Treatment evaluation method, treatment evaluation device, and treatment evaluation program

Abstract

To provide a method for evaluating a treatment, a treatment evaluation device, and a treatment evaluation program capable of automatically determining the effect of treatment.SOLUTION: The method for evaluating a treatment executed by an information processing device includes: a quantification step of quantifying the health condition of a patient as a clinical score; a previous score acquisition step of acquiring a previous score that is a previous clinical score of the patient; a determination step of determining the improvement state of symptoms of the patient on the basis of the difference between the clinical score and the previous score and a predetermined threshold; and an improvement rate calculation step of calculating the improvement rate of the symptoms of the patient from the clinical score and the previous score.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a treatment evaluation method, a treatment evaluation device, and a treatment evaluation program. [Background technology]

[0002] In the field of regenerative medicine, medical institutions are required to regularly report information, including the therapeutic effects of regenerative medicine, to the Ministry of Health, Labor and Welfare, etc. When making these reports, medical professionals are required to calculate clinical scores based on patient-reported outcome measures (PROMs).

[0003] Conventionally, the general method of collecting information using patient-reported outcome measures is for patients to fill out a designated form and for medical professionals to collect information based on the patient's responses. Patent Document 1 discloses a method for collecting higher quality information by having patients enter patient-reported outcome measures electronically. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6929426 Summary of the Invention [Problem to be solved by the invention]

[0005] Analysis of patient-reported outcome measures and output of treatment evaluations are still performed manually by medical professionals. This clerical work is one of the factors that imposes an excessive burden on medical professionals. Furthermore, many medical professionals are in busy environments and can only perform the minimum necessary analysis and output, which often makes it difficult to fully utilize the information collected from patients.

[0006] An object of the present disclosure is to provide a treatment evaluation method, a treatment evaluation device, and a treatment evaluation program that can automatically determine the effectiveness of treatment. [Means for solving the problem]

[0007] [1] A treatment evaluation method executed by an information processing device, a quantification step of quantifying the patient's health status as a clinical score; a previous score acquisition step of acquiring a previous score, which is the patient's previous clinical score; an improvement rate calculation step of calculating an improvement rate of the patient's symptoms from the clinical score and the previous score; a determining step of determining an improvement status of the patient's symptoms based on a difference between the clinical score and the previous score and a predetermined threshold value; A method for evaluating a treatment, comprising:

[0008] [2] The method for evaluating a treatment according to [1] above, In the digitizing step, A method for evaluating treatment, wherein the clinical score is quantified based on a patient-reported outcome measure.

[0009] [3] The method for evaluating a treatment according to [1] or [2] above, In the determining step, A method for evaluating treatment, wherein if the difference between the previous score and the clinical score exceeds a predetermined threshold, it is determined that the patient's symptoms have improved or worsened.

[0010] [4] The method for evaluating a treatment according to any one of [1] to [3] above, In the determining step, When the difference is within a predetermined threshold range, the patient's condition is determined to be stable.

[0011] [5] The method for evaluating a treatment according to any one of [1] to [4], The method for evaluating a treatment further comprises: A method for evaluating a treatment, comprising an output step of outputting the assessment result of the treatment effect on the patient.

[0012] [6] The method for evaluating a treatment according to any one of [1] to [5], storing the calculated improvement rate in the improvement rate calculation step; In the output step, retrieving one or more stored past improvement rates of said patient; The treatment evaluation method further outputs a transition between the retrieved past improvement rate and the improvement rate calculated in the improvement rate calculation step.

[0013] [7] The method for evaluating a treatment according to any one of [1] to [6], In the output step, a predetermined character string corresponding to the determination result is output.

[0014] [8] The method for evaluating a treatment according to any one of [1] to [7], In the output step, The treatment evaluation method includes outputting the assessment result in a preset format.

[0015] [9] The method for evaluating a treatment according to any one of [1] to [8], The information output in the output step includes: A method for evaluating a treatment, comprising information on the progress of the patient's clinical scores.

[0016]

[10] The method for evaluating a treatment according to any one of [1] to [9], The treatment evaluation method further includes a treatment suggestion step of proposing at least one of a treatment method for the patient or a medication content for the patient based on the improvement rate.

[0017]

[11] The method for evaluating a treatment according to any one of [1] to

[10] above, a case reception step of receiving input of information regarding the case of the patient; The method for evaluating a treatment further includes an evaluation method determination step of determining or proposing an evaluation method to be used based on the received case.

[0018]

[12] The method for evaluating a treatment according to

[11] , In the case reception step, A method for evaluating treatment, which accepts input of information about the case of the patient by accepting the selection of one or more options that match the condition of the patient from a plurality of preset options.

[0019]

[13] The method for evaluating a treatment according to

[11] or

[12] above, In the case reception step, A method of evaluating treatment, comprising obtaining information about the patient's case from the patient's medical record.

[0020]

[14] An information processing device having a control unit, The control unit The patient's health status is quantified as a clinical score, Obtain a previous score, which is the patient's previous clinical score; determining an improvement in the patient's symptoms based on a difference between the clinical score and the previous score and a predetermined threshold; Calculating an improvement rate of the patient's symptoms from the clinical score and the previous score. Treatment evaluation device.

[0021]

[15] To the computer, Quantifying the patient's health status as a clinical score; obtaining a previous score, the previous clinical score for the patient; determining an improvement status of the patient's symptoms based on a difference between the clinical score and the previous score and a predetermined threshold; Calculating an improvement rate of the patient's symptoms from the clinical score and the previous score; A treatment evaluation program that allows [Effects of the Invention]

[0022] According to the treatment evaluation method, treatment evaluation device, and treatment evaluation program disclosed herein, the effectiveness of treatment can be automatically determined. [Brief explanation of the drawings]

[0023] [Figure 1] 1 is a block diagram showing a configuration of a treatment evaluation system according to an embodiment of the present disclosure. [Figure 2] FIG. 2 is a block diagram illustrating a configuration of a server device according to an embodiment of the present disclosure. [Figure 3] FIG. 10 is a block diagram illustrating a configuration of a patient terminal according to an embodiment of the present disclosure. [Figure 4] FIG. 2 is a block diagram showing a configuration of a medical staff terminal according to an embodiment of the present disclosure. [Figure 5] 1 is a block diagram illustrating a configuration of a printing device according to an embodiment of the present disclosure. [Figure 6] 10 is a flowchart illustrating an example of the operation of the treatment evaluation system according to an embodiment of the present disclosure. [Figure 7] 10 is a flowchart illustrating an example of the operation of the treatment evaluation system according to an embodiment of the present disclosure. [Figure 8] FIG. 10 is a diagram showing an example of effect assessment information output by a treatment evaluation system according to an embodiment of the present disclosure. [Figure 9] FIG. 10 is a diagram showing an example of effect assessment information output by a treatment evaluation system according to an embodiment of the present disclosure. [Figure 10] FIG. 10 is a diagram showing an example of effect assessment information output by a treatment evaluation system according to an embodiment of the present disclosure. [Figure 11] 10 is a flowchart showing an example of the operation of a treatment evaluation system according to a first modified example of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0024] Hereinafter, an embodiment of the present disclosure will be described with reference to the drawings. In each drawing, parts having the same configuration or function are denoted by the same reference numerals. In the description of this embodiment, duplicated descriptions of the same parts may be omitted or simplified as appropriate.

[0025] FIG. 1 is a diagram illustrating an example of a treatment evaluation system 1 according to an embodiment of the present disclosure.

[0026] The treatment evaluation system 1 includes a server device 10, a patient terminal 20, a medical staff terminal 30, and a printing device 40, which are capable of communicating with each other via a network 2. The network 2 is, for example, the Internet, but may also include an ad-hoc network, a LAN (Local Area Network), a MAN (Metropolitan Area Network), or other networks, or any combination thereof.

[0027] The server device 10 judges the effectiveness of treatment. The patient terminal 20 accepts patient-reported outcome measures from the patient and transmits them to the server device 10. When the server device 10 receives the patient-reported outcome measures from the patient terminal 20, it calculates a clinical score for the patient based on the received patient-reported outcome measures. The server device 10 compares the calculated clinical score with past clinical scores for the same patient to judge the improvement status of the patient's symptoms. The server device 10 transmits the judgment result to the medical staff terminal 30. The medical staff terminal 30 outputs the received judgment result in any format.

[0028] 2 shows an example of the configuration of the server device 10. The server device 10 includes a control unit 11, a storage unit 12, and a communication unit 13. The server device 10 is, for example, a server that belongs to a cloud computing system or other computing system and implements various functions.

[0029] The control unit 11 includes one or more processors, one or more dedicated circuits, or a combination thereof. The processor is a general-purpose processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit), or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). The control unit 11 controls each unit of the server device 10 and executes information processing related to the operation of the server device 10.

[0030] The storage unit 12 includes one or more semiconductor memories, one or more magnetic memories, one or more optical memories, or a combination of at least two of these. The semiconductor memories are, for example, RAM (Random Access Memory) or ROM (Read Only Memory). The RAM is, for example, SRAM (Static RAM) or DRAM (Dynamic RAM). The ROM is, for example, EEPROM (Electrically Erasable Programmable ROM). The storage unit 12 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 12 stores information used in the operation of the server device 10 and information obtained by the operation of the server device 10.

[0031] The memory unit 12 further stores an evaluation method and criteria for calculating a patient's clinical score. The evaluation method includes questions, options, and scores corresponding to the options. Examples of evaluation methods stored in the memory unit 12 include, but are not limited to, the Knee Injury and Osteoarthritis Outcome Score (KOOS), the JOABPEQ (JOA Back Pain Evaluation Questionnaire), the DASH / Quick DASH (Disabilities of the Arm, Shoulder, and Hand questionnaire), Shoulder 36, and the EQ-5D-5L (EuroQol-5 dimensions 5 Level).

[0032] The memory unit 12 further stores clinical score information of the patient. The clinical score information includes patient information, previously calculated clinical scores, assessment date, administration date, therapeutic effect, and evaluation method used as the basis for assessing therapeutic effect. The patient information may include the patient's age, sex, physique information such as height, weight, and muscle mass, the name of the clinic the patient visits, the name of the disease, medical history, sports history, treatment details, number of treatments, treatment period, disease severity, classification of imaging findings, duration of illness, physical findings, blood and biochemical test results, physiological test results, and social situation.

[0033] The communication unit 13 includes one or more communication interfaces. The communication interface is, for example, a LAN interface. The communication unit 13 receives information used in the operation of the server device 10 and transmits information obtained by the operation of the server device 10. The server device 10 is connected to the network 2 by the communication unit 13 and communicates information with other devices via the network 2.

[0034] The operation of the server device 10 is realized by a processor included in the control unit 11 executing a program. The program can be recorded on a computer-readable recording medium. The computer-readable recording medium is, for example, a magnetic recording device, an optical disc, a magneto-optical recording medium, or a semiconductor memory. The program is distributed in a state recorded on a portable recording medium such as a DVD (Digital Versatile Disc) or a CD (Compact Disc)-ROM on which the program is recorded. Some or all of the operation of the server device 10 may be executed by a dedicated circuit included in the control unit 11.

[0035] FIG. 3 shows an example configuration of the patient terminal 20. The patient terminal 20 includes a control unit 21, a storage unit 22, a communication unit 23, an input unit 24, and an output unit 25. The patient terminal 20 is any electronic terminal used by a patient. The patient terminal 20 may be, but is not limited to, a portable information terminal. For example, a general-purpose electronic device such as a smartphone, a tablet terminal, or a PC, or a dedicated electronic device, can be used as the patient terminal 20. The patient terminal 20 may be a smartphone or the like owned by the patient, or a tablet terminal or PC owned by a medical institution.

[0036] The control unit 21 includes one or more processors, one or more dedicated circuits, or a combination of these. The processor is a general-purpose processor such as a CPU or GPU, or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, an FPGA or ASIC. The control unit 21 controls each part of the patient terminal 20 and executes information processing related to the operation of the patient terminal 20.

[0037] The storage unit 22 includes one or more semiconductor memories, one or more magnetic memories, one or more optical memories, or a combination of at least two of these. The semiconductor memories are, for example, RAM or ROM. The RAM is, for example, SRAM or DRAM. The ROM is, for example, EEPROM. The storage unit 22 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 22 stores information used in the operation of the patient terminal 20 and information obtained by the operation of the patient terminal 20.

[0038] The communication unit 23 includes one or more communication interfaces. The communication interfaces are, for example, interfaces compatible with mobile communication standards such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation), or a LAN interface. The communication unit 23 receives information used in the operation of the patient terminal 20 and transmits information obtained by the operation of the patient terminal 20. The patient terminal 20 is connected to the network 2 by the communication unit 23 via a nearby router device or a mobile communication base station, and communicates information with other devices via the network 2.

[0039] The input unit 24 includes one or more input interfaces. The input interface may be, for example, a physical key, a capacitance key, a pointing device, a touch screen integrated with a display, or a microphone for accepting voice input. The input interface may further include a camera for capturing captured images or image codes, or an IC card reader. The input unit 24 accepts an operation for inputting information used in the operation of the patient terminal 20 and sends the input information to the control unit 21.

[0040] The output unit 25 includes one or more output interfaces. The output interface is, for example, an external or built-in display that outputs information as an image or video, a speaker that outputs information as sound, or a connection interface with an external output device. The display is, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) display. The output unit 25 outputs information obtained by the operation of the patient terminal 20.

[0041] The operation of the patient terminal 20 is realized by a processor included in the control unit 21 executing a program. The program can be recorded on a computer-readable recording medium. Examples of the computer-readable recording medium include a magnetic recording device, an optical disk, a magneto-optical recording medium, or a semiconductor memory. The program is distributed in a state recorded on a portable recording medium such as a DVD or CD-ROM on which the program is recorded. The program may be distributed by storing the program in the storage of the server device 10 and transferring the program from the server device 10 to another computer. Some or all of the operation of the patient terminal 20 may be executed by a dedicated circuit included in the control unit 21.

[0042] 4 shows an example of the configuration of the medical staff terminal 30. The medical staff terminal 30 includes a control unit 31, a storage unit 32, a communication unit 33, an input unit 34, and an output unit 35. The medical staff terminal 30 is, for example, a general-purpose electronic device such as a smartphone, a tablet terminal, or a PC, or a dedicated electronic device.

[0043] The control unit 31 includes one or more processors, one or more dedicated circuits, or a combination of these. The processor is a general-purpose processor such as a CPU or GPU, or a dedicated processor specialized for a specific process. The dedicated circuit is, for example, an FPGA or ASIC. The control unit 31 controls each part of the medical staff terminal 30 and executes information processing related to the operation of the medical staff terminal 30.

[0044] The storage unit 32 includes one or more semiconductor memories, one or more magnetic memories, one or more optical memories, or a combination of at least two of these. The semiconductor memories are, for example, RAM or ROM. The RAM is, for example, SRAM or DRAM. The ROM is, for example, EEPROM. The storage unit 32 functions as, for example, a main storage device, an auxiliary storage device, or a cache memory. The storage unit 32 stores information used in the operation of the medical staff terminal 30 and information obtained by the operation of the medical staff terminal 30.

[0045] The communication unit 33 includes one or more communication interfaces. The communication interface is, for example, an interface compatible with a mobile communication standard such as LTE, 4G, or 5G, or a LAN interface. The communication unit 33 receives information used in the operation of the medical staff terminal 30 and transmits information obtained by the operation of the medical staff terminal 30. The communication unit 33 connects the medical staff terminal 30 to the network 2 via a nearby router device or a mobile communication base station, and communicates information with other devices via the network 2.

[0046] The input unit 34 includes one or more input interfaces. The input interface may be, for example, a physical key, a capacitive key, a pointing device, a touch screen integrated with a display, or a microphone for accepting voice input. The input interface may further include a camera for capturing captured images or image codes, or an IC card reader. The input unit 34 accepts operations for inputting information used in the operation of the medical staff terminal 30 and sends the input information to the control unit 31.

[0047] The output unit 35 includes one or more output interfaces. The output interface is, for example, an external or built-in display that outputs information as images or videos, a speaker that outputs information as audio, or an interface for connecting to an external output device. The display is, for example, an LCD or an organic EL display. The output unit 35 outputs information obtained by the operation of the medical staff terminal 30.

[0048] The operation of the medical staff terminal 30 is realized by a processor included in the control unit 31 executing a program. The program can be recorded on a computer-readable recording medium. Examples of the computer-readable recording medium include a magnetic recording device, an optical disk, a magneto-optical recording medium, or a semiconductor memory. The program is distributed in a state recorded on a portable recording medium such as a DVD or CD-ROM on which the program is recorded. The program may be distributed by storing the program in the storage of the server device 10 and transferring the program from the server device 10 to another computer. Some or all of the operation of the medical staff terminal 30 may be executed by a dedicated circuit included in the control unit 31.

[0049] 5 shows an example configuration of a printing device 40. The printing device 40 includes a communication unit 41 and a printing unit 42. The printing device 40 receives printing information from other devices via the network 2 and performs printing. The printing device 40 may be configured as, for example, a facsimile machine, a copier, or the like.

[0050] The communication unit 41 includes one or more communication interfaces. The communication interfaces are, for example, interfaces compatible with mobile communication standards such as LTE, 4G, or 5G, or LAN interfaces. The communication unit 41 receives printing information from other devices. The printing device 40 is connected to the network 2 by the communication unit 41 via a nearby router device or a mobile communication base station, and communicates information with other devices via the network 2.

[0051] The printing unit 42 generates a printed matter based on the printing information received by the communication unit 41. Specifically, the printing unit 42 generates a printed matter by printing based on the printing information on a print medium such as paper that has been placed on it. The printing unit 42 may include a display device such as a digital photo frame, and generate an electronic printed matter as a printed matter by displaying information based on the printing information on the display device.

[0052] Next, the process by which the treatment evaluation system 1 judges the effect of treatment on a patient will be described with reference to Figs. 6 and 7. Fig. 6 is a flowchart showing the process by which the treatment evaluation system 1 judges the effect of treatment. Fig. 7 is a flowchart showing, more specifically, the process by which the server device 10 judges the effect of treatment. In the following description, all "evaluation methods arbitrarily selected by medical professionals" refer to the same evaluation method. Examples of evaluation methods arbitrarily selected by medical professionals include, but are not limited to, KOOS, JOABPEQ, DASH / Quick DASH, and EQ-5D-5L.

[0053] The patient terminal 20 receives a patient-reported outcome measure from the patient via the input unit 24 (step S101). The questions in the patient-reported outcome measure vary depending on the evaluation method. For example, the KOOS, an evaluation method for knee treatment, includes 42 questions about the knee, and the patient inputs answers on a five-point scale based on the condition of their knee. When the control unit 21 of the patient terminal 20 receives the patient-reported outcome measure from the patient, it transmits the patient-reported outcome measure information to the server device 10 via the communication unit 23.

[0054] When the control unit 11 of the server device 10 receives the patient-reported outcome scale information from the patient terminal 20, it calculates a clinical score (step S102). The clinical score is calculated based on an evaluation method arbitrarily selected by a medical professional. The control unit 11 of the server device 10 stores the calculated clinical score in the memory unit 12. The control unit 11 of the server device 10 may retrieve patient information from the memory unit 12 in addition to the patient-reported outcome scale information, and calculate the clinical score taking the patient information into consideration.

[0055] After calculating the clinical score, the control unit 11 of the server device 10 judges the effectiveness of the treatment for the patient (step S103). The judgment is made by calculating the difference between the previous score, which is the clinical score calculated last time for the patient, and the clinical score calculated in step S102. The evaluation method used to calculate the clinical score calculated in step S102 and the previous score is the same.

[0056] More specifically, the control unit 11 of the server device 10 acquires the previous score of the patient (step S201). The previous score of the patient may be stored in the storage unit 12 of the server device 10. In this case, the control unit 11 of the server device 10 acquires the previous score of the patient from the storage unit 12.

[0057] When the control unit 11 of the server device 10 acquires the previous score of the patient, it calculates the difference between the previous score and the clinical score calculated in step S102 (step S202).

[0058] After calculating the difference between the two scores, the control unit 11 of the server device 10 determines the effectiveness of the treatment on the patient based on the calculation result and a predetermined criterion (step S203). The predetermined criterion is, for example, but not limited to, the MCID (Minimal Clinically Important Difference). MCID is the minimum change value at which a change in the patient can be interpreted as beneficial. A threshold value for MCID is set for each evaluation method and each disease. The control unit 11 of the server device 10 determines that there has been some change in the patient's symptoms if the difference between the patient's clinical score and the previous score exceeds the threshold value. When using MCID, the value determined as the threshold value varies depending on the evaluation method and disease, but is generally a value that represents a change of 3.0% to 52.0% of the total score. The control unit 11 of the server device 10 determines whether the patient's symptoms are improving, worsening, or stable based on the difference between the two scores and the threshold value of the predetermined criterion. In addition, the control unit 11 of the server device 10 may determine, for example, that the patient is "under observation" if there is only data from before treatment, or that the patient is "other" if treatment has been discontinued or the patient has died.

[0059] The following describes an example of how to determine whether a patient is improving, worsening, or stable, using the KOOS evaluation method and MCID as the evaluation criterion. The KOOS is an evaluation method in which a maximum score of 100 is used for each of five categories, with higher scores indicating better symptoms. The MCID of the KOOS ranges from 6 to 20. In this example, the threshold is 10 points. The control unit 11 of the server device 10 determines that a patient's symptoms are improving if the patient's clinical score in any of the five categories is 10 points or more higher than the previous score. Conversely, if the score is 10 points or more lower, the control unit 11 determines that a patient's symptoms are worsening. If the difference in any category is less than 10 points, the control unit 11 determines that the patient's symptoms are stable. Note that some evaluation methods consider the lower the clinical score, the better the patient's symptoms. In this case, conversely, if the clinical score is lower than the previous score by more than a predetermined threshold, the control unit 11 determines that the patient's symptoms are improving. If the clinical score is higher than a predetermined threshold, the control unit 11 determines that a patient's symptoms are worsening. If the difference is within the threshold, the control unit 11 determines that the patient's symptoms are stable.

[0060] The predetermined assessment criteria may also be those established by OMERACT (Outcome Measures in Rheumatology). The criteria established by OMERACT are assessment criteria that can be used when WOMAC (Western Ontario and McMaster Universities Arthritis Index) is used as the assessment method. The criteria established by OMERACT can also be used when KOOS, which has similar questions to WOMAC, is used as the assessment method.

[0061] If the difference between the two scores exceeds a threshold derived from a predetermined criterion and indicates an improvement trend (step S203: improvement), the control unit 11 of the server device 10 determines that the patient's symptoms have improved (step S204).

[0062] If the difference between the two scores exceeds a threshold derived from a predetermined criterion and indicates a worsening trend (step S203: worsening), the control unit 11 of the server device 10 determines that the patient's condition has worsened (step S205).

[0063] If the difference between the two scores does not exceed a threshold derived from a predetermined criterion (step S203: stable), the control unit 11 of the server device 10 determines that the patient's condition is stable (step S206).

[0064] After making a determination on the symptoms of the patient, the control unit 11 of the server device 10 stores the determination result in the storage unit 12 (step S207).

[0065] A healthcare professional can refer to the assessment results of the therapeutic effect of a patient stored in the memory unit 12 of the server device 10 through the above process from the healthcare professional terminal 30. When the control unit 31 of the healthcare professional terminal 30 receives an instruction to view the assessment results from the healthcare professional via the input unit 34, it acquires assessment result information from the server device 10. The assessment result information includes, for example, information identifying the patient, the name of the disease or the condition before administration, the result of the efficacy assessment, the scientific basis for the efficacy assessment, the date of administration, the date of assessment, and the details of the diagnosis. The results of the efficacy assessment can be selected from, for example, "improved," "stable," "worsened," "under observation," or "other." "Under observation" is selected when there has not yet been a post-administration visit or when a telephone consultation has been completed and the patient's condition has only been assessed. "Other" is selected when the patient has died of another illness or when details are unknown due to inability to contact the patient. The scientific basis for the efficacy assessment may include information on the evaluation method used for the efficacy assessment. The details of the diagnosis may include an explanation of the therapeutic effect and clinical scores before and after administration. The explanation of the therapeutic effect is a character string corresponding to the result of the effect assessment, and may be preset. When the control unit 31 of the medical staff terminal 30 acquires the assessment result information, it outputs the assessment result information to the output unit 35. At this time, the control unit 31 of the medical staff terminal 30 may store the assessment result information in the storage unit 32.

[0066] The medical professional can also print and output the determination result information that the medical professional terminal 30 has acquired from the server device 10. When the control unit 31 of the medical professional terminal 30 receives an instruction to print the determination result from the medical professional via the input unit 34, it transmits the determination result information and the print instruction to the printing device 40 via the communication unit 33. The print instruction includes information regarding the format in which the determination result information is to be output. The format is set in advance and stored in the memory unit 12 of the server device 10 or the memory unit 32 of the medical professional terminal 30. The format may be, for example, a format used in periodic reports on regenerative medicine to the Ministry of Health, Labor and Welfare, etc.

[0067] FIG. 8 shows an example of the assessment result information output by the output unit 35 and the printing device 40 of the medical staff terminal 30. The assessment result information shown in FIG. 8 includes a patient ID for identifying the patient, the name of the disease or condition before administration, the result of the effectiveness assessment, the scientific basis for the effectiveness assessment, the administration date, the assessment date, the period elapsed since the administration date, and the diagnosis. The explanation of the treatment effect included in the diagnosis is "not improved" if the treatment effect is "stable" or "worsening," and "pain has been alleviated" if the treatment effect is "improved." The assessment result information may also include the improvement rate of the patient's symptoms, which will be described later. Specifically, the VAS values ​​of the patient before and after administration in the original data 27212 in FIG. 8 are 6 and 7 points, respectively. Therefore, the VAS improvement rate is 16.7%. Similarly, the patient's pain scores before and after administration were 53 and 44 points, with an improvement rate of -17.0%, symptom scores were 50 and 57 points, with an improvement rate of 14.0%, ADL scores were 56 and 48 points, with an improvement rate of -14.3%, sport / rec scores were 10 and 18 points, with an improvement rate of 80.0%, and QOL scores were 13 and 0 points, with an improvement rate of -100.0%. The therapeutic effect was determined using the MCID criterion, which will be described later. Details will be provided later.

[0068] FIG. 9 shows an example of displaying past clinical scores for one patient in chronological order based on the assessment result information. FIG. 10 illustrates a composite graph showing the transition of clinical scores for one patient based on the assessment result information. In FIG. 10, the bar graph indicates the VAS score, and the scale on the vertical axis on the left corresponds to the bar graph. The line graph indicates Pain, Symptom, ADL, Sport / Rec, and QOL, respectively, and the scale on the vertical axis on the right corresponds to the line graph. The horizontal axis indicates time. The output unit 35 and printing device 40 of the medical staff terminal 30 may output past clinical scores for one patient in chronological order, as shown in FIG. 9. Furthermore, the control unit 31 of the medical staff terminal 30 may create a graph or the like showing the transition of clinical scores for one patient based on the assessment result information and output it via the output unit 35 or printing device 40.

[0069] The control unit 11 of the server device 10 calculates the improvement rate of the patient's symptoms from the clinical score calculated in step S102 and the previous score (step S104). The method for calculating the improvement rate is not particularly limited, but it may be calculated, for example, by the following formula (1). In the following formula (1), P is the improvement rate, x is the clinical score, and y is the previous score.

number

[0070] The control unit 11 of the server device 10 may output the improvement rate calculated in step S104 via the output unit 35 of the medical staff terminal 30 or the printing device 40. The output format is not particularly limited, and may be, for example, a format in which the improvement rate is displayed together with other determination result information, as shown in Fig. 8. The control unit 11 of the server device 10 may also retrieve one or more past improvement rates for one patient from the storage unit 12 and output the progress of the symptom improvement rate in chronological order.

[0071] The control unit 11 of the server device 10 may tally the improvement rates of patients' symptoms based on predetermined criteria and calculate statistical data. The predetermined criteria may be, for example, "improvement rate by clinic," "improvement rate by treatment method," or "improvement rate by patient attributes (age, sex, physique, treatment period, etc.)." The tallying method is not particularly limited, but patients that form a population may be determined based on patient information, and statistical data may be calculated for that population using predetermined criteria. For example, when calculating statistical data for "improvement rate by clinic," attention is focused on "the clinic visited" among the patient information, and patients are set as a population for each clinic. Then, the improvement rates for that population are tallied, thereby allowing the improvement rate for each clinic to be calculated.

[0072] The control unit 11 of the server device 10 may link the improvement rate of the patient's symptoms calculated in step S104 with the details of the treatment and medication administered to the patient and store the linked data in the storage unit 12. The control unit 11 of the server device 10 may propose at least one of the details of future treatment or medication to be administered to the patient based on the linked data between the improvement rate of the patient's symptoms and the details of the treatment and medication administered to the patient. The method for generating the proposed content is not particularly limited. For example, a learning model may be trained using training data linking past improvement rates and treatment and medication details, including those of other patients, and the proposed content may be generated based on the trained model. The learning model is a model created by machine learning using a machine learning algorithm. The learning model may be, for example, a machine learning model built based on a decision tree. Examples of machine learning models built based on a decision tree include, but are not limited to, Light GBM and XGBoost. Alternatively, the learning model may be a model generated based on a machine learning algorithm such as a convolutional neural network (CNN), a recurrent neural network (RNN), or other deep learning.

[0073] The control unit 11 of the server device 10 may predict the future improvement rate of the patient from data linking the improvement rate of the patient's symptoms with the details of the treatment and medication administered to the patient. The method for predicting the future improvement rate is not particularly limited, but for example, the prediction may be made using a formula calculated by performing regression analysis on data linking past improvement rates, including other patients, with the details of the treatment and medication.

[0074] If a patient has undergone multiple therapies, multiple medications, or a combination thereof, the control unit 11 of the server device 10 may, for example, perform a multiple regression analysis of past therapies or medications and past improvement rates, including those of other patients, to calculate which therapies, medications, or combinations thereof contribute to the patient's improvement rate. In this case, the control unit 11 of the server device 10 may use each of the therapies or medications as an explanatory variable and the improvement rate as a target variable. The explanatory variables may include age, sex, number of treatments, disease severity, classification of imaging findings, duration of illness, etc. The variable selection algorithm is not particularly limited, and may be, for example, a brute force method or a stepwise method.

[0075] The method for calculating the relationship between a treatment method, medication, or a combination thereof and the contribution to the patient's improvement rate is not limited to multiple regression analysis. For example, methods such as logistic regression analysis, decision tree, support vector machine, and Bayesian model may also be used.

[0076] This configuration reduces the administrative burden on medical professionals. Medical professionals can also easily create explanatory materials for patients. Furthermore, patients have more opportunities to receive explanations about their symptoms and progress in treatment, which gives them a sense of satisfaction and peace of mind regarding the treatment. Therefore, one embodiment of the present disclosure having the above configuration contributes to the smooth progress of treatment.

[0077] Next, as a first modified example of the present disclosure, a method for selecting or proposing an evaluation method to be used in accordance with the case of a patient will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the process of determining the therapeutic effect after the treatment evaluation system 1 selects or proposes an evaluation method to be used.

[0078] The control unit 11 of the server device 10 receives information about the patient's case from the patient terminal 20 or the medical staff terminal 30 via the communication unit 13 (step S301). The information about the patient's case includes, for example, the name of the disease, the date of first consultation, the treatment period, the treatment method, the medication history, etc., but is not limited to these.

[0079] The control unit 11 of the server device 10 may acquire, as information related to the patient's case, the patient's medical record information stored in the memory unit 12 or the memory unit 32 of the medical staff terminal 30. As another method, the control unit 11 of the server device 10 may display one or more options indicating the patient's condition on the patient terminal 20 or the medical staff terminal 30, accept the selection of an option that matches the patient's condition, and acquire the selection as information related to the patient's case.

[0080] The control unit 11 of the server device 10 determines the evaluation method to be used from the evaluation methods stored in the storage unit 12 based on the information about the patient case acquired in step S301 (step S302). Instead of selecting one evaluation method to be used, the control unit 11 of the server device 10 may select one or more evaluation methods and output them as candidate evaluation methods to the medical staff terminal 30. In this case, the control unit 11 of the server device 10 may determine the evaluation method to be used as the evaluation method received from the medical staff terminal 30.

[0081] Thereafter, step S303 is the same as step S101, step S304 is the same as step S102, and step S305 is the same as step S103.

[0082] This configuration allows even inexperienced medical professionals to easily select an appropriate evaluation method, and also reduces the time spent considering which evaluation method to use, thereby reducing the administrative burden on medical professionals.

[0083] Next, a first example of a method for calculating a clinical score and a treatment evaluation method using the treatment evaluation system 1 according to this embodiment will be described. In the first example, the KOOS is used as an evaluation method to evaluate treatment for a patient's knee symptoms. The criterion used is MCID.

[0084] First, we will explain the questions that patients answer. Patients answer questions about the condition of their knees. The questions consist of 42 questions, answered on a 5-point scale.

[0085] Next, we will explain how to calculate the clinical score. In the KOOS, questions belong to one of the following categories: "Pain," "Symptoms," "ADL," "Sport / Rec," and "QOL." Each category has nine questions for "Pain," seven for "Symptoms," 17 for "ADL," five for "Sport / Rec," and four for "QOL." The KOOS assigns a maximum score of 100 points to each category, with higher scores indicating better knee condition. Each question is assigned a score of 0, 1, 2, or 3, with the mildest symptom (best condition) being assigned first, and 4 points being assigned to the most severe symptom (worst condition). The clinical score a1 based on the KOOS is calculated for each category using the following formula (2): In the formula, x is the sum of the raw scores corresponding to the patient's responses, and y is the raw score for the most severe symptom for all questions in the category, i.e., "number of questions in the category x 4."

number

[0086] Next, a method for assessing the therapeutic effect will be described. In the first embodiment, the therapeutic effect is assessed using MCID. The MCID of the KOOS is 6 to 20, and in the first embodiment, the threshold is set to 10 points. That is, if the difference between the calculated clinical score and the previously calculated clinical score in any of the scores in each category is an improvement of 10 points or more, the patient's condition is assessed to have improved. On the other hand, if the difference between the calculated clinical score and the previously calculated clinical score in any of the scores in each category is a worsening of 10 points or more, the patient's condition is assessed to have worsened. If the difference between the calculated clinical score and the previously calculated clinical score in all of the scores in each category is an improvement or worsening of less than 10 points, the patient's condition is assessed to be stable.

[0087] If at least one category of the clinical score has improved by more than a threshold value compared to the previously calculated clinical score, the patient's condition may be determined to have improved, even if other categories have worsened by more than a threshold value compared to the previously calculated clinical score. In other words, if at least one category of the clinical score has improved by more than a threshold value compared to the previously calculated clinical score, the patient's condition may be determined to have improved. The determination method when different results are calculated for multiple categories is not limited to this. For example, if the number of categories that have worsened by more than a threshold value is greater than the number of categories that have improved by more than a threshold value, the patient's condition may be determined to have worsened. If the number of categories that have improved by more than a threshold value is the same as the number of categories that have worsened by more than a threshold value, the patient's condition may be determined to have improved.

[0088] 8, the difference between pre- and post-administration scores for "Pain," "Symptom," "ADL," and "Sport / Rec" does not exceed 10 points, and "QOL" has worsened by 10 points or more, so the control unit 11 of the server device 10 determines that the patient's symptoms have worsened. Note that the MCID of VAS is 10 to 30, as described below, but in the above example, the difference between pre- and post-administration is 1 point, which does not reach the minimum MCID and does not affect the above determination.

[0089] A second example of the method for calculating a clinical score and the treatment evaluation method using the treatment evaluation system 1 according to this embodiment will be described. In the second example, the KOOS is used as the evaluation method to evaluate treatment for a patient's knee symptoms. The evaluation criteria used are those established by OMERACT. The method for calculating a clinical score based on the KOOS is the same as in the first example.

[0090] According to the criteria established by OMERACT, a patient's condition is deemed to have improved if the improvement rate in the "Pain" or "ADL" score is 50% or more and the raw score has improved by 20 points or more, or if the score improvement rate for two or more of the three items "Pain," "ADL," and "QOL" is 20% or more and the raw score has improved by 10 points or more. On the other hand, a patient's condition is deemed to have worsened if the score deterioration rate for "Pain" or "ADL" is 50% or more and the raw score has worsened by 20 points or more, or if the score deterioration rate for two or more of the three items "Pain," "ADL," and "QOL" is 20% or more and the raw score has worsened by 10 points or more. If none of the above applies, the patient's condition is deemed to be stable.

[0091] A third example of the method for calculating a clinical score and the treatment evaluation method using the treatment evaluation system 1 according to this embodiment will be described. In the third example, JOABPEQ is used as the evaluation method to evaluate treatment for a patient's lower back pain. The criterion used is MCID.

[0092] First, we will explain the questions that patients will answer. Patients answer questions about their lower back pain. The questionnaire consists of 25 questions. Patients are asked to choose the answer that best applies to them from two to five options.

[0093] Next, we will explain how to calculate the clinical score. In the JOABPEQ, questions belong to one of five categories: "pain-related disorders," "lumbar dysfunction," "gait dysfunction," "social life disorders," and "psychological disorders." Some questions belong to multiple categories. Each category has four questions for "pain-related disorders," six for "lumbar dysfunction," five for "gait dysfunction," four for "social life disorders," and seven for "psychological disorders." Each category is scored out of a maximum of 100 points, and the higher the score, the better the patient's condition of lower back pain.

[0094] Next, a method for assessing the therapeutic effect will be described. In the third embodiment, the therapeutic effect is assessed using MCID. The MCID of JOABPEQ is 20. That is, if the difference between the calculated clinical score and the previously calculated clinical score in any of the scores in each category is an improvement of 20 points or more, the patient's symptoms are assessed to have improved. On the other hand, if the difference between the calculated clinical score and the previously calculated clinical score in any of the scores in each category is a worsening of 20 points or more, the patient's symptoms are assessed to have worsened. If the difference between the calculated clinical score and the previously calculated clinical score in all of the scores in each category is an improvement or worsening of less than 20 points, the patient's symptoms are assessed to be stable.

[0095] A fourth example of the method for calculating a clinical score and the method for evaluating treatment by the treatment evaluation system 1 according to this embodiment will be described. In the fourth example, the Quick DASH is used as the evaluation method to evaluate treatment for the upper limbs of a patient. The criterion used is MCID.

[0096] First, we explain the questions that patients will answer. Patients answer questions about the condition of their upper limbs. The questionnaire consists of 19 questions. Patients choose the answer that best applies to them from five options.

[0097] Next, we will explain how to calculate the clinical score. In the Quick DASH, questions belong to one of three categories: "Impairment / Symptoms," "Work," and "Sports / Performing Activities." Each category has 11 questions for "Impairment / Symptoms," 4 for "Work," and 4 for "Sports / Performing Activities." Each category is scored out of 100 points, with lower scores indicating better upper limb condition. Each question is assigned a score of 1, 2, 3, or 4, with the mildest symptoms (best condition) being assigned first, followed by 5 for the most severe symptoms (worst condition). Among the clinical scores based on the Quick DASH, the "Impairment / Symptoms" score a2 is calculated using the following formula (3): In the formula, x is the sum of the raw scores corresponding to the patient's answers, and n is the number of questions answered by the patient.

number

[0098] Next, a method for assessing the therapeutic effect will be described. In the fourth embodiment, the therapeutic effect is assessed using MCID. The MCID of Quick DASH is 7 to 26, and in the fourth embodiment, the threshold is set to 14 points. That is, if the difference between the calculated clinical score and the previously calculated clinical score in any of the scores in each category is improved by 14 points or more, the patient's symptoms are assessed to have improved. On the other hand, if the difference between the calculated clinical score and the previously calculated clinical score in any of the scores in each category is worsened by 14 points or more, the patient's symptoms are assessed to have worsened. If the difference between the calculated clinical score and the previously calculated clinical score in all of the scores in each category is improved or worsened by less than 14 points, the patient's symptoms are assessed to be stable.

[0099] A fifth example of the method for calculating a clinical score and the method for evaluating treatment using the treatment evaluation system 1 according to this embodiment will be described. In the fifth example, the EQ-5D-5L is used as the evaluation method to evaluate the health-related QOL of a patient. The criterion used is the MCID.

[0100] First, we will explain the questions that patients will answer. Patients will answer questions about their health-related quality of life. The questionnaire consists of five questions. Patients will choose the answer that best applies to them from the five options.

[0101] Next, we will explain how the clinical score is calculated. The EQ-5D-5L questions concern "mobility," "self-care," "usual activities," "pain / discomfort," and "anxiety / depression." The highest QOL (healthy) option is assigned a score of 1, while the lowest QOL option is assigned a score of 5. The EQ-5D-5L assigns a perfect score of 1, representing the best QOL (perfect health). Each option has a coefficient, as shown in Table 1 below. The score is calculated by subtracting the sum of the constant term 0.060924 and the coefficient assigned to each option from 1. Note that if a person answers all questions with a score of 1, i.e., chooses the option with the highest QOL, the score will be 1.

[0102] [Table 1]

[0103] Next, a method for assessing the therapeutic effect will be described. In the fifth embodiment, the therapeutic effect is assessed using MCID. The MCID of EQ-5D-5L is 0.03 to 0.52. In the fifth embodiment, if the difference between the calculated clinical score and the clinical score calculated previously is an improvement of 0.11 points or more, the patient's health-related QOL is assessed to have improved. On the other hand, if the difference between the calculated clinical score and the clinical score calculated previously is a deterioration of 0.11 points or more, the patient's health-related QOL is assessed to have deteriorated. If the calculated clinical score and the clinical score calculated previously show an improvement or deterioration of less than 0.11 points, the patient's health-related QOL is assessed to be stable.

[0104] An embodiment of the present disclosure can be applied to evaluation methods other than those described above. Applicable evaluation methods include, for example, the Visual Analog Scale (VAS) for evaluating pain, the WOMAC for evaluating health-related quality of life, the Health Assessment Questionnaire Disease Index (HAQ-DI) for evaluating physical disability, the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-Fatique) for evaluating fatigue, the Oswestry Disability Index (ODI) and Roland-Morris Disability Questionnaire (RMDQ) for evaluating lower back pain, the Scoliosis Research Society-22r (SRS-22r) for evaluating idiopathic scoliosis, the Zurich Claudication Questionnaire (ZCQ) for evaluating postoperative lumbar spinal stenosis, the Lumbar Stiffness Disability Index (LSDI) for evaluating complaints after lumbar fusion surgery, the Neck Disability Index (NDI) for evaluating neck pain, the JOACMEQ (JOA Cervical Myelopathy Evaluation Questionnaire) for evaluating cervical myelopathy, and the MHQ / brief for evaluating the hand. MHQ (Michigan Hand Outcomes Questionnaire), PRWE (Patient-Rated Wrist Evaluation) to evaluate wrist function, PREE (Patient-Rated Elbow Evaluation) to evaluate elbow function, CTSI / BCTQ (Carpal Tunnel Syndrome Instrument / Boston Carpal Tunnel Questionnaire) to evaluate carpal tunnel syndrome, ASES (American Shoulder and Elbow Score) to evaluate shoulder joints, SST (Simple Shoulder Test), SPADI (Shoulder Pain and Disability Index), Sholder36, iHOT12 (International Hip Outcome Score 12), iHOT33 (International Hip Outcome Score 33), NAHS (Non Arthritis Hip Score) for hip arthroscopic surgery evaluation, mHHS (modified Harris Hip Score) and HOOS (Hip Disability and Osteoarthritis Outcome Score) for hip joint evaluation, FJS-12 (Forgotten Joint Score-12) for artificial joint and knee joint evaluation, IKDC (International Knee Documentation Committee subjective knee form) for knee sports evaluation, Lysholm (Lysholm Knee Scoring Scale), Tegner (Tegner Activity Scale), KSS-2011 (Knee Society Score-2011) and OKS (Oxford Knee Score) for knee joint evaluation, AOFAS (American Orthopaedic Foot & Ankle Society) and JSSF (Japanese Society for Surgery of the Examples include the Self-Administered Foot Evaluation Questionnaire (SAFE-Q), the Pain Disability Assessment Scale (PDAS) to evaluate chronic pain, the Simplified Menopausal Index (SMI) to evaluate menopausal symptoms, the Kansas City Cardiomyopathy Questionnaire (KCCQ) to evaluate heart failure, the Minnesota Living with Heart Failure Questionnaire (MLHFQ), and the Mini-Mental State Examination (MMSE) to evaluate dementia. When using any of the above evaluation methods, the MCID can be used as the assessment criteria. The MCIDs corresponding to each evaluation method are shown in Table 2 below.

[0105] [Table 2] TIFF2025148217000007.tif38170

[0106] The above evaluation methods and criteria are merely examples, and the present disclosure is also applicable to evaluation methods and criteria other than those described above.

[0107] The present disclosure is not limited to the above-described embodiments. For example, multiple blocks shown in the block diagrams may be integrated, or a single block may be divided. Instead of executing multiple steps shown in the flowcharts in chronological order as described, steps may be executed in parallel or in a different order depending on the processing capabilities of the device executing each step, or as needed. Other modifications are possible within the scope of the present disclosure. [Explanation of symbols]

[0108] 1. Treatment Evaluation System 2 Network 10 Server device 11 Control section 12 Storage section 13 Communications Department 20 Patient terminal 21 Control section 22 Memory section 23 Communications Department 24 Input section 25 Output section 30 Medical staff terminals 31 Control Unit 32 Storage section 33 Communications Department 34 Input section 35 Output section 40 Printing device 41 Communications Department 42 Printing Department

Claims

1. A treatment evaluation method executed by an information processing device, a quantification step of quantifying the patient's health status as a clinical score; a previous score acquisition step of acquiring a previous score, which is the patient's previous clinical score; a determining step of determining an improvement status of the patient's symptoms based on a difference between the clinical score and the previous score and a predetermined threshold value; an improvement rate calculation step of calculating an improvement rate of the patient's symptoms from the clinical score and the previous score; A method for evaluating a treatment, comprising:

2. In the digitizing step, The clinical score is quantified based on a patient-reported outcome scale. The method for evaluating a treatment according to claim 1.

3. In the determining step, If the difference between the previous score and the clinical score exceeds a predetermined threshold, it is determined that the patient's symptoms have improved or worsened. The method for evaluating a treatment according to claim 1.

4. In the determining step, If the difference is within a predetermined threshold range, the condition of the patient is determined to be stable. The method for evaluating a treatment according to claim 3.

5. The method for evaluating a treatment further comprises: an output step of outputting the assessment result of the therapeutic effect on the patient; The method for evaluating a treatment according to claim 1.

6. storing the calculated improvement rate in the improvement rate calculation step; In the output step, retrieving one or more stored past improvement rates of said patient; further outputting a transition between the retrieved past improvement rate and the improvement rate calculated in the improvement rate calculation step; The method for evaluating a treatment according to claim 5.

7. In the output step, outputting a predetermined character string corresponding to the determination result; The method for evaluating a treatment according to claim 5.

8. In the output step, outputting the determination result in a preset format; The method for evaluating a treatment according to claim 5.

9. The information output in the output step includes: Including transition information of the patient's clinical score, The method for evaluating a treatment according to claim 5.

10. The method for evaluating a treatment comprises: Further comprising a treatment suggestion step of proposing at least one of a treatment method for the patient or a medication content for the patient based on the improvement rate. The method for evaluating a treatment according to claim 1.

11. The method for evaluating a treatment comprises: a case reception step of receiving input of information regarding the case of the patient; An evaluation method determination step of determining or proposing an evaluation method to be used based on the received case, The method for evaluating a treatment according to claim 1.

12. In the case reception step, accepting input of information about the case of the patient by accepting selection of one or more options that match the condition of the patient from a plurality of preset options; The method for evaluating a treatment according to claim 11.

13. In the case reception step, obtaining information about the patient's case from the patient's medical record; The method for evaluating a treatment according to claim 11.

14. An information processing device including a control unit, The control unit The patient's health status is quantified as a clinical score, Obtain a previous score, which is the patient's previous clinical score; determining an improvement in the patient's symptoms based on a difference between the clinical score and the previous score and a predetermined threshold; Calculating an improvement rate of the patient's symptoms from the clinical score and the previous score. Treatment evaluation device.

15. On the computer, Quantifying the patient's health status as a clinical score; obtaining a previous score, the previous clinical score for the patient; determining an improvement status of the patient's symptoms based on a difference between the clinical score and the previous score and a predetermined threshold; Calculating an improvement rate of the patient's symptoms from the clinical score and the previous score; A treatment evaluation program that allows

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