JP5377600B2 - Service time prediction apparatus and service time prediction method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To accurately predict a service time frame of a user who receives service again. <P>SOLUTION: A consultation time prediction device 1 allocates a consultation time frame of a patient to predicted data 15C (S608 to S613) if not reserved (YES of S607) when receiving a patient on the day (YES of S606). If reserved (NO in S607), the patient has been allocated. Next, whether the patient is reconsulted is determined on the basis of a reconsultation rate 15B2 of a relevant doctor number 15B1 in reconsultation patient data 15B (S614). When reconsultation is determined (YES of S615), time 15B3 until reconsultation of the doctor number 15B1 is added to a predicted start time 15C10 of the patient to calculate a predicted start time of the reconsultation (S616). Then, a reconsultation required time 15B4 of the doctor number 15B1 is added to the predicted start time of the reconsultation to calculate a predicted end time of the reconsultation, and the predicted end time is allocated as a reconsultation time frame (S617). <P>COPYRIGHT: (C)2013,JPO&amp;INPIT

Description

  The present invention relates to an apparatus and method for predicting a service time frame of a user who receives a service again on the same day in a service providing facility.

  Recently, a system for predicting and displaying a waiting time of a user occurring in a service providing facility such as a hospital has been developed. For example, Patent Documents 1 and 2 disclose “waiting time prediction method, waiting time prediction system, and program”. Patent Document 3 discloses “a service time allocation method and a service time allocation device”.

Japanese Patent No. 4,217,689 Japanese Patent No. 4194573 Japanese Patent No. 4143110

  In Patent Documents 1 and 2, the time required for diagnosis is predicted using a neural network or the like, and the waiting time is calculated by multiplying the time required for diagnosis by the number of waiting persons. However, in a hospital that accepts non-reserved patients (non-reserved patients) on the day without a reservation, the reservation patients and non-reserved patients coexist. There is a need to predict the order of appointments for appointment and non-registration patients.

Therefore, in Patent Document 3, the examination order is determined as follows.
(1) The reserved patients are rearranged according to the reservation order and the reception order.
(2) When a non-reserved patient is accepted, a free time frame sandwiched between the reserved time frames is searched at that time, and a non-reserved patient is assigned.
(3) If there is not enough free time frame, forcibly assign to the free time frame that minimizes the total waiting time of each patient. I don't see you. For example, depending on the department, there are patients who will be examined again after the initial examination, so the first patient and the second and subsequent patients coexist. The time frame may not be predicted.

  The present invention has been made in view of the above problems, and a main object thereof is to accurately predict a service time frame of a user who receives a service again.

In order to solve the above problems, the present invention provides a service time prediction for predicting a time frame for providing a service to a re-user who is a user who receives the same service again after receiving the first service in a service providing facility. A device that stores in advance prediction data to which a service time frame of a reserved user is allocated , and provides the re-user with respect to the number of all services provided per day for each staff providing the service. Means for storing a re-service rate that is a ratio of the number of services to be performed in advance; a re-service time interval that is a time from the first service to a re-service for each staff; It means for storing the service required time in advance, upon receiving the user split to provide first service to the user Based on the re-service rate of the staff providing the service to the user and the re-service rate of the first service time frame, which is a time frame to be determined, to determine whether to provide the user again And a second service that is a time frame assigned to provide the user again with the service based on the predicted first service time frame when it is determined that the service is to be provided again to the user. Means for predicting a time frame , wherein the means for predicting the first service time frame includes a service time frame for the reserved user in the prediction data when the accepted user is out of reservation. Means for allocating the service time frame of the non-reserved user as the first service time frame to the free time frame that is between, and predicting the second service time frame, In the prediction data, a service time frame in which a time obtained by adding the re-service time interval of the staff to the start time of the first service time frame is a start time, and the re-service required time of the staff is a time length. The second service time frame is assigned .

  In service providing facilities, the type of service may differ depending on the staff, and depending on the type, after the initial service is provided to the user, the same service may be provided again to the same user as necessary. .

According to this configuration, for each staff member, the probability of re-serving the same user during the day is prepared as a re-service rate, and based on the re-service rate, the user who received the day is re-serviced If it is determined, a re-service time frame is allocated. According to this, the service time frame of the user who receives the service again can be accurately predicted.
Also, at the service providing facility, the time interval until re-service and the time required for re-service when the staff provides service to the same user again has a predetermined tendency depending on the type of service provided by the staff. Conceivable. According to this configuration, a re-service time interval and a re-service required time are prepared for each staff and used when assigning a re-service time frame. According to this, the time frame for re-service can be accurately predicted.

  Further, in the service time prediction apparatus of the present invention, means for acquiring performance data including a service time frame allocated for providing a service to a user for each staff and service day, and the acquired performance data With respect to each staff, means for performing the following processing (1) to (3), and (1) the number of service time frames for all users and the service time frames for the re-users for each service day (2) For each service day, calculate the re-service rate for each service day by dividing the counted number of service hours for the re-user by the number of service hours for all users. (3) The average value of the re-service rate of each service day may be further stored as the re-service rate of the staff.

  According to this configuration, it is possible to calculate a re-service rate that is assumed to be different for each staff member.

  Further, in the service time prediction apparatus of the present invention, means for acquiring performance data including a service time frame allocated for providing a service to a user for each staff and service day, and the acquired performance data And (1) the first service time frame and the second service time frame of the re-user for each service date. (2) For each service day, subtract the start time of the first service time frame from the start time of the second service time frame for each re-user, and re-use each re-user. Calculating a service time interval and subtracting a start time of the second service time frame from an end time of the second service time frame to calculate a re-service required time for each re-user; (3) previous For each service day, the average re-service time interval for each re-user is the re-service time interval for each service day, and the average re-service time for each re-user is the re-service required for each service day. (4) The average value of the re-service time interval of each service day is stored as the re-service time interval of the staff, and the average value of the re-service time of each service day is stored as the re-service required time of the staff. It is good also as storing further as time.

  According to this configuration, it is possible to calculate the re-service time interval and the re-service required time that are assumed to be different for each staff member.

  The present invention includes a service time prediction method. 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, the service time frame of the user who receives a service again can be estimated accurately.

It is a figure which shows the hardware constitutions of the consultation time prediction apparatus. It is a figure which shows the structure of the performance data 15A. It is a figure which shows the structure of the reexamination patient data 15B. It is a figure which shows the structure of the prediction data 15C. It is a flowchart which shows the process which calculates the reexamination rate for every doctor, the time to reexamination, and the time required for reexamination. It is a flowchart which shows the process which estimates the examination time frame of a reexamination patient.

  Hereinafter, embodiments for carrying out the present invention will be described with reference to the drawings. The examination time prediction apparatus according to the embodiment of the present invention calculates the re-examination rate, which is the probability of performing the second and subsequent examinations for each doctor, using the past examination result data, Using the reexamination rate, it is determined whether or not to reexamine the patient who was accepted on the day. Then, using the past examination results data, for each doctor, calculate the time interval from the first examination to the re-examination and the time required for the re-examination, and decide to re-examine the patient who was accepted on the day In addition, the above-mentioned time interval is added to the first examination start time to calculate the re-examination start time, and the re-examination end time is calculated by adding the above-mentioned required time to the re-examination start time. And a re-examination time frame according to the end time.

  According to this, it is possible to accurately determine whether or not to re-examine the received patient, and when it is determined to perform re-examination, the re-examination time frame can be accurately predicted.

≪Device configuration and overview≫
FIG. 1 is a diagram illustrating a hardware configuration of the examination time prediction apparatus 1. The examination 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 is configured so that each unit can transmit and receive data 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, a doctor number for specifying a processing target of examination result data) and an instruction, and is realized by, for example, a keyboard, a mouse, a touch panel, or the like. The processing unit 14 exchanges data between each unit via a predetermined memory and controls the entire examination 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 examination 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.

<< Data structure >>
2-4 is a figure which shows the structure of the data memorize | stored in the memory | storage part 15, in order to estimate a patient's examination time frame. FIG. 2 shows the configuration of the result data 15A. The result data 15A is data related to the examination results performed by the doctors of the hospital, the examination date 15A1, the department code 15A2, the department name 15A3, the doctor number 15A4, the appointment start time 15A5, the appointment end time 15A6, the patient number 15A7, the examination. It consists of a record for each examination result including a frame section 15A8, an actual measurement reception time 15A9, a medical examination waiting time 15A10, and a medical examination actual measurement item (15A11 to 15A16). Note that the actual result data 15A in FIG. 2 is orderly because the results of the medical examination regarding the predetermined doctor on the predetermined medical examination date are extracted and arranged in time series, but the original actual data is a mixture of doctors in charge. It has become.

  The examination date 15A1 is the date of examination. The department code 15A2 is a code unique to the department. The department name 15A3 is the name of the medical department. The doctor number 15A4 is a number unique to the doctor. The reservation start time 15A5 is the start time of the reserved examination time when the examination target is a reserved patient. The reservation end time 15A6 is the end time of the reserved examination time. Patient number 15A7 is a number unique to the patient receiving the examination. The examination frame classification 15A8 is a classification of whether the patient to be examined is reserved or not reserved (visited on the day without reservation), B indicates reserved, and C indicates not reserved. The actual measurement reception time 15A9 is the time when the patient is actually received. The examination waiting time 15A10 is a time for waiting for a patient's examination. For a reserved patient, a time value obtained by subtracting the reservation start time 15A5 from the actual measurement start time 15A11 is set, and for a non-reserved patient, from the actual measurement start time 15A11, A time value is set by subtracting the earlier one of the examination start time (9:00) and the actual measurement reception time 15A9 of the hospital.

  Examination actual measurement items include start time 15A11, end time 15A12, time 15A13, room code 15A14, room name 15A15, and doctor number 15A16. The start time 15A11 is the time when the diagnosis actually starts. The end time 15A12 is the time when the examination is actually ended. The time 15A13 is a time when the medical examination is actually performed, and a time value (minute) obtained by subtracting the start time 15A11 from the end time 15A12 is set. The room code 15A14 is a code unique to the examination room. Room name 15A15 is the name of the examination room. The doctor number 15A16 is a number unique to the doctor.

  The start time 15A11, the end time 15A12, and the time 15A13 of the examination actual measurement items are hereinafter referred to as an actual measurement start time 15A11, an actual measurement end time 15A12, and an actual measurement time 15A13, respectively. Moreover, the record attached with “RE” on the right side of the table of the record data 15A shown in FIG. In the lower part of the table, the average value of the reexamination actual measurement time 15A13 (reexamination required time) is 2.25 (min).

  FIG. 3 shows the structure of the reexamination patient data 15B. The re-examination patient data 15B is data for predicting the examination time frame of the re-examination patient for each doctor related to whether the medical examination is in charge or whether the examination content is a re-examination. It consists of records for each doctor including a consultation rate 15B2, a time 15B3 until the re-examination, and a re-examination required time 15B4. The doctor number 15B1 is a number unique to the doctor. The re-examination rate 15B2 is the ratio of the second and subsequent examinations to the total number of examinations per day, and in practice, an average value for a predetermined number of days is set. The time 15B3 until the re-examination is the time from the first medical examination start time to the second medical examination start time. Actually, an average value for the predetermined number of days and the number of re-examination patients is set. The re-examination required time 15B4 is the time required for re-examination. Actually, an average value for a predetermined number of days and the number of re-examination patients is set.

  FIG. 4 shows the configuration of the prediction data 15C. The prediction data 15C is data that predicts the examination time frame of the patient. The examination date 15C1, department code 15C2, department name 15C3, doctor number 15C4, appointment start time 15C5, appointment end time 15C6, patient number 15C7, examination frame classification 15C8, diagnosis waiting time 15C9, diagnosis prediction items (15C10 to 15C13), actual measurement reception time 15C14, waiting time 15C15 from the reservation start time, and actual measurement items (15C16 to 15C21).

  The examination date 15C1 to the examination frame section 15C8 are the same as the examination date 15A1 to the examination frame section 15A8 in the result data 15A. The examination waiting time 15C9 is a predicted value of the waiting time from the current time, and a time value obtained by subtracting the current time from the prediction start time 15C10 (in the case of minus, 0) is set. In FIG. 4, the current time is 10:01.

  The diagnosis prediction items include a start time 15C10, an end time 15C11, a time 15C12, and a serial number 15C13. The start time 15C10 is the start time of the predicted examination time frame. The end time 15C11 is the predicted end time of the examination time frame. The time 15C12 is a predicted time required for the examination, and a time value obtained by subtracting the start time 15C10 from the end time 15C11 is set. The serial number 15C13 is a parameter for program processing. The start time 15C10, the end time 15C11, and the time 15C12 of the diagnosis prediction item are referred to as a prediction start time 15C10, a prediction end time 15C11, and a prediction time 15C12, respectively.

  The actual measurement reception time 15C14 is the time when the patient is actually received. The waiting time 15C15 from the reservation time is set to a time value obtained by subtracting the reservation start time 15C5 from the actual measurement start time 15C16 for the reserved patient when the examination has already been started, and the actual measurement start time 15C16 for the non-reserved patient. Thus, a time value is set by subtracting the earlier one of the examination start time (9:00) and the actual measurement reception time 15C14 of the hospital. On the other hand, when the examination has not yet started, the time value obtained by subtracting the reservation start time 15C5 from the prediction start time 15C10 is set for the reserved patient, and for the non-reserved patient, the diagnosis of the hospital is performed from the prediction start time 15C10. A time value is set by subtracting the earlier one of the start time (9:00) and the actual measurement reception time 15C14.

  The actual examination items (15C16 to 15C21) are the same as the actual examination items (15A11 to 15A16) in the record data 15A.

≪Device processing≫
5 and 6 are flowcharts showing the processing of the examination time prediction apparatus 1. In this examination time prediction apparatus 1, the processing unit 14 mainly predicts examination time frames of reserved patients, non-reserved patients, and re-examined patients while referring to and updating data in the storage unit 15.

  FIG. 5 is a flowchart showing a batch process performed on the day before the day on which a patient's examination time frame should be predicted, and a process for calculating the re-examination rate for each doctor, the time until the re-examination, and the time required for the re-examination. . First, the examination time prediction apparatus 1 extracts examination result data for a predetermined number of days (D day) from past examination result data accumulated in the storage unit 15 (S501). Next, the staff in the extracted examination result data is extracted (S502). The number of extracted staff is S. The staff is a medical staff specific to the record of the examination results, and is a person related to the response of the reexamination patient. Here, the staff is a doctor representing the person. Actually, a doctor number is set in each record of the examination result data.

  And the consultation time prediction apparatus 1 implements the process of S504-S520 for every staff with S persons (S503-S521). At this time, the variable for specifying the staff is i, and the process is repeated while changing the variable i to 1 to S. First, the performance data of the staff i is extracted (S504). For example, the record data 15A in FIG. 2 is obtained by extracting a record with the doctor number 15A4 of 1027 as the staff i.

  Subsequently, the examination time prediction device 1 performs the processing of S506 to S515 for each examination day for D days (S505 to S516). At this time, the variable for specifying the examination date is j, and the process is repeated while changing the variable j to 1 to D. First, the performance data for one day of the examination date j is extracted (S506). For example, the record data 15A in FIG. 2 is obtained by extracting records with the examination date 15A1 of 2011/3/31 as the examination date j.

  Next, the examination time prediction device 1 counts the total number of examinations in the record data 15A (S507). In the record data 15A of FIG. 2, when all records are counted, the total number of medical examinations is 22 times. And the patient (re-examination patient) received twice or more is searched (S508). At this time, the number of reexamined patients is P, and the number of reexamined patients is R. When all the reexamined patients are up to the second time, P = R. When there are one or more reexamined patients 3 times or more, P <R. In the record data 15A of FIG. 2, P = R = 4. Subsequently, the re-examination rate is calculated by dividing the number of re-examinations by the total number of examinations (S509).

  Furthermore, the examination time prediction apparatus 1 performs the processes of S511 and S512 for each re-examination patient (S510 to S513). At this time, the variable which specifies a reexamination patient is set to k, and a process is repeated changing the variable k to 1-P. First, the time until the re-examination is calculated by subtracting the first actual measurement start time 15A11 from the second actual measurement start time 15A11 of the re-examination patient k (S511). For example, when calculating for a patient whose patient number 15A7 is 1205001, 12: 23-10: 04 = 2: 19. Then, the re-examination required time is calculated by subtracting the second actual measurement start time 15A11 from the second actual measurement end time 15A12 of the re-examination patient k (S510). For example, when calculating for a patient whose patient number 15A7 is 1205001, 12: 28-12: 23 = 0: 05. The second actual measurement time 15A13 may be set as the re-examination required time.

  When all the processes of S510 to S513 are completed, the time until the re-examination and the time required for the re-examination of each of the P reexamination patients are calculated for the staff i and the examination date j. Therefore, the examination time prediction device 1 calculates the average value of the time until re-examination of P reexamination patients (S514), and calculates the average value of the reexamination required time of P reexamination patients (S515).

  Further, when the processes of S505 to S516 are completed, regarding the staff i, the re-examination rate, the time until the re-examination, and the time required for the re-examination are calculated for each examination day for D days. Therefore, the examination time prediction device 1 calculates the average value of the re-examination rate for D days (S517), calculates the average value of the time until the re-examination for D days (S518), The average value of the examination time is calculated (S519). Then, the re-examination rate calculated for the staff i, the time until the re-examination, and the time required for the re-examination are shown in the re-examination patient data 15B. It memorize | stores in the memory | storage part 15 as 15B3 and reexamination required time 15B4 (S520).

  When all the processes of S503 to S521 are completed, the re-examination rate 15B2 of the doctor number 15B1 corresponding to the staff, the time 15B3 until the re-examination, and the re-examination required time 15B4 are stored in the storage unit 15 for each S staff. The

  FIG. 6 is a flowchart showing real-time processing that is performed on the day of the examination, and predicting the examination time frame of the re-examination patient. In addition, as a method for predicting the examination required time of a reserved patient or a non-reserved patient (patient on the day), for example, a method using a neural network is disclosed in paragraphs 0038 to 0045 of Japanese Patent No. 4143110.

  First, the examination time prediction apparatus 1 searches the appointment data stored in the storage unit 15 for each examination room and stores it again (S601). For example, as a representative of the examination room, a search is performed using a doctor number as a key, and prediction data 15C as shown in FIG. Next, the records of the prediction data 15C are rearranged in ascending order of the reservation start time 15C5 (S602). Then, the predicted value of the time required for the appointment of the reserved patient is calculated and stored as the predicted time 15C12 (S603), and the predicted value of the diagnosis start and end times of the reserved patient is calculated and stored as the predicted start time 15C10 and the end time 15C11. (S604). If the prediction time 15C12 and the prediction start time 15C10 are specified, the end time 15C11 is specified by adding the prediction time 15C12 to the prediction start time 15C10.

  Subsequently, the examination time prediction apparatus 1 performs the processing of S606 to S622 until the examination work of the hospital is completed (S605 to S623). First, if there is a new reception patient (YES in S606) and the patient is not a reservation (NO in S607), the process proceeds to S614. If the newly-accepted patient is not scheduled (YES in S607), a predicted value of the examination required time for the non-reserved patient is calculated and stored as the estimated time 15C12 (S608). Then, in the prediction data 15C, an empty time frame between the reserved patients is searched (S609), and it is determined whether or not there is an empty time frame sufficient to allocate the time required for the examination of the non-reserved patient (S610).

  If there is a sufficient vacant time frame (YES in S610), the examination time prediction device 1 allocates the examination time of the non-reserved patient to the vacant time frame (S611). If there is not enough free time frame (NO in S610), the examination time for the non-reserved patient is forcibly assigned after a predetermined time from the reception time (S612). Then, the diagnosis start time of the non-reserved patient is specified and stored as the predicted start time 15C10 (S613). When assigned to an empty time frame (S611), the examination end time of the previous reserved patient becomes the examination start time of the non-reserved patient. On the other hand, when the assignment is forcibly (S612), the start time of the assigned time frame becomes the diagnosis start time of the non-reserved patient.

  Next, the examination time prediction device 1 generates a random number based on the reexamination rate 15B2 of the doctor number 15B1 in the reexamination patient data 15B, and determines whether or not to reexamine the accepted patient ( S614). For example, a random number between 0 and 1 is generated, and whether or not re-examination is performed is determined based on whether or not the random number is less than or equal to the re-examination rate 15B2.

  When it is determined that the patient is to be re-examined (YES in S615), the time 15B3 until the re-examination of the doctor number 15B1 in the re-examination patient data 15B is added to the prediction start time 15C10 of the patient in the prediction data 15C. The prediction start time of reexamination is calculated (S616). Then, the re-examination required end time 15B4 of the doctor number 15B1 in the re-examination patient data 15B is added to the re-examination prediction start time, and the re-examination prediction end time is calculated and assigned as the re-examination time frame (S617). . Specifically, a record including the patient number 15C7, the prediction start time 15C10, the prediction end time 15C11, and the prediction time 15C12 of the reexamination time frame is inserted into the prediction data 15C. Furthermore, the prediction start time 15C10 after the re-examination time frame is recalculated (S618). Specifically, when the already allocated prediction time frame overlaps the re-examination time frame, the prediction end time 15C11 of the re-examination time frame is set as the prediction start time 15C10 of the prediction time frame, and the prediction start time 15C10 is set. A value obtained by adding the prediction time 15C12 is set as a prediction end time 15C11, and the subsequent prediction time frame is shifted. Thereafter, the process returns to S606.

  When it is determined that the patient is not revisited (NO in S615), the process returns to S606.

  When there is no new reception patient (NO of S606), the examination time predicting apparatus 1 determines whether or not there is a patient who starts the examination (S619). If there is a patient who starts a diagnosis (YES in S619), the diagnosis start time (actual value) of the patient is taken in, and the subsequent diagnosis start time (predicted value) of the patient is recalculated (S620). In detail, since the diagnosis start time of the patient is determined from the predicted value to the actual value, the predicted diagnosis end time is predicted by adding the predicted diagnosis time to the diagnosis start time, and based on the diagnosis end time, Recalculate the predicted start time (predicted value) of the patient. Thereafter, the process returns to S606.

  If there is no patient who starts the examination (NO in S619), the examination time predicting apparatus 1 determines whether there is a patient who finishes the examination (S621). If there is a patient who ends the examination (YES in S621), the examination end time (actual value) of the patient is taken in, and the subsequent examination start time (predicted value) of the patient is recalculated (S622). Specifically, since the diagnosis end time of the patient is determined from the predicted value to the actual value, the subsequent prediction start time (predicted value) of the patient is recalculated based on the diagnosis end time. Thereafter, the process returns to S606. If there is no patient who ends the examination (NO in S621), the process returns to S606.

  A process of assigning a re-examination time frame will be described using the prediction data 15C of FIG. First, regarding a patient received at 8:45 as the actual measurement reception time 15C14, a reexamination rate 15B2 corresponding to the doctor number 15C4 (15B1) is compared with a random number generated between 0 and 1, and reexamination is performed. It is determined whether or not to implement. When the random number is less than the re-examination rate 15B2 and it is determined that re-examination, the re-examination time frame is set after the time 15B3 (for example, 2 hours 3 minutes) until the re-examination. At this time, a reexamination time frame having a length of reexamination required time 15B4 (for example, 5 minutes) is set to 12:10 which is 2 hours and 3 minutes after 10:07 of the initial prediction start time 15C10. After the allocation, the prediction start time 15C10 and the prediction end time 15C11 are recalculated in order to shift the subsequent prediction time frame backward.

  In the above embodiment, the program executed by the processing unit 14 is recorded on a computer-readable recording medium in order to cause each unit in the examination time prediction apparatus 1 shown in FIG. 1 to function, and the recorded program The medical examination time prediction apparatus 1 according to the embodiment of the present invention is realized by causing a computer to read and execute the above. 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, it is possible to accurately predict the examination time frame of a patient who undergoes re-examination within a day in a hospital.

  Specifically, whether or not to perform a re-examination on the same patient is considered to have a general tendency depending on the department in charge of the doctor and the content of the medical examination performed at that time. In addition, when a re-examination is performed, the time interval from the first examination to the re-examination and the time required for the re-examination are considered to vary depending on the doctor. For example, depending on the department, when an initial examination is performed, an examination is required, and after receiving an examination, an explanation of the examination result may be received at a reexamination.

  On the other hand, the examination time predicting apparatus 1 calculates the reexamination rate 15B2 for each doctor number 15B1 as shown in the processes of S507 to S509 and S517 on the previous day, and as shown in the processes of S614 and S615 on that day. Based on the reexamination rate 15B2, it is determined whether or not to reexamine the received patient. According to this, since the reexamination rate 15B2 for each doctor who has a predetermined tendency as to whether or not to perform reexamination is used, it is possible to predict whether or not an actual reexamination will be performed with high accuracy.

  Next, as shown in the processing of S510 to S515, S518, and S519 of the previous day, the examination time prediction apparatus 1 calculates a time 15B3 and a reexamination required time 15B4 for each doctor number 15B1, and calculates S616 of the day. And as shown in the process of S617, the re-examination time frame is allocated using the time 15B3 until the re-examination and the re-examination required time 15B4. According to this, since the time until re-examination and the time required for re-examination 15B4 and the time required for re-examination 15B4 for each doctor who has a predetermined tendency in the time until re-examination and the time taken for re-examination are used, the re-examination time frame is accurately predicted can do.

  According to the above, the examination time frame of the reexamination patient can be accurately predicted. According to this, since the accuracy of the prediction display of the waiting time in the waiting room or the like is improved, the service to the patient 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 embodiment, the method for predicting the examination time frame of a reexamination patient in a hospital has been described. However, this method may be applied to other service providing facilities. For example, it can be applied to custom-made or repair of small items in a store that handles small items such as simple accessories.

  When a customer goes to a store to repair an accessory, if it can be repaired immediately, it will wait on the spot, but if the repair takes more than 30 minutes, for example, it will leave the store and return to the time when the repair is completed. Come on. Moreover, the kind of accessory handled for every staff differs, and repair time differs with the kind of the accessory. Therefore, for each staff member, the ratio of the number of repairs required over 30 minutes to the total number of repairs per day is defined as the re-service rate (re-examination rate). The time for explanation regarding repair when it takes 30 minutes or more is used as time required for re-service (time required for re-examination), and is used for predicting the time frame of the service for explaining to the customer after repair.

(2) In the above embodiment, in order to predict whether or not to reexamine the patient based on the reexamination rate, in the process of S614 in FIG. Although it has been described that it is determined whether or not the reexamination rate is 15B2 or less, other methods may be used. For example, it may be determined whether or not a random number generated between 0 and 1 is equal to or greater than (1-revisit rate 15B2). Specifically, assuming that the re-examination rate is 0.3, 1−0.3 = 0.7, and a range of 0 to 1 random numbers of 0.7 or more has a width of 0.3. Therefore, a reexamination rate of 0.3 can be realized.

DESCRIPTION OF SYMBOLS 1 Examination time prediction apparatus 14 Processing part 15 Memory | storage part 15A Performance data 15B Reexamination patient data 15B1 Doctor number (staff)
15B2 Re-examination rate (re-service rate)
15B3 Time until re-examination (re-service time interval)
15B4 Re-examination time (re-service time)
15C prediction data

Claims (6)

A service time prediction device for predicting a time frame for providing a service to a re-user who is a user who receives the same service again after receiving the first service in a service providing facility,
Means for preliminarily storing prediction data to which a service time frame of a reserved user is assigned;
Means for storing in advance a re-service rate, which is a ratio of the number of services provided to the re-user with respect to the total number of services provided per day, for each staff providing the service;
Means for storing in advance a re-service time interval that is a time from the first service to a re-service and a re-service required time that is a time required for the re-service for each staff;
Means for predicting a first service time frame, which is a time frame assigned to provide a service to the user for the first time when a user is received;
Means for determining whether to provide a service to the user again based on the re-service rate of the staff providing the service to the user;
When it is determined that the service is to be provided again to the user, a second service time frame that is a time frame assigned to provide the user with the service again is predicted based on the predicted first service time frame. Means to
Equipped with a,
The means for predicting the first service time frame is:
When the received user is not reserved, the service time frame of the non-reserved user is added to the empty time frame that is between the service time frames of the reserved user in the prediction data. Assigned as a service window,
The means for predicting the second service time frame includes:
In the prediction data, a service time frame in which a time obtained by adding the re-service time interval of the staff to the start time of the first service time frame is a start time, and the re-service required time of the staff is a time length. A service time predicting apparatus, which is assigned as the second service time frame . The service time prediction device according to claim 1,
Means for obtaining performance data including service time slots assigned to provide services to users for each staff and service day;
With respect to the acquired result data, means for performing the following processing (1) to (3) for each staff member;
(1) Counting the number of service time slots for all users and the number of service time slots for re-users for each service day;
(2) For each service day, the re-service rate of each service day is calculated by dividing the counted number of re-user service time frames by the number of service time frames of all users.
(3) memorize | store the average value of the re-service rate of each service day as the re-service rate of the said staff,
The service time prediction apparatus further comprising: The service time prediction device according to claim 1 ,
Means for obtaining performance data including service time slots assigned to provide services to users for each staff and service day;
With respect to the acquired performance data, means for performing the following processes (1) to (4) for each staff member;
(1) For each service day, extract the first service time frame and the second service time frame of the reuser;
(2) For each service day, for each re-user, subtract the start time of the first service time frame from the start time of the second service time frame to determine the re-service time interval of each re-user. Calculating and subtracting the start time of the second service time frame from the end time of the second service time frame to calculate the re-service required time for each user.
(3) For each service day, the average value of the re-service time interval of each re-user is defined as the re-service time interval of each service day, and the average value of the re-service required time of each re-user is determined as each service day. Re-service time for
(4) The average value of the re-service time interval of each service day is stored as the re-service time interval of the staff, and the average value of the re-service time required of each service day is stored as the re-service time of the staff. ,
The service time prediction apparatus further comprising: A service time prediction method for predicting a time frame for providing a service to a re-user who is a user who receives the same service again after receiving an initial service at a service providing facility by a computer,
The computer
Storing in advance prediction data to which a reserved user's service time frame is assigned;
For each staff member who provides a service, pre-store a re-service rate that is a ratio of the number of services provided to the re-user relative to the total number of services provided per day;
For each of the staff, pre-store a re-service time interval that is a time from the first service to a re-service, and a re-service time that is a time required for the re-service;
Predicting a first service time frame, which is a time frame assigned to initially provide a service to the user when the user is accepted;
Determining whether to provide a service to the user again based on the re-service rate of the staff providing the service to the user; and
When it is determined that the service is to be provided again to the user, a second service time frame that is a time frame assigned to provide the user with the service again is predicted based on the predicted first service time frame. And steps to
The execution,
Predicting the first service time frame,
When the received user is not reserved, the service time frame of the non-reserved user is added to the empty time frame that is between the service time frames of the reserved user in the prediction data. Assigned as a service window,
Predicting the second service time frame,
In the prediction data, a service time frame in which a time obtained by adding the re-service time interval of the staff to the start time of the first service time frame is a start time, and the re-service required time of the staff is a time length. And allocating as the second service time frame . The service time prediction method according to claim 4 ,
The computer
Obtaining performance data including service time slots assigned to provide services to users for each staff and service day; and
With respect to the acquired result data, for each staff member, the following steps (1) to (3) are performed:
(1) Counting the number of service time slots for all users and the number of service time slots for re-users for each service day;
(2) For each service day, the re-service rate of each service day is calculated by dividing the counted number of re-user service time frames by the number of service time frames of all users.
(3) memorize | store the average value of the re-service rate of each service day as the re-service rate of the said staff,
A service time prediction method, further comprising: The service time prediction method according to claim 4 ,
The computer
Obtaining performance data including service time slots assigned to provide services to users for each staff and service day; and
With respect to the acquired result data, for each staff, the following steps (1) to (4) are performed:
(1) For each service day, extract the first service time frame and the second service time frame of the reuser;
(2) For each service day, for each re-user, subtract the start time of the first service time frame from the start time of the second service time frame to determine the re-service time interval of each re-user. Calculating and subtracting the start time of the second service time frame from the end time of the second service time frame to calculate the re-service required time for each user.
(3) For each service day, the average value of the re-service time interval of each re-user is defined as the re-service time interval of each service day, and the average value of the re-service required time of each re-user is determined as each service day. Re-service time for
(4) The average value of the re-service time interval of each service day is stored as the re-service time interval of the staff, and the average value of the re-service time required of each service day is stored as the re-service time of the staff. ,
A service time prediction method, further comprising:

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