(1) through 22(m) may each carry out a determination process of determining probabilities that a plurality of patients PA(i) will visit one or more hospitals H(j), and predict a treatment start time of the subject patient PA(i) on the basis of the probabilities which have been determined in the determination process. For example, a wait time may be calculated while setting a visit probability to the hospital H(j) on the basis of information which indicates whether to visit and which is substantially included in the received information.

[0082] For example, in a case where an estimated arrival time is set for only one hospital H(j), a visit probability to the one hospital H(j) may be 100%. In a case where estimated arrival times are set for a plurality of hospitals H(j), visit probabilities may be allocated so that a total of visit probabilities to the plurality of hospitals H(j) is 100%. The allocation may be carried out equally, or may be carried out in accordance with estimated arrival times (namely, travel times taken to travel from a residence to the hospitals H(j)). For example, the setting is made such that a hospital visit probability is reduced as a travel time increases.

[0083] The visit probability can be used for prediction of a treatment start time of another patient PA(i). That is, in prediction of a treatment start time of another patient PA(i), an average wait time may be calculated, and in this calculation, the visit probability may be taken into consideration. For example, in calculation of an average wait time at a certain hospital H(j), an increase in the average wait time caused due to a patient PA(i) having a low visit probability to the certain hospital H(j) is reduced in accordance with that visit probability.

[0084] The reception unit 21 may receive tentative visit reservation information for tentatively reserving a hospital H(j) where a subject patient PA(i) will visit, and the prediction unit 22 may predict a treatment start time for the subject patient PA(i) who has made a tentative visit reservation.

[0085] For example, it is assumed that there are two hospitals H(X) and H(Y). At this time, an estimated treatment start time of a subject patient PA(a) at the hospital H(X) can be obtained under the following conditions: that is, it is predicted that, at a time point 10 minutes before an estimated arrival time of the subject patient PA(a) at the hospital H(X), a patient PA(b) is not in the hospital H(X) and a patient PA(c) is in the hospital H(X).

[0086] In this case, for example, a wait time of the subject patient PA(a) can be calculated under the following conditions: that is, probabilities that the patient PA(b) will visit the hospitals H(X) and H(Y) are both 0.5, and probabilities that the patient PA(c) will visit the hospitals H(X) and H(Y) are 0.9 and 0.1. The probabilities can be set, for example, on the basis of treatment start times of the patients PA(b) and PA(c). This is because it is considered that a patient PA(i) would like to receive treatment sooner and is more likely to visit a hospital H where the treatment start time is earlier.

[0087] As described above, the wait time of the subject patient PA(a) is calculated while taking into consideration triage levels of the patients PA(b) and PA(c).

[0088] (1) If an estimated triage level of the patient PA(c) is equal to or higher than an estimated triage level of the subject patient PA(a), a wait time of the subject patient PA(a) may be calculated while including a treatment time of the patient PA(c). Moreover, in a case where the estimated triage level of the patient PA(c) is lower than that of the subject patient PA(a) and it is estimated that treatment of the patient PA(c) will start before arrival of the subject patient PA(a), the treatment time of the patient PA(c) may be taken into consideration. At this time, in calculation of the wait time of the subject patient PA(a), a treatment time of the patient PA(c) is weighted according to a probability that treatment to the patient PA(c) will start before arrival of the subject patient PA(a).

[0089] (2) If an estimated triage level of the patient PA(b) is higher than the estimated triage level of the subject patient PA(a) and it is estimated that, at an estimated arrival time (20 minutes later) of the patient PA(b), the subject patient PA(a) is already in the hospital but treatment has not started yet (i.e., in a wait state), the wait time of the subject patient PA(a) may include a treatment time of the patient PA(b).

[0090] In a case where it is predicted that the patients PA(b) and PA(c) will be in the hospital H(Y) 30 minutes before the subject patient PA(a) visits the hospital H(Y), if the triage levels of the patients PA(b) and PA(c) are equal to or higher than that of the subject patient PA(a), the wait time of the subject patient PA(a) may include treatment times of the patients PA(b) and PA(c).

[0091] The time management apparatus 20B may present, to a patient PA(i), treatment start times at the respective plurality of hospitals H(j) via the terminal apparatus 10. At this time, the time management apparatus 20B may recommend a hospital H(j) to visit. For example, a recommendation is given to the subject patient PA(a) as follows.

[0092] "If you visit the hospital H(X), the treatment start time is expected to be 10:00. If you visit the hospital H(Y), the treatment start time is expected to be 9:50. It takes longer to travel to the hospital H(Y) than to the hospital H(X) but the wait time is less, so it is recommended that you visit the hospital H(Y) in view of the total time."

[0093] In this recommendation, in a case where a patient PA(i) having a triage level higher than the subject patient PA(a) may visit, such a possibility may be presented together, and a reason why the wait time increases may also be presented together.

[0094] As described above, the time management apparatus 20B employs the configuration in which treatment start times at one or more hospitals H(j) are predicted on the basis of pieces of wait state information of patients PA(i) at the one or more hospitals H(j) associated with self-triage results by a plurality of patients PA(i) including the subject patient PA(i) and estimated arrival times tij at which the plurality of patients PA(i) will arrive at the one or more hospitals H(j). Thus, according to the time management apparatus 20B, it is possible to bring about an example advantage of predicting treatment start times at one or more hospitals H(j) while taking into consideration an estimated triage level prior to a visit of a patient PA(i) to a hospital H(j).

[0095] <Software implementation example> 　　Some or all of the functions of each of the time management apparatuses 20, 20A, and 20B (hereinafter referred to as "each of the apparatuses") may be implemented by hardware such as an integrated circuit (IC chip), or may be implemented by software.

[0096] In the latter case, each of the apparatuses is implemented by, for example, a computer that executes instructions of a program that is software implementing the foregoing functions. Fig. 8 illustrates an example of such a computer (hereinafter, referred to as "computer C"). Fig. 8 is a block diagram illustrating a hardware configuration of the computer C which functions as each of the apparatuses.

[0097] The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for causing the computer C to operate as each of the apparatuses. The processor C1 of the computer C retrieves the program P from the memory C2 and executes the program P, so that the functions of each of the apparatuses are implemented.

[0098] As the processor C1, for example, it is possible to use a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a tensor processing unit (TPU), a quantum processor, a microcontroller, or a combination of these. Examples of the memory C2 include a flash memory, a hard disk drive (HDD), a solid state drive (SSD), and a combination thereof.

[0099] Note that the computer C can further include a random access memory (RAM) in which the program P is loaded when the program P is executed and in which various kinds of data are temporarily stored. The computer C can further include a communication interface for carrying out transmission and reception of data with other apparatuses. The computer C can further include an input-output interface for connecting input-output apparatuses such as a keyboard, a mouse, a display and a printer.

[0100] The program P can be stored in a computer C-readable, non-transitory, and tangible storage medium M. The storage medium M can be, for example, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like. The computer C can obtain the program P via the storage medium M. The program P can be transmitted via a transmission medium. The transmission medium can be, for example, a communication network, a broadcast wave, or the like. The computer C can obtain the program P also via such a transmission medium.

[0101] The above-described functions of each of the apparatuses may be realized by a single processor provided in a single computer, may be realized by causing a plurality of processors provided in a single computer to operate together, or may be realized by causing a plurality of processors provided respectively in a plurality of computers to operate together. A program for causing each of the apparatuses to realize the above-described functions may be stored in a single memory provided in a single computer, may be stored dispersedly in a plurality of memories provided in a single computer, or may be stored dispersedly in a plurality of memories provided respectively in a plurality of computers.

[0102] <Additional remark 1> 　　The present disclosure includes techniques described in supplementary notes below. Note, however, that the present invention is not limited to the techniques described in supplementary notes below, but may be altered in various ways by a skilled person within the scope of the claims.

[0103] (Supplementary note A1) 　　A time management apparatus, including: a reception unit for receiving (i) a self-triage result by a subject patient to whom a treatment start time is to be applied and (ii) an estimated arrival time at which the subject patient will arrive at a hospital, the self-triage result including an estimated triage level that corresponds to a disease condition of the subject patient; and a prediction unit for predicting, on the basis of the self-triage result and the estimated arrival time, the treatment start time at which treatment to the subject patient at the hospital will start.

[0104] Thus, the time management apparatus according to supplementary note A1 can predict, before a visit of a patient, a treatment start time while taking into consideration an estimated triage level.

[0105] (Supplementary note A2) 　　The time management apparatus according to supplementary note A1, in which: the reception unit receives self-triage results by a plurality of patients including the subject patient and estimated arrival times of the plurality of patients; the time management apparatus further includes an information accumulation unit for accumulating pieces of wait state information of patients at the hospital in association with the self-triage results by the plurality of patients and the estimated arrival times of the plurality of patients; and the prediction unit predicts the treatment start time on the basis of the pieces of wait state information.

[0106] Thus, the time management apparatus according to supplementary note A2 can more accurately predict a treatment start time on the basis of pieces of wait state information of patients at the hospital.

[0107] (Supplementary note A3) 　　The time management apparatus according to supplementary note A2, in which: the prediction unit carries out a first prediction process of predicting wait states of patients at the hospital at the estimated arrival time of the subject patient on the basis of the pieces of wait state information; the prediction unit carries out a second prediction process of predicting a wait time of the subject patient caused due to a patient having an estimated triage level which is equal to or higher than the estimated triage level of the subject patient on the basis of the wait states of the patients at the hospital which have been predicted in the first prediction process; and the prediction unit carries out a third prediction process of predicting the treatment start time of the subject patient on the basis of the wait time of the subject patient which has been predicted in the second prediction process.

[0108] Thus, the time management apparatus according to supplementary note A3 can more accurately predict the treatment start time in consideration of a patient having an estimated triage level which is equal to or higher than the estimated triage level of the subject patient.

[0109] (Supplementary note A4) 　　The time management apparatus according to supplementary note A3, in which: the prediction unit carries out an updating process of updating, by predicting wait states of patients including a certain patient, the treatment start time which has been predicted in the third prediction process; and the certain patient has (i) an estimated arrival time which is later than the estimated arrival time of the subject patient and is earlier than the treatment start time of the subject patient and (ii) an estimated triage level which is higher than the estimated triage level of the subject patient.

[0110] Thus, the time management apparatus according to supplementary note A4 can more accurately predict the treatment start time in consideration of a patient who has visited later than the estimated arrival time of the subject patient.

[0111] (Supplementary note A5) 　　The time management apparatus according to supplementary note A3 or A4, in which: the self-triage result includes information indicating a probability that the disease condition of the subject patient corresponds to the estimated triage level; and the prediction unit predicts, in the second prediction process, a wait time of the subject patient on the basis of the probability.

[0112] Thus, the time management apparatus according to supplementary note A5 can more accurately predict the treatment start time on the basis of a probability that the disease condition of the patient corresponds to the estimated triage level.

[0113] (Supplementary note A6) 　　The time management apparatus according to any one of supplementary notes A3 through A5, in which: the information accumulation unit accumulates a statistical distribution of treatment times at the hospital; and the prediction unit predicts, in the second prediction process, a wait time of the subject patient on the basis of the statistical distribution.

[0114] Thus, the time management apparatus according to supplementary note A6 can more accurately predict the treatment start time on the basis of the statistical distribution of treatment times at the hospital.

[0115] (Supplementary note A7) 　　The time management apparatus according to any one of supplementary notes A2 through A6, in which: the prediction unit predicts a treatment end time at which treatment to the subject patient will end.

[0116] Thus, the time management apparatus according to supplementary note A7 can predict a treatment end time at which treatment to the subject patient will end.

[0117] (Supplementary note A8) 　　The time management apparatus according to any one of supplementary notes A1 through A7, further including: an information presentation unit for presenting the treatment start time.

[0118] Thus, the patient can know the treatment start time.

[0119] (Supplementary note A9) 　　The time management apparatus according to supplementary note A8, in which: the information presentation unit presents, together with the treatment start time, a probability that the estimated triage level corresponds to the disease condition of the subject patient.

[0120] Thus, the patient can know the probability that the estimated triage level corresponds to the disease condition and the treatment start time corresponding to the probability.

[0121] (Supplementary note A10) 　　The time management apparatus according to any one of supplementary notes A1 through A9, in which: the reception unit receives self-triage results by a plurality of patients including the subject patient and one or more estimated arrival times at which each of the plurality of patients will arrive at one or more hospitals where the plurality of patients would visit; and the prediction unit predicts treatment start times of the subject patient at the one or more hospitals on the basis of the self-triage results by the plurality of patients and the one or more estimated arrival times.

[0122] Thus, the time management apparatus according to supplementary note A10 can predict treatment start times at the one or more hospitals.

[0123] (Supplementary note A11) 　　The time management apparatus according to supplementary note A10, in which: the prediction unit carries out a determination process of determining probabilities that the plurality of patients will visit the one or more hospitals; and the prediction unit predicts the treatment start times of the subject patient on the basis of the probabilities which have been determined in the determination process.

[0124] Thus, the time management apparatus according to supplementary note A11 can more accurately predict treatment start times at one or more hospitals on the basis of probabilities that the patients will visit the one or more hospitals.

[0125] (Supplementary note A12) 　　The time management apparatus according to supplementary note A10 or A11, in which: the reception unit receives tentative visit reservation information for tentatively reserving a hospital where the subject patient will visit; and the prediction unit predicts a treatment start time of the subject patient who has made a tentative visit reservation.

[0126] Thus, the time management apparatus according to supplementary note A12 can more accurately predict treatment start times of the subject patient at one or more hospitals on the basis of a tentative reservation for a hospital where the subject patient will visit.

[0127] (Supplementary note A13) 　　A time management program for causing a computer to operate as the time management apparatus according to any one of supplementary notes A1 through A12, the time management program causing the computer to function as the prediction unit.

[0128] (Supplementary note B1) 　　A time management apparatus, comprising: 　　a reception unit for receiving (i) a self-triage result by a subject patient to whom a treatment start time is to be applied and (ii) an estimated arrival time at which the subject patient will arrive at a hospital, the self-triage result including an estimated triage level that corresponds to a disease condition of the subject patient; and 　　a prediction unit for predicting, on the basis of the self-triage result and the estimated arrival time, the treatment start time at which treatment to the subject patient at the hospital will start.

[0129] (Supplementary note B2) 　　The time management apparatus according to supplementary note B1, wherein: 　　the reception unit receives self-triage results by a plurality of patients including the subject patient and estimated arrival times of the plurality of patients; 　　said time management apparatus further comprises an information accumulation unit for accumulating pieces of wait state information of patients at the hospital in association with the self-triage results by the plurality of patients and the estimated arrival times of the plurality of patients; and 　　the prediction unit predicts the treatment start time on the basis of the pieces of wait state information.

[0130] (Supplementary note B3) 　　The time management apparatus according to supplementary note B2, wherein: 　　the prediction unit carries out a first prediction process of predicting wait states of patients at the hospital at the estimated arrival time of the subject patient on the basis of the pieces of wait state information; 　　the prediction unit carries out a second prediction process of predicting a wait time of the subject patient caused due to a patient having an estimated triage level which is equal to or higher than the estimated triage level of the subject patient on the basis of the wait states of the patients at the hospital which have been predicted in the first prediction process; and 　　the prediction unit carries out a third prediction process of predicting the treatment start time of the subject patient on the basis of the wait time of the subject patient which has been predicted in the second prediction process.

[0131] (Supplementary note B4) 　　The time management apparatus according to supplementary note B3, wherein: 　　the prediction unit carries out an updating process of updating, by predicting wait states of patients including a certain patient, the treatment start time which has been predicted in the third prediction process; and 　　the certain patient has (i) an estimated arrival time which is later than the estimated arrival time of the subject patient and is earlier than the treatment start time of the subject patient and (ii) an estimated triage level which is higher than the estimated triage level of the subject patient.

[0132] (Supplementary note B5) 　　The time management apparatus according to supplementary note B3 or B4, wherein: 　　the self-triage result includes information indicating a probability that the disease condition of the subject patient corresponds to the estimated triage level; and 　　the prediction unit predicts, in the second prediction process, a wait time of the subject patient on the basis of the probability.

[0133] (Supplementary note B6) 　　The time management apparatus according to supplementary note B3 or B4, wherein: 　　the information accumulation unit accumulates a statistical distribution of treatment times at the hospital; and 　　the prediction unit predicts, in the second prediction process, a wait time of the subject patient on the basis of the statistical distribution.

[0134] (Supplementary note B7) 　　The time management apparatus according to any one of supplementary notes B2 through B4, wherein: 　　the prediction unit predicts a treatment end time at which treatment to the subject patient will end.

[0135] (Supplementary note B8) 　　The time management apparatus according to any one of supplementary notes B1 through B4, further comprising: 　　an information presentation unit for presenting the treatment start time.

[0136] (Supplementary note B9) 　　The time management apparatus according to supplementary note B8, wherein: 　　the information presentation unit presents, together with the treatment start time, a probability that the estimated triage level corresponds to the disease condition of the subject patient.

[0137] (Supplementary note B10) 　　The time management apparatus according to any one of supplementary notes B1 through B4, wherein: 　　the reception unit receives self-triage results by a plurality of patients including the subject patient and one or more estimated arrival times at which each of the plurality of patients will arrive at one or more hospitals where the plurality of patients would visit; and 　　the prediction unit predicts treatment start times of the subject patient at the one or more hospitals on the basis of the self-triage results by the plurality of patients and the one or more estimated arrival times.

[0138] (Supplementary note B11) 　　The time management apparatus according to supplementary note B10, wherein: 　　the prediction unit carries out a determination process of determining probabilities that the plurality of patients will visit the one or more hospitals; and 　　the prediction unit predicts the treatment start times of the subject patient on the basis of the probabilities which have been determined in the determination process.

[0139] (Supplementary note B12) 　　The time management apparatus according to supplementary note B10, wherein: 　　the reception unit receives tentative visit reservation information for tentatively reserving a hospital where the subject patient will visit; and 　　the prediction unit predicts a treatment start time of the subject patient who has made a tentative visit reservation.

[0140] This application is based upon and claims the benefit of priority from Singapore Patent Application No. 10202500127W, filed on January 15, 2025, the disclosure of which is incorporated herein in its entirety by reference.

[0141] 1, 1A, 1B: Triage system 10: Terminal apparatus 20, 20A, 20B: Time management apparatus 21: Reception unit 22: Prediction unit 23: Information accumulation unit 24: Information presentation unit S1: Time management method S11: Reception process S12: Prediction process S12a: First prediction process S12b: Second prediction process S12c: Third prediction process S13: Updating process

Claims

1. A time management apparatus, comprising: 　　a reception unit for receiving (i) a self-triage result by a subject patient to whom a treatment start time is to be applied and (ii) an estimated arrival time at which the subject patient will arrive at a hospital, the self-triage result including an estimated triage level that corresponds to a disease condition of the subject patient; and 　　a prediction unit for predicting, on the basis of the self-triage result and the estimated arrival time, the treatment start time at which treatment to the subject patient at the hospital will start.

2. The time management apparatus according to claim 1, wherein: 　　the reception unit receives self-triage results by a plurality of patients including the subject patient and estimated arrival times of the plurality of patients; 　　said time management apparatus further comprises an information accumulation unit for accumulating pieces of wait state information of patients at the hospital in association with the self-triage results by the plurality of patients and the estimated arrival times of the plurality of patients; and 　　the prediction unit predicts the treatment start time on the basis of the pieces of wait state information.

3. The time management apparatus according to claim 2, wherein: 　　the prediction unit carries out a first prediction process of predicting wait states of patients at the hospital at the estimated arrival time of the subject patient on the basis of the pieces of wait state information; 　　the prediction unit carries out a second prediction process of predicting a wait time of the subject patient caused due to a patient having an estimated triage level which is equal to or higher than the estimated triage level of the subject patient on the basis of the wait states of the patients at the hospital which have been predicted in the first prediction process; and 　　the prediction unit carries out a third prediction process of predicting the treatment start time of the subject patient on the basis of the wait time of the subject patient which has been predicted in the second prediction process.

4. The time management apparatus according to claim 3, wherein: 　　the prediction unit carries out an updating process of updating, by predicting wait states of patients including a certain patient, the treatment start time which has been predicted in the third prediction process; and 　　the certain patient has (i) an estimated arrival time which is later than the estimated arrival time of the subject patient and is earlier than the treatment start time of the subject patient and (ii) an estimated triage level which is higher than the estimated triage level of the subject patient.

5. The time management apparatus according to claim 3 or 4, wherein: 　　the self-triage result includes information indicating a probability that the disease condition of the subject patient corresponds to the estimated triage level; and 　　the prediction unit predicts, in the second prediction process, a wait time of the subject patient on the basis of the probability.

6. The time management apparatus according to claim 3 or 4, wherein: 　　the information accumulation unit accumulates a statistical distribution of treatment times at the hospital; and 　　the prediction unit predicts, in the second prediction process, a wait time of the subject patient on the basis of the statistical distribution.

7. The time management apparatus according to claim 2, wherein: 　　the prediction unit predicts a treatment end time at which treatment to the subject patient will end.

8. The time management apparatus according to claim 1, further comprising: 　　an information presentation unit for presenting the treatment start time.

9. The time management apparatus according to claim 8, wherein: 　　the information presentation unit presents, together with the treatment start time, a probability that the estimated triage level corresponds to the disease condition of the subject patient.

10. The time management apparatus according to claim 1, wherein: 　　the reception unit receives self-triage results by a plurality of patients including the subject patient and one or more estimated arrival times at which each of the plurality of patients will arrive at one or more hospitals where the plurality of patients would visit; and 　　the prediction unit predicts treatment start times of the subject patient at the one or more hospitals on the basis of the self-triage results by the plurality of patients and the one or more estimated arrival times.

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