JP7402645B2 - Medical information processing equipment and programs - Google Patents

Description

Embodiments of the present invention relate to a medical information processing device and a program.

Health checkups or health checkups (hereinafter referred to as health checkups) aim to manage and improve the health condition of examinees by linking diagnostic results based on the results of various tests to disease prevention, early detection, and early treatment. It is done for a purpose.

Furthermore, in recent years, the importance of health checkups has increased as the number of patients suffering from lifestyle-related diseases, whose onset is thought to be closely related to lifestyle, has been on the rise.

By the way, the diagnosis results of the health checkup are explained to the examinee by a doctor (or nurse or public health nurse) while referring to the test results and the like. In addition, when giving explanations, guidance on improving lifestyle habits is provided based on medical evidence such as cases reported at academic conferences. For example, techniques for supporting health guidance have been proposed in the past.

However, for example, for patients who have no subjective symptoms, even if a doctor provides guidance to improve their lifestyle habits, it is difficult for them to realize the necessity and severity of the guidance. Therefore, there was a possibility that the examinee would miss an opportunity to improve his or her lifestyle habits.

Japanese Patent Application Publication No. 2007-193714

The problem to be solved by the present invention is to support guidance on lifestyle improvement for patients receiving medical examinations.

The medical information processing device according to the embodiment includes an acquisition section, a setting section, an extraction section, a classification section, and an output section. The acquisition unit acquires medical data in which the past physical condition and lifestyle history of the patient is recorded, and the setting unit sets the name of the disease of the patient. 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 patients in which information regarding the onset or suspected onset of the disease name is recorded. The classification section classifies the medical data extracted by the extraction section into groups having similar data characteristics. The output unit outputs, based on the medical data belonging to the group, a transition of case information representing a relationship between the physical condition and the lifestyle habits related to the disease name set by the setting unit .

FIG. 1 is a diagram illustrating a configuration example of a medical checkup system according to an embodiment. FIG. 2 is a diagram showing an example of the data structure of the patient data table according to the embodiment. FIG. 3 is a diagram showing an example of the data structure of the test 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 illustrating a configuration example of a medical information processing apparatus according to an 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 a screen output by the output function of the embodiment. FIG. 8 is a flowchart illustrating an example of medical information processing performed by the medical information processing apparatus of the embodiment. FIG. 9 is a diagram illustrating an example of a screen output by the output function of Modification 1.

Embodiments of a medical information processing device and a program will be described below with reference to the drawings.

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

The patient data DB 10 is a database that stores data regarding physical characteristics of patients. Specifically, the patient data DB 10 is a patient data table for storing patient data including information such as age, gender, height, weight, etc. in association with a patient ID that identifies the patient. Hold 11. The patient data of each patient is registered in the patient data table 11, for example, via the medical information processing device 100 or a terminal device installed in a medical facility such as a hospital. In this embodiment, "examiner" refers to an examinee in a healthy condition who undergoes a health checkup such as a regular health checkup or health checkup, an examinee whose disease name is undetermined, a examinee (patient) whose disease name has been determined, etc. It is a concept that includes.

FIG. 2 is a diagram showing an example of the data structure of the patient data table 11. As shown in FIG. 2, the patient data table 11 stores "patient ID" and patient data in association with each other. Here, the patient data includes items such as "age", "sex", "height", and "weight". For example, FIG. 2 shows patient data of patients whose patient IDs are "P0001" and "P0002." Note that the items included in the patient data are not limited to the example shown in FIG. 2, and may include items such as the patient's name and contact information.

The test data DB 20 is a database that stores the history of various test results performed on patients. Specifically, the test data DB 20 associates test data consisting of information such as the date of the test (date of consultation), test method, test items, test site, test results, etc. with the patient ID. It holds an inspection data table 21 for storage. The test data of each examinee is registered in the test data table 21 for each medical checkup (examination), for example, via the medical information processing device 100 or a terminal device installed in a medical facility such as a hospital.

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

For example, as shown in FIG. 3, the test data table 21 includes test data for health checkups conducted periodically (every 6 months) from 2015 to 2018 as test data for patient ID "P0001." Registered in chronological order. In this examination data, "X-ray" and "CT" are used as the examination methods, and it is recorded that the "lungs" were examined by photographing the "chest". In addition, it is recorded that there was a suspicion of an abnormality in the inspections on April 1, 2016 and April 1, 2018, and that it was diagnosed as abnormal in the inspection on October 1, 2018.

For example, as shown in FIG. 3, the examination data table 21 includes examination data for medical examinations conducted periodically (every 6 months) from 2015 to 2016 as examination data for examinee ID "P0002". Data is registered in chronological order. This test data indicates that "blood sugar level" and "neutral fat" were tested by blood test. Additionally, an examination on November 1, 2015 recorded that her triglyceride level was "high."

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

The treatment data DB 30 is a database that stores data related to the patient's lifestyle and treatment. Specifically, the treatment data DB 30 holds a treatment data table 31 for storing the date of the medical examination (examination date), lifestyle data, and treatment data in association with the examinee ID. do. The lifestyle data and treatment data of each examinee are registered in the examination data table 21 for each medical examination, for example, via the medical information processing device 100 or a terminal device installed 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 "patient ID," "date of consultation," lifestyle data, and treatment data in association with each other. In FIG. 4, for convenience of explanation, the "date of consultation" in the treatment data table 31 is matched with the "date of consultation" in the examination data table 21 shown in FIG. 3.

Here, the type and details of the lifestyle reported by the patient are input into the lifestyle data. For example, in Figure 4, the amount of smoking per day and the amount of exercise (number of steps) per day are listed as the types of lifestyle habits, and the smoking reported by each patient with the patient ID "P0001" and "P0002". The amount and number of steps are recorded for each "day of consultation".

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, etc., or the daily calorie intake, sleeping hours, etc.

In this way, the lifestyle data is a record of the lifestyle habits of the patient over time. In other words, the combination of "date of consultation" and lifestyle data in the treatment data table 31 corresponds to an example of medical data in which the history of the lifestyle habits of the patient is recorded.

On the other hand, the treatment data includes items such as "disease name" and "treatment plan." In this embodiment, in the item "Disease name", any one of the following elements is input: "-" meaning not applicable, "Suspected", and "Onset". For example, if no abnormality is detected from the test results, "-" is input. Further, for example, if suspicious symptoms or abnormalities are detected for a specific disease name from the test results, "suspect" is input. Further, for example, when the name of the disease is determined from the results of a test or the like, "onset" is input. In addition, in the case of "suspicion" and "onset", the corresponding disease name is also input.

For example, in FIG. 4, it is recorded that the examinee ID "P0001" was diagnosed with suspected lung cancer on the same date based on the test results on April 1, 2016 shown in FIG. In addition, the test results from October 1, 2016 to October 1, 2017 temporarily resolved the suspicion, but the test results on April 1, 2018 again confirmed that lung cancer was suspected. Based on the test results on the first day of the month, it is recorded that he was diagnosed with lung cancer.

Further, for example, regarding patient ID "P0002," from the test results on November 1, 2015 shown in FIG. 3, it is recorded that the patient was diagnosed with suspected fatty liver on the same date. The test results on May 1, 2016 indicate that the suspicions have been resolved.

In the "Treatment Plan" field, a treatment plan for the examinee (patient) determined by a medical professional such as a doctor, nurse, or public health nurse (hereinafter simply referred to as a doctor) is input. Specifically, in the "treatment plan", follow-up observations, specific treatment methods, etc. are input.

For example, in FIG. 4, it is recorded that follow-up observation has been determined as a "treatment plan" based on the diagnosis result of suspected lung cancer dated April 1, 2016 for the patient ID "P0001." In addition, follow-up observation continued from October 1, 2016 to April 1, 2018, and it was recorded that the "treatment plan" was shifted to treatment due to the onset of lung cancer on October 1, 2018. has been done.

Furthermore, regarding the patient ID "P0002", it is recorded that based on the test result of suspected fatty liver dated November 1, 2015, follow-up observation was decided as a "treatment plan". It is recorded that follow-up observation continued until the examination on May 1, 2016.

In this way, the treatment data is a record of the patient's physical condition over time. In other words, the combination of "date of consultation" and treatment data in the treatment data table 31 corresponds to an example of medical data in which the history of the physical condition of the patient is recorded.

Note that in this embodiment, a mode in which lifestyle data and treatment data are held in the same table has been described, but the present invention is not limited to this, and a mode in which lifestyle data and treatment data are each held in independent tables is also possible. You can also use it as

Furthermore, 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 patient 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 radiology information system (RIS).

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

FIG. 5 is a diagram showing a configuration example of the medical information processing apparatus 100. As shown in FIG. 5, the medical information processing device 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 transmission and communication of various data via the network N. For example, the I/F circuit 110 accesses patient data DB10, examination data DB20, and treatment data DB30. 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 memory 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 an input operation received from an operator into an electrical signal, and outputs the electrical signal 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, or 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, processing circuit 150 is implemented by a processor.

The overall configuration of the medical information processing apparatus 100 according to the present embodiment has been described above. With such a configuration, the medical information processing apparatus 100 according to the present embodiment has a function for supporting 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 in the claims. Further, the setting function 152 is an example of a setting unit in the claims. Further, the extraction function 153 is an example of an extraction unit in the 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 in the claims.

The acquisition function 151 acquires various data related to a medical examinee (hereinafter also referred to as a target examinee). Specifically, when the patient ID of the target patient is specified via the input circuit 130, the acquisition function 151 transfers various data associated with the specified patient ID to the patient data DB 10 and the examination data. It is acquired from DB20 and treatment data DB30. The acquisition function 151 then causes the display 140 to display the acquired data regarding the target patient.

A doctor operating the medical information processing apparatus 100 can check the condition of the target patient by viewing the data acquired and displayed by the acquisition function 151. For example, a doctor can check the current or past physical condition of the target patient based on test data, or can check the current or past lifestyle of the target patient based on lifestyle data. Further, the doctor can also input the target patient's disease name and treatment policy as treatment data via the input circuit 130 or the like.

Incidentally, in order to improve the physical condition of the target patient, a doctor may instruct the target patient on lifestyle improvement measures based on medical evidence such as cases reported at academic conferences and the like. For example, if there is a suspicion of lung cancer, such as patient ID "P0001" in Figure 4, the doctor will instruct the patient to reduce the amount of smoking (from 2 packs/day to 0 packs/day, etc.) . For example, if there is a suspicion of fatty liver, such as patient ID "P0002" in Figure 4, the doctor may ask the patient to increase the amount of exercise (5,000 steps/day → 10,000 steps/day, etc.). Instruct.

However, for example, for patients who do not have any subjective symptoms, it is difficult for them to realize the necessity and severity of improvement measures even if the doctor gives them instructions. Therefore, there was a possibility that target patients would miss opportunities to improve their lifestyle habits.

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

The setting function 152 sets the disease name of the target patient. 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 name of a disease that is predicted to cause the patient based on basic information, test results, lifestyle habits, etc. of the target patient, and set the estimated disease name. Furthermore, if a plurality of disease names are listed for which the patient is predicted to suffer, the setting function 152 may allow the doctor to select one of these disease names.

Note that the method for estimating the disease name is not particularly limited, and various methods can be employed. For example, the setting function 152 may estimate the name of the disease that the patient is expected to suffer from from the test results of the test data using a known method. Furthermore, the setting function 152 may estimate the name of the disease in which the patient is predicted to be affected from the lifestyle data of the target patient based on setting information such as guidelines indicating the relationship between lifestyle habits and disease names.

The extraction function 153 extracts data regarding other patients having similar data characteristics to the target patient. Specifically, the extraction function 153 extracts data based on patient data (age, gender, height, weight), test data, lifestyle data, and treatment data characteristics of the target patient, and the disease name set in the setting function 152. , to search for other patients with data characteristics similar to those of the target patient. Then, the extraction function 153 extracts data regarding the searched other patients from the examination data DB 20 and the treatment data DB 30. The operation of the extraction function 153 will be explained below.

First, the extraction function 153 refers to the treatment data DB 30 and searches for the patient 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 other patients in whom the onset or suspected onset of the disease name set by the setting function 152 is recorded.

Next, the extraction function 153 refers to the patient data DB 10 and narrows down patient IDs similar to the data characteristics of the patient data of the target patient from among the patient IDs of other patients searched. Here, it is possible to set an arbitrary threshold value regardless of the degree to which similarity is determined. For example, if the age of the target examinee is 20 years old, it may be set so that the same age is determined to be similar, or it may be set so that people in their 20s are determined to be similar. Further, threshold values can be similarly set for height and weight. Note that when the patient data includes items such as the patient's name and contact information, the extraction function 153 limits the items to items representing physical characteristics such as age, gender, height, and weight, and performs similarity judgment. shall be carried out.

Next, the extraction function 153 refers to the test data DB 20 and the treatment data DB 30, and selects data similar to the data characteristics of the test data, lifestyle data, and treatment data of the target patient from among the narrowed down patient IDs of other patients. Further narrow down the patient IDs to be examined. Here, the data characteristics of the test data mean, for example, the test method, test items, test site, and test result contents, and the tendency of the test results to change over time. Furthermore, the data characteristics of lifestyle data mean, for example, the type and content of lifestyle habits, and the tendency of changes in lifestyle habits over time. Furthermore, the data characteristics of the treatment data means, for example, the tendency of changes over time in the disease name, treatment plan, etc. until the suspicion of the disease name set by the setting function 152 is registered.

Note that, as the type of lifestyle used for narrowing down, it is preferable to select a type related to the disease name set in the setting function 152. For example, if 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 method for selecting such 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 setting information such as a guideline indicating the relationship between the lifestyle and disease name described above. Further, for example, the extraction function 153 may select the type of lifestyle specified via the input circuit 130.

Through the above-mentioned narrowing down process, the extraction function 153 can narrow down the patient IDs of other patients whose data characteristics of the physical condition and lifestyle habits of the target patient are similar. Note that the degree of similarity to be determined is not particularly limited, and it is possible to set an arbitrary threshold value. Further, in the above example, both the test data and the treatment data were used as the narrowing condition, but only one of the data may be used as the narrowing condition.

Then, the extraction function 153 reads test data, lifestyle data, and treatment data regarding other patients narrowed down by the above-described narrowing down process from the test data DB 20 and the treatment data DB 30.

Note that the extraction function 153 may be configured to extract patient data regarding other patients from the patient data DB 10 along with test data, lifestyle data, and treatment data. In this case, it is preferable that the extraction function 153 performs a masking process to mask personally identifiable information from patient data. For example, the extraction function 153 may perform mask processing to remove names, contact information, etc. from the extracted patient data.

Furthermore, if the number of patient IDs of other patients finally narrowed down is less than a predetermined value, the extraction function 153 may relax the narrowing down conditions until the number reaches a predetermined value. In this case, for example, the extraction function 153 may relax the narrowing down conditions by excluding some or all items of patient data from the narrowing down conditions. Further, for example, the extraction function 153 may relax the narrowing down conditions by changing a threshold value that determines 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 treats test data, lifestyle data, and treatment data with the same patient ID as one unit of data, and classifies data with similar data characteristics into the same group. Furthermore, the classification function 154 totals the number of data belonging to the classified group as the number of cases. Note that the data characteristics used by the classification function 154 for classification are the same as the data characteristics described for the extraction function 153.

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

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

Here, the relationship between physical condition and lifestyle habits means the relationship between the inspection data and treatment data included in each piece of data constituting the same group, and the lifestyle data. More specifically, changes (transitions) over time in lifestyle habits related to the disease name set in the setting function 152, and test results and treatment status over time until the disease name develops or the suspicion of onset is resolved. It means information that expresses the relationship with change (transition).

It should be noted that although each piece of data constituting the same group has similarities, there may be variations due to onset time, etc. In such a case, the output function 155 derives and displays the relationship between representative physical conditions and lifestyle habits from the data forming the same group by performing statistical processing to calculate average values and frequently occurring values. You may let them.

Furthermore, the group to be displayed is not particularly limited and can be set arbitrarily. For example, the output function 155 may set the group with the largest number of cases to be displayed. Further, for example, the output function 155 may set one or both of the groups set as worsening cases and improved cases as display targets.

FIG. 6 is a diagram showing an example of a screen output by the output function 155. FIG. 6 shows an example in which groups of worsening cases and improved cases classified under the disease name "lung cancer" are displayed in parallel, with the upper row corresponding to the worsening cases and the lower row corresponding to the improved cases. Note that the number of worsened cases is 80, and the number of improved cases is 10.

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

Further, in FIG. 6, the data portions of “previous visit,” “previous visit,” and “current visit” mean portions similar to the data characteristics of the target patient. In other words, the worsened cases and improved cases in FIG. 6 are extracted based on the data characteristics of various data obtained from the most recent three health checkups conducted by the target examinee up to the current health checkup. On the other hand, the data portion from "Visited after 6 months" to "Visited after 36 months" is a portion showing data characteristics specific to that group. In other words, by referring to future data characteristics after the "current visit," it is possible to easily confirm the lifestyle that led to the onset of lung cancer or the transition in lifestyle that led to the resolution of the suspected onset of lung cancer.

In addition, the above-mentioned screen can suitably support the doctor's medical examination work by using it during the medical examination of the target patient. For example, when a target patient is diagnosed with a disease such as lung cancer based on test results and lifestyle habits during a medical examination, the doctor sets the name of the assumed disease via the setting function 152. Then, the doctor presents the screen displayed by the output function 155 to the target patient, and gives lifestyle improvement guidance while looking at the worsened cases or improved cases displayed on the screen.

As a result, the medical information processing device 100 can, for example, show the relationship between the physical condition shown in the worsening case and the lifestyle, even if the target patient has no subjective symptoms, and can identify the need for lifestyle improvement. It is possible to make the target patient feel the severity of the problem. In addition, the medical information processing device 100 can present a guideline for improving lifestyle habits by presenting improvement cases, and therefore can provide motivation for improving lifestyle habits to the target patient. Therefore, the medical information processing device 100 can support guidance on improving the lifestyle habits of the target patient, and can increase opportunities for improving the lifestyle habits of the target patient.

Note that the screen displayed by the output function 155 is not limited to the display format shown in FIG. For example, the output function 155 may display the relationship between the onset time and the number of patients affected, based on the onset time of the disease name recorded in each of the data (treatment data) belonging to the group of worsening 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 worsened case shown in the upper part of FIG. Specifically, in FIG. 7, the number of patients diagnosed with lung cancer is displayed at each consultation timing from "6 months later" to "36 months later". For example, in Figure 7, 3 people "visited after 6 months", 8 people "visited after 12 months", 12 people "visited after 18 months", 12 people "visited after 24 months", and 12 people "visited after 30 months". ``Visit a doctor'' indicates that 45 people have developed symptoms.

From this display, the medical information processing apparatus 100 allows doctors and target patients to understand the relationship between the time of onset of the disease and the number of patients suffering from the disease for worsening cases. Thereby, the medical information processing apparatus 100 can make the target patient realize the necessity and seriousness of lifestyle improvement.

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

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

Here, for example, if the doctor determines that a disease is predicted from the past and current (current) test results and lifestyle habits of the subject patient displayed by the acquisition function 151, the doctor may , enter the assumed disease name.

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

Next, the extraction function 153 refers to each data associated with the patient IDs of other patients and narrows down patient IDs having data similar to the data characteristics of the target patient (step S14). Next, the extraction function 153 extracts data regarding patient IDs of other narrowed-down patients (step S15).

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

In this way, when a disease name is set for a target patient, the medical information processing device 100 determines the data characteristics of the history based on the data indicating the history of the target patient's past examinations, lifestyle habits, and treatments. Extract data of other patients similar to the patient. The medical information processing apparatus 100 then classifies the extracted data into groups with similar data characteristics, and outputs a screen showing the relationship between physical condition and lifestyle based on the data belonging to the classified groups.

Thereby, the medical information processing device 100 presents the lifestyle habits of other patients who have developed the set disease name or the lifestyle habits that have led to the resolution of the suspicion of the onset of the disease name to the doctor and the target patient. be able to. Through such presentation, the medical information processing apparatus 100 can make the target patient realize the necessity and seriousness of lifestyle improvement, even if the target patient has no subjective symptoms, for example. Therefore, the medical information processing apparatus 100 can support guidance on improving lifestyle habits of the target patient.

Note that the embodiments described above can be modified and implemented as appropriate by changing some of the configurations or functions of the medical information processing apparatus 100. Therefore, some modified examples of the above-described embodiment will be described below as other embodiments. Note that, below, points that are different from the embodiments described above will be mainly explained, and detailed explanations of points that are common to those already explained will be omitted. Further, the modified examples described below may be implemented individually or in appropriate combinations.

(Modification 1)
The above-described embodiment has been described using FIGS. 6 and 7 as examples of screens output by the output function 155. In this modification, other screens output by the output function 155 will be explained.

In the case of the above-mentioned worsening case, the screen output by the output function 155 will display the test results, lifestyle habits, and transition of treatment status up to the onset of the disease. The output function 155 of this modification causes the screen of this worsened case to be displayed with additional information related to the disease that has developed.

Specifically, as shown in FIG. 9, the output function 155 adds and displays additional information R indicating the risk associated with the onset of the disease name set by the setting function 152 on the aggravated case screen. 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 additional information R is the cost of treatment for the disease name set by the setting function 152. Such additional information R can be generated, for example, based on setting information that associates a disease name with the risk details associated with the onset of the disease name. 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 and displays the generated additional information on the screen of the worsening case.

As a result, the medical information processing device 100 can present the risk of developing the set disease name to the target patient. It can make you feel it.

Note that the risk details displayed in the additional information R are not limited to treatment costs. For example, the output function 155 may display the details of treatment and surgery as additional information R. Further, for example, the output function 155 may display remission rate, mortality rate, etc. as additional information R.

(Modification 2)
In the embodiment described above, the test data and lifestyle data of the target patient are acquired from the test data DB 20 and the treatment data DB 30, but the data acquisition sources are not limited thereto.

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

Furthermore, 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 daily activity amount as test data or lifestyle data from devices 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 test results and lifestyle history in more detail, thereby improving accuracy in determining similarity with data of other patients.

(Modification 3)
In the embodiment described above, an example was explained in which the medical information processing device 100 is used during a medical examination such as a regular health checkup or a health checkup, but the form in which the medical information processing device 100 is used is not limited to this. do not have.

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 patient (user) other than a doctor. In this case, the acquisition function 151 of the medical information processing apparatus 100 acquires and records the user's daily physical condition and lifestyle habits. Note that the physical condition and lifestyle habits may be input manually, or may be automatically input using a function such as the above-mentioned activity meter that the medical information processing apparatus 100 is provided with.

Furthermore, when the setting function 152 of the medical information processing apparatus 100 determines that the user is predicted to suffer from a disease based on the recorded physical condition or lifestyle habits of the user, the setting function 152 sets the name of the assumed disease. As a result, the extraction function 153, classification function 154, and output function 155 of the medical information processing device 100 use data of other patients connected to the network such as the patient data DB 10, the examination data DB 20, and the treatment data DB 30. A screen showing the relationship between physical condition and lifestyle habits is displayed and output.

As a result, the medical information processing device 100 can present to the user the relationship between the physical condition and lifestyle habits based on the user's daily life, so that the medical information processing device 100 can provide lifestyle improvement guidance to the user (examiner) at an appropriate time. It can be done with

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

Note that in the embodiment described above, an example has been described in which the functional configuration of the medical information processing apparatus 100 is realized by the processing circuit 150, but the embodiment is not limited to this. For example, the functional configurations in this specification may be implemented using only hardware or a combination of hardware and software.

Furthermore, the word "processor" used in the above explanation refers to, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an Application Specific Integrated Circuit (ASIC), or a programmable logic device. (For example, it refers to circuits such as Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA)). The processor implements functions by reading and executing programs stored in the storage circuit 120. Note that instead of storing the program in the storage circuit 120, the program may be directly incorporated into the circuit of the processor. In this case, the processor implements its functions by reading and executing a program built into the circuit. Further, the processor of this embodiment is not limited to being configured as a single circuit, but may be configured as a single processor by combining a plurality of independent circuits to realize its functions.

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

According to the embodiment described above, it is possible to support lifestyle improvement guidance for patients receiving medical examinations.

Although several embodiments (modifications) 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 forms, and various omissions, substitutions, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included within the scope and gist of the invention as well as within the scope of the invention described in the claims and its equivalents.

1 Health checkup system 10 Examinee data DB
11 Examinee data table 20 Examination data DB
21 Test 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 (11)

an acquisition unit that acquires medical data recording the physical condition and lifestyle history of the patient;
a setting section for setting a disease name of the patient;
an extraction unit that extracts medical data similar to data characteristics of the medical data acquired by the acquisition unit from among the medical data of other patients 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 having similar data characteristics;
an output unit that outputs a transition of case information representing a relationship between the physical condition and the lifestyle habits related to the disease name set by the setting unit, based on the medical data belonging to the group;
A medical information processing device comprising: The acquisition unit further acquires physical characteristic data indicating physical characteristics of the examinee,
2. The medical information processing apparatus according to claim 1, wherein the extraction unit selects the other patient having similar physical characteristic data conditions of the patient as the extraction target. The medical information processing apparatus according to claim 1 , wherein the extraction unit performs the extraction based on the lifestyle history of a type related to a disease name set by the setting unit. The medical information according to claim 3, wherein the extraction unit selects the lifestyle habits of a type related to the disease name set by the setting unit, based on setting information that associates a relationship between a disease name and a lifestyle type. Processing equipment. The medical information processing apparatus according to claim 1, wherein the output unit sets a group having the largest number of pieces of medical data constituting the group as an output target among the groups classified by the classification unit. The classification unit classifies a group in which the onset of the disease name is recorded as worsening cases, and a group in which it is recorded that the suspected onset of the disease name has been resolved as improved cases;
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. 7. The medical information processing apparatus according to claim 6, wherein the output unit outputs the number of people with symptoms 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 aggravated cases. 7. The output unit further outputs the disease name set by the setting unit based on setting information associating the relationship between the disease name and the risk content related to the onset of the disease name. 7. The medical information processing device according to 7. The output unit is configured to generate information on the case that represents the transition between the physical condition and lifestyle habits related to the disease name set in the setting unit in a chronological relationship based on the medical data belonging to the group. The medical information processing device according to claim 1, which outputs. The extraction unit extracts the medical data of the other patient who is similar to the tendency of change over time in the physical condition and lifestyle of the patient related to the disease name set by the setting unit. The medical information processing device according to claim 9, which is an extraction target. In the computer of the medical information processing device,
an acquisition function that acquires medical data that records the patient's physical condition and lifestyle history;
a setting function for setting the patient's disease name;
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 patients in which information regarding the onset or suspected onset of the disease name is recorded;
a classification function that classifies the medical data extracted by the extraction function into groups with similar data characteristics;
an output function that outputs a transition of case information representing a relationship between the physical condition and the lifestyle habits related to the disease name set by the setting function, based on the medical data belonging to the group;
A program to run.

Images