JP5116782B2 - Service time prediction method and service time prediction apparatus - Google Patents

Description

  The present invention relates to a method and an apparatus for detecting a change of a service provider and correcting a predicted service required time in a service providing facility.

  Devices for predicting waiting time until a user receives a service in a service providing facility such as a hospital, a beauty salon, or a restaurant have been developed. For example, the techniques of Patent Documents 1 and 2 are “waiting time prediction method, waiting time prediction system, and program”, which predicts a service required time for each user, and waits for each user from the predicted result. Is calculated.

Japanese Patent No. 4194573 Japanese Patent No. 4,217,689

By the way, there are usually one or more service providing locations in the service providing location, and the service is often provided to one customer per location. However, due to sudden circumstances, the following situations may occur.
(1) A plurality of service providers provide services to a plurality of customers at one place.
(2) There are places where services cannot be provided.

For example, in a hospital, the service providing location is an examination room.
(1) When one doctor was diagnosing in one examination room, and it became crowded, and a doctor who urgently supported entered, and multiple doctors began to see (2) Multiple examinations When a doctor was scheduled to consult in a room, but one doctor's office was suddenly closed, and a doctor in another doctor's office started to consult a reserved patient in the doctor's office

  In the above case, there are the following problems. First, in the case of (1), when a plurality of doctors perform a diagnosis, the diagnosis proceeds faster than predicted, so the waiting time is shorter than predicted. Next, in the case of (2), since the number of doctors to be examined decreases, the progress of the examination becomes slower than the prediction, so the waiting time becomes longer than the prediction. In addition, since a doctor change as described above occurs suddenly, it is desirable that the system automatically grasps the situation and makes predictions according to the situation as needed, instead of manually correcting the prediction data.

  The present invention has been made in view of the above problems, and its main object is to accurately predict the required service time even if a change occurs in the service provider.

In order to solve the above problems, the present invention, when the service provider at predetermined locations in the service providing facilities to service, the service provider for each user of the location and service, start time, end time And a storage unit that stores actual value data and predicted value data of service time, and a computer including a processing unit, and a service time prediction method for updating the predicted value data, wherein the processing unit from the actual value data, calculating the operating time of the locations is the time the service is provided to the up moment and against the predicted user and receive services in the position for a predetermined period, the actual value data To obtain the service time of the user predicted to receive the service at the location in a predetermined period, and the obtained service time Totaling all of the users who have been selected, dividing the total service time of the users by the operating time of the location, setting the division value as the overlap rate of the location, and the overlap rate of 1 If it is larger, the service time of the predicted value data is divided by the duplication rate and updated to the divided value.

  According to this method, when a plurality of service providers are operating at one service providing location, by detecting it from the actual service value, the service time set in advance can be determined without relying on human resources. The predicted value can be corrected. According to this, it is possible to accurately predict the waiting time of the user using the corrected predicted service time.

  Further, the present invention provides a service provider, a start time, an end time, and a service time record for each location and service user when one service provider performs service for each predetermined location in a service providing facility. A service time prediction method for updating the predicted value data by a computer including a storage unit that stores value data and predicted value data, and a processing unit, wherein the processing unit determines, from each of the actual value data, for each location. , Calculating the operating time of the service provider at the relevant part in the predetermined period, and obtaining the service time of the user predicted to receive the service at the relevant part in the predetermined period from the actual value data Totaling the estimated service times for all of the predicted users, and summing the user service times for the location Dividing the service provider's operating time and setting the division value as the service provider's coverage ratio of the location; and if the ratio is less than 1, the service time of the predicted value data is Dividing by the multiplication rate and updating to the division value.

  According to this method, when a service provider in charge of one service providing location provides a service to a user at another location, it depends on human resources by detecting it from the actual value of the service. In addition, it is possible to correct a preset predicted service time. According to this, it is possible to accurately predict the waiting time of the user using the corrected predicted service time.

The present invention also when the service provider at predetermined locations in the service providing facilities to service, the service provider for each user of the location and service, start time, the actual value of the end time and service time A service time prediction method for updating the predicted value data by a computer including a storage unit that stores data and predicted value data, and a processing unit, wherein the processing unit, for each location, from the actual value data, calculating a service provider operating time of the position of the predetermined time period, from the actual value data, and provides up to a service currently in pairs to the predicted user and receive services in the position for a predetermined period calculating the operating time of the point is the time, from the actual value data, is expected to receive the service in the position for a predetermined period Obtaining the service time of the user, summing the obtained service time for all of the predicted users, dividing the sum of the service time of the user by the operating time of the location, and dividing the result A value as a duplication rate of the part; a step of dividing the total service time of the user by an operating time of the service provider; and setting the divided value as a multiplication rate of the service provider of the part; If the overlap ratio is greater than 1, the service time of the predicted value data is divided by the overlap ratio and updated to the divided value; if the multiplication ratio is less than 1, the predicted value data The service time is divided by the multiplication rate and updated to the divided value.

  According to this method, when a plurality of service providers are operating at one service providing location, or a service provider in charge of one service providing location provides services to users at other locations. In this case, by detecting the service actual value, it is possible to correct the preset predicted service time without relying on human resources. According to this, it is possible to accurately predict the waiting time of the user using the corrected predicted service time.

In the service time prediction method of the present invention, the processing unit further executes a step of updating the start time and the end time in the predicted value data using the updated service time. Also good.
According to this method, the start time and end time of the service can be corrected according to the situation of the service provider.

  In the service time prediction method of the present invention, the processing unit may not update the predicted value data if the overlap rate is 1 or the multiplication rate is 1.

  The present invention includes a service time prediction apparatus. In addition, the problems disclosed by the present application and the solutions thereof will be clarified by the description of the mode for carrying out the invention and the drawings.

  ADVANTAGE OF THE INVENTION According to this invention, even if a service provider changes, service required time can be estimated accurately. According to this, it is possible to accurately predict the waiting time of the user.

It is a figure which shows the hardware constitutions of a service time estimation apparatus. It is a figure which shows the structure of the data memorize | stored in the memory | storage part 15 of the service time prediction apparatus 1, (a) shows the structure of medical examination data 15A, (b) shows the structure of doctor operation time data 15B, (c ) Shows the configuration of the examination room calculation data 15C. It is a flowchart (1/2) which shows the process of the service time prediction apparatus 1. FIG. It is a flowchart (2/2) which shows the process of the service time prediction apparatus 1. FIG. It is a figure which shows the Example when several doctors are examining in 1 examination room, (a) shows the Example at the normal time, (b) is the 1st examination room being crowded, and a doctor An example when B enters support is shown. It is a figure which shows the example of the operation procedure of the medical examination data 15A when a plurality of doctors are conducting a medical examination in one medical examination room, (a) reads the medical examination data 15A, the medical examination ID, the medical examination start time, and the medical examination end The example which rearranges by time is shown, (b) shows the example which calculates a duplication rate and a multiplication rate. It is a figure which shows the example of the operation procedure of the medical examination data 15A when a plurality of doctors are performing medical examinations in one medical examination room. In particular, the medical examination prediction time is calculated and the medical examination start time and the medical examination end time are updated. An example is shown. It is a figure which shows the Example in case one of the examination rooms is a holiday, (a) shows the Example at the normal time, (b) is a doctor's A for the second examination room for a holiday, and the 1st An example when hanging with an examination room is shown. It is a figure which shows the example of the operation procedure of medical examination data 15A when one of the medical examination rooms is a holiday, (a) reads medical examination data 15A, and shows the example rearranged by medical examination ID, medical examination start time, and medical examination end time , (B) shows an example of calculating the duplication rate and the multiplying rate. It is a figure which shows the example of the operation procedure of the diagnosis data 15A when one of the examination rooms is closed, and particularly shows an example in which the examination prediction time is calculated and the examination start time and the examination end time are updated.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. The service time prediction apparatus according to the embodiment of the present invention is a service value facility in which one service provider normally operates at one place where a service is provided, and actual value data such as service time for each place and user. And the predicted value data is stored, and for each location, from the actual value data for a predetermined period, the overlap rate when a plurality of service providers operate in parallel at the location, and the service provider at the location is another location. The service ratio for providing the service to the user is calculated, and the predicted value data of the service time is updated according to at least one of the calculated overlap ratio and service ratio. According to this, the accuracy of the service time prediction can be improved by updating the predicted value according to the actual value such as the service time in real time.

≪Device configuration and overview≫
FIG. 1 is a diagram illustrating a hardware configuration of a service time prediction apparatus. The service time prediction apparatus 1 includes a communication unit 11, a display unit 12, an input unit 13, a processing unit 14, and a storage unit 15, and each unit is connected so that data can be transmitted and received via a bus 16. The communication unit 11 is a part that performs IP (Internet Protocol) communication and the like with other devices via a network, and is realized by, for example, a NIC (Network Interface Card) or the like. The display unit 12 is a part that displays data according to an instruction from the processing unit 14, and is realized by, for example, a liquid crystal display (LCD). The input unit 13 is a part where an operator inputs data (for example, examination room or doctor data), and is realized by, for example, a keyboard or a mouse. The processing unit 14 transfers data between each unit via a predetermined memory and controls the entire service time prediction apparatus 1. A CPU (Central Processing Unit) is stored in the predetermined memory. This is realized by executing the program. The storage unit 15 stores data from the processing unit 14 and reads the stored data, and is realized by a nonvolatile storage device such as an HDD (Hard Disk Drive) or an SSD (Solid State Drive). The The service time prediction device 1 may be a stand-alone device (PC (Personal Computer) or the like), or may be a device (server or the like) that can communicate with a plurality of terminals via a network. Good.

<< Data structure >>
FIG. 2 is a diagram illustrating a configuration of data stored in the storage unit 15 of the service time prediction apparatus 1. FIG. 2A shows the configuration of the diagnosis data 15A. The examination data 15A is data indicating the actual value and predicted value of the examination for each examination room in the hospital, and includes the examination room ID 15A1, the patient ID 15A2, the doctor ID 15A3, the examination start time 15A4, the examination end time 15A5, and the examination time 15A6. Consists of records for each examination room and patient. The examination room ID 15A1 indicates an ID unique to the examination room. The patient ID 15A2 indicates an ID unique to a patient who is initially predicted to receive a medical examination in the examination room (hereinafter referred to as “predicted patient in the examination room”). The doctor ID 15A3 indicates an ID unique to the doctor (actual result) who actually performed an examination on the patient or the doctor (predictor) who is originally in charge of the examination room. The examination start time 15A4 is the time when the examination starts. The examination end time 15A5 is the time when the examination ends. The examination time 15A6 is the time taken for the examination, and the actual value is calculated by “diagnosis end time 15A5—diagnosis start time 15A4”.

  In addition, as a method of acquiring the actual values of the medical examination start time 15A4 and the medical examination end time 15A5, for example, on the terminal screen of the electronic medical chart system, when the doctor opens and closes the patient's medical chart data by his / her ID, The closed times are respectively acquired by the service time prediction apparatus 1 and stored in the storage unit 15 as a diagnosis start time 15A4 and a diagnosis end time 15A5. At that time, the examination room ID 15A1, the patient ID 15A2, and the doctor ID 15A3 are also recognized and stored in the storage unit 15 as the same record as the examination start time 15A4 and the examination end time 15A5.

  FIG. 2B shows the configuration of the doctor operating time data 15B. The doctor operating time data 15B is data indicating an actual operating time for each doctor, and includes a record including a doctor ID 15B1 and an operating time 15B2. The doctor ID 15B1 indicates an ID unique to the doctor. The operating time 15B2 is the total of the times when the doctor actually performed a diagnosis within a predetermined period, and an integrated value of the corresponding diagnosis time 15A6 is set among the actual values of the diagnosis data 15A.

  FIG. 2C shows the configuration of the examination room calculation data 15C. The examination room calculation data 15C is data calculated with respect to the examination room used in the process for each examination room of the service time prediction apparatus 1, and the examination room operating time 15C1, the patient examination time total 15C2, the duplication rate 15C3, and the coverage rate 15C4. including. The examination room operating time 15C1 indicates the duration of the examination for the predicted patient in the examination room within a predetermined period, regardless of the number of doctors examined and whether or not the doctor is the original doctor in charge. The total patient examination time 15C2 indicates the total examination time received by the predicted patient in the examination room within a predetermined period. The duplication rate 15C3 indicates the operation rate of the number of doctors who have actually consulted in the examination room, and is calculated by “total patient examination time 15C2 / examination room operation time 15C1”. The coverage ratio 15C4 indicates the ratio of the time when the doctor examines the predicted patient in the examination room to the entire operation time of one doctor (that is, the degree of devotion to the examination of the predicted patient in the examination room). Calculated as “patient examination time total 15C2 / operating hours of the doctor who examined the predicted patient in the examination room”.

≪Device processing≫
3A and 3B are flowcharts showing processing of the service time prediction apparatus 1. In this service time prediction apparatus 1, the processing unit 14 mainly predicts the examination time while referring to and updating data in the storage unit 15, and corrects the predicted value based on the actual value.

  As illustrated in FIG. 3A, the service time prediction apparatus 1 repeats the processing of S302 to S316 every predetermined time (for example, 5 seconds) until the medical examination work in the hospital starts and ends (S301 to S317). First, the patient's examination time is predicted (S302), and the examination order of each patient is predicted (S303). The prediction result is stored in the storage unit 15 as examination data 15A, but the record value for each predicted patient in each examination room is overwritten on the record that has already been in the past. Next, the examination data 15A including the actual value and the predicted value for the predetermined period is read from the storage unit 15 to a predetermined memory, and the records are rearranged by the examination room ID 15A1, the examination start time 15A4, and the examination end time 15A5 (S304).

  Note that, for example, the method disclosed in paragraph [0072] of Patent Document 1 and paragraph [0080] of Patent Document 2 is used to predict the examination time. In addition, for example, the prediction of the order of diagnosis is based on, for example, a method for estimating a patient's order of diagnosis at a hospital based on past data for the past several years, and predicting the order of diagnosis based on the tendency. Is used.

  Subsequently, the service time prediction apparatus 1 performs the process of S306 for each doctor in charge of each examination room in the hospital (S305 to S307). As the processing, the medical examination time 15A6 of each doctor ID 15A3 is acquired from the actual value within the latest predetermined time (for example, 1 hour) out of the medical examination data 15A on the predetermined memory, and the total value is obtained. It memorize | stores in the memory | storage part 15 as operation time 15B2 of each doctor ID15B1 in operation time 15B (S306).

  And the service time prediction apparatus 1 repeats the process of S309-S315 and S321-S333 for every examination room (S308-S316). First, in the examination data 15A, the operating time of the examination room is calculated from the actual value within the latest predetermined time (for example, 1 hour) and stored in the storage unit 15 as the examination room operating time 15C1 of the examination room calculation data 15C. (S309). The operation time of the examination room is calculated based on the examination start time 15A4 and the examination end time 15A5, the details of which will be described later. The examination room calculation data 15C stores data necessary for processing for each examination room, and the stored contents are renewed for each process. Next, the total of examination time 15A6 is calculated for all predicted patients (patient ID15A2) in the examination room (examination room ID15A1) from the actual value within the latest predetermined time (for example, 1 hour) in the examination data 15A. The patient examination time 15C2 of the examination room calculation data 15C is stored in the storage unit 15 (S310).

  Subsequently, the service time prediction apparatus 1 calculates the duplication rate by “patient examination time total 15C2 / examination room operating time 15C1” and stores it in the storage unit 15 as the duplication rate 15C3 of the examination room calculation data 15C (S311). If the duplication rate is 1 or less (NO in S312), the process of S321 is performed. Details of the processing after S321 will be described later.

  If the overlap rate is greater than 1 (YES in S312), the service time predicting apparatus 1 determines that there is an overlap in the examination room, that is, another doctor is performing a support examination (S313). According to this, since the predicted value of the initial examination time is calculated on the premise of one doctor per examination room, it is necessary to correct the estimated value of the examination time. Therefore, among the predicted values of the examination data 15A, the examination time 15A6 is recalculated by dividing by the overlap rate, and the examination time 15A6 in the storage unit 15 is updated (S314). Further, among the predicted values of the examination data 15A, the examination start time 15A4 and the examination end time 15A5 are recalculated by the new examination time 15A6, and the examination start time 15A4 and the examination end time 15A5 in the storage unit 15 are updated (S315). .

  As shown in FIG. 3B, if the overlap rate of the examination room is not greater than 1, that is, 1 (NO in S312 of FIG. 3A), the service time prediction device 1 determines the initial doctor in charge of the examination room ( For the doctor ID 15B1), the operating time 15B2 of the doctor operating time data 15B is read from the storage unit 15 (S321). If the operating time of the doctor in charge is greater than 0 (YES in S322), the holding ratio is calculated by “total patient examination time 15C2 / doctor operating time 15B2”, and the storage section 15 is set as the holding ratio 15C4 of the examination room calculation data 15C. (S323). On the other hand, if the operating time of the doctor in charge is not greater than 0, that is, 0 (NO in S322), it is determined that the examination room is closed (S324). Therefore, the operating time 15B2 of the doctor operating time data 15B is read from the storage unit 15 for the doctor who examined the predicted patient in the examination room (S325). Then, the holding ratio is calculated based on “total patient examination time 15C2 / doctor operating time 15B2”, and is stored in the storage unit 15 as the holding ratio 15C4 of the examination room calculation data 15C (S326).

  If the coverage ratio calculated in S323 or S326 is smaller than 1 (YES in S327), the service time prediction device 1 is associated with the doctor who examined the predicted patient in the examination room, that is, the doctor is in another examination room. It is determined that the predicted patient is being examined while being held (S328). According to this, since the predicted value of the initial examination time is calculated on the assumption that one doctor is in charge of one examination room, it is necessary to correct the estimated value of the examination time. Therefore, among the predicted values of the examination data 15A, the examination time 15A6 is recalculated by dividing by the duplication rate (1) and the multiplying ratio, and the examination time 15A6 in the storage unit 15 is updated (S329). Furthermore, among the predicted values of the examination data 15A, the examination start time 15A4 and the examination end time 15A5 are recalculated based on the new examination time 15A6, and the examination start time 15A4 and the examination end time 15A5 in the storage unit 15 are updated (S330). .

  If the above-mentioned holding ratio is not smaller than 1, that is, 1 (NO in S327), the service time predicting apparatus 1 determines that there is no overlap and no holding (S331). According to this, among the predicted values of the examination data 15A, when the examination time 15A6 is recalculated by dividing it by the overlap rate (1) and the multiplication rate (1) (S322), the examination time 15A6 is the same as the original value. The predicted values of the examination start time 15A4 and the examination end time 15A5 remain unchanged (S333).

<Example>
4-7 is a figure which shows the Example of a process of the service time prediction apparatus 1. FIG. FIG. 4 shows an embodiment in the case where a plurality of doctors conduct examinations in one examination room. As shown in FIG. 4 (a), at the normal time, only the doctor A is consulting, the operating time of the first examination room is 20 minutes, and the total examination time of each patient is 10 minutes + 10 minutes = Since it is 20 minutes, the overlap rate is the total examination time / operation time = 20/20 = 1, so that it is understood that there is no overlap, that is, the examination is being performed as usual. Therefore, the predicted value of the consultation time of the patient waiting for the diagnosis is the original diagnosis time predicted value × the duplication rate (1), and therefore remains the original diagnosis time.

However, as shown in FIG. 4 (b), when the first examination room is congested and the doctor B enters support, the operation time of the first examination room is 10 minutes. Since the total value is 10 minutes + 10 minutes = 20 minutes, the overlap rate is the total examination time / operation time = 20/10 = 2> 1, so there is an overlap, that is, other doctors are consulting with support I understand. Accordingly, the predicted consultation time value of the patient waiting for the diagnosis is the original predicted consultation time value / duplication rate (2), the predicted consultation time value is updated, and the diagnosis start time and the diagnosis end time are recalculated.

  FIG. 5A and FIG. 5B show an example of an operation procedure of the examination data 15A when a plurality of doctors conduct examinations in one examination room. As shown in FIG. 5A (a), first, the examination data 15A is read out and rearranged according to the examination ID, the examination start time, and the examination end time. In the data of the first examination room, the first to fourth records are the actual values, and the fifth and subsequent records are the predicted values. In the data of the second examination room, the first record to the third record are the actual values, and the fourth and subsequent records are the predicted values.

  Next, as shown in FIG. 5A (b), the operating time is 15 minutes in the data of the first examination room. The operating time is basically the duration from the (first) examination start time of the first first record to the (latest) examination end time of the last fourth record among the actual values. In the middle of the examination, there may be overlap of examination times (when the examination end time of a record is greater than the examination start time of the next record), but if there is an empty period when no examination is performed (exclusion of an examination of a record) If time <the next record's examination start time), the free time is subtracted from the duration. The total examination time is 30 minutes when the examination time of the actual value is totaled. Thereby, since the duplication rate is total examination time / operation time = 30/15 = 2> 1, it is determined that there is duplication.

  On the other hand, in the data of the second examination room, the operation time is 20 minutes and the total examination time is 20 minutes. Thereby, since the duplication rate is 20/20 = 1, it is determined that there is no duplication. Further, since the operating time of the doctor B is 20 minutes, the coverage rate is determined as “no consultation” since the total examination time / the operating time of the doctor B = 20/20 = 1.

  As shown in FIG. 5B, in the data of the first examination room, the examination time after the fifth record is recalculated and updated by the original examination time / duplication rate = original examination time / 2, and accordingly The examination start time and examination end time are also updated. On the other hand, in the data of the second examination room, the new examination time = the original examination time / duplicate ratio / the coverage ratio = the original examination time / 1/1 = the original examination time, so the predicted value of the examination time remains unchanged, The examination start time and the examination end time remain unchanged.

  FIG. 6 shows an embodiment when one of the examination rooms is closed. As shown in FIG. 6 (a), in the normal time, the doctor A is consulting in the first examination room, the operation time of the doctor A is 10 minutes, and the total examination time of each patient in the first examination room. Is 10 minutes, and the holding rate of the first examination room is the total examination time / operation time = 10/10 = 1, so that it is understood that there is no holding, that is, the examination is being performed as usual. Accordingly, the predicted examination time of the patient waiting for the examination in the first examination room is the original examination time predicted value / the holding ratio = the original examination time predicted value / 1 = the original examination time predicted value. Will remain. On the other hand, doctor B is consulting in the second examination room, and the operation time of doctor B is 10 minutes, and the total examination time of each patient in the second examination room is 10 minutes. Since the total examination time / operation time = 10/10 = 1, it can be seen that there is no holding, that is, the examination is being performed as usual. Therefore, the predicted examination time of the patient waiting for the examination in the second examination room is the original examination time predicted value / multiplication ratio = the original examination time predicted value / 1 = the original examination time predicted value. Will remain.

  However, as shown in FIG. 6 (b), the second examination room is closed, so when doctor A accepts the patient in the second examination room with support, In one examination room, the operating time of doctor A is 10 minutes + 10 minutes = 20 minutes, and the total examination time of each patient in the first examination room is 10 minutes, so the coverage rate of the first examination room is the total examination time / Since the operation time = 10/20 = 0.5 <1, it is determined that the doctor A is holding another examination room. Therefore, the predicted consultation time of the patient waiting for the examination in the first examination room becomes the original examination time predicted value / multiplication ratio = the original examination time predicted value / 0.5, and the examination time predicted value is updated, and the examination start time And recalculate the consultation end time.

  On the other hand, in the second examination room, since the operation time of the doctor B is 0 minutes, it is determined that the doctor is absent. The total examination time of each patient in the second examination room is 10 minutes, and the operating hours of the examination doctor (doctor A) in the second examination room is 20 minutes, so the coverage rate of the second examination room is the total examination time. Since the operation time is 10/20 = 0.5 <1, it is determined that the doctor A is engaged with another examination room. Therefore, the predicted examination time of the patient waiting for the examination in the second examination room is the original examination time predicted value / the holding ratio = the original examination time predicted value / 0.5, and the examination time predicted value is updated, and the examination start time And recalculate the consultation end time.

  In addition, when the doctor X who is not in charge of the other examination room comes in place of the doctor B in the second examination room that is closed, the estimated examination time remains the same as the original examination time. It becomes.

  7A and 7B show an example of an operation procedure for the examination data 15A when one of the examination rooms is closed. As shown in FIG. 7A (a), first, the examination data 15A is read out and rearranged by the examination ID, the examination start time, and the examination end time. In the data of the first examination room, the first record to the third record are the actual values, and the fourth and subsequent records are the predicted values. In the data of the second examination room, the first and second records are actual values, and the third and subsequent records are predicted values.

  Next, as shown in FIG. 7A (b), the operating time of the first examination room ranges from 50 minutes from 9:00 to 9:50 to 10 minutes from 9:05 to 9:15 and from 9:25 to 9:40. 25 minutes minus 15 minutes. The total examination time is 5 + 10 + 10 = 25 minutes when the examination time is totaled. Thereby, since the overlap rate is the total examination time / operation time = 25/25 = 1, it is determined that there is no overlap. The operating time of the doctor A is 25 minutes for the predicted patient in the first examination room + 25 minutes = 50 minutes for the predicted patient in the second examination room, so the coverage ratio is the total examination time / operating time of the doctor A = 25. Since /50=0.5<1, it is determined that there is hanging.

  On the other hand, in the data of the second examination room, since the operating time of the doctor B who is in charge is 0, it is determined that the doctor is absent. Therefore, since doctor A consulted the predicted patient in the second examination room with support, the operating time of the first examination room originally handled by doctor A is from 9:05 to 9:40 from 35 minutes to 9:15 to 9: 9. 25 minutes minus 25 minutes 10 minutes. The total examination time is 10 minutes + 15 minutes = 25 minutes. Thereby, since the duplication rate is the total examination time / operation time = 25/25 = 1, it is determined that there is no duplication. In addition, since the operation time of the doctor A is 25 minutes for the predicted patient in the first examination room + 25 minutes = 50 minutes for the prediction patient in the second examination room, the coverage ratio is the total examination time / operation time of the doctor A = 25. Since /50=0.5<1, it is determined that there is hanging.

  Then, as shown in FIG. 7B, in the data of the first examination room, the examination time after the fourth record is the original examination time / duplication ratio / holding ratio = original examination time / 1 / 0.5 = original It is recalculated and updated by the examination time × 2, and the examination start time and the examination end time are also updated accordingly. In the data of the second examination room, the examination time, the examination start time, and the examination end time after the third record are similarly updated.

  In the above embodiment, in order to cause each unit in the service time prediction apparatus 1 shown in FIG. 1 to function, a program executed by the processing unit 14 is recorded on a computer-readable recording medium, and the recorded program The service time predicting apparatus 1 according to the embodiment of the present invention is realized by causing a computer to read and execute. In this case, the program may be provided to the computer via a network such as the Internet, or a semiconductor chip or the like in which the program is written may be incorporated in the computer.

  According to the embodiment of the present invention described above, the service time predicting apparatus 1 is configured such that not only the original doctor A but also the doctor B came to support (duplication) in the first examination room, and the second examination room. The fact that the doctor A in the first examination room is supporting (holding) is recognized from the doctor's operating time and the actual value of the patient's examination time without relying on manual data input. That is, when a doctor who examines a patient who is initially predicted to receive a medical examination in each examination room is suddenly changed, the service time prediction apparatus 1 automatically determines the change status (duplication or holding). In detail, the duplication rate of the doctors actually operating in one examination room and the rate of the examination room when one doctor examines the predicted patient in the other examination room are calculated. Of the examination data 15A stored in the storage unit 15, it is reflected in the predicted values of the examination time 15A6, the examination start time 15A4, and the examination end time 15A5. According to this, prediction values, such as consultation time 15A6, can be corrected in real time according to a doctor's examination situation, and also the prediction accuracy of a patient's waiting time can be improved.

<< Other embodiments >>
As mentioned above, although the form for implementing this invention was demonstrated, the said embodiment is for making an understanding of this invention easy, and is not for limiting and interpreting this invention. The present invention can be changed and improved without departing from the gist thereof, and equivalents thereof are also included in the present invention. For example, the following embodiments can be considered.

(1) In the above-described embodiment, the example in which the service time prediction apparatus 1 is applied to the examination of the patient by the doctor in each examination room of the hospital is described. However, the present invention is not limited to this, and other service providing facilities, that is, beauty You may apply the service time prediction apparatus 1 about the service in a hospital, a restaurant, etc. In the case of a hospital, the service providing location is an examination room, but in the case of a beauty salon, it is a seat, and in the case of a restaurant, it is a table or a private room.

(2) The service time prediction device 1 may be applied when the overlapping rate> 1 and the multiplying rate <1, that is, when overlapping and hanging occur simultaneously. For example, when the second examination room is closed, not only the doctor A in charge of the first examination room but also the doctor X not in charge of the examination room of the hospital may come to support from the outside. In addition, when the second examination room is closed, doctor A in the first examination room and doctor C in the third examination room may examine the predicted patient in the second examination room with support. In such a case, since both the overlap ratio and the coverage ratio are valid, it is determined whether or not the coverage ratio <1 even if the overlap ratio> 1.

1 Service Time Prediction Device 14 Processing Unit 15 Storage Unit 15A Examination Data (Service Data)
15A1 Examination room ID (location where services are provided)
15A2 Patient ID (user)
15A3 Doctor ID (service provider)
15A4 Examination start time (start time)
15A5 Examination end time (end time)
15A6 Consultation time (service time)
15B Doctor operating time data 15B1 Doctor ID (service provider)
15B2 Operating hours (service provider operating hours)
15C Examination room calculation data 15C1 Examination room operation time (part operation time)
15C2 Total patient examination time (total service time for users)
15C3 Overlap ratio 15C4 Multiply ratio

Claims (8)

Service provider at predetermined locations in the service providing facilities for performing service, the service provider for each user of the location and service, start time, the actual value data and the predicted value data of the end time and service time A service time prediction method for updating the predicted value data by a computer including a storage unit and a processing unit,
The processing unit, for each location,
From the actual value data, calculating the operating time of the locations is the time the service is provided to the up moment and against the predicted user and receive services in the position for a predetermined period,
Obtaining the service time of the user predicted to receive service at the location in a predetermined period from the actual value data, and totaling the acquired service time for all of the predicted users;
Dividing the total service time of the user by the operating time of the location, and setting the division value as the overlap rate of the location;
If the overlap rate is greater than 1, the service time of the predicted value data is divided by the overlap rate and updated to the divided value;
The service time prediction method characterized by performing. When one service provider performs service for each predetermined location in a service providing facility, the service provider for each location and service user, actual value data and predicted value data of start time, end time and service time A service time prediction method for updating the predicted value data by a computer including a storage unit and a processing unit,
The processing unit, for each location,
From the actual value data, calculating the operating time of the service provider in the relevant part for a predetermined period;
Obtaining the service time of the user predicted to receive service at the location in a predetermined period from the actual value data, and totaling the acquired service time for all of the predicted users;
Dividing the total service time of the user by the operating time of the service provider at the location, and setting the divided value as the coverage rate of the service provider at the location;
If the multiplying ratio is less than 1, the service time of the predicted value data is divided by the multiplying ratio and updated to the divided value;
The service time prediction method characterized by performing. Service provider at predetermined locations in the service providing facilities for performing service, the service provider for each user of the location and service, start time, the actual value data and the predicted value data of the end time and service time A service time prediction method for updating the predicted value data by a computer including a storage unit and a processing unit,
The processing unit, for each location,
From the actual value data, calculating the operating time of the service provider in the relevant part for a predetermined period;
From the actual value data, calculating the operating time of the locations is the time the service is provided to the up moment and against the predicted user and receive services in the position for a predetermined period,
Obtaining the service time of the user predicted to receive service at the location in a predetermined period from the actual value data, and totaling the acquired service time for all of the predicted users;
Dividing the total service time of the user by the operating time of the location, and setting the division value as the overlap rate of the location;
Dividing the total service time of the user by the operating time of the service provider, and setting the division value as the service provider's coverage ratio at the location;
If the overlap rate is greater than 1, the service time of the predicted value data is divided by the overlap rate and updated to the divided value;
If the multiplying ratio is less than 1, the service time of the predicted value data is divided by the multiplying ratio and updated to the divided value;
The service time prediction method characterized by performing. The service time prediction method according to any one of claims 1 to 3,
The processor is
The service time prediction method further comprising the step of updating the start time and the end time in the predicted value data using the updated service time. The service time prediction method according to any one of claims 1 to 3,
The service time prediction method, wherein the processing unit does not update the prediction value data if the overlap rate is 1 or the multiplication rate is 1. Service provider at predetermined locations in the service providing facilities for performing service, the service provider for each user of the location and service, start time, the actual value data and the predicted value data of the end time and service time A storage unit for storing;
A processing unit for updating the predicted value data;
With
The processing unit, for each location,
From the actual value data, means for calculating the operating time of the locations is the time the service is provided to the up moment and against the predicted user and receive services in the position for a predetermined period,
Means for obtaining service time of a user predicted to receive service at the location in a predetermined period from the actual value data, and summing the obtained service time for all of the predicted users;
Means for dividing the total service time of the user by the operating time of the location, and setting the division value as the overlap rate of the location;
Means for dividing the service time of the predicted value data by the duplication rate if the duplication rate is greater than 1, and updating to the division value;
A service time prediction apparatus comprising: When one service provider performs service for each predetermined location in a service providing facility, the service provider for each location and service user, actual value data and predicted value data of start time, end time and service time A storage unit for storing
A processing unit for updating the predicted value data;
With
The processing unit, for each location,
Means for calculating the operating hours of the service provider in the predetermined period from the actual value data;
Means for obtaining service time of a user predicted to receive service at the location in a predetermined period from the actual value data, and summing the obtained service time for all of the predicted users;
Means for dividing the total service time of the user by the operating time of the service provider at the location, and setting the division value as a multiplication rate of the service provider at the location;
Means for dividing the service time in the predicted value data by the multiplication rate and updating the division value to the division value if the multiplication rate is less than 1;
A service time prediction apparatus comprising: Service provider at predetermined locations in the service providing facilities for performing service, the service provider for each user of the location and service, start time, the actual value data and the predicted value data of the end time and service time A storage unit for storing;
A processing unit for updating the predicted value data;
With
The processing unit, for each location,
Means for calculating the operating hours of the service provider in the predetermined period from the actual value data;
From the actual value data, means for calculating the operating time of the locations is the time the service is provided to the up moment and against the predicted user and receive services in the position for a predetermined period,
Means for obtaining service time of a user predicted to receive service at the location in a predetermined period from the actual value data, and summing the obtained service time for all of the predicted users;
Means for dividing the total service time of the user by the operating time of the location, and setting the division value as the overlap rate of the location;
Means for dividing the total service time of the user by the operating time of the service provider, and setting the divided value as the coverage ratio of the service provider at the location;
Means for dividing the service time of the predicted value data by the duplication rate if the duplication rate is greater than 1, and updating to the division value;
Means for dividing the service time in the predicted value data by the multiplication rate and updating the division value to the division value if the multiplication rate is less than 1;
A service time prediction apparatus comprising:

Images