JP5328867B2 - Time prediction apparatus, time prediction method, and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a time prediction device capable of accurately predicting the time of a service delivery or duration of waiting time, a time prediction method and a program. <P>SOLUTION: The time prediction device performs the steps of: calculating a cumulative probability of medical examination time or waiting time until the examination starts; generating normal-time probability distribution (medical examination time/waiting time) in which the cumulative probability becomes equal to or more than a reference probability (medical examination time/waiting time) and abnormal-time probability distribution (medical examination time/waiting time) in which the cumulative probability becomes less than the reference probability; calculating a prediction value of the medical examination time/waiting time using the normal-time probability distribution (medical examination time/waiting time); and recalculating the medical examination time/waiting time using the abnormal-time probability distribution (medical examination time/waiting time) if the medical examination time/waiting time exceeds a prescribed reference time (medical examination time/waiting time). <P>COPYRIGHT: (C)2013,JPO&amp;INPIT

Description

  The present invention relates to a time prediction apparatus, a time prediction method, and a program.

  A system for predicting and displaying a waiting time occurring in a hospital or the like has been developed. For example, Patent Documents 1 and 2 disclose systems for predicting waiting time using a neural network.

Japanese Patent No. 4194573 Japanese Patent No. 4217689

  For example, when examining a patient who takes time to understand symptoms, or when an outpatient doctor treats an inpatient, the examination time and waiting time may become extremely longer than usual. . However, in the systems disclosed in Patent Documents 1 and 2, since the predicted values for the same examination time and waiting time are always calculated regardless of the situation of the examination, the prediction accuracy at the time of abnormality is deteriorated.

  The present invention has been made in view of such a background, and an object thereof is to provide a time prediction device, a time prediction method, and a program capable of accurately predicting a service providing time or a waiting time. .

  A main invention of the present invention for solving the above problem is an apparatus for predicting a service providing time or a waiting time until the service is provided, and for calculating the past providing time or the waiting time. A record storage unit for storing the record information, a generation probability calculation unit for calculating the occurrence probability of the provision time or the waiting time based on the record information, and a cumulative occurrence probability by sorting the provision time or the wait time A probability distribution creating unit that creates a normal probability distribution that is a probability distribution in which the cumulative occurrence probability is equal to or greater than a predetermined threshold and an abnormal probability distribution that is a probability distribution in which the cumulative occurrence probability is less than the threshold; A prediction unit that calculates a predicted value of the provision time or the waiting time using the normal probability distribution, and the prediction unit includes the provision time for a certain service or If the serial latency exceeds a predetermined set time, and recalculating the predicted value of the providing time or the waiting time by using the abnormality probability distribution.

  According to the time predicting apparatus of the present invention, a probability distribution at normal times and abnormal times is created based on past service provision time or waiting time results, and prediction is first made using the normal probability distribution. Can do. Therefore, it is possible to eliminate the time that does not occur so much and perform the prediction based on the occurrence probability distribution of the time that is likely to occur, so that the prediction accuracy can be improved. Further, according to the time prediction device of the present invention, when the actual provision time or waiting time exceeds the set time, the predicted value can be recalculated using the probability distribution at the time of abnormality. Therefore, at the time of abnormality exceeding the set time, the predicted value can be corrected using the occurrence probability distribution of time that hardly occurs in normal times. Therefore, the prediction accuracy at the time of abnormality can also be improved.

In the temporal prediction device of the present invention, the prediction unit may calculate the predicted value by generating a random number in the normal probability distribution or the abnormal probability distribution.
In the time prediction device of the present invention, the probability distribution creation unit may sort the provision time or the waiting time in ascending order.
In this case, in normal prediction, the possibility that a long provision time or waiting time is calculated as a predicted value can be reduced. On the other hand, at the time of abnormality, a long provision time or waiting time can be recalculated as a predicted value.

In the time prediction device of the present invention, the set time may be the provision time or the waiting time when the cumulative occurrence probability is equal to or higher than the threshold.
In this case, when the provision time or waiting time in which the occurrence probability falls below the threshold value actually occurs, it can be determined that an abnormality has occurred. Therefore, since the separation between the normal time and the abnormality can be matched with the probability distribution, the prediction accuracy of the provision time or the waiting time can be improved.

In the time prediction device of the present invention, the probability distribution creation unit may sort the provision time or the waiting time in descending order of the occurrence probability.
In this case, since a thing with a high generation | occurrence | production probability can be used for prediction at normal time, the prediction precision at normal time can be improved more.

  According to another aspect of the present invention, there is provided a method for predicting a service providing time or a waiting time until the service is provided, wherein the computer calculates the past providing time or the waiting time. Record actual information, calculate the occurrence probability of the provision time or the waiting time based on the actual information, calculate the cumulative occurrence probability by sorting the provision time or the waiting time, and the cumulative occurrence probability is predetermined A normal probability distribution that is a probability distribution that is greater than or equal to a threshold value and an abnormal probability distribution that is a probability distribution that the cumulative occurrence probability is less than the threshold value, and the provision time or the waiting time is generated using the normal probability distribution. When a predicted value of time is calculated, and the provision time or the waiting time for a certain service exceeds a predetermined set time, the provision time or And to re-calculate the predicted value of the serial latency.

  Another aspect of the present invention is a program for predicting a service providing time or a waiting time until the service is provided, and calculates a past providing time or the waiting time in a computer. Storing the performance information for calculating the probability of occurrence of the provision time or the waiting time based on the performance information, calculating the cumulative occurrence probability by sorting the provision time or the waiting time, Creating a normal probability distribution that is a probability distribution in which the cumulative occurrence probability is greater than or equal to a predetermined threshold and an abnormal probability distribution that is a probability distribution in which the cumulative occurrence probability is less than the threshold; and the normal probability distribution And calculating a predicted value of the provision time or the waiting time using the provision time or the waiting time for a certain service when the predetermined time is set. If it exceeds, and thereby execute the steps of: recalculating the predicted value of the providing time or the waiting time by using the abnormality probability distribution.

  Other problems and solutions to be disclosed by the present application will be made clear by the embodiments of the invention and the drawings.

  According to the present invention, it is possible to accurately predict service provision time or waiting time.

It is a figure showing the whole information service system 1 composition concerning this embodiment. It is a figure which shows the hardware constitutions of the patient terminal. 3 is a functional block diagram of a patient terminal 10. FIG. 2 is a hardware configuration diagram of an employee terminal 20. FIG. 3 is a functional block diagram of an employee terminal 20. FIG. 2 is a hardware configuration diagram of a reception terminal 30. FIG. 3 is a functional block diagram of a reception terminal 30. FIG. 2 is a hardware configuration diagram of a doctor terminal 40. FIG. 3 is a functional block diagram of a doctor terminal 40. FIG. 2 is a hardware configuration diagram of a server device 50. FIG. 3 is a functional block diagram of a server device 50. FIG. It is a figure which shows the structural example of the reception history information memorize | stored in the reception history database. It is a figure which shows the structural example of the medical history information memorize | stored in the medical history database. The structural example of the waiting time performance information memorize | stored in the waiting time performance database 553 is shown. It is a flowchart which shows the flow of the registration process of the waiting time track record information to the waiting time track record database 553. It is a figure which shows the structural example of the waiting information memorize | stored in the waiting list database 554. FIG. It is a figure which shows an example of the waiting list output by the waiting list output part 515. It is a figure which shows the structural example of the reservation information memorize | stored in the reservation database. It is a figure which shows the structural example of the weather information memorize | stored in the weather database. It is a figure which shows the structural example of the total information memorize | stored in the total database 557. FIG. It is a flowchart of the waiting time prediction process by the waiting time prediction unit 522. It is a flowchart of the patient number prediction process by the patient number prediction part 525. It is a flowchart of the waiting time prediction process in case prediction date is the current day. It is a flowchart of the process which produces generation | occurrence | production probability distribution of a medical examination interval. It is a graph which shows the example of occurrence frequency distribution of a medical examination interval. It is a flowchart of the waiting time prediction process in case prediction date is the current day. It is a figure explaining the process of the information provision system 1 on the examination day. It is a flowchart of the registration process of waiting information based on reservation information. It is a flowchart of the registration process of waiting information. It is a flowchart of the monitoring process of a medical condition. It is a flowchart of a medical examination end process. It is a flowchart of the time-up process of an interval timer. It is a flowchart of the update process of waiting information. It is a flowchart of the time-up process of a medical examination timer. It is a flowchart of a medical examination start process.

  In the present embodiment, a description will be given of an information providing system 1 that provides a diagnosis waiting time and a predicted value of the number of visiting patients to a patient who receives a medical examination or a medical institution employee at a medical institution. Moreover, in the information provision system 1 of this embodiment, the interval (interval) from the end of a certain diagnosis to the next diagnosis is predicted and reflected in the prediction of the waiting time.

== System configuration ==
FIG. 1 is a diagram illustrating an overall configuration of an information providing system 1 according to the present embodiment. As shown in FIG. 1, the information providing system 1 includes a patient terminal 10 used by a patient (user) who receives a diagnosis (service) in a medical institution, an employee terminal 20 used by an employee of the medical institution, A reception terminal 30 that performs reception processing when visiting an institution, a doctor terminal 40 that is operated by a doctor that examines a patient, and a server device 50 are configured. The patient terminal 10, the employee terminal 20, the reception terminal 30, the doctor terminal 40, and the server device 50 are computers such as a personal computer, a workstation, a mobile phone terminal, a PDA (Personal Digital Assistant), a smartphone, and a set top box. is there.

  The employee terminal 20, the reception terminal 30, the doctor terminal 40, and the server device 50 are installed inside a medical institution and are connected via a LAN (Local Area Network) 60 so that they can communicate with each other. Yes. The LAN 60 is connected to the Internet 70, and the employee terminal 20, the reception terminal 30, the doctor terminal 40, and the server device 50 can communicate with a computer connected to the Internet 70. The patient terminal 10 is connected to the Internet 70 and can communicate with at least the server device 50.

  As will be described later, the patient can access the server device 50 using the patient terminal 10 and browse the prediction of the waiting time until the diagnosis when he / she goes to the hospital. In addition, employees of medical institutions such as doctors, nurses, pharmacists, managers, and offices can access the server device 50 using the employee terminal 20 and browse the prediction of the number of patients to visit. It has become.

  The reception terminal 30 performs reception when the patient visits, and registers the reception time and the like in the database managed by the server device 50. Further, as will be described later, a waiting time is displayed on the reception terminal 30 so that the patient can check the waiting time until the examination.

  The doctor terminal 40 receives the patient ID at the start and end of the patient's medical care, and in response to this, the doctor terminal 40 registers the patient visit history in the database managed by the server device 50. Further, the doctor terminal 40 may be used, for example, for receiving input of medicines prescribed by a doctor or displaying an electronic medical record.

== Patient terminal 10 ==
FIG. 2 is a diagram illustrating a hardware configuration of the patient terminal 10. As shown in FIG. 2, the patient terminal 10 includes a CPU 101, a memory 102, a storage device 103, a communication interface 104, an input device 105, and an output device 106.

  The storage device 103 stores programs and data, for example, a hard disk drive, a solid state disk, a flash memory, a CD-ROM drive, a semiconductor storage device, or the like. The CPU 101 implements various functions by reading a program stored in the storage device 103 into the memory 102 and executing it. The communication interface 104 is a modem, an ATM interface, an Ethernet (registered trademark) adapter, or the like for connecting to a communication network, for example, a public telephone line or a dedicated telephone line. The patient terminal 10 is connected to the Internet 70 via the communication interface 104 and performs communication with other computers connected to the Internet.

  The input device 105 is a keyboard, mouse, touch panel, tablet, microphone, or the like that accepts data input. A plurality of input devices 105 may be provided. The output device 106 is, for example, a CRT or a liquid crystal display, a printer, a speaker or the like that outputs data. A plurality of output devices 106 may be provided.

  FIG. 3 is a functional block diagram of the patient terminal 10. As illustrated in FIG. 3, the patient terminal 10 includes a predicted waiting time acquisition request transmission unit 111, a predicted waiting time reception unit 112, and a predicted waiting time output unit 113.

  The predicted waiting time acquisition request transmission unit 111 is a command (hereinafter referred to as a predicted waiting time acquisition request) for acquiring a predicted value (hereinafter referred to as a predicted waiting time) of waiting time until a medical examination when visiting a medical institution. .) To the server device 50. In the prediction waiting time acquisition request, a date to be predicted (hereinafter referred to as a prediction target date) is set. As the prediction target date set in the prediction waiting time acquisition request, for example, the patient terminal 10 may receive an input from the patient via the user interface, or the current date may be used.

The predicted waiting time reception unit 112 receives the predicted waiting time transmitted from the server device 50 in response to the predicted waiting time acquisition request.
The predicted waiting time output unit 113 outputs the predicted waiting time received by the predicted waiting time receiving unit 112 to the output device 106.

  As described above, since the patient using the patient terminal 10 of the present embodiment can refer to the waiting time using the patient terminal 10 before the visit, it is easy to schedule the visit time and the like, which is convenient.

== Employee terminal 20 ==
FIG. 4 is a hardware configuration diagram of the employee terminal 20. As shown in FIG. 4, the hardware configuration of the employee terminal 20 is the same as that of the patient terminal 10, but the destination to which the communication interface 104 is connected is the LAN 60.

  FIG. 5 is a functional block diagram of the employee terminal 20. As shown in FIG. 5, the employee terminal 20 includes a predicted patient number acquisition request transmission unit 211, a predicted patient number reception unit 212, and a predicted patient number output unit 213.

The predicted patient number acquisition request transmission unit 211 obtains a command value (hereinafter referred to as a predicted patient number acquisition request) for acquiring a predicted value (hereinafter referred to as a predicted patient number) of the number of patients (number of users) who visits a medical institution. ) To the server device 50. In the predicted patient number acquisition request, the prediction target date and the time zone to be predicted (in this embodiment, the time zone is specified in units of 1 hour, from 0 minutes to 1 hour per hour. The time zone is hereinafter referred to as a prediction target time zone).
The predicted patient number receiving unit 212 receives the predicted patient number transmitted from the server device 50 in response to the predicted patient number acquisition request.
The predicted patient number output unit 213 outputs the predicted patient number received by the predicted patient number receiving unit 212 to the output device 106.

  The predicted patient number acquisition request is transmitted according to an instruction from an employee who operates the employee terminal 20. The predicted number of patients is output from the output device 106 of the employee terminal 20, and the employee can refer to it. Therefore, the employee can be used as a reference for shift management of doctors, nurses, pharmacists, office workers and other employees, for example.

  In addition, for example, the predicted patient number acquisition request may be periodically transmitted to the server device 50 for each time slot in a medical institution opening time (for example, 9:00 to 16:00). In this way, the predicted value can always be output from the output device 106 of the employee terminal 20.

== Reception terminal 30 ==
FIG. 6 is a hardware configuration diagram of the reception terminal 30. The hardware configuration of the reception terminal 30 is substantially the same as that of the patient terminal 10 shown in FIG. 1 described above, but includes a card reader 301 that is a reading device for the examination ticket 302 in addition to the configuration of the patient terminal 10. The examination ticket 302 is, for example, a magnetic card, an IC card, a memory card, and the like, and information for identifying a patient (hereinafter referred to as a patient ID) is recorded. As the card reader 301, a magnetic card reader, a contact type IC card reader, a non-contact type IC card reader, a memory card reader, or the like can be used in accordance with the examination ticket 302. Instead of the card reader 301, an input device that reads a biological signal such as a fingerprint or an iris as a patient ID may be employed.

  FIG. 7 is a functional block diagram of the reception terminal 30. As illustrated in FIG. 7, the reception terminal 30 includes a patient ID input unit 311, a doctor ID input unit 312, a predicted waiting time acquisition request transmission unit 313, a predicted waiting time reception unit 314, a predicted waiting time output unit 315, and a reception history registration. A request transmission unit 316 is provided.

  The patient ID input unit 311 reads out the patient ID recorded on the examination ticket 302 via the card reader 301. Note that the reception terminal 30 may include an input device 105 such as a keyboard, a mouse, and a microphone, and the patient ID input unit 311 may receive an input of a patient ID from the input device 105. Moreover, the patient ID input part 311 will allocate a reception number to patient ID, if the input of patient ID is received.

The doctor ID input unit 312 receives input of information (hereinafter, referred to as a doctor ID) that identifies a doctor who receives a patient. The doctor ID input unit 312 receives a doctor ID input from the input device 105 such as a keyboard, a mouse, or a touch panel.
The predicted waiting time acquisition request transmission unit 313 transmits a predicted waiting time acquisition request to the server device 50. The prediction target date and the prediction target time zone are set in the prediction waiting time acquisition request.
The predicted waiting time receiving unit 314 receives the predicted waiting time transmitted from the server device 50 in response to the predicted waiting time acquisition request.
The predicted waiting time output unit 315 outputs the predicted waiting time received by the predicted waiting time receiving unit 314 to the output device 106.

  The predicted waiting time acquisition request transmission unit 313 detects that the examination ticket 302 has been inserted into the card reader 301 and transmits a predicted waiting time acquisition request to the server device 50. This is convenient because the patient can check the current waiting time at the stage of reception.

  Further, as will be described later, a predicted waiting time is periodically transmitted from the server device 50. The predicted waiting time receiving unit 314 receives the predicted waiting time periodically transmitted, and the predicted waiting time output unit 315 outputs the predicted waiting time to the output device 106. Thereby, the patient in the vicinity of the reception terminal 30 can grasp the prospect of the current waiting time.

  The reception history registration request transmitting unit 316 registers a command (hereinafter referred to as reception history registration request) for registering information (hereinafter referred to as reception history information) indicating that the patient having inserted the examination ticket has been received. 50. In the reception history registration request, a patient ID, a doctor ID, a reception number, the current date and time, and the like are set. Details of the reception history information will be described later.

== Doctor terminal 40 ==
FIG. 8 is a hardware configuration diagram of the doctor terminal 40. As shown in FIG. 8, the hardware configuration of the doctor terminal 40 is the same as that of the reception terminal 30.

  FIG. 9 is a functional block diagram of the doctor terminal 40. As illustrated in FIG. 9, the doctor terminal 40 includes a diagnosis start instruction input unit 411, a diagnosis end instruction input unit 412, and a diagnosis history registration request transmission unit 413.

  The medical examination start instruction input unit 411 receives an input (hereinafter referred to as a medical examination start instruction) indicating that the doctor has started the medical examination of the patient. In the present embodiment, it is assumed that a diagnosis start instruction is input when the diagnosis ticket 302 is inserted into the card reader 301. The examination start instruction may be received from the input device 105 such as a keyboard or a mouse. In addition, the examination start instruction input unit 411 also receives an input of a patient ID that is a subject of examination. In the present embodiment, the patient ID is read from the examination ticket 302 inserted in the card reader 301. When a diagnosis start instruction is input, the diagnosis start instruction input unit 411 stores the patient ID and the current time (hereinafter referred to as a diagnosis start time) in the memory 102. In addition, the diagnosis start instruction input unit 411 transmits a message indicating that a diagnosis start instruction has been input (hereinafter referred to as diagnosis start information) to the server device 50 including the doctor ID, patient ID, and diagnosis start time.

  The examination end instruction input unit 412 receives an input (hereinafter referred to as a diagnosis end instruction) indicating that the doctor has finished the patient's examination. In the present embodiment, it is assumed that the examination end instruction is input when the examination ticket 302 inserted in the card reader 301 is extracted. The examination end instruction may be received from the input device 105.

  The diagnosis history registration request transmission unit 413 transmits a command (hereinafter referred to as a diagnosis history registration request) for registering information (hereinafter referred to as diagnosis history information) indicating that the patient has been examined to the server device 50. The examination history registration request includes the current date, the current time (hereinafter referred to as the examination end time), the doctor ID of the doctor who performed the examination, the patient ID and the medical treatment start time stored in the memory 102. Is set. The diagnosis history registration request transmission unit 413 transmits the above-described diagnosis history registration request to the server device 50 when the diagnosis end instruction is input to the diagnosis end instruction input unit 412. Accordingly, the medical history information is registered in the database managed by the server device 50 at the timing when the patient's medical examination is completed.

== Server device 50 ==
FIG. 10 is a hardware configuration diagram of the server device 50. As shown in FIG. 10, the hardware configuration of the server device 50 is a general computer configuration similar to the above-described patient terminal 10, employee terminal 20, reception terminal 30, doctor terminal 40, etc. 105, the card reader 301, etc. can be omitted. In the present embodiment, the output device 106 included in the server device 50 is assumed to be a display device that provides information to a patient who is waiting for a diagnosis, such as an electronic bulletin board or a large display.

  FIG. 11 is a functional block diagram of the server device 50. As illustrated in FIG. 11, the server device 50 includes an acceptance history registration request receiving unit 511, a medical history registration request receiving unit 512, a waiting time result registration unit 513, a waiting list update unit 514, a waiting list output unit 515, and reservation information registration. Unit 516, weather information registration unit 517, total information registration unit 518, examination time prediction unit 519, neural network calculation unit 520, prediction waiting time acquisition request reception unit 521, waiting time prediction unit 522, prediction waiting time transmission unit 523, prediction Functional units of a patient number acquisition request receiving unit 524, a patient number predicting unit 525, a predicted patient number transmitting unit 526, and an interval predicting unit 527, a reception history database 551, a diagnosis history database 552, a waiting time result database 553, a waiting list database 554, reservation database 555, weather database 556, tabulation data It is equipped with a database each of the base 557.

  The reception history database 551 stores reception history information. FIG. 12 shows a configuration example of reception history information stored in the reception history database 551. As shown in FIG. 12, the reception history information includes the date the patient came to the hospital, the reception number, the patient ID, the doctor ID of the doctor who receives the medical examination, and the reception time.

  The reception history registration request receiving unit 511 receives the reception history registration request transmitted from the reception terminal 30, and receives the reception history based on the date, reception number, patient ID, and doctor ID set in the received reception history registration request. Information is created, and the created reception history information is registered in the reception history database 551.

  The examination history database 552 stores examination history information (corresponding to the performance information of the present invention). FIG. 13 shows a configuration example of the examination history information stored in the examination history database 552. As shown in FIG. 13, the examination history information includes the date of the examination, the doctor ID of the doctor in charge of the examination, the patient ID of the patient who received the examination, the examination start time, and the examination end time.

  The diagnosis history registration request receiving unit 512 receives the diagnosis history registration request transmitted from the doctor terminal 40, and the date, doctor ID, patient ID, diagnosis start time, diagnosis end time set in the received diagnosis history registration request. The medical examination history information is created based on the information, and the created medical examination history information is registered in the medical examination history database 552. The medical examination history registration request receiving unit 512 receives medical examination start information transmitted from the doctor terminal 40.

  The waiting time result database 553 stores information including a result value of the waiting time of the patient from reception to examination (hereinafter referred to as waiting time result information). FIG. 14 shows a configuration example of the waiting time record information stored in the waiting time record database 553. As shown in FIG. 14, the waiting time record information includes date, doctor ID, patient ID, reception number, reception time zone, and waiting time. The waiting time result information is created by the waiting time result registration unit 513 based on the reception history information and the examination history information described above. FIG. 15 is a flowchart showing the flow of registration processing of waiting time record information in the waiting time record database 553.

  When the reception history information is registered in the reception history database 551 (S611) and the diagnosis history information is registered in the diagnosis history database 552 (S612), the difference between the diagnosis start time of the diagnosis history information and the reception time of the reception history information is calculated. It is calculated as the actual value of the waiting time (S613), and the time zone including the reception time of the reception history information is specified (S614). The waiting time record registration unit 513 reads the reception history information corresponding to the patient ID of the registered medical history information from the reception history database 551 (S615), and specifies the date, doctor ID, and patient ID of the medical history information. The waiting time record information in which the band, the reception number of the reception history information, and the waiting time record information in which the calculated waiting time record value is set is registered in the waiting time record database 553 (S616).

  The waiting list database 554 stores waiting information including a patient who is scheduled to receive a medical examination, a scheduled time for starting medical examination, and the like. FIG. 16 shows a configuration example of waiting information stored in the waiting list database 554. As shown in FIG. 16, in the waiting information, the patient ID of the patient scheduled to be examined, the reception number, the scheduled start time for the diagnosis, the time when the diagnosis is completed, whether the appointment is an appointment, in correspondence with the doctor ID of the doctor performing the examination. A reservation flag indicating whether or not, and a predicted waiting time are included. Since the diagnosis end time is set to the time when the patient's diagnosis is actually ended, a value is not set for the diagnosis end time when the patient has not yet received a diagnosis. When “o” is set in the reservation flag, it indicates that a medical examination is reserved, and a reservation time is set as the scheduled medical examination time. When the reservation flag “x” is set, a predicted value of a diagnosis start time, which will be described later, is set as the scheduled diagnosis start time. The waiting list database 554 is updated by the waiting list update unit 514. The waiting list update unit 514 periodically sets the time from the current time to the scheduled start time of the diagnosis as a waiting time. Details of the waiting list update unit 514 for updating the waiting list database 554 will be described later.

  The waiting list output unit 515 outputs a list of waiting information registered in the waiting list database 554 to the output device 106. FIG. 17 shows an example of a waiting list output by the waiting list output unit 515. FIG. 17 shows an example of a screen displayed on an electronic bulletin board or a display. In addition, as shown in FIG. 17, the waiting list output unit 515 displays information indicating that a patient who has been scheduled for examination registered in the appointment database 555, which will be described later, is a reserved patient in the reception number column or the like. It may be. Further, when the doctor terminal 40 receives an input of a diagnosis start instruction, the doctor ID of the patient who has started the diagnosis is transmitted to the server device 50, and the waiting list output unit 515 corresponds to the patient ID that has been examined. The fact that the patient is being examined may be displayed in the examination end time column or the like.

  The reservation database 555 stores information related to appointments for diagnosis (hereinafter referred to as reservation information). FIG. 18 shows a configuration example of reservation information stored in the reservation database 555. As shown in FIG. 18, the reservation information includes a reservation date, a doctor ID of a doctor in charge of the examination, a patient ID of the reserved patient, and a reservation time (scheduled start time of the diagnosis). The reservation information registration unit 516 registers reservation information in the reservation database 555. As the reservation information, for example, an input from an operator who has received a reservation by telephone can be received, or the reservation information can be received from the patient terminal 10 via the Internet 70. The reservation information registration unit 516 can use a general reservation acceptance processing mechanism.

  The weather database 556 stores weather information related to weather forecasts, weather measurement values, and the like. FIG. 19 shows a configuration example of weather information stored in the weather database 556. As shown in FIG. 19, the weather information stored in the weather database 556 includes date, time zone, weather in the time zone, maximum temperature, and minimum temperature. In this embodiment, meteorological information includes both weather data based on forecast and measurement data based on measurement. However, the data based on forecast and the data based on measurement are managed as separate databases. Also good.

  The weather information registration unit 517 acquires, for example, weather information by accessing a server of a weather company that provides weather data, and registers the acquired weather information in the weather database 556. The weather information registration unit 517 can use a general weather data acquisition process mechanism.

  The total database 557 stores information (hereinafter referred to as total information) obtained by totaling the above-described reception history information and waiting time record information. FIG. 20 shows a configuration example of total information stored in the total database 557. As shown in FIG. 20, the total information includes the date, the day of the week specified by the date, the time zone, the weather, the maximum temperature and the minimum temperature, and the average value of the waiting time (waiting time average) in the date and time zone. The number of patients visiting the hospital is included.

  The total information is periodically registered by the total information registration unit 518. In this embodiment, it is assumed that the total information registration unit 518 creates total information for each hour using the immediately preceding time zone as the time zone to be totaled and registers it in the total database 557. The total information registration unit 518 generates total information, reads the weather, maximum temperature, and minimum temperature corresponding to the date and time zone to be totaled from the weather database 556, and sets them in the generated total information. In addition, the total information registration unit 518 calculates an average value of the waiting time corresponding to the date and time zone to be counted from the waiting time record database 553 and sets the average value in the total information. The total information registration unit 518 counts the number of the reception history information corresponding to the date to be counted from the reception history database 551, the reception time being included in the time zone to be counted, and the total information as the number of visiting patients. The total information is set and registered in the total database 557.

  The examination time prediction unit 519 predicts the examination time. The examination time prediction unit 519 reads out the examination history information for a predetermined number of days in the examination history database 552 and sets the examination time (time from the examination start time to the examination end time) for each of the read examination history information in minutes (3 It is possible to use a unit of an arbitrary length such as a minute unit, a unit of 5 minutes, etc.), and a frequency distribution in order of short examination time is created. The examination time prediction unit 519 calculates the occurrence probability for each examination time, and the cumulative probability obtained by accumulating the occurrence probabilities in the order of the examination time is referred to as a predetermined probability (hereinafter referred to as a reference probability. Probability (consultation time) ". The reference probability can be set to an arbitrary value such as 90% or 75%, for example (hereinafter referred to as normal probability distribution). The normal probability distribution of is expressed as “normal probability distribution (consultation time)”) and the probability distribution that the cumulative probability is less than a predetermined value (hereinafter referred to as abnormal probability distribution, the abnormal probability of the examination time) The distribution is expressed as “probability distribution at abnormal time (examination time)”). Hereinafter, a consultation time when the cumulative occurrence probability is equal to or higher than the reference probability is referred to as a reference time, and the reference time for the consultation time is referred to as “reference time (examination time)”. The examination time prediction unit 519 predicts the examination time based on the normal probability distribution (examination time), and if the examination time exceeds the reference time (examination time), the abnormal time probability distribution (examination time) is obtained. Re-predict the consultation time based on

The predicted waiting time acquisition request receiving unit 521 receives a predicted waiting time acquisition request transmitted from the patient terminal 10 or the receiving terminal 30.
The waiting time prediction unit 522 performs waiting time prediction. As will be described later, the waiting time prediction unit 522 waits based on the predicted value of the examination time by the examination time prediction unit 519 when the prediction target date is the current day and a doctor who examines the patient is specified. Make predictions. The waiting time prediction unit 522 calculates the occurrence frequency and the occurrence probability of the waiting time based on the actual value of the waiting time when the prediction target date is the current day and the doctor is not specified, and in order of increasing waiting time. The normal probability distribution (waiting time) in which the accumulated cumulative probability is greater than or equal to the predetermined reference probability (waiting time) and the abnormal probability distribution (waiting time) in which the cumulative probability is less than the reference probability (waiting time) are calculated. Initially, the waiting time is predicted based on the normal probability distribution (waiting time), and if the waiting time exceeds a predetermined reference time (waiting time), the abnormal probability distribution (waiting time) Re-predict latency based on The reference time (waiting time) is a waiting time when the cumulative probability (waiting time) is equal to or higher than the reference probability (waiting time). When the prediction target date is not the current day, the waiting time prediction unit 522 predicts the waiting time based on the aggregate information. The waiting time prediction unit 522 predicts the waiting time every predetermined time (for example, 30 seconds, 1 minute, 5 minutes, etc.), and when receiving a predicted waiting time acquisition request, it waits even when triggered by the reception. Make time predictions.

The predicted waiting time transmission unit 523 transmits the waiting time predicted by the waiting time prediction unit 522 to the patient terminal 10 or the reception terminal 30 that is the transmission source of the predicted waiting time acquisition request. In addition, the predicted waiting time transmission unit 523 transmits the predicted waiting time to the receiving terminal 30 every time the waiting time is predicted by the waiting time prediction unit 522 every predetermined time.
The predicted patient number acquisition request receiving unit 524 receives the predicted patient number acquisition request transmitted from the employee terminal 20.
The number-of-patients prediction unit 525 predicts the number of patients visiting the hospital based on the total information and the like. The details of the process for predicting the number of visiting patients will be described later.
The predicted patient number transmission unit 526 transmits the predicted value of the number of visiting patients predicted by the patient number prediction unit 525 (hereinafter referred to as the predicted patient number) to the employee terminal 20.

  The interval prediction unit 527 (predicts the interval. The interval prediction unit 527 calculates the interval result based on the past examination results, calculates the occurrence frequency and occurrence probability of the interval, and the normal probability distribution of the interval The interval prediction unit 527 generates a random number corresponding to the normal probability distribution (interval) and predicts the interval, and the interval prediction unit 527 generates an interval. When a predetermined reference time (interval) is exceeded, a random number corresponding to the abnormal probability distribution (interval) is generated to recalculate the predicted value of the interval.

  The examination time prediction unit 519 and the waiting time prediction unit 522 correspond to the occurrence probability calculation unit, the probability distribution creation unit, and the prediction unit of the present invention.

== Waiting time prediction process ==
A patient who visits a medical institution accesses the server device 50 using the patient terminal 10 in advance and refers to the predicted value of the waiting time to determine whether or not to go to the hospital and when to go to the hospital. It can be performed. In addition to the waiting time of the day, the patient can also request a prediction of waiting time at a future date. The waiting time prediction unit 522 performs different processing depending on the prediction target date set in the prediction waiting time acquisition request.

<When the forecast date is a future date>
When the prediction target date is a date later than the current day, the waiting time prediction unit 522 predicts the waiting time based on the aggregation information stored in the aggregation database 557. FIG. 21 shows the flow of the waiting time prediction process when the prediction target date is a date later than the current day.

  The waiting time predicting unit 522 reads the total information corresponding to the date that is the same day of the week before the prediction target day from the total database 557, and sums the number of patients in the read total information to n1 (S621). The waiting time prediction unit 522 reads weather information corresponding to the prediction target date and the prediction target time zone. The waiting time prediction unit 522 is a month in which the month specified from the date is specified from the prediction target date, and the day of the week is specified from the prediction target day, among the total information stored in the total database 557. The time zone is the prediction target time zone, and the weather, the maximum temperature, and the minimum temperature are read out as the weather information of the read out weather information (S622). The waiting time prediction unit 522 calculates the number of visiting patients for each date from the read total information (S623), and specifies three dates corresponding to the calculated number of visiting patients close to n1 (S624).

The waiting time prediction unit 522 calculates, for each of the specified three dates, the total of the calculated number of visiting patients as n2 (S625), and calculates the quotient of n1 and n2 as a correction coefficient (S626). The waiting time prediction unit 522 calculates the average value of the waiting time average of the counting information corresponding to the date from the counting database 557 (S627), and calculates the corrected waiting time by multiplying the calculated average value by the correction coefficient. (S628).
The waiting time prediction unit 522 calculates an average value of the corrected waiting times corresponding to the three identified dates and sets it as a predicted value of the waiting time (S629).

  In this way, the waiting time prediction unit 522 can predict the waiting time based on the aggregation information stored in the aggregation database 557. As described above, the waiting time prediction unit 522 predicts the waiting time of the prediction target day based on the waiting time in the same past season or weather. Therefore, the waiting time can be predicted in consideration of the correlation with factors such as the season and the weather, so that it is possible to make a prediction with higher accuracy than the rule of thumb of the employee of the medical institution.

  In the above prediction process, the predicted value of the waiting time is corrected by using the ratio of the number of visiting patients on the same day of the most recent day and the number of visiting patients on the past date in the same season or weather. . Therefore, it is possible to eliminate the fluctuation of the predicted value due to the change in the number of patients coming to the hospital and perform a more accurate prediction. Note that the waiting time may be predicted without correction. In this case, the processing of (S625) to (S628) is omitted, and the average value of the waiting time average of the total information corresponding to any of the dates specified in (S624) is calculated as the predicted value of the waiting time. .

  Note that the same processing as the waiting time prediction processing by the waiting time prediction unit 522 shown in FIG. 21 described above is also used for the patient number prediction processing by the patient number prediction unit 525. FIG. 22 shows the flow of patient number prediction processing by the patient number prediction unit 525.

  The patient number prediction unit 525 performs the processes of (S621) to (S624) in the same manner as in FIG. 21, and performs the processes of (S625) and (S626) for each of the three dates. Here, the patient number predicting unit 525 calculates the total number of visiting patients in the tabulated information instead of calculating the average waiting time in (S627) (S631), and replaces (S628) with the total number of visiting patients. Is multiplied by the correction coefficient to obtain the corrected number of visiting patients (S632), and instead of (S629), the average of the corrected number of visiting patients is used as a predicted value of the number of visiting patients (S633). In this way, as with waiting time prediction, it is possible to predict the number of patients with high accuracy in consideration of correlation with factors such as seasons and weather.

<When the prediction date is the current day 1: When no doctor is identified>
On the other hand, when the prediction target date is the current day, the waiting time prediction unit 522 performs the prediction process in consideration of the actual value of the waiting time of the day. In the present embodiment, when the prediction target date is the current day, the date of the current day is set in the predicted waiting time acquisition request transmitted from the patient terminal 10, and the card reader 301 of the reception terminal 30 It is assumed that a prediction waiting time acquisition request is transmitted from the reception terminal 30 when the patient's examination ticket 302 is inserted, and a case where the waiting time prediction unit 522 predicts the waiting time every predetermined time.

  FIG. 23 shows the waiting time when the prediction target date is the current day and no doctor is specified, that is, when the doctor ID is not set in the prediction waiting time acquisition request, or when waiting time prediction is performed periodically. The flow of the prediction process is shown.

  The waiting time prediction unit 522 acquires the waiting time of the waiting time record information last registered in the waiting time result database 553 as the latest waiting time (S641), and the acquired latest waiting time is equal to or greater than the reference time (waiting time). If this is the case (S642: YES), random numbers are generated in the abnormal probability distribution (waiting time) to obtain a predicted value of the waiting time (S643), and if the latest waiting time is less than the reference time (waiting time) (S642: NO), a random number is generated with a normal probability distribution (waiting time) to obtain a predicted value of the waiting time (S644).

  As described above, the waiting time is predicted based on the normal probability distribution (waiting time) in the normal state, and when the latest waiting time is equal to or longer than the reference time (waiting time) The waiting time can be predicted based on (waiting time). As a result, the waiting time can be predicted based on the normal probability distribution in which the waiting time with low occurrence frequency is excluded in normal times, so that, for example, sudden illness occurs without significantly predicting the waiting time. If the patient has a special condition that takes a long time to see the doctor, or if the doctor needs to see the inpatient during the visit of the outpatient, If it occurs, the waiting time can be predicted based on the abnormal probability distribution (waiting time). Therefore, the waiting time can be accurately predicted.

<When the prediction date is the current day 2: When a doctor is specified>
The waiting time prediction unit 522 assumes a predicted waiting time acquisition request transmitted from the reception terminal 30 when a doctor ID is set in the predicted waiting time acquisition request. In addition, the following processing may be performed for each doctor ID.) In order to predict the examination time, the waiting time is predicted from the number of patients actually waiting. Also, in this case, in the information providing system 1 of the present embodiment, the interval occurrence frequency and occurrence probability are calculated from the past interval results, and the cumulative probability (interval) in which the occurrence probabilities are accumulated in the shortest interval is the reference probability (interval). Create a normal probability distribution (interval) that is greater than or equal to (interval) and an abnormal probability distribution (interval) that is less than the reference probability (interval), and first predict the interval based on the normal probability distribution (interval) And reflect on the waiting time. The abnormal probability distribution (interval) is used when the waiting list database 554 is updated.

  FIG. 24 shows a flow of processing for creating a normal probability distribution (interval) and an abnormal probability distribution (interval). The interval prediction unit 527 stores the examination history information for a predetermined number of days D (for example, any value such as one year, six months, one month, ten days, one week) from the examination history database 552 from the present time. Reading (S661), doctor IDs are extracted without duplication (S662). The interval prediction unit 527 repeats the following process with each doctor ID as “i”. The interval prediction unit 527 extracts the examination history information whose doctor ID matches “i” from the examination history database 552 as a list (S663), and sets each day of the date of the examination history information included in the list as j. Perform the process. The interval prediction unit 527 extracts the examination history information whose date matches “j” from the list (S664), and sorts the extracted examination history information (S665). The interval prediction unit 527 subtracts the examination end time of the “k” th examination history information from the examination start time of the “k + 1” th examination history information for “k” from 1 to the number of examination history information, The interval “I (k)” for the “k” th medical history information is calculated (S666). In the present embodiment, the interval is calculated in units of minutes, but may be in units of seconds or units of 5 minutes, for example.

  The interval prediction unit 527 creates a frequency distribution of the interval “I” calculated as described above (S667). FIG. 25 is a graph showing an example of the frequency distribution of the interval “I” in minutes. As shown in FIG. 25, the frequency of occurrence of the interval often decreases as the interval becomes longer. However, there are medical institutions that do not exhibit such characteristics. The interval prediction unit 527 calculates the occurrence probability for each interval and calculates the cumulative occurrence probability for the intervals in descending order of occurrence probability or in ascending order of interval (S668). The interval prediction unit 527 creates a distribution of occurrence probabilities up to an interval where the cumulative occurrence probability is equal to or higher than the reference probability (interval) as a normal probability distribution (interval) (S669), and the cumulative occurrence probability is equal to or higher than the reference probability (interval). A distribution of occurrence probabilities after the next interval (that is, an occurrence probability distribution of an interval in which the cumulative occurrence probability is less than the reference probability) is created as an abnormal probability distribution (interval) (S670). The interval predicting unit 527 sets the interval having the cumulative occurrence probability closest to the reference probability (interval) as the reference time (interval) (S671).

  Through the above processing, a normal probability distribution (interval) and an abnormal probability distribution (interval) are created for each doctor.

FIG. 26 shows a flow of waiting time prediction processing when a doctor ID is set in the prediction waiting time acquisition request.
The consultation time prediction unit 519 calculates a prediction value “a” of the consultation time by generating a random number with a normal probability distribution (consultation time) (S681).

The waiting time prediction unit 522 calculates the number of waiting patients “X” from the waiting list database 554 by counting the number of waiting information for which the examination end time is not set and the reservation flag is “x”. (S682). In addition, the waiting time prediction unit 522 calculates the number of reserved patients “b” from the waiting list database 554 by counting the number of waiting information whose reservation flag is “◯” (S683). The interval prediction unit 527 generates a random number corresponding to the normal probability distribution (interval) to calculate the predicted value “I” of the interval (S684).
The waiting time prediction unit 522 adds the reserved patient number “b” and the predicted value “I” of the interval to the number of waiting patients “X” obtained as described above, and is predicted by the neural network. A value obtained by multiplying the examination time “a” is set as a predicted value of the waiting time (S685).

  As described above, the waiting time prediction unit 522 can perform prediction according to the number of patients actually waiting and being reserved, and thus can perform prediction with higher accuracy. In addition, since the interval predicted according to the normal probability distribution can be added to the predicted value of the waiting time, the accuracy of the waiting time prediction can be further improved.

== Waiting list update processing ==
As described above, in this embodiment, waiting information is displayed on, for example, an electric bulletin board or a display, and the patient can check the waiting time even during the waiting. The waiting information is read from the waiting list database 554 by the waiting list output unit 515 and output to the output device 106. The waiting list database 554 is updated by the waiting list update unit 514 when the server device 50 receives a reception history registration request from the reception terminal 30 or receives a medical history registration request from the doctor terminal 40. The reception terminal 30 also always displays the predicted waiting time. The waiting time prediction value is periodically transmitted from the waiting time prediction unit 522 to the reception terminal 30.

FIG. 27 is a diagram for explaining processing of the information providing system 1 on the day of medical examination. 27 is performed for each doctor ID (hereinafter, a doctor ID to be processed is referred to as a target doctor ID). That is, the process of FIG. 27 is executed by the number of doctors.
First, the waiting list update unit 514 performs waiting information registration processing based on the reservation information shown in FIG. 28 (S701).

  The waiting list update unit 514 reads the reservation information corresponding to the current day and the target doctor ID from the reservation database 555 (S721), and performs the following processing for each of the read reservation information. The waiting list update unit 514 creates waiting information based on the reservation information (S722). Here, the waiting list update unit 514 creates waiting information including the doctor ID and the patient ID included in the reservation information, sets a reservation flag of the waiting information to “○”, and sets the reservation time at the scheduled start time of the examination. Can be set. The interval prediction unit 527 generates a random number corresponding to the normal probability distribution (interval) corresponding to the target doctor ID to calculate the interval “I” (S723), and sets “I” as the interval of the waiting information. It is registered in the waiting list database 554 (S724). By repeating the above processing, waiting information corresponding to the reservation information is registered in the waiting list database 554.

  Next, the consultation time prediction unit 519 calculates a prediction value “a” of the consultation time by generating a random number with a normal probability distribution (consultation time) (S702). The waiting list update unit 514 sets the first waiting information corresponding to the target doctor ID as “end information” (S703). Thereafter, the following processing is repeated until the examination work on that day is completed.

  When the reception history registration request receiving unit 511 receives a reception history registration request corresponding to the target doctor ID (S704: YES), the waiting information registration process shown in FIG. 29 is executed (S705).

  The waiting list update unit 514 sets the current time as “t” (S741), generates a random number corresponding to the normal probability distribution (interval) corresponding to the target doctor ID, and calculates the interval “I” (S742). . The waiting list update unit 514 reads the waiting information corresponding to the next target doctor ID from the waiting list database 554 in order of the scheduled start time of the diagnosis (S743), and if the waiting information exists (S744: YES), “t”. It is determined whether or not “t + a + I” obtained by adding the predicted value “a” of the examination time and the predicted value “I” of the interval exceeds the scheduled examination start time of the waiting information (S745). YES), a value obtained by adding the predicted value of the examination time and the predicted value of the interval to the scheduled examination start time of the waiting information is set to “t” (S746), and the processing from step S743 is repeated.

  When the next waiting information cannot be read (S744: NO), or when “t + a + I” is before the scheduled diagnosis start time of the waiting information (S745: NO), the waiting list update unit 514 displays the reception history information. Based on this, waiting information is created (S747). That is, the waiting list update unit 514 creates waiting information in which the patient ID, the doctor ID, and the receiving number of the receiving history information are set. The waiting list update unit 514 sets “I” as the waiting information interval and registers it in the waiting list database 554 (S748).

  As described above, the waiting information is registered in the waiting list database 554 according to the reception information.

  Next, the examination status monitoring process shown in FIG. 30 is performed (S706). When the examination is completed, that is, when the examination history registration request receiving unit 512 receives the examination history registration request corresponding to the target doctor ID (S761: YES), the examination termination process shown in FIG. 31 is performed (S762). The waiting list update unit 514 reads the waiting information corresponding to the doctor ID and patient ID of the registered medical history information from the waiting list database 554 as “end information” (S781), and the medical history at the end time of the medical check of the end information. The examination end time of information is set and the waiting list database 554 is updated (S782). The waiting list update unit 514 sets a reference time (interval) to a first timer (hereinafter referred to as an interval timer) (S783). The waiting list update unit 514 cancels the second timer (hereinafter referred to as a “consultation timer”) (S784), and the consulting time prediction unit 519 uses a random probability with a normal probability distribution (consultation time). Is generated to calculate the predicted value “a” of the examination time (S785).

  When the interval timer expires, that is, when the reference time (interval) has elapsed since the end of the examination (S763: YES), the interval timer time-up process shown in FIG. 32 is performed (S764). The interval prediction unit 527 generates a random number corresponding to the abnormal probability distribution (interval) corresponding to the target doctor ID, and calculates an abnormal interval “I” (S801). The interval prediction unit 527 sets “I” to the “end information” interval and updates the waiting list database 554 (S802). The waiting list update unit 514 executes the waiting information update process shown in FIG. 33 (S803).

  In FIG. 33, the waiting list update unit 514 sets “t” as a value obtained by adding the interval of the end information to the end time of the end information (S821). The waiting information corresponding to the ID is read (S822), and if the waiting information exists (S823: YES), the scheduled diagnosis start time of the waiting information is set to “t” (S824), and the diagnosis is set to “t”. The predicted time value “a” and the interval of the waiting information are added (S825), and the processing from step S822 is repeated. As a result of the above processing, when the interval after the “end information”, that is, the waiting information corresponding to the most recently completed examination exceeds the reference time (interval), the interval is recalculated by the abnormal probability distribution (interval) (normally The longer interval is recalculated.) In response to this, the scheduled examination start time of the waiting information after the “end information” is readjusted.

  When the examination timer expires, that is, when the reference time (diagnosis time) has elapsed since the examination started (S765: YES), the examination timer time-up process shown in FIG. 34 is performed (S766). The consultation time predicting unit 519 generates a random number from the probability distribution (diagnosis time) at the time of abnormality to calculate a predicted value “a” of the consultation time (S841), and the waiting list update unit 514 receives the waiting information in FIG. 33 described above. Update processing is performed (S842). As a result, when the examination time exceeds the reference time (examination time), the examination time is recalculated based on the abnormal probability distribution (examination time) (longer examination time is recalculated). The scheduled examination start time of the waiting information after “Information” is readjusted.

  When the diagnosis is started, that is, when the diagnosis history registration request receiving unit 512 receives the diagnosis start information corresponding to the target doctor ID (S767: YES), the diagnosis start process shown in FIG. 35 is executed (S768). ). The waiting list update unit 514 cancels the interval timer (S861), and on the other hand, sets the reference time (examination time) to the examination timer (S862). The waiting list update unit 514 updates the waiting list database 554 by setting an interval of “end information” obtained by subtracting the diagnosis start time included in the diagnosis start information from the end time of the end information (S863). The update process shown in FIG. 33 is executed (S864). The waiting list update unit 514 sets the scheduled diagnosis start time of the waiting information corresponding to the doctor ID and the patient ID included in the diagnosis start information to the diagnosis start time included in the diagnosis start information (S865).

  The above processing is repeated until the end of business.

  As described above, in the information providing system 1 according to the present embodiment, the diagnosis time is predicted based on the normal probability distribution (diagnosis time), and the waiting time is predicted based on the normal probability distribution (wait time). Yes. Therefore, since the examination time and waiting time that have a high probability of occurring in the past performance can be provided as predicted values, the prediction accuracy of the examination time and waiting time can be improved. In addition, in normal times, examination times and waiting times that have a low probability of occurring in past performance are not used as predicted values, so prediction of extremely long examination times and waiting times should not be performed. be able to. On the other hand, when the examination time and waiting time become more than the reference time (diagnosis time) and the reference time (waiting time), it is based on the abnormal probability distribution (diagnosis time) and abnormal probability distribution (waiting time). Predictions of consultation time and waiting time can be recalculated. That is, at the time of abnormality, the predicted values of the examination time and the waiting time are recalculated from the probability distribution at the time of abnormality. Therefore, improvement of the prediction accuracy of the examination time and waiting time after recalculation is expected.

  In the information providing system 1 of the present embodiment, the waiting list update unit 514 updates the waiting information registered in the waiting list database 554 to the latest state. The waiting list database 554 sets a reference time (interval) when the reception history registration request receiving unit 511 receives a reception history registration request or when the medical history registration request receiving unit 512 receives medical examination start information or a medical history registration request. It is updated when an interval is exceeded. Since the waiting list update unit 514 sets the scheduled start time of the patient based on the predicted value of the check time, the predicted start time of the diagnosis can be predicted with high accuracy. In addition, since waiting information is displayed based on the waiting list database 554 by the waiting list output unit 515, a patient waiting for a medical examination can grasp the waiting time.

  In the information providing system 1 according to the present embodiment, the waiting time prediction value is calculated in consideration of the interval prediction value. Accordingly, it is possible to display a predicted value of waiting time with high accuracy.

  Moreover, in the information provision system 1 of this embodiment, since the interval is predicted based on a normal probability distribution (interval), an interval having a high probability of occurring in the past performance can be provided as a predicted value. . Since the frequency of occurrence of a long interval is usually low, it is less likely that a long interval is added to the waiting time as a predicted value. When the normal occurrence probability distribution is created by calculating the cumulative occurrence probability in the order of short intervals, an interval exceeding the reference time (interval) can be prevented from being added to the waiting time.

  In the information providing system 1 according to the present embodiment, when the actual interval exceeds the reference time (interval), the interval can be recalculated using the abnormal probability distribution (interval). Thereby, the waiting time is also recalculated. Therefore, when an abnormality in which the actual interval exceeds the reference time (interval) occurs, a value that is not calculated as a normal predicted value can be calculated as a predicted value. Thereby, the prediction accuracy at the time of abnormality can be improved. In addition, when an abnormality occurs, it is updated to a long waiting time and displayed as waiting information, so that a patient waiting for a medical examination can also grasp that some abnormality has occurred.

  In addition, although this embodiment demonstrated prediction of the waiting time of a medical examination, it can be easily applied to the waiting time of a pharmacy etc., for example.

  In addition, the prediction of the examination time and the waiting time prediction of the present embodiment can be easily applied to the prediction of the waiting time until various services other than the medical institution are provided.

  Moreover, you may enable it to acquire the predicted value of the number of patients and waiting time also from the reception terminal 30, the doctor terminal 40, etc.

  In the present embodiment, in FIG. 21 and FIG. 22 (S624), three dates that are the number of visiting patients close to n1 are specified. However, the number is not limited to three, and any number of dates may be specified. Can be specified. Similarly, in the process of (S646) in FIG. 23, two pieces of total information are selected, but one or any plurality of pieces of total information may be selected.

  Further, in FIG. 21 and FIG. 22 (S622) and FIG. 23 (S644), the weather and the maximum temperature are not completely the same. For example, the maximum temperature is within a predetermined range such as ± 5 degrees. May be selected. Also, weather data other than the weather and the maximum temperature may be added to the conditions.

  In the present embodiment, weather data for each time zone is used, but daily weather data may be used. In this case, the time zone condition is not used when searching the weather database 556.

  In the present embodiment, the reception terminal 30 receives the input of the doctor ID from the input device 105. However, for example, a list of doctor IDs of doctors that can be examined is stored, and the order is determined from the list. You may make it read out to. Further, the reception terminal 30 may receive an input of a medical department to be consulted and search for doctors who can consult. In this case, for example, the reception terminal 30 can access the server device 50, refer to the waiting list database 554, and assign a doctor ID of a doctor with a small number of waiting patients to the patient.

  Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes equivalents thereof.

10 Patient terminal 20 Employee terminal 30 Reception terminal 40 Doctor terminal 50 Server device 60 LAN
70 Internet 101 CPU
DESCRIPTION OF SYMBOLS 102 Memory 103 Memory | storage device 104 Communication interface 105 Input device 106 Output device 111 Predictive waiting time acquisition request transmission part 112 Predictive waiting time reception part 113 Predictive waiting time output part 211 Predictive patient number acquisition request transmission part 212 Predictive patient number reception part 213 Prediction Number of patients output unit 301 Card reader 302 Examination ticket 311 Patient ID input unit 312 Doctor ID input unit 313 Predictive waiting time acquisition request transmission unit 314 Predictive waiting time reception unit 315 Predictive waiting time output unit 316 Reception history registration request transmission unit 411 Start of diagnosis Instruction input unit 412 Examination end instruction input unit 413 Examination history registration request transmission unit
511 Reception history registration request reception unit 512 Examination history registration request reception unit 513 Waiting time result registration unit 514 Waiting list update unit 515 Waiting list output unit 516 Reservation information registration unit 517 Weather information registration unit 518 Total information registration unit 519 Examination time prediction unit 521 Predictive waiting time acquisition request receiving unit 522 Waiting time prediction unit 523 Predictive waiting time transmission unit 524 Predicted patient number acquisition request receiving unit 525 Patient number prediction unit 526 Predicted patient number transmission unit 527 Interval prediction unit 551 Reception history database 552 Examination history database 553 Waiting time record database 554 Waiting list database 555 Reservation database 556 Weather database 557 Total database

Claims (7)

An apparatus for predicting a service providing time or a waiting time until the service is provided,
A record storage unit for storing record information for calculating the past provision time or the waiting time;
An occurrence probability calculation unit for calculating an occurrence probability of the provision time or the waiting time based on the record information;
The provisional time or the waiting time is sorted to calculate a cumulative occurrence probability, and a normal probability distribution that is a probability distribution in which the cumulative occurrence probability is equal to or greater than a predetermined threshold and a probability distribution in which the cumulative occurrence probability is less than the threshold A probability distribution creation unit for creating a probability distribution during abnormal time,
A prediction unit that calculates a predicted value of the provision time or the waiting time using the normal probability distribution;
With
The predicting unit recalculates the predicted value of the providing time or the waiting time using the abnormal probability distribution when the providing time or the waiting time for a service exceeds a predetermined set time. ,
A time prediction apparatus characterized by the above. The time prediction device according to claim 1,
The prediction unit calculates the predicted value by generating a random number in the normal probability distribution or the abnormal probability distribution;
A time prediction apparatus characterized by the above. The time prediction device according to claim 1 or 2,
The probability distribution creation unit sorts the provision time or the waiting time in a short order;
A time prediction apparatus characterized by the above. The time prediction device according to claim 3,
The set time is the provision time or the waiting time when the cumulative occurrence probability is equal to or higher than the threshold;
A time prediction apparatus characterized by the above. The time prediction device according to claim 1 or 2,
The probability distribution creation unit sorts the provision time or the waiting time in descending order of occurrence probability,
A time prediction apparatus characterized by the above. A method for predicting a service providing time or a waiting time until the service is provided,
Computer
Store past information for calculating the past provision time or the waiting time,
Calculate the probability of occurrence of the provision time or the waiting time based on the performance information,
The provisional time or the waiting time is sorted to calculate a cumulative occurrence probability, and a normal probability distribution that is a probability distribution in which the cumulative occurrence probability is equal to or greater than a predetermined threshold and a probability distribution in which the cumulative occurrence probability is less than the threshold Create an abnormal probability distribution that is
Using the normal probability distribution to calculate the predicted value of the provided time or the waiting time;
Recalculating the predicted value of the providing time or the waiting time using the abnormal probability distribution when the providing time or the waiting time for a service exceeds a predetermined set time;
A time prediction method characterized by A program for predicting a service providing time or a waiting time until the service is provided,
On the computer,
Storing past information for calculating the past provision time or the waiting time;
Calculating an occurrence probability of the provision time or the waiting time based on the record information;
The provisional time or the waiting time is sorted to calculate a cumulative occurrence probability, and a normal probability distribution that is a probability distribution in which the cumulative occurrence probability is equal to or greater than a predetermined threshold and a probability distribution in which the cumulative occurrence probability is less than the threshold Creating an abnormal probability distribution that is
Calculating a predicted value of the provided time or the waiting time using the normal probability distribution;
Recalculating the predicted value of the providing time or the waiting time using the abnormal probability distribution when the providing time or the waiting time for a service exceeds a predetermined set time;
A program for running

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