Electronic medical chart system, information processing device, information processing method, and information processing program

Abstract

To perform an objective evaluation of the QOL of a patient by a medical worker to improve the medical quality.SOLUTION: An electronic medical chart system includes: an input unit that receives an input of an evaluation of a patient by a medical worker or a communication between the medical worker and the patient; a language processing unit that performs language processing for either the evaluation of the patient or communication which is input through the input unit; and a numeralization unit that numeralizes the quality of life (QOL) of the patient by using a result of processing by the language processing unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an electronic medical record system, an information processing device, an information processing method, and an information processing program. [Background technology]

[0002] In the above technical field, Non-Patent Document 1 discloses a technology for evaluating a patient's quality of life (QOL) by performing language processing on text data of a blog about a patient's battle with illness based on EQ-5D-5L (EuroQol-5 dimensions-5 level, see Non-Patent Document 2). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Shinichi, Matsuda. et al. (2021), "Incorporating Unstructured Patient Narratives and Health Insurance Claims Data in Pharmacovigilance: Natural Language Processing Analysis of Patient-Generated Texts About Systemic Lupus Erythematosus", Journal of Medical Interest Research, 7(6). doi: 10.2196 / 29238 [Non-Patent Document 2] Ikeda et al., "Development of a scoring method for the Japanese version of EQ-5D-5L," Health and Medical Sciences 2015 Vol.64 No.1 p.47-55 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technique described in the above document was an evaluation based on the patient's subjective report, a blog about their battle with illness, and was not an objective evaluation of the patient's QOL.

[0005] An object of the present invention is to provide a technique for solving the above-mentioned problems. [Means for solving the problem]

[0006] In order to achieve the above object, the system according to the present invention comprises: an input unit for inputting an evaluation of the patient by a medical professional or a communication between the medical professional and the patient; a language processing unit that performs language processing on the evaluation or communication of the patient inputted by the input unit; a quantification unit that quantifies a patient's quality of life (QOL) using a processing result of the language processing unit; An electronic medical record system equipped with

[0007] In order to achieve the above object, the device according to the present invention comprises: A language processing unit that performs language processing on the evaluation of the patient by the medical professional or the communication between the medical professional and the patient; a quantification unit that quantifies the QOL of a patient using a processing result of the language processing unit; The information processing device is provided with:

[0008] In order to achieve the above object, the method according to the present invention comprises: A language processing step in which a language processing unit performs language processing on an assessment of a patient by a medical professional or a communication between a medical professional and a patient; a digitization step in which a digitization unit digitizes the QOL of the patient using a processing result of the language processing step; The present invention relates to an information processing method.

[0009] In order to achieve the above object, the program according to the present invention comprises: A language processing step of language processing the assessment of the patient by the medical professional or the communication between the medical professional and the patient; a digitization step of digitizing the patient's QOL using a processing result of the language processing step; It is an information processing program that causes a computer to execute the above. Effect of the Invention

[0010] According to the present invention, objective patient QOL evaluation by medical professionals can be used to improve the quality of medical care. [Brief description of the drawings]

[0011] [Figure 1] 1 is a block diagram showing the configuration of an electronic medical record system according to a first embodiment. [Diagram 2] FIG. 11 is a diagram for explaining an outline of the operation of the electronic medical record system according to the second embodiment. [Diagram 3] FIG. 11 is a block diagram showing the configuration of an electronic medical record system according to a second embodiment. [Figure 4] FIG. 11 is a sequence diagram showing the operation procedure of the electronic medical record system according to the second embodiment. [Diagram 5] FIG. 11 is a block diagram showing a configuration of an information processing device according to a second embodiment. [Figure 6A] FIG. 11 is a block diagram showing a configuration of a language processing unit according to a second embodiment. [Figure 6B] FIG. 11 is a diagram showing a procedure for generating a learning model for language processing according to the second embodiment. [Figure 6C] FIG. 11 is a diagram illustrating an overview of processing by a language processing unit according to the second embodiment. [Figure 6D] FIG. 11 is a diagram showing a processing result of the language processing unit according to the second embodiment. [Figure 6E] FIG. 11 is a diagram showing an evaluation of the processing accuracy of the language processing unit according to the second embodiment. [Figure 7A] FIG. 11 is a block diagram showing the configuration of a digitizing unit according to the second embodiment. [Figure 7B] FIG. 11 is a block diagram showing an example of a score conversion table according to the second embodiment. [Figure 8A]FIG. 11 is a block diagram showing the configuration of a notification unit according to the second embodiment. [Figure 8B] 13A and 13B are diagrams illustrating an example of a display on a notification unit according to the second embodiment. [Figure 8C] 13A and 13B are diagrams illustrating other display examples in the notification unit according to the second embodiment. [Figure 9] 10 is a flowchart showing a processing procedure of an information processing device according to a second embodiment. [Figure 10] FIG. 11 is a sequence diagram showing the operation procedure of the electronic medical record system according to the third embodiment. [Figure 11] FIG. 13 is a sequence diagram showing the operation procedure of the electronic medical record system according to the fourth embodiment. [Figure 12] FIG. 13 is a sequence diagram showing the operation procedure of the electronic medical record system according to the fifth embodiment. [Figure 13] FIG. 13 is a sequence diagram showing the operation procedure of the electronic medical record system according to the sixth embodiment. [Figure 14] 13A and 13B are diagrams illustrating examples of displays on a notification unit according to another embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. However, the components described in the following embodiment are merely examples, and are not intended to limit the technical scope of the present invention.

[0013] [First embodiment] An electronic medical record system 100 according to a first embodiment of the present invention will be described with reference to Fig. 1. The electronic medical record system 100 is a system for evaluating the QOL of patients by medical professionals.

[0014] As shown in Fig. 1, the electronic medical record system 100 includes an input unit 101, a language processing unit 102, and a digitizing unit 103. The input unit 101 inputs a patient evaluation 111 by a medical professional or a communication 112 between a medical professional and a patient. The language processing unit 102 performs language processing on the patient evaluation 111 or communication 112 input by the input unit 101. The digitizing unit 103 digitizes a QOL 132 of a patient 131 using a processing result 121 of the language processing unit 102.

[0015] According to this embodiment, the objective patient QOL evaluation by medical professionals can be used to improve the quality of medical care.

[0016] [Second embodiment] Next, an electronic medical record system 200 according to a second embodiment of the present invention will be described. The electronic medical record system 200 according to this embodiment performs language processing on document data generated in a medical institution, which describes the input evaluation of a patient by a medical professional or communication between a medical professional and a patient, and quantifies the patient's QOL using the language processing result. Then, the medical professional is notified according to the change in the quantified QOL. In addition, the medical professional is notified according to the change in QOL and at least one of the medical care-related information, which is the medication content, the patient's vitals, the diet content, the treatment content, the operation content, the dementia score, the hospital visit content, the hospitalization content, the number of days of hospitalization, and the hospital room information. Alternatively, the patient's QOL is quantified using the language processing result and the patient's vitals. At least one of the input of medical information and the notification of the QOL score is performed from a terminal used by the medical professional or a bedside terminal.

[0017] <Electronic medical record system> The electronic medical record system 200 of this embodiment will be described below with reference to FIGS.

[0018] (Operation overview) FIG. 2 is a diagram for explaining an outline of the operation of the electronic medical record system 200 according to this embodiment.

[0019] In the electronic medical record system 200, the patient ID and the medical department for which the QOL score is to be calculated are input from the doctor terminal 230 to the display screen 231. Furthermore, as an option, the display mode of the QOL score is selected as to whether to output the calculated QOL value of the patient or to output the QOL change from the patient's QOL history, for example, in a graph.

[0020] Upon receiving a request from the doctor terminal 230 to calculate the QOL of the target patient, the information processing device 210 reads out medical information such as an electronic medical record corresponding to the target patient from the medical information database 220, performs language processing on the medical information, and applies it to the EQ-5D-5L to generate a QOL score. Then, the QOL score calculated for the target patient is presented to the doctor terminal 230.

[0021] A display screen 232 of the doctor terminal 230 is an example of a display when the patient's QOL value is selected as the display mode, and a display screen 233 is an example of a display when the patient's QOL change is selected as the display mode.

[0022] (composition) FIG. 3 is a block diagram showing the configuration of an electronic medical record system 200 according to this embodiment.

[0023] The electronic medical record system 200 includes an information processing device 210, a medical information database 220, a doctor terminal 230, and a patient management system 340, which communicate with each other via a network 350.

[0024] The information processing device 210 is a device that calculates the QOL score of each patient based on the medical information stored in the medical information database 220 in this embodiment. The medical information database 220 stores information on electronic medical records input from the doctor terminal 230 and other medical related information. The doctor terminal 230 is a terminal where a doctor inputs electronic medical records and observes (monitors) the QOL score of each patient. The medical information may be input from a nurse terminal carried by a nurse or other medical staff. It may also be input from a bedside terminal placed on the bed of each patient. The patient management system 340 is a system for managing the medical information and medical related information for each patient, and an existing system is used. It includes the details of medication, the patient's vital signs, the details of meals, the details of treatment, the details of surgery, the dementia score, the details of outpatient visits, the details of hospitalization, the number of days of hospitalization, and hospital room information.

[0025] (Operation sequence) 4 is a sequence diagram showing the operation procedure of the electronic medical record system 200 according to this embodiment. The electronic medical record system 200 according to this embodiment includes an electronic medical record generation function 410 and a QOL score calculation function 430.

[0026] In the electronic medical record generation function 410, in step S411, the doctor terminal 230 requests the patient management system 340 to view the patient information. In step S413, the patient management system 340 requests acquisition of the medical information stored in the medical information database 220, and in step S415 acquires the patient information of the target patient. Then, in step S417, the patient management system 340 causes the doctor terminal 230 to display the acquired patient information.

[0027] In step S421, the doctor terminal 230 requests the patient management system 340 to input medical information corresponding to the patient information. In step S423, the patient management system 340 requests the medical information database 220 to update the medical information of the target patient, and in step S425, acquires the update result of the patient information of the target patient. Then, in step S427, the patient management system 340 notifies the doctor terminal 230 of the update result.

[0028] In the QOL score calculation function 430, in step S431, the doctor terminal 230 instructs the information processing device 210 to calculate a QOL score based on the patient information of the target patient. In step S433, the information processing device 210 having the QOL score calculation function requests acquisition of the medical information of the target patient stored in the medical information database 220, and in step S435 acquires the medical information of the target patient. Then, in step S436, the information processing device 210 performs language processing on the medical information of the target patient, generates a QOL score by correlating the language processing result with the EQ-5D-5L, and notifies the patient management system 340 of the calculated QOL score in step S437. In step S439, the patient management system 340 notifies the doctor terminal 230 of the QOL score of the target patient.

[0029] <Configuration of information processing device> FIG. 5 is a block diagram showing the configuration of an information processing device 210 according to this embodiment.

[0030] The information processing device 210 includes a communication control unit 501 , a medical information acquisition unit 502 , a language processing unit 503 , a database 504 , a digitization unit 505 , and a QOL output unit 506 .

[0031] The communication control unit 501 controls communication between the information processing device 210, the medical information database 220, the patient management system 340, and the doctor terminal 230. The medical information acquisition unit 502 has a medical information table 521, and reads out the medical information of the target patient from the medical information database 220. The language processing unit 503 has a language processing table 531, and based on the medical information of the target patient acquired by the medical information acquisition unit 502, performs language processing on the sentences included in the medical information using a sentence extractor that has fine-tuned a BERT (Bidirectional Encoder Representations from Transformers) pre-learning model with the medical information as teacher data, for example, to extract words to be used for calculating the QOL score.

[0032] The database 504 stores language processing data 541 used by the language processing unit 503, digitization data 542 used by the digitization unit 505, and generated QOL score data 543. The digitization data 542 includes a score conversion table.

[0033] The digitizing unit 505 has a digitizing table 551, and calculates the QOL score of each item of the target patient using digitizing data 542 including a score conversion table, and converts it into the patient's QOL score. The patient's QOL score is stored in QOL score data 543 of the database 504 in association with the patient ID. The QOL output unit 506 has an output generation table 561, and generates a screen according to the display mode designation based on the patient's QOL score, and outputs it to the doctor terminal 230.

[0034] (Language Processing Unit) FIG. 6A is a block diagram showing the configuration of the language processing unit 503 according to this embodiment.

[0035] The language processing unit 503 includes a learning model fine tuning unit 601, a sentence extractor 602 in which a BERT pre-learning model 611 is fine-tuned, and a question item scoring unit 603. The learning model fine tuning unit 601 fine-tunes the BERT pre-learning model 611 based on teacher data 610 selected from medical information. In this embodiment, the medical information includes, but is not limited to, medical information annotated as the teacher data 610 and medical information generated by data augmentation. The sentence extractor 602 receives the medical information acquired by the medical information acquisition unit 502, and extracts sentences related to each question item of EQ-5D-5L using the sentence extractor 602 in which the BERT pre-learning model 611 is fine-tuned. The question item scoring unit 603 determines the question item score 630 of each question item based on the sentences related to each question item of EQ-5D-5L, and outputs the score to the quantification unit 505. In this embodiment, the language processing unit 503 has been described as having a question item scoring unit 603 and outputting the question item score 630 from the medical information, but the language processing unit 503 may also be configured to output sentences related to each question item of EQ-5D-5L to the quantification unit 505, and the quantification unit 505 may have the question item scoring unit 603.

[0036] FIG. 6B is a diagram showing a procedure for tuning the learning model of language processing according to this embodiment.

[0037] Preliminary examination step: After extracting sentences from the electronic medical record database using content analysis and text mining analysis software (e.g., KHCoder) for doctor's findings and nursing records, a list was created, and 66 keywords were set based on the EQ-5D-5L (Japanese version) questionnaire 621, and the average monthly frequency of keyword appearance was calculated for each patient. Then, 29 keywords were set based on the frequency of appearance to select teaching data for the learning model.

[0038] Fine tuning steps: (S601): Based on the 29 set keywords, text is extracted from the medical information 622 as teacher data for fine-tuning the learning model. (S602): Annotation (addition of tags and metadata) is performed on the extracted text, and teacher data 623 including learning data and verification data is generated. (S603): A large-scale language model (for example, GPT-3.5) is utilized for the extracted text to generate data-augmented training data 624. Note that the data-augmented training data 624 includes training data including negation word correspondence. (S604): Fine-tuning of the BERT pre-training model 611 is performed using the training data of the training data 623 and the data-augmented training data 624 as training data. (S605): Using the validation data of the training data 623, the processing of the sentence extractor 602, which is a fine-tuned version of the BERT pre-training model 611, is verified.

[0039] FIG. 6C is a diagram showing an outline of processing by the language processing unit 503 according to this embodiment.

[0040] In the sentence extractor 602 utilizing the BERT pre-learning model, medical information 620 of the target patient is input, and sentences necessary for scoring each question item of the EQ-5D-5L questionnaire 621 are extracted. Each question item of the EQ-5D-5L questionnaire 621 has five dimensions: degree of mobility, management of personal affairs, usual activities, pain / discomfort, anxiety / gloominess, and five levels: each dimension has a score of 1 to 5 (1 is no problem / 5 is a big problem), and the dimension and level closely related to the extracted keyword are selected. Then, the question item score 630 of each dimension is output.

[0041] FIG. 6D is a diagram showing the processing result of the language processing unit 503 according to this embodiment.

[0042] When the sentences of the input medical information 620 are input to the language processing unit 503, the extracted sentences and question item scores 630 are output as follows: for "speed of movement" "score 3" ← extracted sentence 'Could walk with a walker, but' + 'difficulty', and for "usual activities" "score 4" ← extracted sentence 'chew slowly and well'.

[0043] FIG. 6E is a diagram showing an evaluation of the processing accuracy of the language processing unit 503 according to this embodiment.

[0044] In Fig. 6E, the processing accuracy of the language processing unit 503 is evaluated for each EQ-5D item using evaluation indices such as accuracy, precision, recall, and f1-score. Evaluation 640 is the result of evaluating each question item using one standard, and evaluation 650 is the case where the accuracy is divided into the case where there is no problem (low QOL score) (shown as "A", e.g., score "1") and the case where there is a problem (high QOL score, e.g., score "2 to 5") (shown as "B"). The accuracy is low in the case where there is no problem (low QOL score) due to the lack of data, but the accuracy is sufficient in the case where there is a problem (high QOL score).

[0045] One index of the processing accuracy of the language processing unit 503 is whether it can make a judgment regardless of the score, and in particular whether it can distinguish between a score of "1" and the other scores of "2 to 5." According to the evaluation 640 and evaluation 650 in FIG. 6E, it is possible to judge with high accuracy whether the setting item is included in the range of 5 or not. For example, the f1-score is 0.6 or more for the question items "degree of mobility" and "pain / discomfort," which is a sufficient accuracy.

[0046] (Quantification Department) FIG. 7A is a block diagram showing the configuration of the digitizing unit 505 according to this embodiment.

[0047] The quantification unit 505 has an EQ-5D-5L question score acquisition unit 701 and a score conversion unit 702. The EQ-5D-5L question score acquisition unit 701 acquires the scores of the question items output from the language processing unit 503. The score conversion unit 702 has a score conversion table 721 and converts a set of scores of five question items into one score. As described above, the quantification unit 505 may have a question item scoring unit 603. The score conversion unit 702 may convert the scores of each question item using the score conversion table 721 into a score of the entire question items, and may perform a process of quantifying the patient's QOL using the processing result of the language processing unit and the patient's vitals, etc.

[0048] FIG. 7B is a block diagram showing an example of the score conversion table 721 according to this embodiment.

[0049] In Figure 7B, "Status" is the value that arranges the scores of the five question items (5D), and "Score" is the score after conversion. By using the score conversion table, it is possible to visualize the transition of the overall evaluation of the five items of EQ-5D-5L.

[0050] (Notification Department) FIG. 8A is a block diagram showing the configuration of a notification unit 800 according to this embodiment.

[0051] The notification unit 800 is realized by a combination of the QOL output unit 506 of the information processing device 210 and the doctor terminal 230. The QOL output unit 506 also receives medical care related information together with the QOL score from the digitization unit 505, and generates an output screen. The medical care related information includes at least one of the following: medication details, patient vitals, diet details, treatment details, surgery details, dementia score, outpatient details, hospitalization details, number of days of hospitalization, and hospital room information.

[0052] Fig. 8B is a diagram showing a display example 833 in the notification unit 800 according to this embodiment. Fig. 8B is an example in which the QOL score digitized by the digitizing unit 505 is presented as is.

[0053] Fig. 8C is a diagram showing another display example of the notification unit 800 according to this embodiment. Fig. 8C shows a patient's history read from the medical information database 220, and the calculated QOL score is presented as a QOL change for a specific item (the item "anxiety / gloominess" in Fig. 8C) superimposed on medical care-related information ("surgery" and "medication" in Fig. 8C), thereby supporting doctors in analyzing the cause of the QOL change and the effect of medical care. In other words, it is possible to compare before and after, for example, drug intervention for each EQ-5D-5L item.

[0054] <Processing procedure of information processing device> 9 is a flowchart showing a processing procedure of the information processing device 210 according to this embodiment. This flowchart is executed by a CPU (Central Processing Unit) of the information processing device 210 using a RAM (Random Access Memory) to realize the components of FIG.

[0055] In step S901, the information processing device 210 executes fine-tuning processing of the BERT pre-training model 611 of the language processing unit 503. The fine-tuning processing of the BERT pre-training model 611 has been described in detail in FIG. 6B. In step S903, the information processing device 210 determines whether or not fine-tuning of the BERT pre-training model 611 has been completed, and if not completed, returns to step S901 to continue the fine-tuning. Note that the completion of fine-tuning is performed by inputting verification data (see FIG. 6B) to the sentence extractor 602 that has fine-tuned the BERT pre-training model 611 and verifying the accuracy of the output result.

[0056] When the fine tuning of the BERT pre-training model 611 is completed, the information processing device 210 acquires medical information including the electronic medical record of the target patient from the medical information database 220 in step S905. In step S907, the information processing device 210 utilizes the BERT pre-training model 611 of the language processing unit 503 to extract sentences related to each question item in EQ-5D-5L based on the acquired medical information. Then, in step S908, the information processing device 210 determines the question item score corresponding to the EQ-5D-5L questionnaire 621 based on the sentences related to each question item. In step S909, the information processing device 210 converts the question item score into a QOL score of each patient using the score conversion table 721 by the quantification unit 505. Then, in step S911, the information processing device 210 accumulates the QOL score in association with each patient in the medical information database 220.

[0057] In step S913, the information processing device 210 determines the selection of the presentation format in the QOL viewing request. If the presentation format in the QOL viewing request is to output QOL numerical values, in step S915, the information processing device 210 outputs QOL numerical values ​​and / or statistical values ​​from the QOL output unit 506. On the other hand, if the presentation format in the QOL viewing request is to output QOL changes, in step S917, the information processing device 210 generates and outputs a graph of QOL changes from the QOL history in the QOL output unit 506.

[0058] According to this embodiment, medical professionals can objectively evaluate the patient's QOL, leading to improved quality of medical care. That is, doctors can monitor the objective QOL score or changes in QOL score for each patient, and therefore can work to improve QOL. Furthermore, changes in QOL score can be monitored in conjunction with timing of medical care-related information such as medication details, patient vitals, diet details, treatment details, surgery details, dementia score, outpatient details, hospitalization details, number of days hospitalized, and hospital room information, making it possible to analyze the cause of QOL decline and the impact of medical care on QOL.

[0059] That is, in this embodiment, by fine-tuning the Japanese pre-training model of BERT, which can obtain distributed representations taking into account the context, by utilizing medical records, an algorithm is created that automatically judges EQ-5D-5L, which is a type of value-based scale, and a retrospective QOL value that contributes to the economic evaluation of medical technology is calculated. Therefore, according to this embodiment, it is possible to accurately measure QOL from medical records using natural language processing, and it is expected to be useful for verifying the effectiveness of treatment. Furthermore, accurate QOL also improves the accuracy of quality-adjusted life years (QALY), which is an effectiveness indicator used in cost-effectiveness analysis.

[0060] [Third embodiment] Next, an electronic medical record system according to a third embodiment of the present invention will be described. The electronic medical record system according to this embodiment differs from the second embodiment in that medical information is acquired from a bedside terminal or a QOL score is notified. That is, at least one of an input unit for inputting medical information and a notification unit for notifying a QOL score is provided in the bedside terminal in addition to the terminal used by the medical staff. Since the other configurations and operations are the same as those in the second embodiment, the same reference numerals are used for the same configurations and operations, and detailed explanations thereof will be omitted.

[0061] <Operation sequence of electronic medical record system> 10 is a sequence diagram showing the operation procedure of the electronic medical record system 1000 according to this embodiment. The electronic medical record system 1000 includes a bedside terminal 1030, a medical information database 1020 that also accumulates measurement information (vital signs) of the patient from the bedside terminal 1030, and an information processing device 1010 that calculates a QOL score using the measurement information (vital signs) of the patient from the bedside terminal of the medical information database 1020.

[0062] The bedside terminal function 1031 performs the following process. When a request for measurement value registration using the bedside terminal 1030 is made in step S1031, the patient management system 340 requests the medical information database 1020 to update the measurement value in step S1033. The patient management system 340 acquires the update result from the medical information database 1020 in step S1035, and notifies the bedside terminal 1030 of confirmation of the update result in step S1037.

[0063] Furthermore, in step S1041, when a request to view the test results is made using the bedside terminal 1030, the patient management system 340 in step S1043 requests the medical information database 1020 to acquire the test results. In step S1045, the patient management system 340 acquires the test results from the medical information database 1020, and in step S1047 notifies the bedside terminal 1030 of confirmation of the test results.

[0064] In addition, the QOL score calculation function 430 issues a QOL calculation instruction from the bedside terminal 1030 and notifies the bedside terminal 1030 of the patient's QOL score.

[0065] According to this embodiment, it is possible to collect medical information from a bedside terminal and to notify the bedside terminal of a QOL score.

[0066] [Fourth embodiment] Next, an electronic medical record system according to a fourth embodiment of the present invention will be described. The electronic medical record system according to this embodiment differs from the second and third embodiments in that home medical information is reflected in the QOL score. That is, the QOL score can be calculated as medical information for data generated outside a medical institution. Since the other configurations and operations are the same as those of the second and third embodiments, the same reference numerals are used for the same configurations and operations, and detailed explanations thereof will be omitted.

[0067] <Operation sequence of electronic medical record system> 11 is a sequence diagram showing the operation procedure of the electronic medical record system 1100 according to this embodiment. The electronic medical record system 1100 includes a medical information database 1120 that also stores medical information on home medical care, and an information processing device 1110 that also uses the medical information on home medical care in the medical information database 1120 to calculate a QOL score.

[0068] The home medical care function 1150 performs the following process. When there is a request to input home medical care information in step S1151, the patient management system 340 requests the medical information database 1120 to update the home medical care information in step S1153. The patient management system 340 acquires the home medical care information from the medical information database 1120 in step S1155, and notifies the doctor terminal 230 of the home medical care information in step S1157.

[0069] It should be noted that the QOL score calculation function 430 also uses the home medical care information accumulated in the medical care information database 1120 to calculate the patient's QOL score.

[0070] According to this embodiment, an objective QOL score for a patient can be calculated from medical information from home medical care, which can lead to an improvement in the quality of home medical care.

[0071] [Fifth embodiment] Next, an electronic medical record system according to a fifth embodiment of the present invention will be described. The electronic medical record system according to this embodiment differs from the second to fourth embodiments in that voice data during medical treatment is reflected in the QOL score. Other configurations and operations are the same as those of the second to fourth embodiments, so the same configurations and operations are denoted by the same reference numerals and detailed explanations thereof will be omitted.

[0072] <Operation sequence of electronic medical record system> 12 is a sequence diagram showing the operation procedure of the electronic medical record system 1200 according to this embodiment. The electronic medical record system 1200 includes a medical information database 1220 that accumulates medical information as audio information, and an information processing device 1210 that performs audio processing on the audio medical information in the medical information database 1220 and calculates a QOL score using the audio processing result.

[0073] In step S1236, the information processing device 1210 performs voice processing on the voice information included in the medical information, and calculates a QOL score using the voice processing result.

[0074] According to the present embodiment, since audio medical information is used, it is possible to calculate a QOL score that is more in line with the patient's condition, including their emotions, which can lead to further improvement in the quality of medical care.

[0075] [Sixth embodiment] Next, an electronic medical record system according to a sixth embodiment of the present invention will be described. The electronic medical record system according to this embodiment differs from the second to fifth embodiments in that video data (or image data) during medical treatment is reflected in the QOL score. Other configurations and operations are the same as those of the second to fifth embodiments, so the same configurations and operations are denoted by the same reference numerals and detailed explanations thereof will be omitted.

[0076] <Operation sequence of electronic medical record system> 13 is a sequence diagram showing the operation procedure of an electronic medical record system 1300 according to this embodiment. The electronic medical record system 1300 includes a medical information database 1320 that also stores medical information as video information (image information), and an information processing device 1310 that performs video processing on the video medical information in the medical information database 1320 and calculates a QOL score using the video processing result.

[0077] In FIG. 13, in step S1336, the information processing device 1310 performs image processing on the image information included in the medical information, and calculates the QOL score using the image processing result.

[0078] According to this embodiment, since video medical information is used, it is possible to calculate a QOL score based on information including the patient's direct movements, which can lead to further improvement in the quality of medical care.

[0079] [Other embodiments] The display of the QOL score calculated from the medical information is not limited to the above embodiment. For example, as shown in FIG. 14, the display may be a graph showing the history of the frequency of occurrence of pre-consideration words of EQ-5D.

[0080] Although we have explained an example of using BERT as a learning model for language processing, other large-scale language models (LLMs) such as the GPT series, Transformer, PaLM (Scaling Language Modeling with Pathways), and LLaMA (Language Large Models Meta AI) can also be used. In addition, RoBERTa, XLM, TS, XLNet, BART, ALBERT, ELECTRA, etc. can also be used.

[0081] In the above embodiment, the calculation of the QOL score by EQ-5D-5L has been described, but the ones used for the QALY calculation may be EQ-5D-3L, HUI (Health Utilities Index), SF-6D (Short Form 6 Dimension), etc., and the ones not used for the QALY calculation may be SF-36 (Short Form 36), WHOQOL, etc. Furthermore, EORTC (European Organisation for Research and Treatment of Cancer) and FACTORY-Tax (Functional Assessment of Cancer Therapy-taxane) used for cancer, SGRO (St. George's Respiratory Questionnaire) used for respiratory diseases, etc. may also be used.

[0082] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the technical scope of the present invention. In addition, systems or devices that combine the separate features included in each embodiment in any way are also included in the technical scope of the present invention.

[0083] The present invention may be applied to a system consisting of multiple devices, or to a single device. Furthermore, the present invention is also applicable to a case where an information processing program for implementing the functions of the embodiments is supplied to a system or device and executed by a built-in processor. In order to implement the functions of the present invention with a computer, a program installed on a computer, a medium storing the program, a server for downloading the program, and a processor for executing the program are also included in the technical scope of the present invention. In particular, at least a non-transitory computer readable medium storing a program for causing a computer to execute the processing steps included in the above-mentioned embodiments is included in the technical scope of the present invention.

Claims

1. an input unit for inputting an evaluation of the patient by a medical professional or a communication between the medical professional and the patient; a language processing unit that performs language processing on the evaluation or communication of the patient inputted by the input unit; a quantification unit that quantifies a patient's quality of life (QOL) using a processing result of the language processing unit; An electronic medical record system equipped with

2. The electronic medical record system according to claim 1 , further comprising a notification unit that notifies a medical professional in accordance with a change in the quantified QOL.

3. The electronic medical record system according to claim 2 , wherein the notification unit notifies a medical professional based on the change in QOL and medical care-related information.

4. The electronic medical record system of claim 3 , wherein the medical care-related information includes at least one of medication details, patient vital signs, diet details, treatment details, surgery details, dementia score, outpatient details, hospitalization details, length of hospitalization, and hospital room information.

5. The electronic medical record system according to claim 1 , wherein the quantification unit quantifies the patient's QOL using the processing result of the language processing unit and the patient's vital signs.

6. 2. The electronic medical record system according to claim 1, wherein the data inputted from the input unit includes at least one of text data, audio data and video data.

7. 2. The electronic medical record system according to claim 1, wherein the data inputted from the input unit includes at least one of data generated within a medical institution and data generated outside the medical institution.

8. 3. The electronic medical record system according to claim 2, wherein at least one of the input unit and the notification unit is provided in a terminal used by the medical staff or a bedside terminal.

9. A language processing unit that performs language processing on the evaluation of the patient by the medical professional or the communication between the medical professional and the patient; a quantification unit that quantifies the QOL of a patient using a processing result of the language processing unit; An information processing device comprising:

10. A language processing step in which a language processing unit performs language processing on an assessment of a patient by a medical professional or a communication between a medical professional and a patient; a digitization step in which a digitization unit digitizes the patient's QOL using a processing result of the language processing step; An information processing method comprising:

11. A language processing step of language processing the assessment of the patient by the medical professional or the communication between the medical professional and the patient; a digitization step of digitizing the patient's QOL using a processing result of the language processing step; An information processing program that causes a computer to execute the above.

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