JP2021051394A - Medical information processing apparatus and program - Google Patents

Abstract

To support guidance to improve lifestyle for a medical examinee.SOLUTION: A medical information processing apparatus according to an embodiment comprises: an acquisition unit; a setting unit; an extraction unit; a classification unit; and an output unit. The acquisition unit acquires medical data in which the state of the body of a medical examinee and the history of lifestyle of the medical examinee are recorded. The setting unit sets the name of the medical examinee's disease. The extraction unit extracts, from the medical data of the other medical examinee in which information on the onset or suspected onset of the name of a disease is recorded, medical data similar to the data characteristics of the medical data acquired by the acquisition unit. The classification unit classifies the medical data extracted by the extraction unit for every group similar in data characteristics. The output unit outputs information representing the relationship between the state of the body and the lifestyle based on the medical data belonging to the groups.SELECTED DRAWING: Figure 5

Description

Embodiments of the present invention relate to medical information processing devices and programs.

Health examination or health examination (hereinafter referred to as "health examination") manages and improves the health condition of the examinee by linking the diagnosis result based on the result of various tests to the prevention, early detection and early treatment of the disease. It is done for the purpose.

In recent years, the number of patients with lifestyle-related diseases, whose lifestyle is considered to be deeply involved in the cause of the onset, has been increasing, and the importance of medical examinations is increasing.

By the way, the diagnosis result of the medical examination is explained to the examinee by a doctor (or a nurse or a public health nurse) with reference to the test result and the like. In addition, at the time of explanation, guidance is given to improve lifestyle habits based on medical grounds such as cases reported at academic societies. For example, conventionally, a technique for supporting health guidance has been proposed.

However, for example, it is difficult for a examinee who has no subjective symptoms to realize the necessity and seriousness of the guidance even if the doctor gives guidance for improving lifestyle habits. Therefore, the examinee may miss the opportunity to improve the lifestyle.

JP-A-2007-193714

The problem to be solved by the present invention is to support the guidance for improving lifestyle habits to the examinee.

The medical information processing apparatus according to the embodiment includes an acquisition unit, a setting unit, an extraction unit, a classification unit, and an output unit. The acquisition unit acquires medical data in which the history of the examinee's physical condition and lifestyle so far is recorded, and the setting unit sets the disease name of the examinee. The extraction unit extracts medical data similar to the data characteristics of the medical data acquired by the acquisition unit from the medical data of other examinees in which information regarding the onset or suspected onset of the disease name is recorded. The classification unit classifies the medical data extracted by the extraction unit into groups having similar data characteristics. The output unit outputs information representing the relationship between the physical condition and the lifestyle based on the medical data belonging to the group.

FIG. 1 is a diagram showing a configuration example of a medical examination system according to an embodiment. FIG. 2 is a diagram showing an example of the data structure of the examinee data table according to the embodiment. FIG. 3 is a diagram showing an example of the data structure of the inspection data table according to the embodiment. FIG. 4 is a diagram showing an example of the data structure of the treatment data table according to the embodiment. FIG. 5 is a diagram showing a configuration example of the medical information processing device according to the embodiment. FIG. 6 is a diagram showing an example of a screen output by the output function of the embodiment. FIG. 7 is a diagram showing another example of the screen output by the output function of the embodiment. FIG. 8 is a flowchart showing an example of medical information processing performed by the medical information processing device of the embodiment. FIG. 9 is a diagram showing an example of a screen output by the output function of the modified example 1.

Hereinafter, embodiments of the medical information processing apparatus and the program will be described with reference to the drawings.

FIG. 1 is a diagram showing a configuration example of a medical examination system according to an embodiment. As shown in FIG. 1, the medical examination system 1 includes a examinee data DB (Data Base) 10, an examination data DB 20, a treatment data DB 30, and a medical information processing device 100. Here, the examinee data DB (Data Base) 10, the examination data DB 20, the treatment data DB 30, and the medical information processing device 100 are connected to each other via a network N such as an in-hospital LAN provided in a medical facility such as a hospital. Are connected so that they can communicate with each other. The number of each device is not limited to the number shown in FIG.

The examinee data DB 10 is a database that stores data related to the physical characteristics of the examinee. Specifically, the examinee data DB 10 is a examinee data table for storing examinee data composed of information such as age, gender, height, and weight in association with a examinee ID that identifies the examinee. Holds 11. The examinee data of each examinee is registered in the examinee data table 11 via, for example, a medical information processing device 100 or a terminal device provided in a medical facility such as a hospital. In the present embodiment, the “examinee” is a person who is in a state of health who undergoes a medical examination such as a regular health examination or a health examination, a person whose disease name is unconfirmed, a person whose disease name is confirmed (patient), or the like. Is a concept that includes.

FIG. 2 is a diagram showing an example of the data structure of the examinee data table 11. As shown in FIG. 2, the examinee data table 11 stores the “examinee ID” and the examinee data in association with each other. Here, the examinee data has items such as "age", "gender", "height", and "weight". For example, FIG. 2 shows examinee data of examinees whose examinee IDs are “P0001” and “P0002”. The items of the examinee data are not limited to the example of FIG. 2, and may include items such as the examinee's name and contact information.

The test data DB 20 is a database that stores a history of various test results performed on the examinee. Specifically, the examination data DB 20 associates examination data composed of information such as the date (examination date) when the examination was performed, the examination method, the examination item, the examination site, and the examination result with the examinee ID. Holds the inspection data table 21 for storage. The examination data of each examinee is registered in the examination data table 21 for each medical examination (examination) via, for example, a medical information processing device 100 or a terminal device provided in a medical facility such as a hospital.

FIG. 3 is a diagram showing an example of the data structure of the inspection data table 21. As shown in FIG. 3, the examination data table 21 stores the “examinee ID” and the examination data in association with each other. Here, the test data has items such as "examination date", "test method", "test item", "test site", and "test result".

For example, in the examination data table 21, as shown in FIG. 3, as the examination data of the examinee ID “P0001”, the examination data of the medical examinations performed regularly (every 6 months) from 2015 to 2018 is displayed. Registered in chronological order. In this examination data, "X-ray" and "CT" are used as the examination method, and it is recorded that the examination of "lung" was performed by photographing the "chest". In addition, the suspicion of abnormality was recorded in the inspection on April 1, 2016 and April 1, 2018, and it is recorded that the diagnosis of abnormality was made in the inspection on October 1, 2018.

Further, for example, as shown in FIG. 3, in the examination data table 21, the examination of the medical examination conducted regularly (every 6 months) from 2015 to 2016 as the examination data of the examinee ID “P0002”. Data is registered in chronological order. This test data shows that the blood test tested for "blood glucose" and "triglyceride". In addition, it is recorded that the triglyceride level was "high" in the inspection on November 1, 2015.

In this way, the test data is a record of the physical condition of the examinee at the time of the test over time. That is, the examination data corresponds to an example of medical data in which the history of the physical condition of the examinee is recorded.

The treatment data DB 30 is a database that stores data related to the lifestyle and treatment of the examinee. Specifically, the treatment data DB 30 holds a treatment data table 31 for storing the date (consultation date) when the medical examination was performed, the lifestyle data, and the treatment data in association with the examinee ID. To do. The lifestyle data and treatment data of each examinee are registered in the examination data table 21 for each medical examination via, for example, a medical information processing device 100 or a terminal device provided in a medical facility such as a hospital.

FIG. 4 is a diagram showing an example of the data structure of the treatment data table 31. As shown in FIG. 4, the treatment data table 31 stores the “examinee ID”, the “examination date”, the lifestyle data, and the treatment data in association with each other. In FIG. 4, for convenience of explanation, the “consultation date” of the treatment data table 31 is matched with the “consultation date” of the examination data table 21 shown in FIG.

Here, in the lifestyle data, the type and content of the lifestyle declared by the examinee are input. For example, in FIG. 4, the amount of smoking per day and the amount of exercise per day (number of steps) are listed as the types of lifestyle habits, and smoking declared by each examinee with examinee IDs "P0001" and "P0002". The amount and number of steps are recorded for each "visit date".

The type of lifestyle is not limited to the example shown in FIG. 4, and other types of lifestyle may be registered. For example, the type of lifestyle may be the daily intake of alcohol, the frequency of intake, or the like, or the daily calorie intake, sleep time, or the like.

In this way, the lifestyle-related data is a record of the lifestyle-related habits of the examinee over time. That is, the set of the "consultation date" and the lifestyle data in the treatment data table 31 corresponds to an example of medical data in which the history of the lifestyle of the examinee is recorded.

On the other hand, the treatment data has items such as "disease name" and "treatment plan". In the present embodiment, any element of "-", "suspect", or "onset" meaning not applicable is input in the item of "disease name". For example, if no abnormality is detected from the inspection result, "-" is input. Further, for example, when a suspicious symptom or abnormality is detected for a specific disease name from the test result, "suspicion" is input. Further, for example, when the name of the disease is confirmed from the result of the test or the like, "onset" is input. In the case of "suspicion" and "onset", the name of the corresponding disease is also input.

For example, in FIG. 4, it is recorded that the examinee ID “P0001” was diagnosed as suspected of having lung cancer on the same date from the test results on April 1, 2016 shown in FIG. In addition, although the suspicion was once resolved in the test results from October 1, 2016 to October 1, 2017, it was diagnosed as suspicion of lung cancer again in the test results on April 1, 2018, and the following 10 From the test results on the 1st of the month, it is recorded that the patient was diagnosed with lung cancer.

Further, for example, it is recorded that the examinee ID "P0002" was diagnosed as having a suspicion of fatty liver on the same date from the test results on November 1, 2015 shown in FIG. And, from the inspection result on May 1, 2016, it is recorded that the suspicion was resolved.

In the item of "treatment plan", the treatment plan of the examinee (patient) decided by the medical staff (hereinafter, simply referred to as a doctor) such as a doctor, a nurse, and a public health nurse is input. Specifically, follow-up observation, a specific treatment method, and the like are input to the "treatment plan".

For example, in FIG. 4, it is recorded that the follow-up observation was decided as a “treatment plan” for the examinee ID “P0001” from the diagnosis result of suspicion of lung cancer dated April 1, 2016. In addition, follow-up was continued from October 1, 2016 to April 1, 2018, and it was recorded that the "treatment plan" was shifted to treatment with the onset of lung cancer on October 1, 2018. Has been done.

In addition, it is recorded that the follow-up observation of the examinee ID "P0002" was decided as a "treatment plan" from the test result of suspicion of fatty liver dated November 1, 2015. It is recorded that the follow-up was continued until the inspection on May 1, 2016.

In this way, the treatment data is a record of the physical condition of the examinee over time. That is, the "consultation date" and the set of treatment data in the treatment data table 31 correspond to an example of medical data in which the history of the physical condition of the examinee is recorded.

In the present embodiment, the form in which the lifestyle data and the treatment data are held in the same table has been described, but the present invention is not limited to this, and the form in which the lifestyle data and the treatment data are held in independent tables. May be.

Further, the examinee data DB 10, the examination data DB 20, and the treatment data DB 30 may be realized by one server device (database server) or may be realized by a plurality of server devices. In the latter case, for example, the examinee data DB 10 and the treatment data DB 30 may be realized by a server device such as a hospital information system (HIS). Further, the examination data DB 20 may be realized by a server device such as a radiological information system (RIS).

The medical information processing apparatus 100 executes various processes using the medical data stored in the examinee data DB 10, the examination data DB 20, and the treatment data DB 30. Specifically, the medical information processing apparatus 100 is used by a doctor for medical examination work of a examinee. The medical information processing device 100 is realized by, for example, a computer device such as a workstation.

FIG. 5 is a diagram showing a configuration example of the medical information processing device 100. As shown in FIG. 5, the medical information processing apparatus 100 includes an I / F (interface) circuit 110, a storage circuit 120, an input circuit 130, a display 140, and a processing circuit 150.

The I / F circuit 110 is connected to the processing circuit 150 and controls the transmission and communication of various data via the network N. For example, the I / F circuit 110 accesses the examinee data DB 10, the examination data DB 20, and the treatment data DB 30. For example, the I / F circuit 110 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like.

The storage circuit 120 is connected to the processing circuit 150 and stores various data. For example, the storage circuit 120 stores various setting information related to the operation of the medical information processing apparatus 100. For example, the storage circuit 120 is realized by a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like.

The input circuit 130 is connected to the processing circuit 150, converts the input operation received from the operator into an electric signal, and outputs the input operation to the processing circuit 150. For example, the input circuit 130 is realized by a trackball, a switch button, a mouse, a keyboard, a touch panel, and the like.

The display 140 is connected to the processing circuit 150 and displays various information and various image data output from the processing circuit 150. For example, the display 140 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like.

The processing circuit 150 comprehensively controls the operation of the medical information processing apparatus 100. For example, the processing circuit 150 is realized by a processor.

The overall configuration of the medical information processing apparatus 100 according to the present embodiment has been described above. Under such a configuration, the medical information processing apparatus 100 according to the present embodiment has a function for supporting the medical examination work.

Specifically, the processing circuit 150 has an acquisition function 151, a setting function 152, an extraction function 153, a classification function 154, and an output function 155. Here, the acquisition function 151 is an example of an acquisition unit within the scope of claims. Further, the setting function 152 is an example of a setting unit within the scope of claims. Further, the extraction function 153 is an example of an extraction unit within the scope of claims. Further, the classification function 154 is an example of a classification unit in the claims. Further, the output function 155 is an example of an output unit within the scope of claims.

The acquisition function 151 acquires various data regarding the examinee to be examined (hereinafter, also referred to as the target examinee). Specifically, when the examinee ID of the target examinee is designated via the input circuit 130, the acquisition function 151 converts various data associated with the designated examinee ID into the examinee data DB 10 and the examination data. Obtained from DB20 and treatment data DB30. Then, the acquisition function 151 causes the acquired data regarding the target examinee to be displayed on the display 140.

The doctor who operates the medical information processing apparatus 100 can confirm the state of the target examinee by looking at the data acquired and displayed by the acquisition function 151. For example, the doctor can confirm the current or past physical condition of the target examinee based on the examination data, and can confirm the present or past lifestyle of the target examinee based on the lifestyle data. In addition, the doctor can also input the disease name and treatment policy of the target examinee as treatment data via the input circuit 130 or the like.

By the way, in order to improve the physical condition of the target examinee, the doctor may instruct the target examinee on measures for improving lifestyle habits based on medical grounds such as cases reported at academic societies and the like. For example, when there is a suspicion of lung cancer as in the case of the examinee ID “P0001” in FIG. 4, the doctor instructs the target examinee to reduce the amount of smoking (2 boxes / day → 0 boxes / day, etc.). .. In addition, for example, when there is a suspicion of fatty liver as in the case of the examinee ID "P0002" in FIG. 4, the doctor asks the target examinee to increase the amount of exercise (5,000 steps / day → 10,000 steps / day, etc.). Guidance.

However, for example, it is difficult for a subject examinee who has no subjective symptom to realize the necessity and severity of the improvement measures even if the doctor gives guidance on the improvement measures. Therefore, there is a possibility that the target examinees may miss the opportunity to improve their lifestyle.

Therefore, in the medical information processing apparatus 100 of the present embodiment, the setting function 152, the extraction function 153, the classification function 154, and the output function 155 support the guidance for improving the lifestyle of the target examinee.

The setting function 152 sets the disease name of the target examinee. For example, the setting function 152 may set a disease name designated by a doctor via the input circuit 130. Further, for example, the setting function 152 may estimate the disease name predicted to be affected from the basic information of the target examinee, the test result, the lifestyle, and the like, and set the estimated disease name. In addition, when a plurality of disease names that are predicted to be affected are listed, the setting function 152 may allow a doctor to select these disease names.

It should be noted that various methods can be adopted regardless of the method for estimating the disease name. For example, the setting function 152 may estimate the name of the disease predicted to be affected from the test result of the test data by a known method. In addition, the setting function 152 may estimate the disease name predicted to be affected from the lifestyle data of the target examinee based on the setting information such as the guideline showing the relationship between the lifestyle and the disease name.

The extraction function 153 extracts data on other examinees that have similar data characteristics to the target examinee. Specifically, the extraction function 153 is based on the examinee data (age, gender, height, weight) of the target examinee, the data characteristics of the examination data, the lifestyle data and the treatment data, and the disease name set by the setting function 152. , Search for other examinees who have data characteristics similar to the data characteristics of the target examinee. Then, the extraction function 153 extracts data related to the searched other examinees from the examination data DB 20 and the treatment data DB 30. Hereinafter, the operation of the extraction function 153 will be described.

First, the extraction function 153 refers to the treatment data DB 30, and searches for the examinee ID of the treatment data in which the disease name set by the setting function 152 is registered. Here, the treatment data to be searched is the treatment data of another examinee in which the onset or suspicion of the onset of the disease name set by the setting function 152 is recorded.

Next, the extraction function 153 refers to the examinee data DB 10 and narrows down the examinee IDs similar to the data characteristics of the examinee data of the target examinee from the examinee IDs of the other examinees searched. Here, it is possible to set an arbitrary threshold value regardless of the degree of determination of similarity. For example, when the age of the target examinee is 20 years old, the same age may be set to be judged to be similar, or the 20s may be set to be judged to be similar. Similarly, threshold values can be set for height and weight. When the examinee data includes items such as the examinee's name and contact information, the extraction function 153 limits the items indicating physical characteristics such as age, gender, height, and weight to determine the similarity. Shall be done.

Next, the extraction function 153 refers to the examination data DB 20 and the treatment data DB 30, and is similar to the data characteristics of the examination data, lifestyle data, and treatment data of the target examinee from among the examinee IDs of the other examinees narrowed down. Further narrow down the examinee IDs to be performed. Here, the data characteristics of the inspection data mean, for example, the inspection method, the inspection item, the inspection site, the content of the inspection result, and the tendency of the inspection result to change with time. Further, the data characteristics of the lifestyle data mean, for example, the type and content of the lifestyle and the tendency of the lifestyle to change over time. Further, the data characteristic of the treatment data means, for example, the tendency of changes over time such as the disease name and the treatment plan until the suspicion of the disease name set by the setting function 152 is registered.

As the type of lifestyle used for narrowing down, it is preferable to select the type related to the disease name set by the setting function 152. For example, when the disease name is "lung cancer", it is preferable to select the type of lifestyle related to smoking. Further, for example, when the disease name is "fatty liver", it is preferable to select the type of lifestyle related to the amount of exercise and eating habits. The selection method of such a type is not particularly limited, and various methods can be adopted.

For example, the extraction function 153 may select the type of lifestyle related to the disease name set by the setting function 152 based on the setting information such as the guideline showing the relationship between the lifestyle and the disease name described above. Further, for example, the extraction function 153 may select a lifestyle type designated via the input circuit 130.

By the above-mentioned narrowing process, the extraction function 153 can narrow down the examinee IDs of other examinees who are similar to the data characteristics of the physical condition and lifestyle of the target examinee. It is possible to set an arbitrary threshold value regardless of the degree of similarity. Further, in the above example, both the examination data and the treatment data are set as the narrowing conditions, but only one of the data may be used as the narrowing condition.

Then, the extraction function 153 reads out the examination data, the lifestyle data, and the treatment data regarding the other examinees narrowed down by the above-mentioned narrowing process from the examination data DB 20 and the treatment data DB 30.

The extraction function 153 may be in the form of extracting the examinee data regarding other examinees from the examinee data DB 10 together with the examination data, the lifestyle data, and the treatment data. In this case, it is preferable that the extraction function 153 performs a masking process that masks information that can identify an individual from the examinee data. For example, the extraction function 153 may execute a mask process for removing the name, contact information, and the like from the extracted examinee data.

Further, when the number of examinee IDs of other examinees finally narrowed down does not reach the predetermined value, the extraction function 153 may relax the narrowing condition until the predetermined value is reached. In this case, for example, the extraction function 153 may relax the narrowing condition by excluding some or all items of the examinee data from the narrowing condition. Further, for example, the extraction function 153 may relax the narrowing-down condition by changing the threshold value for determining the degree of similarity.

The classification function 154 classifies the data extracted by the extraction function 153 into groups having similar data characteristics. Specifically, the classification function 154 uses test data, lifestyle data, and treatment data having the same examinee ID as one unit of data, and classifies data having similar data characteristics into the same group. In addition, the classification function 154 aggregates the number of data belonging to the classified group as the number of cases. The data characteristics used by the classification function 154 for classification are the same as the data characteristics described in the extraction function 153.

In addition, the classification function 154 determines whether or not the treatment data of the data belonging to each group includes the content indicating the onset of the disease name set by the setting function 152. Then, the classification function 154 classifies the group into aggravated cases (bad cases) when the content indicating the onset is included, and classifies the group as an improved case (good case) when the content indicating the onset is not included. Classify into.

The output function 155 outputs information representing the relationship between the physical condition and the lifestyle based on the data belonging to the group classified by the classification function 154. Specifically, the output function 155 selects a group to be displayed from the groups classified by the classification function 154, and displays a screen showing the relationship between the physical condition and the lifestyle based on the data belonging to the group. Display and output to 140.

Here, the relationship between the physical condition and the lifestyle means the relationship between the test data and the treatment data included in each data constituting the same group and the lifestyle data. More specifically, the change (transition) of the lifestyle related to the disease name set by the setting function 152 over time, the test result until the onset or suspected onset of the disease name is resolved, and the treatment status over time. It means information that represents the relationship with change (transition).

Although the data constituting the same group have similarities, they may have variations depending on the time of onset and the like. In such a case, the output function 155 derives and displays the relationship between the representative physical condition and the lifestyle from the data constituting the same group by performing statistical processing for calculating the average value and the frequent value. You may let me.

In addition, the group to be displayed can be arbitrarily set regardless of the group. For example, the output function 155 may set the group with the largest number of cases as the display target. Further, for example, the output function 155 may set one or both of the groups set for the worsening case and the improving case as the display target.

FIG. 6 is a diagram showing an example of a screen output by the output function 155. FIG. 6 shows an example in which a group of aggravated cases and improved cases classified under the disease name “lung cancer” is displayed in parallel, and the upper row corresponds to the worsened case and the lower row corresponds to the improved case. The number of worsening cases is 80, and the number of improving cases is 10.

As shown in FIG. 6, the output function 155 displays the relationship between the physical condition and the lifestyle for each group of aggravated cases and improved cases. Here, as an example of the display form, the transitions of the examination data, the lifestyle data, and the treatment data are displayed in parallel in chronological order. In FIG. 6, the test data is represented by the items of "lung X-ray" and "lung CT". In addition, lifestyle-related data is represented by the item of "smoking amount" per day. In addition, the treatment data is represented by the item of "treatment status".

Further, in FIG. 6, the data portions of "previous consultation", "previous consultation", and "current consultation" mean parts similar to the data characteristics of the target examinee. That is, it means that the aggravated cases and the improved cases in FIG. 6 were extracted based on the data characteristics of various data obtained in the last three medical examinations up to the current medical examination conducted by the subject examinee. On the other hand, the data part of "visit after 6 months" to "visit after 36 months" is a part showing data characteristics peculiar to the group. In other words, by referring to the future data characteristics after the "visit this time", it is possible to easily confirm the transition of the lifestyle that led to the onset of lung cancer or the lifestyle that led to the elimination of the suspicion of onset.

It should be noted that the above-mentioned screen can be used at the time of the medical examination of the target examinee to preferably support the medical examination work of the doctor. For example, at the time of a medical examination of a target examinee, if the morbidity of lung cancer or the like is predicted from the test results and lifestyle, the doctor sets the expected disease name through the setting function 152. Then, the doctor presents the screen displayed by the output function 155 to the target examinee, and gives guidance for improving the lifestyle while observing the aggravated case or the improved case displayed on the screen.

As a result, the medical information processing apparatus 100 needs to improve the lifestyle by presenting the relationship between the physical condition and the lifestyle shown in the aggravated case, even for the subject examinee who has no subjective symptom, for example. And the severity can be felt by the target examinee. Further, since the medical information processing apparatus 100 can present a guideline for lifestyle-related improvement by presenting an improvement case, it is possible to give the target examinee a motivation for improving the lifestyle-related habit. Therefore, the medical information processing apparatus 100 can support the guidance for improving the lifestyle of the target examinee, and can increase the chances of improving the lifestyle of the target examinee.

The screen displayed by the output function 155 is not limited to the display form shown in FIG. For example, the output function 155 may display the relationship between the onset time and the number of onsets based on the onset time of the disease name recorded in each of the data (treatment data) belonging to the group of exacerbated cases.

FIG. 7 is a diagram showing another example of the screen output by the output function 155. FIG. 7 shows another display form of the aggravated case shown in the upper part of FIG. Specifically, in FIG. 7, the number of examinees diagnosed with the onset of lung cancer is displayed at each consultation timing of “6 months later consultation” to “36 months later consultation”. For example, in FIG. 7, "6 months later" is 3 people, "12 months later" is 8 people, "18 months later" is 12 people, "24 months later" is 12 people, and "30 months later". "Visit" shows that 45 people have developed the disease.

From such a display, the medical information processing apparatus 100 can make the doctor and the target examinee grasp the relationship between the onset time of the disease and the number of onsets of the aggravated case. As a result, the medical information processing apparatus 100 can make the target examinee realize the necessity and the degree of seriousness of the lifestyle habit improvement.

Next, the medical information processing performed by the medical information processing apparatus 100 will be described with reference to FIG. FIG. 8 is a flowchart showing an example of medical information processing performed by the medical information processing device 100.

First, when the examinee ID of the target examinee is designated, the acquisition function 151 acquires various data related to the examinee ID from the examinee data DB 10, the examination data DB 20, and the treatment data DB 30 (step S11).

Here, for example, when the doctor determines that the morbidity of the disease is predicted from the past and present (this time) test results and lifestyle of the target examinee displayed by the acquisition function 151, the doctor determines that the illness is predicted through the input circuit 130. , Enter the expected disease name.

When the disease name is input via the input circuit 130, the setting function 152 sets the disease name as the disease name of the target examinee (step S12). When the disease name is set in step S12, the extraction function 153 refers to the treatment data DB 30, and obtains the examinee ID of another examinee whose "suspicion" or "onset" of the set disease name is recorded in the treatment data. Search (step S13).

Subsequently, the extraction function 153 refers to each data associated with the examinee IDs of other examinees, and narrows down the examinee IDs having data similar to the data characteristics of the target examinees (step S14). Next, the extraction function 153 extracts data related to the examinee IDs of the other examinees narrowed down (step S15).

Subsequently, the classification function 154 classifies the data extracted in step S15 into groups having similar data characteristics (step S16). Then, the output function 155 causes the display 140 to display and output a screen showing the relationship between the physical condition and the lifestyle of the group based on the group classified in step S16 (step S17), and ends this process.

As described above, when the disease name is set for the target examinee, the medical information processing apparatus 100 has the data characteristics of the history based on the data indicating the history of the examination, lifestyle and treatment of the target examinee. Extract data from other patients who are similar to. Then, the medical information processing apparatus 100 classifies the extracted data into groups having similar data characteristics, and outputs a screen showing the relationship between the physical condition and the lifestyle based on the data belonging to the classified groups.

As a result, the medical information processing apparatus 100 presents to the doctor and the target examinee the lifestyle habits of other examinees who have developed the set disease name, or the lifestyle habits which have resolved the suspicion of developing the disease name. be able to. By such presentation, the medical information processing apparatus 100 can make the target examinee realize the necessity and the seriousness of the improvement of the lifestyle even if the subject examinee has no subjective symptom, for example. Therefore, the medical information processing apparatus 100 can support the guidance for improving the lifestyle of the target examinee.

The above-described embodiment can be appropriately modified and implemented by changing a part of the configuration or function of the medical information processing apparatus 100. Therefore, in the following, some modifications according to the above-described embodiment will be described as other embodiments. In the following, points different from the above-described embodiment will be mainly described, and detailed description of points common to the contents already described will be omitted. Further, the modifications described below may be carried out individually or in combination as appropriate.

(Modification example 1)
In the above-described embodiment, FIGS. 6 and 7 have been described as examples of screens output by the output function 155. In this modification, another screen output by the output function 155 will be described.

In the case of the aggravated case described above, the screen output by the output function 155 displays the test result, lifestyle, and transition of the treatment status up to the onset of the disease. The output function 155 of the present modification adds and displays additional information related to the developed disease on the screen of this exacerbated case.

Specifically, as shown in FIG. 9, the output function 155 adds additional information R indicating the risk content associated with the onset of the disease name set by the setting function 152 to the screen of the aggravated case and displays it. Here, FIG. 9 is a diagram showing an example of a screen output by the output function 155 of this modification.

FIG. 9 shows an example in which the cost for treatment of the disease name set by the setting function 152 is displayed as the additional information R. The additional information R can be generated, for example, based on the setting information in which the relationship between the disease name and the risk content associated with the onset of the disease name is associated. Specifically, the output function 155 generates additional information R indicating the risk content corresponding to the disease name set by the setting function 152, based on the setting information stored in the storage circuit 120 or the like. Then, the output function 155 adds the generated additional information to the screen of the deteriorated case and displays it.

As a result, the medical information processing apparatus 100 can present the risk when the set disease name develops to the target examinee, so that the necessity and severity of lifestyle-related improvement can be more strongly conveyed to the target examinee. You can feel it.

The risk content displayed in the additional information R is not limited to the treatment cost. For example, the output function 155 may display the treatment content and the operation content as additional information R. Further, for example, the output function 155 may display the remission rate, the mortality rate, and the like as additional information R.

(Modification 2)
In the above-described embodiment, the mode of acquiring the test data and the lifestyle data of the target examinee from the test data DB 20 and the treatment data DB 30 has been described, but the data acquisition destination is not limited to this.

For example, the target examinee may be using a device such as a smartphone or an activity meter that measures the amount of activity such as heart rate, blood pressure, and number of steps. In this case, the acquisition function 151 may acquire data indicating the daily activity amount from the device of the target examinee as examination data or lifestyle data via a communication device (not shown).

Further, for example, data acquired by a device such as an activity meter may be stored and managed by a device on a network such as a cloud. In this case, the acquisition function 151 may acquire data indicating the daily activity amount as inspection data or lifestyle data from a device on the network via an I / F circuit or a communication device (not shown).

As a result, the medical information processing apparatus 100 can collect the test results and the history of lifestyle habits in more detail, so that it is possible to improve the accuracy of determining the degree of similarity with the data of other examinees.

(Modification example 3)
In the above-described embodiment, an example in which the medical information processing device 100 is used at the time of a medical examination such as a regular health examination or a health examination is described, but the form in which the medical information processing device 100 is used is not limited to this. Absent.

For example, the medical information processing device 100 may be provided in the form of a smartphone, a PC (Personal Computer), or the like used by a examinee (user) other than a doctor. In this case, the acquisition function 151 of the medical information processing apparatus 100 acquires and records the daily physical condition and lifestyle of the user. The physical condition and lifestyle may be manually input, or may be automatically input by a function of the activity meter or the like described separately provided in the medical information processing apparatus 100.

Further, the setting function 152 of the medical information processing apparatus 100 sets, for example, the name of the assumed illness when it is determined that the illness is predicted based on the recorded physical condition or lifestyle of the user. As a result, the extraction function 153, the classification function 154, and the output function 155 of the medical information processing apparatus 100 use the data of other examinees such as the network-connected examinee data DB10, examination data DB20, and treatment data DB30. Display and output a screen showing the relationship between physical condition and lifestyle.

As a result, the medical information processing apparatus 100 can present the relationship between the physical condition and the lifestyle to the user from the daily life of the user, and therefore, the user (examinee) is instructed to improve the lifestyle at an appropriate timing. Can be done with.

(Modification example 4)
In the above-described embodiment, the output function 155 displays and outputs the relationship between the physical condition and the lifestyle on the display 140, but the output destination is not limited to this. For example, when the medical information processing device 100 includes a printing device, the output function 155 may cause the printing device to print the relationship between the physical condition and the lifestyle. Further, for example, the output function 155 may transmit the relationship between the physical condition and the lifestyle to another device connected to the network N via the I / F circuit 110.

In the above-described embodiment, an example in which the functional configuration included in the medical information processing apparatus 100 is realized by the processing circuit 150 has been described, but the embodiment is not limited to this. For example, the functional configuration in the present specification may be hardware alone or a mixture of hardware and software to realize the same function.

Further, the word "processor" used in the above description means, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an integrated circuit for a specific application (Application Specific Integrated Circuit: ASIC), or a programmable logic device. (For example, it means a circuit such as a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)). The processor realizes the function by reading and executing the program stored in the storage circuit 120. Instead of storing the program in the storage circuit 120, the program may be directly incorporated in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program embedded in the circuit. Further, the processor of the present embodiment is not limited to the case where it is configured as a single circuit, and a plurality of independent circuits may be combined to be configured as one processor to realize its function.

Here, the program executed by the processor is provided by being incorporated in a ROM (Read Only Memory), a storage circuit, or the like in advance. This program is a file in a format that can be installed or executed on these devices, such as CD (Compact Disk) -ROM, FD (Flexible Disk), CD-R (Recordable), DVD (Digital Versatile Disk), etc. It may be recorded and provided on a computer-readable storage medium. Further, this program may be provided or distributed by being stored on a computer connected to a network such as the Internet and downloaded via the network. For example, this program is composed of modules including each of the above-mentioned functional parts. In actual hardware, the CPU reads a program from a storage medium such as a ROM and executes it, so that each module is loaded on the main storage device and generated on the main storage device.

According to the embodiment described above, it is possible to support the guidance for improving the lifestyle of the examinee.

Although some embodiments (mods) of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other embodiments, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, as well as in the scope of the invention described in the claims and the equivalent scope thereof.

1 Medical examination system 10 Examiner data DB
11 Examiner data table 20 Examination data DB
21 Examination data table 30 Treatment data DB
31 Treatment data table 100 Medical information processing device 151 Acquisition function 152 Setting function 153 Extraction function 154 Classification function 155 Output function

Claims (9)

The acquisition department that acquires medical data that records the physical condition and lifestyle history of the examinee,
A setting unit that sets the disease name of the examinee,
An extraction unit that extracts medical data similar to the data characteristics of the medical data acquired by the acquisition unit from the medical data of other examinees in which information regarding the onset or suspected onset of the disease name is recorded.
A classification unit that classifies the medical data extracted by the extraction unit into groups with similar data characteristics, and a classification unit.
An output unit that outputs information representing the relationship between the physical condition and the lifestyle based on the medical data belonging to the group, and an output unit.
Medical information processing device equipped with. The acquisition unit further acquires physical characteristic data indicating the physical characteristics of the examinee, and obtains the physical characteristics.
The medical information processing device according to claim 1, wherein the extraction unit targets the other examinee who is similar to the condition of the physical characteristic data of the examinee to be extracted. The medical information processing apparatus according to claim 1 or 2, wherein the extraction unit performs the extraction based on the history of the lifestyle of the type related to the disease name set in the setting unit. The medical information according to claim 3, wherein the extraction unit selects the lifestyle of the type related to the disease name set by the setting unit based on the setting information associated with the relationship between the disease name and the type of lifestyle. Processing equipment. The medical information processing apparatus according to claim 1, wherein the output unit sets an output target is a group having the largest number of medical data constituting the group among the groups classified by the classification unit. The classification unit classifies the group in which the onset of the disease name is recorded as an aggravated case, and classifies the group in which the suspicion of the onset of the disease name is resolved as an improved case.
The medical information processing apparatus according to claim 1, wherein the output unit sets one or both of the groups of the worsened cases and the improved cases as output targets. The medical information processing device according to claim 6, wherein the output unit outputs the number of onsets for each onset time based on the onset time of the disease name recorded in each of the medical data belonging to the group of worsening cases. Claim 6 or claim 6 or the output unit adds and outputs the risk content corresponding to the disease name set in the setting unit based on the setting information in which the relationship between the disease name and the risk content related to the onset of the disease name is associated. 7. The medical information processing apparatus according to 7. For computers of medical information processing equipment
An acquisition function that acquires medical data that records the physical condition and lifestyle history of the examinee, and
A setting function for setting the disease name of the examinee and
An extraction function that extracts medical data similar to the data characteristics of the medical data acquired by the acquisition function from the medical data of other examinees in which information regarding the onset or suspected onset of the disease name is recorded, and an extraction function.
A classification function that classifies medical data extracted by the extraction function into groups with similar data characteristics, and
An output function that outputs information indicating the relationship between the physical condition and the lifestyle based on the medical data belonging to the group, and
A program to execute.

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