WO2022195664A1

WO2022195664A1 - Medical assistance system and medical assistance method

Info

Publication number: WO2022195664A1
Application number: PCT/JP2021/010349
Authority: WO
Inventors: 俊哉西村; 圭司奥村; 聖杉本
Original assignee: オリンパスメディカルシステムズ株式会社
Priority date: 2021-03-15
Filing date: 2021-03-15
Publication date: 2022-09-22

Abstract

An accumulating unit 32 accumulates patient information about a plurality of patients, and past examination information including past examination implementation times and diagnostic results for the plurality of patients. A determining unit 20 determines a level of disease risk for a subject patient on the basis of at least one of patient information of the subject patient for whom an examination time is to be set and past examination information of the subject patient, and the patient information and past examination information accumulated in the accumulating unit 32. An examination time deriving unit 22 derives examination time information regarding a next examination time for the subject patient, on the basis of the level of disease risk and the past examination implementation time.

Description

Medical support system and medical support method

This disclosure relates to a medical support system and a medical support method.

Patent Literature 1 acquires eating information indicating foods ingested by a user, refers to etiological information and eating information that define a degree of food contribution indicating the degree of correlation between a disease and foods, and determines whether the user is affected by the disease. Disclosed is a disease prediction device that predicts diseases that are likely to occur. The disease prediction device proposes optional examinations in health checkups corresponding to predicted diseases.

JP 2018-124702 A

It is preferable for medical facilities to conduct endoscopic examinations at appropriate times for patients who may develop cancer (high-risk patients). Examination intervals vary among doctors. In order to detect the onset of cancer at an early stage, examinations should be performed at high frequency, but high frequency examinations impose a burden on patients and may lead to wasteful use of medical resources. The present disclosure has been made in view of such circumstances, and an object thereof is to provide a technique for assisting setting of the next examination time.

In order to solve the above problems, a medical support system according to one aspect of the present disclosure includes an accumulation unit that accumulates patient information of a plurality of patients and past examination information including past examination execution times and diagnosis results of a plurality of patients. , based on at least one of the patient information of the target patient whose examination time is to be set and the past examination information of the target patient, and the patient information and the past examination information accumulated in the accumulation unit, the degree of disease risk of the target patient and an examination timing derivation section for deriving examination timing information regarding the next examination timing of the subject patient based on the degree of disease risk and the past examination execution timing.

A medical support method according to another aspect of the present disclosure accumulates patient information of a plurality of patients and past examination information including past examination implementation times and diagnostic results of a plurality of patients, and sets examination times. Based on at least one of the patient's patient information and the target patient's past examination information, and the accumulated patient information and past examination information, the high disease risk of the target patient is determined, and the high disease risk and past examination information are determined. Examination time information regarding the next examination time for the target patient is derived based on the implementation time.

It should be noted that any combination of the above-described components and expressions of the present disclosure converted between methods, devices, systems, recording media, computer programs, etc. are also effective as aspects of the present disclosure.

It is a figure which shows the structural example of the medical assistance system concerning embodiment. It is a figure which shows the example of patient information and examination information. FIG. 10 is a diagram showing a conversion table of time interval-diagnosis interval; It is a figure which shows the example of statistical data. It is a figure which shows another example of statistical data. FIG. 4 is a diagram showing an example of a combination of information elements; It is a figure which shows the example of a notification screen.

FIG. 1 shows a configuration example of a medical support system 1 according to an embodiment. A medical support system 1 of the embodiment is provided in a medical facility such as a hospital, manages patient information of patients, and includes an information processing device 10 , a storage device 30 and a terminal device 40 . The information processing device 10, the storage device 30 and the terminal device 40 are communicably connected by a network 2 such as a LAN (local area network). The information processing device 10 may be connected via the network 2 to an endoscopy management system (not shown) installed in an endoscopy department within a medical facility.

The information processing device 10 includes a patient information acquisition unit 12 , an examination information acquisition unit 14 , a statistical processing unit 16 , an information element identification unit 18 , a determination unit 20 and an examination time derivation unit 22 . The storage device 30 is an auxiliary storage device such as a HDD (Hard Disk Drive) or SSD (Solid State Drive), and has a storage section 32, a statistical data storage section 34, an information element storage section 36, and an examination time storage section . The accumulation unit 32, the statistical data storage unit 34, the information element storage unit 36, and the examination time storage unit 38 may all be configured in the same auxiliary storage device, or may be configured in separate auxiliary storage devices. In the configuration example shown in FIG. 1, the information processing device 10 and the storage device 30 are connected to the network 2 in the medical facility. The processing device 10 and the storage device 30 may be communicably connected.

The configuration of the medical support system 1 shown in FIG. 1 can be implemented by arbitrary processors, memories, auxiliary storage devices, and other LSIs in terms of hardware, and can be implemented by programs loaded in the memory in terms of software. , here, the functional blocks realized by their cooperation are drawn. Therefore, those skilled in the art will understand that these functional blocks can be realized in various forms by hardware alone, software alone, or a combination thereof.

The patient information acquisition unit 12 acquires the patient's patient information. The patient information may include attribute information such as age and sex of the patient, health management information such as BMI (Body Mass Index), systolic blood pressure, and information indicating infection status of Helicobacter pylori. Example patient information is also referred to as "patient background information" and is typically obtained prior to an endoscopy.

Before the endoscopy, the patient measures the health condition using the wearable device, and the patient information acquisition unit 12 acquires patient information such as health management information from the wearable device. The patient information acquisition unit 12 may also acquire patient information including patient attribute information and information indicating the infection status of Helicobacter pylori from a patient database of a medical facility, interview results, and the like. Further, the patient information acquisition unit 12 may acquire patient information from a terminal device such as a tablet. The patient information acquiring unit 12 stores the acquired patient information in the storage unit 32 in association with an examination ID that identifies the endoscopy.

The examination information acquisition unit 14 acquires the patient's examination information. The examination information includes at least examination execution time and diagnosis results. In the embodiment, the inspection implementation time is the day when the inspection is performed (inspection date), but it may be the week or month when the inspection is performed. In the embodiment, the examination information is result information registered in the endoscopy management system by the doctor after the endoscopy. Get inspection information. Note that the examination information may be result information of examinations other than endoscopy. The examination information acquisition unit 14 associates the acquired examination information with the examination ID and causes the accumulation unit 32 to store the obtained examination information.

FIG. 2 shows an example of patient information and examination information acquired during past examinations accumulated in the accumulation unit 32 . The accumulation unit 32 accumulates past patient information of a plurality of patients and past examination information including past examination implementation times and diagnosis results of a plurality of patients. Patient information and examination information are associated with each other and stored in association with examination IDs of examinations performed in the past. The past patient information and examination information accumulated in the accumulation unit 32 are not limited to those obtained at one medical facility, and may include information obtained at a plurality of medical facilities.

　Patient information includes information elements for multiple items. In the example shown in FIG. 2, items of "age", "systolic blood pressure", "BMI", and "pylori infection status" are set in the patient information, and the patient information includes information elements of each item. be done. In the embodiment, the information element is an item value input to the item, the age at the time of examination is registered as an information element in "age", and the systolic blood pressure measured before the examination is registered in "systolic blood pressure". BMI at the time of examination is registered as an information element in "BMI", and an infection status value at the time of examination is registered in the "H. pylori infection status" item as an information element. In addition to these items, items such as the patient's sex and medical history may be set in the patient information.

Similarly, examination information also includes information elements for multiple items. In the example shown in FIG. 2, items of "examination date" and "diagnosis result" are set in the examination information, and the examination information includes information elements of each item. In the "examination date", the date on which the endoscopy was performed is registered as an information element. As described above, time information indicating the week or month in which the examination was performed may be registered instead of the date of examination. In the "diagnosis result", the diagnosis result of a disease diagnosed by a doctor is registered as an information element. In the "diagnosis result" in the embodiment, any one of "no abnormality", "adenoma", and "gastric cancer" is registered as an information element, but other information elements may be registered. In addition to these items, the examination information may include items such as the size of the lesion indicating the examination result and the site of the lesion.

In this way, the accumulation unit 32 accumulates patient information and examination information during examinations of a plurality of patients. Focusing on one patient, since patient information and examination information in a plurality of past examinations are stored, the accumulated information in the accumulation unit 32 can be used to show how the patient's disease has progressed over time. It can be used as history data (diagnosis history information) shown in series. Therefore, the statistical processing unit 16 of the embodiment has a function of calculating statistical data based on past patient information and past examination information of a plurality of patients accumulated in the accumulation unit 32 . The statistical data storage section 34 stores statistical data calculated by the statistical processing section 16 .

First, the statistical processing unit 16 calculates the time interval between examinations based on the past examination times of each patient. Specifically, the statistical processing unit 16 calculates the time interval between the two examinations from the two examination dates included in the plurality of examination information of each patient. Time interval means the period between two tests. For example, the statistical processing unit 16 may calculate the time interval between two test dates when the diagnosis result of the disease changes. The statistical processing unit 16 determines whether the diagnosis result changes from the first diagnosis to the second diagnosis (the second diagnosis is different from the first diagnosis) from a plurality of past examination information for one patient. 2 from the date of examination on which the first diagnosis was made, the date of examination following the date of examination on which the first diagnosis was made, and the date of examination on which the second diagnosis was made. Calculate the interval between two inspection days. For example, referring to patient A's examination history,
Inspection date 2019/12/1: No abnormality Inspection date 2020/5/1: Adenoma Inspection date 2020/11/1: Gastric cancer The diagnosis results have changed. Therefore, the statistical processing unit 16 calculates the time interval when the diagnosis result of the patient A changes as follows.
Time interval at which "no abnormality" changed to "adenomas": 5 months Time interval at which "adenomas" changed to "gastric cancer": 6 months It may be configured to calculate not only the interval, but also the time interval between two tests in which the diagnostic result did not change.

After calculating the time interval, the statistical processing unit 16 derives the diagnosis interval from the calculated time interval.
FIG. 3 shows a time interval-diagnosis interval conversion table. After calculating the time interval I, the statistical processing unit 16 derives the diagnosis interval according to the conversion table shown in FIG. For example, if the time interval I is 4 months, the diagnostic interval is derived as 6 months, and if the time interval I is 10 months, the diagnostic interval is derived as 12 months. In this way, the statistical processing unit 16 determines the examination date on which the first diagnosis was made, the examination date following the examination date on which the first diagnosis was made, and the examination date on which the second diagnosis was made. to derive the diagnostic interval.

Taking patient A as an example, the statistical processing unit 16 converts the time intervals calculated for patient A into the following diagnostic intervals.
Diagnosis interval when “No abnormality” changed to “Adenoma”: 6 months Diagnosis interval when “Adenoma” changed to “Stomach cancer”: 6 months

The statistical processing unit 16 calculates the time interval between the last examination date of adenoma diagnosis and the first examination date of stomach cancer diagnosis in the examination history in which the diagnosis result changed from “adenomas” to “stomach cancer”. , derive the diagnostic interval from the time interval. Note that the statistical processing unit 16 calculates the time interval between the first examination date when adenoma was diagnosed and the first examination date when stomach cancer was diagnosed in the examination history in which the diagnosis result changed from "adenomas" to "stomach cancer". and the diagnosis interval may be derived from the time interval. For example, in the examination history of a certain patient, if adenoma is diagnosed again in examination 6 months after the date of diagnosis of adenoma, and gastric cancer is diagnosed in examination 6 months after that, the statistical processing unit 16 may derive the diagnostic interval based on the 12 months from the date of first diagnosis of adenoma to the date of first diagnosis of gastric cancer. In this case, the statistical processing unit 16 derives the diagnosis interval as 12 months.

The statistical processing unit 16 refers to the examination history of all patients and derives the diagnosis interval when the disease diagnosis result changes. Based on the patient information accumulated in the accumulation unit 32, the statistical processing unit 16 calculates statistical data regarding the combination of the derived diagnosis interval and one or more information elements of the patient information. The statistical processing unit 16 causes the statistical data storage unit 34 to store the calculated statistical data.

FIG. 4 shows an example of statistical data stored in the statistical data storage unit 34. FIG. 4 shows data obtained by statistically processing patient information of patients whose diagnosis has changed from "adenomas" to "gastric cancer". The statistical data includes the number and ratio of patients who match the information elements of "diagnosis interval", "age", "systolic blood pressure", "BMI", and "H. pylori infection status". The patient information subject to statistical processing by the statistical processing unit 16 is the patient information before the diagnosis result changes, specifically the patient information when the adenoma was first diagnosed. In this example, among the patients whose patient information has been accumulated, there are 200 patients whose diagnosis results have changed from "adenoma" to "gastric cancer". , the number of patients and the ratio of patients (ratio to the number of patients) that match the information elements of "diagnosis interval", "age", "systolic blood pressure", "BMI", and "H. pylori infection status" are calculated.

Statistical data in the first row includes age 60 to 69 years at the time of diagnosis of adenoma, systolic blood pressure of 130 to 139, BMI of 30 or more, H. pylori infection status positive, and Examination after 6 months shows that the number of patients diagnosed with gastric cancer is 124. These 124 patients account for 62% of the total number of 200 patients. This indicates that a patient aged 60 to 69 years, with a systolic blood pressure of 130 to 139, a BMI of 30 or more, a positive H. It means that if you get tested, you are very likely to be diagnosed with stomach cancer.

The statistical data in the second row show that the age at the time of diagnosis of adenoma was 60 to 69 years, the maximum blood pressure was 120 to 129, the BMI was 25 to 29, the H. pylori infection status was positive, and the adenoma was diagnosed. Examination 6 months later shows that the number of patients diagnosed with gastric cancer is 30. These 30 patients account for 15% of the total number of 200 patients. Compared to the patient group in the first row, the patient group in the second row has the same age range and infection status of Helicobacter pylori, but shows lower values for both systolic blood pressure and BMI. In addition, the percentage of the total number of 200 patients was 62% in the first stage patient group, while the second stage patient group was 15%. From this, patients who are 60 to 69 years old, have a positive H. pylori infection status, have a diagnosis interval of 6 months, have a maximum blood pressure of 130 to 139, and a BMI of 30 or more have a maximum blood pressure of 120 to 129 and a BMI of 30 or more. It can be seen that the risk of carcinogenesis is higher than that of patients 25-29.

The information element identifying unit 18 identifies a combination of a diagnosis interval and one or more information elements in which the ratio of patients whose diagnosis results have changed is higher than a predetermined threshold value in association with the diagnosis result before the change, and stores the information in the information element storage unit 36. Remember. In the statistical data of patients who changed from "adenoma" to "gastric cancer", the diagnosis result before the change was "adenoma". The information element identifying unit 18 refers to the item of "ratio of people" in the statistical data, and identifies combinations of diagnosis intervals for which a ratio of people higher than a predetermined threshold is calculated and one or more information elements in the patient information. Here, when the predetermined threshold is 40%, the information element identifying unit 18 identifies a combination of the diagnosis interval and one or more information elements identified by the statistical data on the first stage shown in FIG.

Combinations of specified diagnostic intervals and one or more information elements are shown below.
"Diagnosis interval": 6 months "Age": 60-69
"Maximal blood pressure": 130-139
“BMI”: 30≧
"Hylori infection status": positive The number of patients in the patient group corresponding to this combination is 124 patients. The information element identifying unit 18 identifies a combination of a diagnosis interval and one or more information elements in which the ratio of patients whose diagnosis results have changed is higher than a predetermined threshold value in association with the diagnosis result before the change, and stores the information in the information element storage unit 36. Memorize.

The statistical data shown in FIG. 4 includes the number and ratio of patients who match the information elements of "diagnosis interval", "age", "systolic blood pressure", "BMI", and "H. pylori infection status". However, the statistical processing unit 16 may change items of patient information used for calculating statistical data. For example, the statistical processing unit 16 excludes the item "H. pylori infection status" and calculates the number and ratio of patients who match the information elements "diagnosis interval", "age", "systolic blood pressure", and "BMI". can be calculated. In addition, the statistical processing unit 16 excludes the item "H. pylori infection status" and adds the item "sex" instead, and adds the item "diagnosis interval", "age", "systolic blood pressure", "BMI", " The number and ratio of patients who match the "sex" information element may be calculated. Items used for calculating statistical data may be appropriately set for each medical facility. The statistical processing unit 16 calculates statistical data for combinations of various items, thereby making it possible to search for combinations of information elements with a high possibility of developing cancer. In this way, the statistical processing unit 16 may specify diagnostic history information including information about progression of disease from a plurality of diagnostic results, and calculate statistical data about a combination of patient information and diagnostic history information.

FIG. 5 shows another example of statistical data stored in the statistical data storage unit 34. FIG. 5 shows data obtained by statistically processing patient information of patients whose diagnosis results have changed from "no abnormality" to "adenomas". The statistical data includes the number and ratio of patients who match the information elements of "diagnosis interval", "age", "systolic blood pressure", "BMI", and "H. pylori infection status". The patient information subject to statistical processing by the statistical processing unit 16 is the most recent patient information before the diagnosis result changes, specifically the patient information when the patient was finally diagnosed as having no abnormality. In this example, among the patients whose patient information has been accumulated, there are 400 patients whose diagnosis results have changed from "no abnormality" to "adenomas", and the statistical processing unit 16 has derived the diagnosis interval for each patient. In addition, the number of patients and the ratio of patients (ratio to the number of patients) that match the information elements of "diagnosis interval", "age", "systolic blood pressure", "BMI", and "H. pylori infection status" are calculated.

Statistical data in the first row includes age 50 to 59 years old when diagnosed as normal, systolic blood pressure 130 to 139, BMI 30 or more, Helicobacter pylori infection status positive, and diagnosed as normal. Examination 12 months later shows that the number of patients diagnosed with adenoma is 200. These 200 patients account for 50% of the total number of 400 patients. This means that a patient aged 50-59 years, with a systolic blood pressure of 130-139, a BMI of 30 or more, who is positive for H. Having a microscopy means that you are very likely to be diagnosed with an adenoma.

The statistical data on the second row includes age 60 to 69 years old, systolic blood pressure of 130 to 139, BMI of 30 or higher, and positive H. pylori infection status at the time of diagnosis of no abnormalities, and no abnormalities were diagnosed. A test 12 months after being treated shows that the number of patients diagnosed with adenoma is 40. These 40 patients account for 10% of the total number of 400 patients. Compared to the patient group in the first row, the patient group in the second row has the same systolic blood pressure, BMI, and H. pylori infection status, but the age range is higher. Based on this, patients aged 50 to 59 with a systolic blood pressure of 130 to 139, a BMI of 30 or higher, and a positive H. pylori infection status have a higher risk of carcinogenesis than patients aged 60 to 69. I understand.

It should be noted that the method of setting the parameter when calculating the ratio is not limited to this. For example, in the statistical data in the first row, the age at the time of diagnosis of no abnormality is 50 to 59 years old, the maximum blood pressure is 130 to 139, the BMI is 30 or more, the infection status of Helicobacter pylori is positive, and the diagnosis is without abnormality. We set the total number of patients who underwent examination 12 months after being diagnosed as the population parameter, and calculated the percentage of patients who were diagnosed with adenoma in examination 12 months after being diagnosed with no abnormalities. good too. Age 50 to 59 years old at the time of diagnosis, systolic blood pressure 130 to 139, BMI 30 or more, H. pylori infection status positive, examination 12 months after the diagnosis. When setting the total number of patients who have undergone the test as the population parameter, the information on the time interval between two examinations in which the diagnostic results did not change, calculated by the statistical processing unit 16, may also be used to calculate the population parameter. .

The information element identifying unit 18 identifies a combination of a diagnosis interval and one or more information elements in which the ratio of patients whose diagnosis results have changed is higher than a predetermined threshold value in association with the diagnosis result before the change, and stores the information in the information element storage unit 36. Remember. In the statistical data of patients who changed from "no abnormality" to "adenomas", the diagnostic result before the change was "no abnormality". The information element identifying unit 18 refers to the item of "ratio of people" in the statistical data, and identifies combinations of diagnosis intervals for which a ratio of people higher than a predetermined threshold is calculated and one or more information elements in the patient information. Here, when the predetermined threshold is 40%, the information element identifying unit 18 identifies a combination of the diagnosis interval and one or more information elements identified by the statistical data on the first stage shown in FIG.

Combinations of specified diagnostic intervals and one or more information elements are shown below.
"Diagnosis interval": 12 months "Age": 50-59
"Maximal blood pressure": 130-139
“BMI”: 30≧
"H. pylori infection status": positive The information element identification unit 18 identifies a combination of a diagnosis interval and one or more information elements for which the ratio of patients whose diagnosis results have changed is higher than a predetermined threshold, in association with the diagnostic results before the change. and stored in the information element storage unit 36.

FIG. 6 shows an example of combinations of information elements stored in the information element storage unit 36. FIG. The information element storage unit 36 stores combinations of diagnostic intervals and one or more information elements in association with diagnostic results. Specifically, the information element storage unit 36 stores a combination of a diagnosis interval in which the percentage of patients whose diagnosis results have changed in the past is higher than a predetermined threshold value and one or more information elements in association with the diagnosis result before the change.

The information element storage unit 36 stores a combination of information elements with a high possibility that the disease condition will progress. As described above, the patient information of the patient diagnosed as "no abnormality" is stored in association with "no abnormality" in FIG. , the patient is likely to be diagnosed with adenoma when undergoing endoscopy 12 months later. In addition, the patient information of the patient diagnosed with "adenoma" corresponds to the information elements of "age", "systolic blood pressure", "BMI", and "pylori infection status" stored in association with "adenoma" in FIG. If so, the patient is likely to be diagnosed with an adenoma when undergoing endoscopy 6 months later. Therefore, the combination of information elements shown in FIG. 6 becomes useful statistical data that can be used when deriving the timing of the next examination.

The determination unit 20 determines that a plurality of information elements included in the patient information of the target patient for whom the next examination time is to be set are associated with the diagnosis results of the examinations that the patient has undergone in the past in the information element storage unit 36. It is determined whether or not the above information elements are met. The determination unit 20 searches for the latest patient information and examination information of the patient accumulated in the accumulation unit 32, and the information elements of the latest diagnosis result and patient information are stored in the information element storage unit 36 and the diagnosis result and 1 It is determined whether or not the combination of the above information elements is suitable. That is, the determination unit 20 determines the high disease risk of the target patient by determining whether the patient for whom the examination time is to be set is included in the high-risk patient group. If the target patient is included in the high-risk patient group, the determination unit 20 may determine the target patient as a high-risk patient whose disease risk is equal to or higher than a predetermined threshold. This is because the determination unit 20 stores at least one of the patient information of the target patient for whom the examination time is to be set and the past examination information of the target patient, the past patient information of the plurality of patients accumulated in the accumulation unit 32, and the It means determining the high disease risk of the target patient based on past examination information.

Referring to the patient information and examination information of patients A to E shown in FIG. matches Specifically, the latest patient information and examination information of patient E whose examination date is 2021/2/1 is
"Diagnostic result": Adenoma "Age": 65 "Maximal blood pressure": 135
"BMI": 35
"H. pylori infection status": Positive and compatible with the combination of information elements associated with "Adenoma" shown in FIG. Therefore, the determination unit 20 determines that the patient information of the patient E matches the combination of information elements stored in the information element storage unit 36, and determines that the patient E has a high risk of developing stomach cancer. Identify yourself as a patient.

In this way, the determination unit 20 determines the degree of disease risk of a target patient having predetermined information elements of patient information and predetermined information elements of past examination information based on patient information and past examination information of a plurality of patients. do. In the embodiment, the determination unit 20 searches for the latest patient information and examination information of the patient stored in the storage unit 32, and the information elements of the latest diagnosis results and patient information are stored in the information element storage unit 36. It is determined whether the combination of the diagnostic result and one or more information elements matches. The present disclosure is not limited to this, and the determination unit 20 identifies a group of patients whose latest diagnosis results and information elements of patient information match the statistical data stored in the statistical data storage unit 34, and identifies a group of patients with a high disease risk. You may judge the sturdiness. Specifically, the determining unit 20 may determine the level of disease risk based on information on the ratio of the number of people in the matched patient group, which indicates the level of risk.

For example, in the statistical data shown in FIG. The ratio of the number of patients in this group is 6%. A patient group with a high population ratio has a relatively high disease risk, and a patient group with a low population ratio has a relatively low disease risk. Therefore, the disease risk is highest in the patient group in the first row, and decreases in order from the patient group in the second row, the patient group in the third row, and the patient group in the fourth row. The determination unit 20 may determine the degree of disease risk by identifying a patient group to which the diagnostic result of the target patient for whom the examination time is to be set and the information elements of the patient information match. In this way, the determination unit 20 determines the height of the disease risk of the target patient based on at least one of the patient information of the target patient, the past examination information, and the statistical data stored in the statistical data storage unit 34. may be configured to

The examination timing derivation unit 22 derives examination timing information regarding the timing of the next examination for the target patient based on the target patient's disease risk level and the target patient's past examination implementation time. In an embodiment, the examination timing derivation unit 22 derives examination timing information regarding the next examination timing for the patient E who has been determined to be included in the high-risk patient group. For example, the inspection time derivation unit 22 may derive inspection time information regarding the next inspection time using statistical data including diagnostic intervals. Specifically, the examination timing derivation unit 22 derives timing information regarding the next examination timing by adding the diagnosis interval to the most recent examination execution timing of the patient. Regarding patient E, the most recent examination date is February 1, 2021, and the diagnosis interval associated with "adenomas" shown in FIG. 6 is six months. The next inspection date is derived as 2021/8/1. The inspection time derivation unit 22 may derive the next inspection time as a period (for example, during August 2021) instead of deriving a specific inspection date.

In addition, the examination time derivation unit 22 adds timing information regarding the next examination time to the most recent examination date as a diagnosis interval, which is an appropriate examination interval predetermined according to the height of the disease risk based on the statistical data. Information may be derived. When the diagnostic interval of the high-risk patient group based on statistical data is long, instead of the diagnostic interval based on statistical data, by using the appropriate examination interval determined according to the height of the disease risk as the diagnostic interval, It becomes possible to set the next examination of the target patient at an earlier timing. For example, if the interval between diagnoses for a group of high-risk patients based on statistical data is 24 months, using a shorter appropriate inspection interval (for example, 6 months) will have the effect of setting the next inspection time earlier. be. In this manner, the examination timing derivation unit 22 may derive examination timing information regarding the next examination timing of the target patient whose examination timing is to be set, based on the degree of disease risk and the past examination execution timing. Note that the past inspection implementation time may be a past inspection date.

When the examination timing derivation unit 22 derives the timing information regarding the next examination timing, it associates the timing information with the patient ID and stores it in the examination timing storage unit 38 . For example, the information processing apparatus 10 may send an endoscopy guide to the patient when the timing information stored in the examination timing storage unit 38 approaches.

For a patient whose latest patient information and examination information do not match the combination of information elements stored in the information element storage unit 36, the examination timing deriving unit 22 extracts timing information regarding the next examination time from the most recent examination date. It may be set after a predetermined period of time. The predetermined period is a period of time, such as 12 months or 24 months, which is predetermined as a period for periodic examinations at a hospital facility. In this manner, the examination timing derivation unit 22 may derive timing information regarding the next examination timing according to the determination result by the determination unit 20 .

In the above example, the determination unit 20 searches for the latest patient information and examination information of the patient stored in the storage unit 32, and the information elements of the latest diagnosis results and patient information are stored in the information element storage unit 36. It was determined whether the combination of the diagnostic results obtained and one or more information elements conformed. In another example, in response to a request from a doctor, the determination unit 20 converts the information elements of the diagnosis result and patient information reported by the doctor into the diagnosis result and one or more pieces of information stored in the information element storage unit 36. It may be determined whether or not the combination of elements is suitable.

When the endoscopy is completed, the doctor uses the terminal device 40 installed in the medical office to enter the examination report. The doctor causes the display of the terminal device 40 to display the report input screen, and inputs the examination results such as the diagnostic results and findings of the endoscopy to the report input screen. At this time, the terminal device 40 supplies patient information and examination information to the information processing apparatus 10 in order to acquire information about the next examination time.

In the information processing apparatus 10, when the patient information acquisition unit 12 receives the patient information and the examination information acquisition unit 14 receives the examination information, the determination unit 20 determines that the information elements of the diagnosis result and the patient information reported by the doctor are: It is determined whether or not the combination of the diagnostic result stored in the information element storage unit 36 and one or more information elements is suitable. When the judgment unit 20 judges that the information elements of the diagnosis result and patient information inputted in the report match the combination of the diagnosis result and one or more information elements stored in the information element storage unit 36, the examination time The derivation unit 22 derives time information regarding the patient's next examination time and provides it to the terminal device 40 .

FIG. 7 shows an example of a notification screen 60 displayed on the report input screen. When the doctor confirms the next scheduled examination date of the patient E and operates the OK button, the next scheduled examination date of the patient E may be stored in the examination time storage unit 38 .

The present disclosure has been described above based on multiple examples. Those skilled in the art will understand that these examples are illustrative, and that various modifications can be made to combinations of each component and each treatment process, and such modifications are within the scope of the present disclosure. be.

In the example, the test information includes the test date and diagnosis results, but may also include items related to the test results. In this case, the information element storage unit 36 may store a combination of a diagnosis interval, one or more information elements of patient information, and one or more test results in association with the diagnosis result. Items related to test results may include at least one of a lesion size item and a lesion site item. In this case, the information element storage unit 36 stores a combination of a diagnosis interval, one or more information elements of patient information, and an information element of the size of a lesion or an information element of a lesion site in association with the diagnosis result. good too.

　Patient information may include information elements of items related to chief complaints. In this case, the information element storage unit 36 may store the combination of the diagnosis interval and the information element of the item related to the chief complaint in association with the diagnosis result. The information elements of the main complaint item are, for example, lower abdominal pain, belching, and tarry stools.

In the embodiment, the statistical processing unit 16 calculates statistical data, and the determination unit 20 determines the level of the patient's disease risk based on the statistical data. You may For example, the statistical processing unit 16 converts the age-based disease risk level accumulated in the accumulation unit 32 into a function, and the determination unit 20 determines the disease risk level of the patient whose examination time is to be set based on the function. It may be configured to determine the degree of

The present disclosure can be used in the technical field of deriving information on the next inspection time.

DESCRIPTION OF SYMBOLS 1... Medical support system 10... Information processing apparatus 12... Patient information acquisition part 14... Examination information acquisition part 16... Statistical processing part 18... Information element identification part , 20... Judgment unit, 22... Inspection time derivation unit, 30... Storage device, 32... Accumulation unit, 34... Statistical data storage unit, 36... Information element storage unit, 38 . . . Examination time storage unit.

Claims

an accumulation unit for accumulating patient information of a plurality of patients and past examination information including past examination implementation times and diagnostic results of the plurality of patients;
Based on at least one of the patient information of the target patient for whom the examination time is to be set and the past examination information of the target patient, and the patient information and the past examination information accumulated in the storage unit, the disease of the target patient is determined. a determination unit that determines the level of risk;
an examination time deriving unit that derives examination time information regarding the next examination time of the target patient based on the disease risk level and the past examination implementation time;
medical support system.
the patient information is acquired from a wearable device and stored in the storage unit;
The medical support system according to claim 1, characterized by:
the past examination information is acquired from an endoscopy management system and accumulated in the accumulation unit;
The medical support system according to claim 1, characterized by:
a statistical processing unit that calculates statistical data based on the patient information and the past examination information;
further comprising a statistical data storage unit that stores the statistical data calculated by the statistical processing unit;
The determination unit determines the disease risk of the target patient based on at least one of the patient information of the target patient and the past examination information of the target patient, and the statistical data stored in the statistical data storage unit. determine height,
The medical support system according to claim 1, characterized by:
The statistical processing unit derives a diagnosis interval based on the past examination implementation time, calculates the statistical data related to the combination of the patient information and the diagnosis interval,
The inspection time derivation unit derives the inspection time information using the statistical data,
5. The medical support system according to claim 4, characterized by:
The statistical processing unit identifies diagnostic history information including information about disease progression from the plurality of diagnostic results, and calculates statistical data about a combination of the patient information and the diagnostic history information,
The determination unit determines the level of the disease risk of the target patient based on the patient information of the target patient, the latest diagnosis result, and the statistical data.
5. The medical support system according to claim 4, characterized by:
the patient information is acquired from a terminal device and stored in the storage unit;
The medical support system according to claim 1, characterized by:
The determination unit determines a high-risk patient whose disease risk is equal to or higher than a predetermined threshold,
The medical support system according to claim 1, characterized by:
The inspection time derivation unit derives a time obtained by adding a predetermined diagnostic interval to the past inspection implementation time as the inspection time information.
The medical support system according to claim 1, characterized by:
The examination time derivation unit sets the diagnosis interval according to the degree of the disease risk, and derives a date obtained by adding the diagnosis interval to the past examination implementation time as the examination time information.
The medical support system according to claim 9, characterized by:
Further comprising an inspection time storage unit that accumulates the inspection time information derived by the inspection time derivation unit,
The medical support system according to claim 9, characterized by:
The accumulation unit accumulates information elements related to past examination dates as the past examination execution time,
2. The medical support system according to claim 1, wherein the examination time deriving unit outputs the examination time information based on the degree of disease risk and the past examination date.
The patient information includes information elements of items related to Helicobacter pylori infection status,
The determination unit determines the patient information including information elements of items related to Helicobacter pylori infection status based on the patient information including information elements of items related to Helicobacter pylori infection status and the past examination information accumulated in the storage unit. Determining the high disease risk of the subject patient with
The medical support system according to claim 1, characterized by:
The information elements of the diagnosis result include adenoma and cancer,
The statistical processing unit calculates the statistical data on progression from the adenoma to the cancer,
The determination unit determines the level of disease risk of the target patient based on the statistical data regarding progression from the adenoma to the cancer.
5. The medical support system according to claim 4, characterized by:
the past examination information includes at least one of a lesion size item and a lesion site item;
Based on the patient information and the past examination information related to at least one of the lesion size item and the lesion site item stored in the storage unit, the determination unit determines the lesion size item and the lesion site item. Determining the high disease risk of the target patient having the patient information including information about at least one of the lesion site items;
The medical support system according to claim 1, characterized by:
The patient information includes information elements of items related to chief complaints,
Based on the patient information and the past examination information including the information element of the item related to the chief complaint accumulated in the storage unit, the determination unit determines the target patient having the patient information including the information element of the item related to the chief complaint based on the past examination information. determine the high disease risk of
The medical support system according to claim 1, characterized by:
The statistical processing unit derives the diagnostic interval from two test implementation times,
6. The medical support system according to claim 5, characterized by:
The statistical processing unit performs a test date on which the first diagnosis is performed, and a second diagnosis that is a test date following the test date on which the first diagnosis is performed and is different from the first diagnosis. deriving the diagnostic interval from the examination date on which the
18. The medical support system according to claim 17, characterized by:
accumulating patient information of a plurality of patients and past examination information including past examination implementation times and diagnostic results of the plurality of patients;
Based on at least one of patient information of a target patient for whom an examination time is set, past examination information of the target patient, and the accumulated patient information and the past examination information, the high disease risk of the target patient to determine the
Deriving examination time information regarding the next examination time of the target patient based on the high disease risk and the past examination implementation time;
medical assistance method.