JP5434742B2 - Work amount estimation program, work amount estimation method, and work amount estimation device - Google Patents

Description

  The present invention relates to a work amount estimation program, a work amount estimation method, and a work amount estimation device for estimating a work amount related to an inspection work.

  The medical institution charges a fee other than the patient's self-payment, that is, a charge for the medical insurance to the health insurance association that is the insurer. And when requesting, a medical institution submits a receipt (medical fee summary). The receipt has patient information, medical reward points, and a summary of the receipt. A medical institution prepares a monthly receipt for each patient who is an insured person, and submits the previous month's receipt by a predetermined date. For example, by the 10th of every month, all receipts generated in the previous month at the medical institution must be submitted.

  Therefore, the person in charge of receipt check at the medical institution performs the check of the receipt in preparation for the receipt of the receipt on the 10th of every month. However, in many cases, the person in charge of receiving a check has a job other than the check of the receipt, and the check of the receipt tends to accumulate before the deadline for submitting the receipt.

  If the number of created receipts and the amount described in the receipt summary column described for each receipt are large, the time required for the inspection of the receipt becomes longer. On the other hand, the person in charge of receipt inspection must complete the inspection work by the deadline for submission of the receipt. Accordingly, the person in charge of receipt inspection is required to estimate the time required for the inspection of the receipt and to proceed with the inspection of the receipt in a planned manner.

  Patent Document 1 discloses a technique for estimating a required time required for development and other work (see, for example, Patent Document 1).

JP-A-9-198441

  If the person in charge of receipt inspection can grasp the time required for the inspection of the receipt, the inspection of the reception can be proceeded systematically. For example, the person in charge of receipt inspection starts the receipt inspection for the current month during the current month so that the inspection work is not concentrated before the deadline for submission of the next month.

  Here, the description of the medical action (treatment, surgery, medication, injection, rehabilitation, etc.) for the patient is described in the receipt summary column of the receipt. Many details are described in the receipt summary column of patients who have undergone many medical practices. In addition, in the receipt summary column for a patient who has performed a small amount of medical practice, a few details are described. The main subject of the inspection work of the receipt is the receipt summary column.

  Therefore, a patient's receipt for which many medical practices have been performed has a large amount of details (work amount) in the receipt summary column for performing the inspection work, and the inspection work often takes a long time. In addition, a patient's receipt for which a small amount of medical practice has been performed often requires a short time for the inspection work because the amount of the details (work amount) in the receipt summary column for performing the inspection work is small. As described above, the time required for the inspection of the receipt depends on the amount (work amount) of the specification in the receipt summary column.

  Therefore, in order to estimate the time required for the inspection of the receipt with high accuracy, it is necessary to accurately predict the amount of details in the receipt summary column. However, there is a problem that it is very difficult to predict accurately because the amount of the description in the receipt summary column varies depending on the medical practice to be performed on the patient.

  On the other hand, the technique described in Patent Document 1 cannot estimate the amount of work in consideration of the amount of specification in the receipt summary column.

  An object of one embodiment of the present invention is to provide a work amount estimation program, a work amount estimation method, and a work amount estimation apparatus that can accurately estimate a work amount related to an inspection work.

  In order to solve the above-mentioned problem, an embodiment of the present invention refers to a first storage unit that stores, in a computer, the average length of stay of discharged patients and the attributes of the discharged patients in association with each other. Based on the number of hospitalization days related to a hospitalized patient and the attributes of the hospitalized patient, the number of remaining hospitalization days calculating step for calculating the expected number of remaining hospitalization days for the hospitalized patient; for the hospitalized patient, A first specification amount acquisition step of acquiring a first specification amount that is an amount of a medical practice specification that has occurred in the number of hospitalization days; and the discharged patient having the same attribute as the hospitalized patient from the first storage means A second specification amount acquisition step of acquiring a second specification amount that is a specification amount of medical treatment to a patient of the patient, and the hospitalization that occurs in the remaining hospitalization days of the hospitalized patient based on the second specification amount Is the amount of statement of medical practice to patients A third specific amount calculating step for calculating a third specific amount, and an output step for outputting a detailed amount of medical practice for the hospitalized patient based on the first specific amount and the third specific amount are executed. This is a work amount estimation program.

  In addition, what applied the component, the expression, or the arbitrary combinations of the component of one Embodiment of this invention to a method, an apparatus, a system, a computer program, a recording medium, a data structure, etc. is also effective as an aspect of this invention. .

  According to one embodiment of the present invention, it is possible to provide a work amount estimation program, a work amount estimation method, and a work amount estimation device that can accurately estimate the amount of work related to inspection work.

It is a hardware block diagram of an example of PC. It is a block diagram of an example of the work amount estimation apparatus of a present Example. It is a block diagram of an example of movement history DB. It is a block diagram of an example of accounting card DB. It is a block diagram of an example of patient DB. It is a block diagram of an example of disease name DB. It is a block diagram of an example of a wound name master. It is a block diagram of an example of DPC information DB. It is a block diagram of an example of a DPC master. It is a block diagram of an example of data amount storage DB. It is a block diagram of an example of a classification master. It is a block diagram of an example of intermediate | middle DB. It is a flowchart of an example of the data amount storage process which the work amount estimation apparatus of a present Example performs. It is a flowchart of an example of the classification master preparation process which the work amount estimation apparatus of a present Example performs. It is a block diagram of an example of the record created by step S11. FIG. 12 is a graph showing the amount of receipt data when the minimum hospitalization days acquired from the classification master of FIG. 11 are 1st and 6th. It is a flowchart of an example of the prediction process which the work amount estimation apparatus of a present Example performs. It is a block diagram of an example of a record with a discharge date blank extracted from data amount storage DB58 in step S21. It is a block diagram of an example of the record extracted from a classification master in step S23. It is explanatory drawing of an example showing the amount of receipt data (average) before conversion and after conversion. It is a graph of an example showing the amount of receipt data (average) before conversion and after conversion. It is a flowchart of an example of intermediate | middle DB creation processing which the work amount estimation apparatus of a present Example performs. A record showing the number of confirmed inpatients with bronchial asthma who were hospitalized in the past month. It is a flowchart of an example of the screen display process which the work amount estimation apparatus of a present Example performs. It is an image figure of an example of the screen of a work amount estimation result.

  Next, modes for carrying out the present invention will be described based on the following embodiments with reference to the drawings. In this embodiment, a receipt will be described as an example. However, any document or data having a different amount of specification depending on a medical practice for a patient may be used. The work amount estimation apparatus of this embodiment can be applied to a receipt inspection apparatus or the like.

(Hardware configuration)
Here, a personal computer (PC) that executes a work amount estimation program will be described as an example. The work amount estimation program has the same function as the work amount estimation device. FIG. 1 is a hardware configuration diagram of an example of a PC.

  The PC 10 includes an input device 21, a display device 22, and a PC main body 23. The PC main body 23 includes a main storage device 31, an arithmetic processing device 32, an interface device 33, a recording medium reading device 34, and an auxiliary storage device 35 that are connected to each other via a bus 37. The input device 21 and the display device 22 are connected to the bus 37.

  The input device 21, the display device 22, the main storage device 31, the arithmetic processing device 32, the interface device 33, the recording medium reading device 34, and the auxiliary storage device 35 that are mutually connected by the bus 37 are managed by the arithmetic processing device 32. Can exchange data with each other. The arithmetic processing unit 32 is a central processing unit that controls operation of the entire PC 10.

  The interface device 33 receives data from another PC, server, etc., and passes the contents of the data to the arithmetic processing device 32. Further, the interface device 33 transmits data to other PCs, servers, etc. in accordance with instructions from the arithmetic processing device 32.

  The auxiliary storage device 35 stores a work amount estimation program that causes a computer to execute at least work amount estimation processing in the work amount estimation device as a part of a program that causes the PC 10 to perform the same function as the work amount estimation device. .

  The arithmetic processing device 32 reads out the work amount estimation program from the auxiliary storage device 35 and executes it, thereby functioning as a work amount estimation device. The work amount estimation program may be stored in the main storage device 31 accessible to the arithmetic processing device 32. The input device 21 receives data input under the control of the arithmetic processing device 32. The work amount estimation program can be recorded in a recording medium 36 that can be read by the PC 10.

  Examples of the recording medium readable by the PC 10 include a magnetic recording medium, an optical disk, a magneto-optical recording medium, and a semiconductor memory. Magnetic recording media include HDDs, flexible disks (FD), magnetic tapes (MT) and the like. Examples of the optical disc include a DVD (Digital Versatile Disc), a DVD-RAM, a CD-ROM (Compact Disc-Read Only Memory), and a CD-R (Recordable) / RW (ReWriteable). Magneto-optical recording media include MO (Magneto-Optical disk).

  When distributing the work amount estimation program, for example, it is conceivable to sell a portable recording medium 36 such as a DVD or a CD-ROM in which the work amount estimation program is recorded.

  Then, the PC 10 that executes the work amount estimation program reads the work amount estimation program from the recording medium 36 on which the recording medium reader 34 has recorded the work amount estimation program, for example. The arithmetic processing device 32 stores the read work amount estimation program in the main storage device 31 or the auxiliary storage device 35. Then, the PC 10 reads the work amount estimation program from the main storage device 31 or the auxiliary storage device 35, which is its own storage device, and executes processing according to the work amount estimation program.

(Block configuration)
In the following description, the work amount estimation apparatus 40 in which the work amount estimation program is installed in at least the PC 10 will be described as an example.

  In this embodiment, the amount of work that occurs during a predetermined period is estimated. For example, the work amount estimation device 40 estimates a receipt detail amount generated per month. Further, when the work amount estimation process (described later) is executed on a certain reference day within a predetermined period, the work amount estimation device 40 performs the work amount generated from the beginning of the predetermined period to the reference day, and the desired period from the reference date. And the amount of work that will occur by the end of. Then, the work amount estimation device 40 uses the added value as the work amount generated within a predetermined period. With the mechanism described above, the person in charge of receipt inspection can grasp the amount of work that occurs every month, for example.

  In the present embodiment, it is assumed that the work amount per month is estimated in view of submitting the receipts per month, but the present invention is not limited to this. Therefore, a unit period determined by the country or the like corresponds to a predetermined period.

  FIG. 2 is a block diagram of an example of the work amount estimation apparatus of this embodiment. 2 includes a data amount storage processing unit 41, a classification master creation processing unit 42, a prediction processing unit 43, an intermediate DB creation processing unit 44, a screen display processing unit 45, a movement history database (DB) 51, There are an accounting card DB 52, a patient DB 53, a disease name DB 54, an injury and disease name master 55, a DPC information DB 56, a DPC master 57, a data amount storage DB 58, a classification master 59, and an intermediate DB 60.

  The data amount storage processing unit 41, the classification master creation processing unit 42, the prediction processing unit 43, the intermediate DB creation processing unit 44, and the screen display processing unit 45 are realized by the arithmetic processing device 32 executing the work amount estimation program. .

  The data amount storage processing unit 41 creates a data amount storage DB 58 from the movement history DB 51, accounting card DB 52, disease name DB 54, and injury / illness name master 55. The classification master creation processing unit 42 creates a classification master 59 from the data amount storage DB 58. The prediction processing unit 43 uses the data amount storage DB 58 and the classification master 59 to predict the amount of details in the receipt summary column of the hospitalized patient's receipt. Hereinafter, the amount of the specification in the receipt summary column of the receipt is simply referred to as a receipt data amount.

  The intermediate DB creation processing unit 44 performs daily batch processing at, for example, midnight. The intermediate DB creation processing unit 44 uses the data amount storage DB 58 and the classification master 59 to calculate the receipt data amount of the patient who has been discharged in the corresponding year, and to the patient who is hospitalized in the corresponding year and the corresponding month. The amount of receipt data of a patient who is expected to be hospitalized is predicted and stored in the intermediate DB 60 as a prediction of the amount of receipt data for the corresponding year that requires inspection work.

  The screen display processing unit 45 creates a work amount estimation result screen from the intermediate DB 60 and displays the screen on the display device 22. The screen of the work amount estimation result includes, for example, the amount of receipt data for the corresponding year as an estimate of the work amount related to the inspection work of the receipt. Hereinafter, the work amount related to the inspection work of the receipt is simply referred to as the work amount.

  The movement history DB 51 stores movement history information such as the hospitalization date and discharge date of the patient. The accounting card DB 52 stores accounting card information representing the details of medical treatment for the patient. The patient DB 53 stores patient information. The disease name DB 54 stores disease name information that represents the patient's injury / illness name in various codes. The wound name master 55 stores the wound name associated with the code included in the disease name DB 54. The DPC information DB 56 stores DPC information representing DPC names with various codes. The DPC master 57 stores the DPC name in association with the code included in the DPC information DB 56.

  The data amount storage DB 58 stores a record representing the amount of receipt data for each hospitalization of a patient. The classification master 59 is created based on records having the same classification key extracted from the records stored in the data amount storage DB 58. The classification master 59 stores an average of the amount of hospitalized reception data classified by a certain classification key. The intermediate DB 60 stores a record representing the prediction of the amount of receipt data for the corresponding year that requires inspection work.

  FIG. 3 is a configuration diagram of an example of the movement history DB. The movement history DB 51 of FIG. 3 stores movement history information having a patient ID, a hospitalization date, and a discharge date. The patient ID represents patient identification information. The hospitalization date and discharge date are the patient's hospitalization date and discharge date. In the movement history DB 51, the patient ID and the date of hospitalization are set when hospitalized, and the date of discharge is set when discharged. The movement history DB 51 stores the entry / exit date for each patient.

  FIG. 4 is a configuration diagram of an example of the accounting card DB. The accounting card DB 52 in FIG. 4 stores accounting card information having the amount of details for each year, patient ID, details, and date. The statement represents the medical action performed on the patient. The amount of details for each day is represented by, for example, the number, amount, score, etc. of medical practices performed on the patient. In FIG. 4, “−” represented as the daily amount is the same as that in the left column. The accounting card DB 52 is set with year, month, patient ID, details, and amount of details for each day at the time of medical treatment, treatment, drug administration, and the like. The accounting card DB 52 represents the amount of details for each hospitalization for each year and month.

  FIG. 5 is a configuration diagram of an example of a patient DB. The patient DB 53 of FIG. 5 stores patient information having a patient ID, date of birth, and gender. The date of birth and gender represent the date of birth and gender of the patient. The patient DB 53 represents the date of birth and sex for each patient.

  FIG. 6 is a configuration diagram of an example of a disease name DB. The disease name DB 54 in FIG. 6 stores patient name, hospitalization date, wound name code, and disease name information having ICD 10. The wound name code identifies the wound name of the patient and is associated with the wound name code of the wound name master 55. The ICD 10 identifies the name of the patient's injury and is associated with the ICD 10 of an ICD 10 master (not shown). The disease name DB 54 represents a code representing the hospitalization date and injury name for each patient.

  FIG. 7 is a configuration diagram of an example of a wound name master. The wound name master 55 in FIG. 7 stores the wound name and wound name. The wound name code is identification information of the wound name. The wound name master 55 represents the wound name for each wound name code.

  FIG. 8 is a configuration diagram of an example of the DPC information DB. The DPC information DB 56 in FIG. 8 stores DPC information having a patient ID, hospitalization date, and DPC code. The DPC code identifies the patient's DPC name. The DPC code of the DPC information DB 56 is associated with the DPC code of the DPC master 57. The DPC information DB 56 represents a DPC code indicating the hospitalization date and the DPC name for each patient.

  FIG. 9 is a configuration diagram of an example of the DPC master. The DPC master 57 in FIG. 9 stores a DPC code and a DPC name. The DPC code is identification information of a DPC name. The DPC master 57 represents a DPC name for each DPC code.

  FIG. 10 is a configuration diagram of an example of the data amount storage DB. The data amount storage DB 58 in FIG. 10 stores records having patient ID, classification key, hospitalization days, hospitalization date, discharge date, and receipt data amount. The classification key is information for classifying the records stored in the data amount storage DB 58. The hospitalization days, hospitalization dates, and discharge dates represent the patient's hospitalization days, hospitalization dates, and discharge dates. FIG. 10 shows an example in which the name of a disease is used as the classification key, but age, sex, ICD10, DCP code, and the like may be used. In addition, the classification key may use a combination of injury / illness name, age, sex, ICD10, DCP code, and the like.

  The amount of receipt data represents the amount of receipt data for each day from hospitalization (day 1) to discharge (day n). The data amount storage DB 58 represents the amount of receipt data for each hospitalization of the patient for each day.

  FIG. 11 is a configuration diagram of an example of the classification master. The classification master 59 in FIG. 11 stores records having a classification key, a minimum hospitalization period, an average hospitalization period, and a daily receipt data amount (average). The minimum hospitalization days indicate that the corresponding records whose hospitalization days are n (n = 1 to 10 in the example of FIG. 11) or more are targeted. The corresponding record is a record having the same classification key extracted from the record stored in the data amount storage DB 58.

  The average length of hospitalization is the average number of days of hospitalization of the corresponding records having hospitalization days of n days or more. The daily receipt data amount (average) is the average of the daily receipt data amount from hospitalization to discharge of the corresponding records having hospitalization days of n days or more. The classification master 59 represents the average length of hospital stay and the amount of receipt data (average) for each corresponding record with the number of hospitalization days being n days or more.

  FIG. 12 is a configuration diagram of an example of the intermediate DB. The intermediate DB 60 in FIG. 12 includes the date of medical care, classification key, the number of confirmed inpatients, the expected number of inpatients, the expected number of inpatients after the date of medical treatment, the sum of the daily receipt data amount before the medical date, A record having the sum of the daily receipt data amount after the year, month, day, the sum of the receipt data amount of the medical year and month, and the daily receipt data amount is stored.

  The date of medical treatment represents a reference date for estimating the amount of work. The number of confirmed inpatients is the number of patients admitted even before the medical treatment (corresponding) date among patients admitted before the medical treatment date. The expected number of hospitalized patients is the number of patients expected to be hospitalized during the medical year. The expected number of hospitalized patients is obtained from, for example, the number of hospitalized patients in the same month of the previous year. Note that the expected number of hospitalized patients may be obtained by taking into account the expansion of the scale of medical institutions and the rate of increase in the number of patients. The expected number of inpatients after the date of treatment is the number of expected inpatients minus the number of confirmed inpatients.

  The sum of the daily receipt data amounts before the medical treatment date is the sum of the daily receipt data amounts determined before the medical treatment date. The sum of the daily receipt data amounts after the medical treatment date is the sum of the expected daily receipt data amounts after the medical treatment date. The sum of the amount of receipt data for the medical date is a number obtained by adding the sum of the daily receipt data amount before the medical date and the sum of the daily receipt data amounts after the medical date. The daily receipt data amount represents a confirmed or expected receipt data amount for each day of the medical treatment date.

  The intermediate DB 60 calculates the amount of receipt data before the date of medical treatment and the expected amount after the date of medical treatment, which are calculated from the amount of receipt data that has been confirmed or predicted for each day of the medical date requiring inspection. Stores the amount of receipt data and the amount of receipt data for the entire medical treatment date.

(Processing flow)
FIG. 13 is a flowchart of an example of a data amount storage process performed by the work amount estimation apparatus of this embodiment. The data amount storage processing unit 41 of the work amount estimation device 40 proceeds to step S1 and acquires the patient ID and hospitalization date from the movement history DB 51 using the medical date as a key. Further, the data amount storage processing unit 41 acquires the patient ID and hospitalization date of the movement history information in which the discharge date is not stored from the movement history DB 51. The data amount storage processing unit 41 sets the acquired patient ID and hospitalization date in the data amount storage DB 58.

  In step S2, the data amount storage processing unit 41 acquires and counts the detailed amount from the accounting card DB 52 using the patient ID and the medical date as a key, and receives the receipt of the corresponding patient ID of the corresponding patient ID in the data amount storage DB 58. Set as the amount of data.

  In step S3, the data amount storage processing unit 41 sets the classification key acquired using the patient ID and the hospitalization date as keys as the classification key of the corresponding patient ID in the data amount storage DB 58. For example, in the case of the disease name DB 54 in FIG. 6 and the disease name master 55 in FIG. 7, the data amount storage processing unit 41 obtains the injury name code from the disease name DB 54 using the patient ID and hospitalization date as keys, and the injury name code as a key. The disease name is acquired from the wound disease name master 55, and the disease name is set as the classification key of the corresponding patient ID in the data amount storage DB 58.

  Proceeding to step S4, the data amount storage processing unit 41 acquires from the movement history DB 51 if there is a discharge date of the patient ID acquired in step S1. In step S5, the data amount storage processing unit 41 acquires the number of hospitalization days counted from the hospitalization date and the discharge date. If there is no discharge date, the length of hospital stay is blank. The data amount storage processing unit 41 sets the acquired discharge date and hospitalization date in the data storage DB 58 as the discharge date and hospitalization date of the corresponding patient ID.

  By the processing of the flowchart shown in FIG. 13, the data amount storage processing unit 41 can create a data amount storage DB 58 as shown in FIG.

  FIG. 14 is a flowchart illustrating an example of a classification master creation process performed by the work amount estimation apparatus according to this embodiment. Proceeding to step S11, the classification master creation processing unit 42 obtains the patient ID, the number of hospitalization days, and the amount of receipt data from the record in which the number of hospitalization days exists in the data amount storage DB 58 using the classification key as a key. The classification master creation processing unit 42 creates a record including the classification key as a key, the acquired patient ID, the number of days of hospitalization, and the amount of receipt data.

  Proceeding to step S12, the classification master creation processing unit 42 extracts records in which the received data amount exists up to the minimum hospitalization days from the created records. The initial value of the minimum hospital stay is 1 day.

  Proceeding to step S13, the classification master creation processing unit 42 calculates the average of the amount of receipt data after the minimum hospitalization day for each extracted record. The classification master creation processing unit 42 sets the classification key, the minimum number of days of hospitalization, and the daily receipt data amount (average) in the record of the classification master 59.

  Proceeding to step S14, the classification master creation processing unit 42 calculates the average number of hospitalization days for the extracted records, and sets the average number of hospitalization days in the record of the classification master 59 set in step S13. In step S15, the classification master creation processing unit 42 adds 1 day to the minimum hospitalization days. Proceeding to step S16, the classification master creation processing unit 42 obtains the maximum hospitalization days from the hospitalization days included in the record created at step S11.

  Then, the classification master creation processing unit 42 determines whether or not the minimum hospital stay is larger than the maximum hospital stay. If the minimum hospitalization days are not larger than the maximum hospitalization days, the classification master creation processing unit 42 returns to step S12 and continues the processing. If the minimum hospitalization days are larger than the maximum hospitalization days, the classification master creation processing unit 42 ends the process of the flowchart shown in FIG.

  FIG. 15 is a configuration diagram of an example of the record created in step S11. The record of FIG. 15 is created by acquiring the patient ID, the number of days of hospitalization, and the amount of receipt data from the record having the number of days of hospitalization in the data amount storage DB 58 of FIG. 10 using the classification key “bronchial asthma” as a key.

  For example, the first record in FIG. 15 includes the patient ID “03” acquired from the data amount storage DB 58 using the classification key “bronchial asthma” as a key, the number of hospitalization days “5”, and the first to fifth days of hospitalization. The amount of receipt data for each day of “5”, “6”, “3”, “3”, and “2”. In the case of the record in FIG. 15, the maximum hospitalization period obtained in step S16 is 10 days.

  In the case of the record in FIG. 15, the processes in steps S12 to S16 are repeated from the minimum hospitalization period “1 day” to “10 days”. In the process of step S12 when the minimum hospitalization period is "1 day", all records are extracted as records having a receipt data amount up to the first day.

  Therefore, in step S13, the classification master creation processing unit 42 averages the receipt data amounts from the first day to the tenth day of all the records, and receives the receipt data amount (average) of the classification master 59 from the first day to the tenth day. ). Further, in the process of step S12 when the minimum hospital stay is “2 days”, all records are extracted as records where the amount of receipt data exists until the second day, as in the case of the minimum hospital stay “1 day”.

  Therefore, in step S13, the classification master creation processing unit 42 averages the receipt data amounts of all records from the second day to the tenth day, respectively, and the classification master 59 receives the receipt data amounts (average) from the first day to the ninth day. ). Further, in the process of step S12 when the minimum hospitalization period is “3 days”, records excluding the record of the patient ID “12” are extracted as records in which the amount of receipt data exists until the third day.

  Accordingly, in step S13, the classification master creation processing unit 42 averages the amount of receipt data from the third day to the tenth day of the records excluding the record of the patient ID “12”, and the first day from the first day of the classification master 59. Set as the amount of eye receipt data (average).

  Further, in the process of step S12 when the minimum hospital stay is "4 days", the record with the patient ID "12" is recorded as the record data amount exists until the fourth day, as in the case of the minimum hospital stay "3 days". Records other than are extracted. Therefore, in step S13, the classification master creation processing unit 42 averages the amounts of receipt data from the 4th day to the 10th day of the records excluding the record of the patient ID “12”, and the 7th day from the 1st day of the classification master 59. Set as the amount of eye receipt data (average).

  Further, in the process of step S12 when the minimum hospitalization period is “5 days”, the record with the patient ID “12” is recorded as the record data amount exists until the fifth day, as in the case of the minimum hospitalization period “3 days”. Records other than are extracted. Therefore, in step S13, the classification master creation processing unit 42 averages the amount of receipt data from the fifth day to the tenth day of the records excluding the record of the patient ID “12”, and the first day from the first day of the classification master 59. Set as the amount of eye receipt data (average).

  In the process of step S12 when the minimum hospitalization period is “6 days”, records excluding records with patient IDs “03” and “12” are extracted as records in which the amount of receipt data exists until the sixth day. Therefore, in step S13, the classification master creation processing unit 42 averages the receipt data amounts on the sixth to tenth days of the records excluding the records with the patient IDs “03” and “12”, and the first day of the classification master 59 Set as the amount of receipt data (average) on the fifth day.

  In the process of step S12 when the minimum hospitalization period is “7 days”, records with patient IDs “04” and “05” are extracted as records in which the amount of receipt data exists until the seventh day.

  Therefore, in step S13, the classification master creation processing unit 42 averages the receipt data amounts from the seventh day to the tenth day of the records with the patient IDs “04” and “05”, respectively, and the first day from the first day of the classification master 59. Set as the amount of eye receipt data (average).

  Further, in the process of step S12 when the minimum hospital stay is “8 days”, the records with the receipt data amount up to the eighth day are the records with the patient IDs “04” and “05” as in the case of the minimum hospital stay “7 days”. "Is extracted.

  Therefore, in step S13, the classification master creation processing unit 42 averages the receipt data amounts from the 8th day to the 10th day of the records of the patient IDs “04” and “05”, respectively, and the classification master 59 starts the 3rd day from the 1st day. Set as the amount of eye receipt data (average).

  Further, in the process of step S12 when the minimum hospitalization period is “9 days”, the record of the patient ID “04” is extracted as a record in which the amount of receipt data exists until the ninth day.

  Accordingly, in step S13, the classification master creation processing unit 42 averages the receipt data amounts of the records of the patient ID “04” from the ninth day to the tenth day, respectively, and the classification master 59 receives the receipts from the first day to the second day. Set as data volume (average).

  Further, in the process of step S12 when the minimum hospitalization period is “10 days”, the record of the patient ID “04” is recorded as the record where the receipt data amount exists until the 10th day, as in the case of the minimum hospitalization period “9 days”. Is extracted.

  Therefore, the classification master creation processing unit 42 sets the receipt data amount on the 10th day of the record of the patient ID “04” as the receipt data amount (average) on the first day of the classification master 59 in step S13.

  In the case of the record in FIG. 15, the row of maximum hospitalization days and the column of days of receipt data amount in the classification master 59 are “10” which is the maximum hospitalization days obtained in step S16. The matrix for setting the daily receipt data amount (average) of the classification master 59 is a 10 × 10 matrix in the case of the record of FIG.

  The process of step S14 is the same as the process of step S13, and the number of days of hospitalization of the records extracted in step S12 (for example, all records for which the amount of receipt data exists until the first day when the minimum number of days of hospitalization is "1 day"). The average of “6.2 days” is calculated. The classification master creation processing unit 42 sets the average “6.2 days” of the obtained hospitalization days as the average length of hospitalization of the record of the minimum hospitalization days “1 day” in the classification master 59.

  Here, the processing of steps S12 to S14 when the minimum hospitalization period is “6 days” will be specifically described by taking the record of FIG. 15 as an example. When the minimum hospitalization period is “6 days”, in step S12, the classification master creation processing unit 42 has a patient ID “04” as a record (for patients who have been hospitalized for 6 days or more) until the sixth day. Records “05” and “09” are extracted.

  In step S13, the classification master creation processing unit 42 receives “18”, “6”, “6”, which is the amount of receipt data on the sixth day after the minimum hospitalization days of the records whose extracted patient IDs are “04”, “05”, and “09”. The average “9.67” of “5” is calculated and set in the column of “first day” of the receipt data amount (average) in the row of the minimum hospitalization period “6” of the classification master 59.

  In addition, the classification master creation processing unit 42 calculates the average “4.” of “8”, “5”, and “0” that is the amount of receipt data on the seventh day of the records with the extracted patient IDs “04”, “05”, and “09”. 33 ”is calculated and set in the column of“ second day ”of the receipt data amount (average) in the row of the minimum hospitalization period“ 6 ”of the classification master 59.

  Further, the classification master creation processing unit 42 calculates the average “1 ..” of “2”, “2”, and “0” that are the receipt data amounts on the eighth day of the records with the extracted patient IDs “04”, “05”, and “09”. 33 ”is calculated and set in the column of“ 3rd day ”of the receipt data amount (average) in the row of the minimum hospitalization period“ 6 ”of the classification master 59.

  Further, the classification master creation processing unit 42 calculates the average “1 ..” of “3”, “0”, “0”, which is the amount of receipt data on the ninth day of the records with the extracted patient IDs “04”, “05”, “09”. “00” is calculated and set in the column of “Day 4” of the receipt data amount (average) in the row of the minimum hospitalization period “6” of the classification master 59.

  In addition, the classification master creation processing unit 42 calculates the average “0.0” of “2”, “0”, and “0” that is the amount of receipt data on the 10th day of the records with the extracted patient IDs “04”, “05”, and “09”. 67 ”is calculated and set in the column of“ 5th day ”of the receipt data amount (average) in the row of the minimum hospitalization period“ 6 ”of the classification master 59.

  Furthermore, the classification master creation processing unit 42 calculates an average “8” of “10”, “8”, and “6” that are the hospitalization days of the records with the extracted patient IDs “04”, “05”, and “09”. It is set to the average length of stay in the row of the minimum hospital stay “6” of the master 59.

  The patients whose patient IDs are “04”, “05”, and “09” have been hospitalized for a minimum of 6 days, in other words, 6 days or more. Therefore, the average length of stay for patients with a minimum hospital stay of 6 days is 6 days or more.

  When the work amount is estimated, the receipt data amount of the classification master 59 in FIG. 11 may be referred to using the classification key and the minimum hospitalization days as keys. FIG. 16 is a graph showing the amount of receipt data when the minimum hospitalization days acquired from the classification master of FIG. 11 are 1st and 6th. The amount of receipt data with a minimum hospital stay of 1 day is the average for all patients, and includes the amount of receipt data for patients discharged early. On the other hand, the amount of receipt data for a minimum hospitalization period of 6 days is the average of the amount of receipt data for patients who have been hospitalized for 6 days or more after the sixth day. At the time when the number of days of hospitalization has passed, the amount of receipt data (Fb, 6 (t)) with a minimum hospitalization date of 6 days is not the amount of receipt data (Fb, 1 (t)) with a minimum hospitalization date of 1 day. Use.

  FIG. 17 is a flowchart illustrating an example of a prediction process performed by the work amount estimation apparatus according to this embodiment. Proceeding to step S 21, the prediction processing unit 43 extracts a record with a discharge date blank from the data amount storage DB 58. That is, in step S21, a record of hospitalized patients is extracted.

  Proceeding to step S22, the prediction processing unit 43 acquires the number of days in which the receipt data amount is stored from the record extracted at step S21. Proceeding to step S23, the prediction processing unit 43 searches for the minimum number of hospitalization days and the classification key in the classification master 59 using the number of days acquired in step S22 and the classification key as keys, and extracts matching records.

  Proceeding to step S24, the prediction processing unit 43 calculates the number of remaining hospitalization days obtained by subtracting the minimum hospitalization days from the average hospitalization days of the records extracted in step S23. Proceeding to step S25, the prediction processing unit 43 obtains a unit number of days obtained by dividing the number of days of the record extracted in step S23 (the number of days in which the receipt data amount (average) is stored) by the number of days remaining in hospital. The prediction processing unit 43 divides the day of the record extracted in step S23 by the obtained unit days.

  Proceeding to step S26, if there is a decimal point on the day before conversion, which is the result of the division performed at step S25, the prediction processing unit 43 carries out the decimal point and calculates the day after conversion. In step S27, the prediction processing unit 43 determines whether or not the day before conversion is an integer, and the day after conversion is different from the previous day.

  If the day before conversion is not an integer, and the day after conversion is different from the previous day, the prediction processing unit 43 proceeds to step S28, and (the day after the advance-1-before the advancement). The day before the day) / (the day before the advance-the day before the day before the advance) is set as a proration ratio and multiplied by the amount of receipt data on the day before the advance. The prediction processing unit 43 adds the receipt data amount on the day before the advance multiplied by the proration ratio to the receipt data amount on the day after the advance. Further, the prediction processing unit 43 adds the remainder of the receipt data amount on the day before the multiplication multiplied by the proration ratio to the receipt data amount on the day after the advancement.

  On the other hand, if the day before conversion is an integer, or the day after conversion is not different from the previous day, the prediction processing unit 43 proceeds to step S29 and receives the amount of receipt data on the day before conversion. Is added to the amount of receipt data on the day after conversion. Proceeding to step S30 following steps S28 and S29, if there is a next day, the prediction processing unit 43 returns to step S27 and performs processing for the next day. If there is no next day, the prediction processing unit 43 ends the processing of the flowchart shown in FIG. Note that the flowchart shown in FIG. 17 is sequentially performed for records in which the discharge date in the data amount storage DB 58 is blank.

  FIG. 18 is a block diagram showing an example of a record with a blank discharge date extracted from the data amount storage DB 58 in step S21. The records in FIG. 18 are for patients who have been hospitalized for bronchial asthma since January 26, 2010. In the record of FIG. 18, the amount of receipt data from the first day (January 26, 2010) to the sixth day (January 31, 2010) is set.

  The amount of receipt data for January 2010 is obtained by adding the amount of receipt data for the first day to the sixth day. Here, the process of predicting the amount of receipt data for February 2010 on February 1 (first month) will be described. When the record of FIG. 18 is extracted in step S21, six days are acquired as the number of days in which the amount of receipt data is stored in step S22.

  In step S23, the minimum number of hospitalization days and the classification key in the classification master 59 of FIG. 11 are searched using the classification key “bronchial asthma” and the number of days “6 days” in which the amount of receipt data is stored as a key. 19 records are extracted.

  FIG. 19 is a configuration diagram of an example of a record extracted from the classification master in step S23. The record in FIG. 19 corresponds to a patient who has been hospitalized for more than 6 days with bronchial asthma. In step S24, the number of days remaining in the hospital is defined as the number of days remaining. The number of days remaining in hospital obtained by subtracting the minimum number of days in hospital “6 days” from the average number of days in hospital “8 days” in the record of FIG. calculate.

  In the case of the record of FIG. 19, the amount of receipt data expected until the patient is discharged is “17” obtained by adding the amount of receipt data (average) from the first day to the fifth day. When the patient's remaining hospitalization days are 2, the daily receipt data amount is converted to the receipt data amount for the 2.5th day out of the 5th day.

  In the case of the record of FIG. 19, in step S25, the unit number of days “2.5 days” is calculated by dividing the number of days “5 days” in which the amount of receipt data (average) is stored by the number of days remaining in hospital “2 days”. That is, 2.5 days before conversion corresponds to one day after conversion. Then, the date before conversion (from the first day to the fifth day) of the record in FIG. 19 is divided by the unit number of days “2.5 days”.

  For example, the day before conversion on the first day is 1 / 2.5 = 0.4 day. The day before conversion on the second day is 2 / 2.5 = 0.8 day. The day before conversion on the third day is 3 / 2.5 = 1.2 day. The day before conversion on the fourth day is 4 / 2.5 = 1.6 day. The day before conversion on the fifth day is 5 / 2.5 = 2.0 days.

  In the case of the record in FIG. 19, in step S26, since the decimal point exists on the day before conversion calculated in step S25, the decimal point is raised and the day after conversion is calculated. For example, the day after the conversion on the first day becomes the 1.0th day by raising the 0.4th day. The day after the conversion on the second day becomes the 1.0th day by raising the 0.8th day. The day after conversion on the third day becomes the 2.0th day due to the advance of the 1.2th day. The day after conversion on the fourth day becomes the 2.0th day due to the advance of the 1.6th day. The day after conversion on the fifth day becomes the 2.0th day due to the 2.0th day advance.

  In step S27, the pre-conversion day from the first day to the fifth day and the post-conversion day to the pre-conversion day are not integers, and the post-conversion day is the previous day. As different days, the day “1.2 day” before the conversion on the third day and the day “2.0 day” after the conversion are extracted. The receipt data amount (average) other than the third day is added to the receipt data amount on the converted day by the process of step S29.

  For example, the receipt data amount (average) on the first day is added to the converted day “1.0 day”. Further, the amount of receipt data (average) on the second day is added to the converted day “1.0 day”. Further, the amount of receipt data (average) on the fourth day is added to the “2.0th day” after the conversion. Further, the amount of receipt data (average) on the fifth day is added to the “2.0th day” after the conversion.

  The amount of receipt data (average) on the third day is apportioned into the first day and the second day after conversion by the processing in step S27. In the case of the record of FIG. 19, since the receipt data amount (average) from the 0.8th day to the 1.2th day before conversion is “1.33”, it is from the 0.8th day to the 1.0th day. The amount of receipt data (average) is obtained by the following equation (1).

(1.0−0.8) / (1.2−0.8) × 1.33 = 0.2 / 0.4 × 1.33 = 0.67 ... (1)
“0.67”, which is the amount of receipt data (average) obtained by the equation (1), is added as the amount of receipt data (average) of “1.0 day” that is the day after conversion. Further, the amount of receipt data (average) from the 1.0th day to the 1.2th day is obtained by the following equation (2).

1.33−0.67 = 0.66… (2)
“0.66”, which is the amount of receipt data (average) obtained by the equation (2), is added as the amount of receipt data (average) of “2.0 days” that is the day after conversion. FIG. 20 is an explanatory diagram of an example showing the amount of receipt data (average) before and after conversion. FIG. 21 is a graph showing an example of the amount of receipt data (average) before and after conversion.

  In the prediction process shown in FIG. 17, the number of days until a hospitalized patient is discharged is predicted from the average length of stay of discharged patients classified by the same classification key, and discharged by the same classification key. Since the amount of receipt data of hospitalized patients is predicted from the amount of received data (average) of completed patients, the amount of receipt data can be accurately predicted every day.

  FIG. 22 is a flowchart illustrating an example of an intermediate DB creation process performed by the work amount estimation apparatus according to this embodiment. Proceeding to step S41, the intermediate DB creation processing unit 44 extracts from the data amount storage DB 58 a record with the date of hospitalization as the key and a record with a blank date of discharge as the date of medical care.

  Proceeding to step S42, the intermediate DB creation processing unit 44 sets one classification key included in the record extracted at step S41. Proceeding to step S43, the intermediate DB creation processing unit 44 performs the process of step S44 on the record in which the discharge date is set among the records extracted at step S41. In addition, the intermediate DB creation processing unit 44 causes the process of step S45 to be performed on the record that is not set in the discharge date among the records extracted in step S41.

  In step S44, the intermediate DB creation processing unit 44 obtains the receipt data amount (determined value) for each day of the relevant year of the record in which the discharge date extracted in step S41 is set, and the relevant year and month. Calculate the number of hospitalized patients who have been discharged (confirmed).

  In step S45, the intermediate DB creation processing unit 44 causes the prediction processing unit 43 to perform the prediction processing illustrated in FIG. 17 to predict the amount of receipt data after the medical date. Progressing to step S46 following step S44 or S45, the intermediate DB creation processing unit 44 calculates the number of patients expected to be hospitalized (expected number of inpatients) in the corresponding year by a statistical / mathematical method such as Fourier transform. Calculate the expected number of hospitalized patients after the date of medical treatment.

  Proceeding to step S47, the intermediate DB creation processing unit 44 acquires the median between the date of medical care and the end of the month as the expected hospitalization date of the expected inpatient. The intermediate DB creation processing unit 44 acquires from the classification master 59 records that have the classification key and the minimum hospitalization period “1 day”. The intermediate DB creation processing unit 44 obtains the sum of calculation results obtained by multiplying the amount of receipt data (average) from the first day to the nth day of the acquired records by the expected number of hospitalized patients.

  Proceeding to step S48, the intermediate DB creation processing unit 44 calculates the sum of daily receipt data amounts (determined values) from the beginning of the month. The intermediate DB creation processing unit 44 calculates the sum of the amount of receipt data (predicted value) for each day from the date of medical treatment to the end of the month.

  In step S49, the intermediate DB creation processing unit 44 receives the date of medical care, the classification key, the number of confirmed inpatients, the number of expected inpatients, the number of expected inpatients after the date of medical treatment, and daily receipts before the date of medical treatment. The sum of the data amount, the sum of the daily receipt data amount after the medical treatment date, the sum of the reception data amount of the medical treatment date, and the daily receipt data amount are output to the intermediate DB 60.

  In step S50, the intermediate DB creation processing unit 44 refers to the intermediate DB 60, determines the number of confirmed inpatients, the expected number of inpatients, the expected number of inpatients after the medical date, and the medical date before the medical date of all the classification keys. Calculate the sum of the daily receipt data amount, the sum of the daily receipt data amount after the medical treatment date, the sum of the reception data amount of the medical treatment month and the daily receipt data amount, and the classification key “total” Number of confirmed inpatients, expected number of inpatients, expected number of inpatients after the date of medical treatment, sum of the amount of daily receipt data before the date of medical treatment, sum of the amount of daily receipt data after the date of medical treatment, medical treatment The sum of the year and month receipt data amount and the daily receipt data amount are output to the intermediate DB 60.

  In step S51, if there is another classification key, the intermediate DB creation processing unit 44 returns to step S42 and continues the processing. If there is no other classification key, the intermediate DB creation processing unit 44 ends the processing of the flowchart of FIG.

  In step S46, for example, the expected number of hospitalized patients is calculated as follows. The intermediate DB creation processing unit 44 calculates the number of confirmed inpatients hospitalized in the past month from the data amount storage DB 58 for each classification key. FIG. 23 is a record showing the number of confirmed inpatients with bronchial asthma hospitalized in the past month.

  In step S46, the number of inpatients in the corresponding year is calculated from the number of confirmed inpatients with bronchial asthma who have been hospitalized in the past month by a statistical and mathematical method. In step S46, the number of hospitalized patients for the corresponding year may be calculated from the average of the same month in the past, or may be obtained by an exponential smoothing method. Here, by substituting the average number of hospitalized patients for the corresponding month in the same month, the number of hospitalized patients for the corresponding month can be obtained by fast Fourier transforming the entire past.

  In the example of FIG. 23, the expected number of hospitalized patients in February 2010 is found to be 24 by fast Fourier transform. The remaining expected number of hospitalized patients for the relevant year is obtained by subtracting the number of patients admitted in the relevant month from the expected number of inpatients. For example, when three people are hospitalized by February 5, 2010, the expected number of inpatients hospitalized from February 6, 2010 to February 28, 2010 is the expected number of inpatients in February 2010 “ From “24”, “21” is obtained by subtracting the number of confirmed inpatients “3” who are hospitalized by February 5, 2010.

  Patients who are expected to be hospitalized for the remainder of the year (from the date of medical care to the end of the month) expect the median date of the rest of the year to be the expected date of hospitalization of the expected inpatient . In the process of step S47, when the expected inpatient is admitted from the expected hospitalization date, the record of the classification master 59 that is the classification key and the minimum hospitalization date “1 day” is referred to, and the expected number of inpatients is determined as the amount of receipt data ( By multiplying by (average), the amount of receipt data can be predicted.

  For example, in the case of the classification master 59 of FIG. 11, the addition value of the receipt data amount (average) of the classification master 59 having the classification key “bronchial asthma” and the minimum hospitalization period “1 day” is added from February 6, 2010 to 2010. By multiplying the expected number of inpatients hospitalized by February 28, “21”, the sum of daily receipt data amounts after the medical date can be predicted.

  FIG. 24 is a flowchart illustrating an example of a screen display process performed by the work amount estimation apparatus according to this embodiment. In step S61, the screen display processing unit 45 sets the number of confirmed inpatients, the expected number of inpatients, the expected number of inpatients after the medical date, and the days before the medical date from the intermediate DB 60 using the medical date as a key. The sum of the receipt data amount, the sum of the daily receipt data amount after the medical treatment date, the sum of the reception data amount of the medical treatment date, and the daily receipt data amount are acquired.

  In step S62, the screen display processing unit 45 creates a screen of the work amount estimation result and sets the number of confirmed inpatients, the expected number of inpatients, and the expected number of inpatients after the medical date. In step S63, the screen display processing unit 45 sets the daily receipt data amount on the work amount estimation result screen.

  In step S64, the screen display processing unit 45 displays the sum of the daily receipt data amount before the medical date, the daily receipt data amount after the medical date, the medical date on the work amount estimation result screen. After the sum of the receipt data amounts is set, for example, it is displayed on the display device 22.

  FIG. 25 is an image diagram of an example of a work amount estimation result screen. The work amount estimation result screen 100 in FIG. 25 displays the receipt data amount as the work amount. A portion 101 on the screen 100 displays a daily receipt data amount. A portion 102 on the screen 100 represents the sum of daily receipt data amounts before the medical date. A portion 103 on the screen 100 represents the sum of daily receipt data amounts after the medical date. A portion 104 on the screen 100 represents the sum of the amount of receipt data for the medical year and month.

  A portion 105 on the screen 100 represents the number of confirmed inpatients. A portion 106 on the screen 100 represents the expected number of inpatients after the medical date. A portion 107 on the screen 100 represents the expected number of hospitalized patients for the medical treatment date.

  In the portion 108 on the screen 100, the classification key “whole” is selected as an initial value. When the classification key is selected from the combo box of the portion 108, the screen display processing unit 45 changes the content displayed in the portions 101 to 107 on the screen 100 based on the classification key.

  The work amount estimation program in the present embodiment can be provided not only by package software but also by a WEB service or the like.

  In addition, the remaining hospitalization number calculating means, the first detailed amount acquiring means, the second specified amount acquiring means, and the third specified amount calculating means described in the claims correspond to the prediction processing unit 43. The output means corresponds to the intermediate DB creation processing unit 44.

10 PC
DESCRIPTION OF SYMBOLS 21 Input device 22 Display device 23 PC main body 31 Main storage device 32 Arithmetic processing device 33 Interface device 34 Recording medium reading device 35 Auxiliary storage device 36 Recording medium 37 Bus 40 Work amount estimation device 41 Data amount storage processing unit 42 Classification master creation processing Unit 43 Prediction processing unit 44 Intermediate DB creation processing unit 45 Screen display processing unit 51 Movement history database (DB)
52 Accounting Card DB
53 Patient DB
54 Disease Name DB
55 Wound and Sick Name Master 56 DPC Information DB
57 DPC master 58 Data volume storage DB
59 Classification master 60 Intermediate DB
100 Work amount estimation result screen

Claims (7)

On the computer,
With reference to the first storage means for storing the average length of stay of discharged patients and the attributes of the discharged patients in association with each other, based on the number of days of hospitalization for the hospitalized patient and the attributes of the hospitalized patient, A remaining hospitalization calculating step for calculating the expected number of days remaining in the hospitalized patient; and
For the hospitalized patient, a first specification amount acquisition step of acquiring a first specification amount that is a specification amount of medical practice that occurred during the hospitalization days; and
A second specification amount acquisition step of acquiring a second specification amount, which is a specification amount of a medical action from the first storage means to the hospitalized patient and the discharged patient who has the same attribute;
Based on the second specification amount, a third specification amount calculation step for calculating a third specification amount that is the amount of the medical treatment details to the hospitalized patient that occurs in the remaining hospitalization days of the hospitalized patient;
A work amount estimation program for executing an output step of outputting a detailed amount of medical practice for the hospitalized patient based on the first specific amount and the third specific amount. The first storage means includes, for each discharged patient group having a hospitalization period of a predetermined number or more among the plurality of discharged patients, an average length of stay in the discharged patient group and the second specified amount per day. The work amount estimation program according to claim 1, wherein: The work amount estimation program according to claim 1 or 2, wherein the third detailed amount calculating step allocates the second detailed amount for each day of the number of days remaining in hospitalization of the hospitalized patient. In the remaining hospitalization days calculating step, calculating the remaining hospitalization days from a reference date within a predetermined period,
In the first item quantity acquisition step, the first item prize up to the reference date within the predetermined period is acquired;
In the third item amount calculating step, calculating the third item amount from the reference date of the predetermined period,
The work amount estimation program according to any one of claims 1 to 3, wherein in the output step, a detailed amount of the medical practice for the hospitalized patient in the predetermined period is output. The remaining days of hospitalization calculation step, the first specification amount acquisition step, the second specification amount acquisition step, and the third specification amount calculation step are executed for each hospitalized patient, and the hospitalized patient The total amount of medical practice details to the patient is calculated, and the total amount of medical practice details to the patient who has been discharged by the reference date within the predetermined period and hospitalization after the reference date within the predetermined period. The work amount estimation program according to claim 4, wherein a total amount of medical treatment details for expected patients is calculated, and a total amount of medical treatment details for all patients within the predetermined period is output. A work amount estimation method executed by a computer,
The computer is
Based on the number of days of hospitalization related to the hospitalized patient and the attributes of the hospitalized patient, the average storage days of the discharged patient and the attributes of the discharged patient are stored in association with each other from the first storage means for storing the hospitalized A remaining hospitalization calculating step for calculating the expected remaining hospitalization days of the patients,
For the hospitalized patient, a first specification amount acquisition step of acquiring a first specification amount that is a specification amount of medical practice that occurred during the hospitalization days; and
A second specification amount acquisition step of acquiring a second specification amount, which is a specification amount of a medical action from the first storage means to the hospitalized patient and the discharged patient who has the same attribute;
Based on the second specification amount, a third specification amount calculation step for calculating a third specification amount that is the amount of the medical treatment details to the hospitalized patient that occurs in the remaining hospitalization days of the hospitalized patient;
A work amount estimation method comprising: performing an output step of outputting a detailed amount of medical practice for the hospitalized patient based on the first specific amount and the third specific amount. Based on the number of days of hospitalization related to the hospitalized patient and the attributes of the hospitalized patient, the average storage days of the discharged patient and the attributes of the discharged patient are stored in association with each other from the first storage means for storing the hospitalized A means for calculating the number of days remaining in hospital for calculating the expected number of days remaining in hospital,
First specification amount acquisition means for acquiring a first specification amount that is the amount of the details of the medical practice that occurred in the hospitalization days for the hospitalized patient;
Second specification amount acquisition means for acquiring from the first storage means a second specification amount that is a specification amount of medical practice to the discharged patient having the same attribute as the hospitalized patient;
Based on the second specification amount, a third specification amount calculation means for calculating a third specification amount that is the amount of the medical treatment specification to the hospitalized patient that occurs in the remaining hospitalization days of the hospitalized patient;
A work amount estimation device comprising: an output unit that outputs a detailed amount of medical practice for the hospitalized patient based on the first specific amount and the third specific amount.

Images