JP5631635B2 - Medical work support device - Google Patents

Description

  The present invention relates to a medical service support technology, and more particularly to a medical service support apparatus that manages examination data.

In medical institutions such as hospitals, past examination results and medical treatment contents are recorded and managed for each examinee in order to perform appropriate treatment. Computers are widely used to support such management work of inspection result data and the like.
For example, Patent Document 1 discloses a filing device that manages an endoscopic image and a reexamination appointment by recording an endoscopic image of a patient imaged by an endoscopic device in association with patient information. Yes.
Further, Patent Literature 2 discloses a medical information support method for storing inputted personal information, treatment information, treatment progress information, and medical information as a database, and performing classification and statistical processing.

In addition, the idea of “medical evidence-based medicine” (evidence-based medicine) has become commonplace, and past medical data that has been accumulated should be provided as judgment materials for doctors to predict the progression of medical conditions. Has been done.
For example, Patent Document 3 describes, for each internal tissue at an arbitrary future time point, from a database on the self-growth rate of lesion tissue, statistics on metastasis frequency between tissues, and metastasis frequency statistics between organs, and the volume ratio of lesion tissues. A disease state prediction system for predicting and calculating the volume ratio of a diseased tissue is disclosed.

JP 2002-203046 A JP 2006-221471 A Japanese Patent Laid-Open No. 5-266002

Early detection is extremely important for the treatment of all diseases including cancer and arteriosclerosis. Therefore, if minor abnormalities are found and follow-up is necessary, it is necessary to conduct follow-up examinations and examinations frequently. However, the financial situation in Japan and other developed countries is tight, and the pressure to reduce medical expenses is increasing. In addition, since the test places a burden on the subject's body, it is not desirable to repeat the test unnecessarily.
Therefore, it is required that the inspection interval is not too short and not too long and is performed at an optimum interval.

  The present invention has been made in view of such circumstances, and an object thereof is to provide a technique for supporting the determination of an optimal inspection interval.

  In order to solve the above-described problems, a medical operation support apparatus according to an aspect of the present invention is provided for each type of anomaly based on examination data of a plurality of subjects who have undergone a plurality of examinations and anomalies are recognized. A statistical data storage unit that stores statistical data statistically processed for correlation between the inspection interval and the parameter indicating the state of the anomalous site, and one inspection specified by the user from the statistical data stored in the statistical data storage unit An extraction unit that extracts statistical data that matches data and specified conditions, a correlation between the examination interval and a parameter indicating the state of the anomalous site based on the statistical data extracted by the extraction unit, and one specified by the user And a presentation information generation unit that generates information to be presented to the user using the inspection data.

  It should be noted that any combination of the above-described constituent elements and a conversion of the expression of the present invention between a method, an apparatus, a system, a recording medium, a computer program, etc. are also effective as an aspect of the present invention.

  According to the present invention, determination of an optimal inspection interval can be supported.

It is a block diagram which shows the structure of the medical service support system containing the medical service support apparatus concerning embodiment. It is a table | surface which shows the example of the test | inspection data regarding the one subject, and the sample data produced | generated by the medical service assistance apparatus of FIG. 1 based on the test | inspection data. FIG. 2 is a diagram showing an example of sample data plotted on the graph with the horizontal axis representing the examination interval and the vertical axis representing the risk score, and an estimated curve calculated based on these sample data. It is a figure which shows the example of the screen for a presentation which the drawing part of the medical service assistance apparatus of FIG. 1 produces | generates and an output part outputs. It is a figure which shows another example of the screen for a presentation which the drawing part of the medical service assistance apparatus of FIG. 1 produces | generates and an output part outputs. It is a flowchart explaining the process which the medical service assistance apparatus of FIG. 1 produces | generates statistical data. It is a flowchart explaining the process which produces | generates the presentation information for assisting determination of the next test time by the medical service assistance apparatus of FIG.

  When minor abnormalities (hereinafter also referred to as “abnormalities”) are found in an examination and follow-up is required, a doctor generally determines the next examination date based on his / her experience. The medical operation support apparatus according to the embodiment statistically processes the accumulated past examination data, and presents a judgment material when the doctor determines the next examination date.

  Specifically, the medical operation support apparatus according to the embodiment classifies past examination data according to the type of anomaly, and for each case, a parameter indicating the status of the anomalous part such as the size, size, number of the anomaly parts, etc. Statistically investigate how this changes over time. Then, the time at which the anomaly progresses to a level that requires treatment such as surgery, the time at which the parameter indicating the state of the anomalous site changes abruptly, and the like are statistically obtained.

  The medical work support apparatus according to the embodiment uses a statistical data of a case similar to the case of the subject when instructed by a user such as a doctor, as a judgment material for determining the next examination date. Present. The medical service support apparatus according to the embodiment may also calculate and present a recommended next examination time.

Hereinafter, the configuration of the medical operation support apparatus 100 according to the embodiment will be described with reference to the drawings.
FIG. 1 is a block diagram showing a configuration of a medical work support system 10 including a medical work support apparatus 100 according to an embodiment of the present invention.
The medical service support system 10 includes a medical service support apparatus 100, a subject information storage unit 210, an examination data storage unit 220, an operation unit 230, and a display device 240.

  The subject information storage unit 210 stores attribute information of a plurality of subjects who have been examined. The subject attribute information includes, for example, subject identification information for uniquely identifying each subject (hereinafter also referred to as “subject ID”), the date of birth of the subject, gender, and contact information. It is information about the subject such as medical history, medical history, examination record, etc., which is necessary when contacting or examining the subject at a medical institution such as a hospital.

  The inspection data storage unit 220 stores the inspection data of each subject. Examination data refers to, for example, examination identification information (hereinafter, also referred to as “examination ID”) that uniquely identifies each examination, examination date, examination type, subject ID, and an abnormal part observed in the examination. And shape, size, doctor's findings, and images.

The operation unit 230 is configured such that the user can input an instruction to the medical work support apparatus 100 by manual input or voice input. The display device 240 displays information to be presented to the user generated by the medical work support device 100.
The operation unit 230 and the display device 240 may be configured as part of a user terminal used by the user. Although one operation unit 230 and display device 240 are illustrated in FIG. 1, a plurality of operation units 230 and display devices 240 may be provided. Further, instead of the display device 240, other output means such as a printer or a speaker may be provided.
These components are communicably connected by a network such as an intranet, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), and the Internet.

The medical service support apparatus 100 includes an instruction reception unit 110, a statistical processing unit 112, a statistical data storage unit 114, an extraction unit 116, a condition registration unit 118, a condition holding unit 120, and a presentation information generation unit 130.
The instruction receiving unit 110 receives an instruction input by the user from the operation unit 230.
For example, when the user inputs a statistical processing instruction instructing to statistically process the accumulated examination data from the operation unit 230, the instruction receiving unit 110 receives the statistical processing instruction and notifies the statistical processing unit 112.

  Note that the statistical processing instruction may instruct to perform statistical processing periodically. In this case, the instruction receiving unit 110 notifies the statistical processing instruction to the statistical processing unit 112 every time a set period such as one day, one week, or one month elapses.

  Alternatively, the statistical processing instruction may instruct to perform statistical processing at a timing when a predetermined amount of inspection data is added that is suitable for performing statistical processing. In this case, for example, the instruction receiving unit 110 receives an instruction to store the newly obtained inspection data in the inspection data storage unit 220 when a predetermined number of statistical processing triggers are received. To the statistical processing unit 112. The number of statistical processing triggers serving as a threshold may be appropriately determined based on experience and experiments, such as 10 cases, 100 cases, 1000 cases, and the like based on the scale of the system, the inspection frequency, the system usage cost for statistical processing, and the like.

  In addition, the instruction receiving unit 110 transmits an instruction to store inspection data to an inspection data registration unit (not shown) every time an instruction to store newly obtained inspection data in the inspection data storage unit 220 is input. At the same time, a statistical processing instruction may be generated and transmitted to the statistical processing unit 112.

When the statistical processing unit 112 receives the statistical processing instruction, the statistical processing unit 112 searches the subject information storage unit 210, extracts the subject who has undergone a plurality of tests, searches the test data storage unit 220, Examination data relating to a subject in which an abnormality is found in at least one of the extracted subjects is acquired.
Here, “abnormal change was recognized” means that some abnormality was recognized to the extent that follow-up or treatment was necessary, for example, when a tumor or cancer was found in the examination.

  Next, the statistical processing unit 112 classifies the extracted inspection data into a plurality of categories according to the recognized type of anomaly. The types of abnormalities include, for example, examination types, abnormal sites, types of diseases expected from abnormalities, and the like. When the examination type is endoscopic examination of colorectal cancer, for example, the abnormal site may be classified as “large intestine”, or more finely, “appendiceal opening”, “cecum”, “ascending It may be classified into “colon”, “transverse colon”, “descending colon”, “sigmoid colon”, “rectum” and the like. In addition, the type of disease expected in this case may be classified as “early cancer of the large intestine”, or more specifically, “type I (raised type)”, “type II (surface type)”, “ It may be finely classified into “III type (concave type)”.

  In the classification of the extracted data, other elements may be used in addition to the type of anomaly. For example, you may classify | categorize by the item contained in a subject's attribute information, such as a subject's sex and age.

Then, the statistical processing unit 112 generates sample data used for statistical processing from the inspection data belonging to each category for each classified category.
The sample data includes, for example, “examination interval” and “parameter indicating the state of an abnormal site”.

Here, the “inspection interval” is used as an index indicating a period of time required for a certain anomalous site to progress to the state observed in the inspection in the statistical processing. It is defined as the interval between two examinations among a plurality of examinations taken by a person.
For example, the examination interval is defined as a period between the latest examination among the examinations in which no abnormality is found and the examination used as a sample. Alternatively, the examination interval may be defined as a period between an examination in which an abnormality is first discovered and an examination used as a sample.

  The “parameter indicating the state of the anomalous site” is used as an index indicating the progress until the anomaly is determined to be a disease requiring some kind of treatment in statistical processing. Hereinafter, the parameter indicating the state of the abnormal site is also referred to as “progression index”. As the progress index, for example, test values such as the size of the tumor, the number of tumors, the depth of the tumor, and the like may be used as they are, or the test values are converted and a score (hereinafter, “ What is called “risk score” may be used.

A table 300 in FIG. 2 shows an example of examination data regarding a subject acquired by the statistical processing unit 112 and sample data generated by the statistical processing unit 112 based on the examination data.
The examination data 310 relating to one subject acquired by the statistical processing unit 112 includes items of examination ID, examination date, tumor size, and diagnosis result. Here, an example in which the size of a tumor is used as an examination data item for deriving a risk score used as a progress index is shown.

The sample data 312 generated by the statistical processing unit 112 includes items of inspection interval and risk score.
The statistical processing unit 112 calculates an inspection interval based on each inspection date recorded in the inspection data 310.
FIG. 2 shows an example in which the examination interval is calculated with reference to the examination immediately before an abnormality is found on the examination date diagnosed as having no abnormality. That is, in the example of FIG. 2, the inspection date of inspection ID “ABC01” is February 10, 2009, and the number of days elapsed from this date to each inspection date is shown as the inspection interval.

  In the example of FIG. 2, the risk score calculated from the size of the tumor is shown as the progress index. As described above, the value obtained in the examination may be converted to determine the risk score for each examination, and the risk score may be used as the progress index. Thereby, when presenting a plurality of progress indicators based on a plurality of examination items, the scale and time change characteristics of each progress indicator can be aligned and presented in a form that allows the user to easily compare the plurality of indicators. . In addition, when predicting the next inspection time by using a plurality of progress indicators in combination, it is possible to use the calculation in a state in which the difference in the time change characteristics of each progress indicator is absorbed.

  In addition, without using the risk score, for example, a value obtained by an examination such as a tumor size may be used as it is as a progress index. Thereby, it is easy to understand intuitively and objective data can be presented to the user.

In FIG. 2, when the tumor size is 0 mm, the risk score is 0, and when the tumor size is 20 mm, the risk score is 10, and the risk score is determined so that the tumor size is proportional to the risk score. An example is shown.
Thus, by defining the risk score so that the inspection value and the risk score are proportional, the risk score can be set to a value that is easy to imagine intuitively. In addition, the scale of each progress index can be easily aligned. By aligning the scale of each progress index, for example, when presenting statistically processed data to the user, a plurality of types of risk scores can be presented in an easy-to-view form, such as plotting on a single graph.

The risk score is not limited to the example shown in FIG. 2, and may be determined using various methods.
For example, the risk score may be determined so that the risk score and the test interval are in a proportional relationship depending on where each test date is located between the reference date and the test date diagnosed as a malignant tumor.

When this method is applied to the example of FIG. 2, for example, the period 313 days from the reference date to the fourth inspection date is evenly divided, but the fractions are rounded off to be divided into 10 periods. In addition, the inspection risk score performed in the first period close to the reference date, that is, the period from the reference date to the first to 31st days is one point, and the risk score in the second period adjacent thereto is two points. Set the risk score to increase one point at a time. In the example of FIG. 2, the risk score is 0 for the first inspection with the inspection ID “ABC01”, 3 for the second inspection with the inspection ID “ABC02”, and 7 for the third inspection with the inspection ID “ABC03”. The point is determined as 10 points for the fourth inspection of the inspection ID “ABC04”.
In this example, the time dependence of the progress index is uniformly proportional.

  The period dependence of the risk score can be determined so as to be uniform among a plurality of progress indicators other than the proportional relationship. In this case, for example, after deriving an estimation curve, which will be described later, the conversion formula is determined so that the estimation curves of a plurality of progress indicators have the same shape.

  By determining the risk score calculation method in this way, it is possible to absorb the difference in the period dependence of the parameters indicating the state of each abnormal part. Therefore, when the statistically processed data is presented to the user, a plurality of types of risk scores can be plotted on a single graph and presented in a form that is easy to compare. Further, when predicting the next inspection time, a plurality of progress indices can be reflected in the prediction in a state in which differences in time change characteristics of the progress indices are absorbed.

  Subsequently, the statistical processing unit 112 determines a correlation between the examination interval and the progress index for each progress index using a plurality of sample data included in each classified category. For example, the statistical processing unit 112 plots sample data on a graph in which the horizontal axis indicates the inspection interval and the vertical axis indicates the progress index, and calculates an equation of an estimated curve indicating the correlation between the inspection interval and the progress index. To do. Various known methods may be used for calculating the equation of the estimated curve. For example, a polynomial function may be assumed as a model function, and a coefficient of a polynomial may be obtained using a least square method as a fitting method.

  FIG. 3 shows an example of sample data plotted on a graph with the horizontal axis as the inspection interval and the vertical axis as the risk score, and an estimated curve calculated based on these sample data.

The statistical processing unit 112 further determines a point that serves as a guideline in determining the time of re-examination in the calculated estimated curve. In this specification, this point is also referred to as a “change point”.
In the estimated curve shown in FIG. 3, the change points determined by the statistical processing unit 112 are marked with a circle. The change point is defined as, for example, a point where the slope of the estimated curve changes greatly, a point where the slope becomes steep, or a point where the risk score is equal to or greater than a threshold value.

When a change point is a point at which the slope of the estimated curve changes greatly, for example, a point at which the rate of change of the slope of the estimated curve becomes maximum, that is, a point at which the second derivative of the function representing the estimated curve takes a maximum value. Determine as
When the change point is a point where the slope of the estimated curve becomes steep, it is determined as a point where the slope of the estimated curve, that is, the first derivative of the function indicating the estimated curve becomes equal to or greater than a set threshold.

  The model function and fitting method assumed in calculating the estimated curve, and the risk score used for determining the change point and the threshold value of the slope of the estimated curve may be appropriately determined based on statistics and experience. These are determined in advance or set by the user and stored in the condition holding unit 120. The statistical processing unit 112 reads out and uses these functions and methods from the condition holding unit 120.

  When the model function, fitting method, change point calculation method, and the like used for calculating the estimated curve are set by the user, the instruction receiving unit 110 receives the setting input from the operation unit 230 by the user, and the condition registration unit 118. Send to. The condition registration unit 118 stores the setting received from the instruction reception unit 110 in the condition holding unit 120.

  In this way, the statistical processing unit 112 statistically processes the correlation between the examination interval and the progress index for each type of abnormality such as the type of examination, the site of the abnormality, the type of disease suspected from the examination result, The obtained statistical data is stored in the statistical data storage unit 114.

The instruction receiving unit 110 notifies the extraction unit 116 when receiving the next examination time determination support instruction input from the operation unit 230 by the user.
Support for determining the next inspection time is the presentation of statistical data of past cases similar to the case of a specific subject to be surveyed (hereinafter referred to as “survey target”), and the next test for the survey target. Any one of the recommended times may be presented, or both of them may be included.

Specifically, for example, the user designates one examination by inputting an examination ID from the operation unit 230. The user may designate the latest examination of the subject by inputting the subject ID.
The instruction receiving unit 110 receives the input subject ID and examination ID and notifies the extraction unit 116 of them. The extraction unit 116 extracts, from the subject information storage unit 210 and the examination data storage unit 220, the latest examination data of the subject designated by the subject ID or the examination data designated by the examination ID. The extraction unit 116 also extracts the subject information of the subject specified by the subject ID or the subject read from the examination data designated by the examination ID.

The extraction unit 116 acquires from the condition holding unit 120 an index (hereinafter also referred to as “key index”) that is used when generating presentation information to be presented to the user from among a plurality of progress indexes. The extraction unit 116 also acquires items (hereinafter, also referred to as “key items”) used when generating the presentation information among the items of the subject information and the items of the examination data.
The extraction unit 116 also acquires a calculation condition and the like used when calculating the recommended examination time from the condition holding unit 120.

Conditions used when generating presentation information such as key indexes, key items, and calculation conditions are specified in advance or specified by the user and stored in the condition holding unit 120.
When these are specified by the user, the instruction receiving unit 110, when receiving an instruction for specifying the key index, key item, and calculation condition input from the operation unit 230 by the user, transmits the instruction to the condition registration unit 118, and the condition registration unit 118 stores the received key index, key item, and calculation condition in the condition holding unit 120.

  The extraction unit 116 refers to the key item acquired from the condition holding unit 120 and uses the subject information extracted from the subject information storage unit 210 and the test data storage unit 220 and the latest test data to be the subject to be investigated. Acquire the data corresponding to the examiner's key items.

The extraction unit 116 also acquires, from the statistical data stored in the statistical data storage unit 114, statistical data that matches the key index and matches the data corresponding to the key item of the survey target person.
For example, the key index is “tumor size”, the key items are the type and gender of the suspected disease, and the data corresponding to the key items of the survey subjects are “colorectal cancer” and “male”, respectively. Assume that there is. At this time, the extraction unit 116 refers to the statistical data generated based on the examination data classified into the categories “colon cancer” and “male” in the past examinations, and the degree of progression is included in the statistical data. Statistical data such as formulas of estimated curves and change points derived for the index “tumor size” are acquired. Then, the acquired statistical data is transmitted to the presentation information generation unit 130.

  If there is no statistical data in the statistical data storage unit 114 that matches both the key index and the data corresponding to the key item of the survey subject, the extraction unit 116 instructs the statistical processing unit 112 to require it. Statistical data may be generated and the generated statistical data may be acquired.

Returning to FIG.
The presentation information generation unit 130 includes a drawing unit 132, an examination interval calculation unit 134, and an output unit 136.
Based on the statistical data received from the extraction unit 116, the drawing unit 132 presents the correlation between the examination interval and the progress index in the past examination data and the latest examination data of the survey target person to the user. Generate screen and print formats. Then, the output unit 136 outputs the presentation information generated by the presentation information generation unit 130 by causing the display device 240 to display the presentation information, for example.

FIG. 4 shows an example of a presentation screen 400 generated by the drawing unit 132 and output by the output unit 136.
FIG. 4 shows a correlation between a plurality of progress indexes obtained from statistics of past examination data and examination intervals. Specifically, each progression index is expressed on a graph in which the three progress indices of tumor size, number, and depth of arrival are represented by risk scores, the horizontal axis is the examination interval, and the vertical axis is the risk score. The estimated curve is displayed. In addition, change points are shown on the estimated curves of the progress indicators. And the progress index in the latest test result of the survey subject is presented with the scale of the risk score on the vertical axis aligned with the graph.

In this way, the inspection data of the survey subject is displayed by arranging the inspection data of the survey subject and the statistics of the inspection data of similar cases in the past side by side, and indicating the change points that serve as a guideline for the re-examination time. Data can be presented in a form that makes it easy to compare past statistics. In addition, by displaying multiple progress indicators at the same time, it is easy to compare materials for determining which progress indicator should be used to determine the next examination time and treatment policy according to the case. Can be presented.
As a result, it is possible to support decision making regarding the next examination time and treatment policy by a doctor.

  When the instruction receiving unit 110 receives an instruction for estimating the next recommended test time for a specific subject to be investigated, the test interval calculating unit 134 is a standard test interval that is a period until the recommended next test time. Is calculated. Specifically, the inspection interval calculation unit 134 acquires the calculation conditions used when calculating the next recommended inspection time from the condition holding unit 120, and uses the acquired conditions and the statistical data extracted by the extraction unit 116. The standard inspection interval is calculated based on the latest inspection data of the survey subject. Then, the output unit 136 reflects the standard examination interval calculated by the examination interval calculation unit 134 and outputs the presentation information to the display device 240.

  As a calculation condition used when calculating the standard inspection interval, for example, the period from the point where the key index is equal to the value of the key index of the survey subject on the estimated curve to the change point may be defined as the standard inspection interval. . In addition, the standard test interval is the period from the point on the estimated curve where the key index is equal to the value of the survey subject's key index to the point where the estimated curve reaches a threshold that can be determined as a disease such as cancer. May be defined.

FIG. 5 shows another example of the presentation screen 400 generated by the drawing unit 132 and output by the output unit 136.
In this example, a threshold value indicating a boundary that may be determined as a disease such as cancer is indicated as a boundary line 402, and an intersection between the boundary line and the estimated curve is indicated as a next recommended test time 406. ing. The standard inspection interval is a period from the time 404 corresponding to the key index value of the survey subject to the next recommended inspection time 406, and the date obtained by adding the standard inspection interval to the most recent inspection is the next recommended inspection. It is displayed in the day column 410.

  The inspection interval calculation unit 134 may calculate a standard inspection interval for each of the plurality of progress indicators. In this case, the output unit 136 outputs the presentation information by reflecting the standard examination interval for each of the plurality of progress indicators on the screen.

When presenting information related to a plurality of progress indicators, the output unit 136 may switch and display a graph or a standard inspection interval for each of the plurality of progress indicators according to a user instruction from the operation unit 230. .
As a result, a user such as a doctor can select a progress index that he / she should refer to for the case based on his / her own experience and display it in an easy-to-read state alone. The work of checking the index can be easily performed.

  Further, the output unit 136 may display only a graph and a standard examination interval for an arbitrary combination of progress indicators specified by the user via the operation unit 230 among a plurality of progress indicators. As a result, the user can decide which progress indicator should be used by concentrating on the confirmation of the progress indicator that needs to be confirmed without displaying the progress indicator that seems unnecessary for the case. .

  The output unit 136 may also display a graph and a standard inspection interval for each of the plurality of progress indicators on one screen. At this time, by displaying the correlation between a plurality of progress indexes and examination intervals on one graph, the user can easily compare the respective progress indexes and select the optimal progress index. be able to. In addition, it becomes easy for the user to determine the optimum next inspection time by comparing and examining the standard inspection intervals derived from a plurality of progress indicators.

  In addition, the output unit 136 may display a graph and a standard examination interval on an arbitrary combination of progress indicators designated by the user via the operation unit 230 among a plurality of progress indicators. Thereby, the user can select only the progress index that should be considered for the case based on his / her experience, and can display it for comparison and examination in an easy-to-read form.

  The output unit 136 also displays a graph of one progress indicator according to the conditions specified in advance or specified by the user and stored in the condition holding unit 120 even when presentation information regarding a plurality of progress indicators is created. Alternatively, the presentation information may be output by reflecting the standard examination interval obtained from the one progress degree index on the screen.

  In selecting one progress index, for example, information related to the progress index having the minimum standard inspection interval among the standard inspection intervals calculated by the inspection interval calculation unit 134 is reflected and the presentation information is output. Conditions may be defined. As a result, a suitable standard test interval can be calculated and presented when the degree of disease progression depends only on one of the designated degree of progress indicators.

  Further, as one standard inspection interval, a condition may be set so that presentation information is output by reflecting the maximum standard inspection interval among the standard inspection intervals calculated by the inspection interval calculation unit 134. This makes it possible to calculate a standard test interval that is suitable when the degree of disease progression depends on all of the designated degree-of-progress indicators.

In addition, the examination interval calculation unit 134 performs weighting processing on each progression degree index to the estimation curves for the plurality of progression degree indexes extracted by the extraction unit 116, and adds them together to calculate one estimation curve. One standard inspection interval may be calculated from the one estimated curve.
In this case, the output unit 136 reflects the graph of one progress index obtained using a plurality of progress indices and the standard examination interval on the screen to provide presentation information.

At this time, the inspection interval calculation unit 134 acquires and uses the weighting processing conditions from the condition holding unit 120.
The conditions for the weighting process are designated in advance or designated by the user and stored in the condition holding unit 120.
When specified by the user, the instruction receiving unit 110, when receiving an instruction for specifying the weighting processing condition input from the operation unit 230 by the user, transmits the instruction to the condition registration unit 118, and the condition registration unit 118 receives the received weighting process. The condition is stored in the condition holding unit 120.

  Thereby, it is possible to present a determination material suitable for a case where the degree of progression of disease depends on a plurality of degree of progression indicators but the degree of dependence varies depending on the degree of progression indicator. In addition, a standard inspection interval suitable for such a case can be calculated. In addition, when a standard model is not applicable, a doctor can calculate a standard inspection interval in consideration of his / her experience and the progress of a specific subject.

  Each block shown in FIG. 1 can be realized in terms of hardware by an element and a machine device such as a CPU of a computer, and in terms of software, it is realized by a computer program or the like. Draw functional blocks. Therefore, those skilled in the art will understand that these functional blocks can be realized in various forms by a combination of hardware and software.

The operation according to the above configuration is as follows.
FIG. 6 is a flowchart for explaining processing in which the medical work support apparatus 100 of FIG. 1 generates statistical data.

  First, the instruction receiving unit 110 receives a statistical processing instruction of accumulated examination data input by the user from the operation unit 230 (S10), and notifies the statistical processing unit 112 of it. The statistical processing unit 112 has received a plurality of examinations from the data stored in the subject information storage unit 210 and the examination data storage unit 220, and identifies subjects who have been found to have changed in at least one examination. Extraction is performed, and previous examination data regarding the extracted subject is acquired (S12).

  The statistical processing unit 112 classifies the extracted data into a plurality of categories based on the examination type, the abnormal part, the type, and the like (S14). Then, for each examination belonging to each category (S16), sample data including an examination interval and a progress index is generated (S18). Then, the statistical processing unit 112 uses the plurality of sample data to calculate an equation of an estimated curve indicating the correlation between the examination interval and the progress index (S20). Then, the statistical processing unit 112 determines a change point that serves as a guideline for determining the timing of the reexamination (S22), and stores statistical data such as an estimated curve formula and a change point in the statistical data storage unit 114 (S24). ). The statistical processing unit 112 executes the statistical processing from step S16 to step S22 on the data of each category (S24), and the statistical processing ends.

FIG. 7 is a flowchart for explaining processing in which the medical work support apparatus 100 in FIG. 1 generates presentation information for supporting the investigation subject person to determine the next examination time.
When the instruction receiving unit 110 receives an inspection time determination support instruction input by the user from the operation unit 230 (S30), the instruction receiving unit 110 notifies the extraction unit 116 of the instruction. The extraction unit 116 extracts the latest examination data and the subject information of the subject designated as the survey target from the subject information storage unit 210 and the examination data storage unit 220 (S32).

  The extraction unit 116 acquires, from the condition holding unit 120, key indicators and key items used when generating information to be presented to the user, calculation conditions used when calculating the next recommended inspection time, and the like (S34). The acquisition of the key index or the like in step S34 may be processed before the acquisition of the inspection data or the like in step S32, or may be processed in parallel with the acquisition of the inspection data or the like in step S32.

  If statistical data that matches both the key index and the data corresponding to the key item of the survey subject exists in the statistical data storage unit 114 (Y in S36), the extraction unit 116 extracts the statistical data, that is, the estimated curve. An expression, a change point, etc. are acquired (S40). When there is no statistical data that matches both the key index and the data corresponding to the key item of the survey target in the statistical data storage unit 114 (N in S36), the extraction unit 116 instructs the statistical processing unit 112 to Statistical data matching the data corresponding to the key index and the key item of the survey subject is generated (S38), and the generated statistical data is acquired (S40).

  When the instruction receiving unit 110 has received a next recommended inspection time estimation instruction (Y in S42), the inspection interval calculating unit 134 calculates a standard inspection interval that is a recommended inspection interval until the next time (S44). . When the instruction receiving unit 110 has not received the next recommended inspection time estimation instruction (N in S42), the inspection interval calculation unit 134 does not calculate the standard inspection interval. The drawing unit 132 generates presentation information by using an estimated curve indicating the correlation between the examination interval and the progress index in the past examination data, and the latest examination data of the survey target person (S46). The output unit 136 outputs the presentation information generated by the presentation information generation unit 130 by causing the display device 240 to display the information (S48). At this time, if the instruction receiving unit 110 has received the next recommended inspection time estimation instruction, the output unit 136 outputs the presentation information by reflecting the standard inspection interval determined by the inspection interval calculation unit 134 ( S48).

The usage scenes of the present apparatus with the above configuration and operation are as follows.
First, a user such as a doctor or a person in charge of examination data instructs the medical work support apparatus 100 to perform statistical processing on the accumulated examination data. The medical service support apparatus 100 statistically processes the examination data through the procedure of FIG. As a result, the past examination data is classified, and an estimated curve, a change point, or the like indicating an average progress of anomalies is calculated for each classified category.

  For example, a user such as a doctor instructs the medical work support apparatus 100 to support the determination of the next examination time of the subject when the examination of the subject is finished. Through the procedure of FIG. 7, the medical service support apparatus 100 causes the display device to display the examination data of the survey target person and the statistical data belonging to the similar category in the past, and serves as a guideline for the re-examination time. Indicates the change point. The medical service support apparatus 100 may further display a recommended period until the next examination and a recommended next examination time. A user such as a doctor can efficiently determine the next examination date based on statistics by viewing the displayed presentation information.

  The present invention has been described based on the embodiments. The embodiments are exemplifications, and it will be understood by those skilled in the art that various modifications can be made to combinations of the respective constituent elements and processing processes, and such modifications are within the scope of the present invention. .

  DESCRIPTION OF SYMBOLS 100 Medical service support apparatus, 112 Statistical processing part, 114 Statistical data storage part, 116 Extraction part, 130 Presentation information generation part, 134 Examination interval calculation part, 136 Output part, 210 Subject information storage part, 220 Examination data storage part 312 Sample data.

Claims (5)

Statistics that statistically processed the correlation between the examination interval and the parameter indicating the state of the anomaly site for each type of anomaly based on the examination data of multiple subjects who had undergone multiple examinations and anomalies were observed A statistical data storage unit for storing data;
From the statistical data stored in the statistical data storage unit, an extraction unit that extracts statistical data that matches one inspection data specified by the user under a specified condition;
Based on the statistical data extracted by the extraction unit , a plurality of changes serving as a guideline for the re-examination time derived using the first parameter indicating the inspection interval and the plurality of second parameters indicating the state of the anomalous site A medical work support apparatus comprising: a presentation information generation unit configured to generate information to be presented to a user using a point and one examination data designated by the user.   The presentation information generating unit is a graph in which one axis indicates the value of the first parameter and the other axis indicates the value of the second parameter, and the plurality of pieces obtained from the statistical data extracted by the extraction unit 2. A screen for presenting an estimated curve of the second parameter, the change point derived for each of the estimated curves, and a piece of inspection data designated by a user is generated. The medical operation support apparatus described in 1. The presentation information generation unit
Based on the statistical data extracted by the extraction unit and one inspection data designated by the user, an inspection interval calculation unit that calculates a standard inspection interval that is a recommended inspection interval until the next time,
Medical service support system according to claim 1 or 2, characterized in that an output unit for outputting the presentation information to reflect the standard test interval of the inspection interval calculating unit has calculated. Statistical data the statistical data storage unit is stored, and the first parameter is a statistical data obtained by statistically processing the correlation between the plurality of second parameters,
The inspection interval calculating unit calculates a standard test interval for each of the plurality of second parameters,
The medical operation support apparatus according to claim 3 , wherein the output unit outputs presentation information by reflecting a minimum standard examination interval among the standard examination intervals calculated by the examination interval calculation unit. A subject information storage unit for storing attribute information of a plurality of subjects;
An examination data storage unit for storing examination data of a plurality of subjects;
Statistical processing for statistically processing the correlation between the first parameter and the second parameter for each type of anomaly based on data read from the subject information storage unit and the test data storage unit Further comprising
The statistical data storage unit, a medical service support system according to any one of claims 1 to 4, characterized by characterized by storing statistical data processed in the statistical processing unit.

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