JP2006293612A - Health enhancement plan decision device - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a device for totally and efficiently deciding concrete measures necessary for health enhancement. <P>SOLUTION: This health enhancement plan decision device is provided with a database 2 for storing policy definition 21 constituted of definition for deciding the conformity of each health/living condition to each policy for health promotion and concrete measure definition 22 for deciding the relevancy of each policy to each concrete measure for health promotion, a policy selecting means 11 for searching the conformity of each policy to an inputted health/living condition status as a whole from the conformity of each policy to each inputted health/living condition of a user based on the policy definition, and for introducing a policy list showing the conformity of each policy and a concrete measure selecting means 13 for searching the conformity of each concrete measure to the policy in the policy list from relevancy relating to the policy based on the concrete measure definition, and for introducing the concrete measure guide list showing the conformity of each concrete measure. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a health promotion plan formulation device that formulates improvement measures for daily life based on health conditions and living conditions in order to prevent lifestyle-related diseases.

In the conventional health promotion plan formulation method, improvement measures were selected by one-step processing (continuous processing on the system side without any confirmation or correction by the user in the middle) with the subject's health condition and living conditions as input ( For example, Non-Patent Document 1).
Alternatively, the first step consisting of the development of health problems and living conditions into abstract problem areas (such as alcohol saving, exercise, smoking cessation, stress, etc.) and the re-examination of specific measures based on the results of alternative problem areas. Improvement measures were selected by the second stage of processing consisting of development (for example, Non-Patent Document 2).

http://www.health-net.or.jp/kenko_check/health-check/ “Challenge Program” at http://www.sankenjin.ne.jp/pub/tour/tour.html

  In the health promotion plan formulation method as in Non-Patent Document 1, improvement measures remain in the direction of abstract behavior (such as “refrain from drinking” and “lower blood pressure”). It was not possible to develop specific actions such as whether they could “lower blood pressure”. Even in the detailed explanation part of the improvement measures, only general explanation about each directionality is described, and information on what the target user should specifically do is not obtained.

  On the other hand, in order to expand to individual specific measures with one-stage configuration, it is necessary to obtain information for selection of all specific measures, but there are many situations where individual information is not required As a result, there is also a problem that various kinds of information input (unnecessary) is required.

  Further, in the health promotion plan formulation method as in Non-Patent Document 2, the first stage comprising the display area of the problem area and the selection area of the problem area for selecting one second-stage process to be called. Assuming that one problem area has been identified, the second stage process for selecting a specific measure required there is independent, so the same information is used in the first and second stages. There were problems such as requiring input and not being able to select comprehensive concrete measures that took into account the situation of other problem areas that were not selected but had no problems.

  The present invention has been made to solve the above-mentioned problems, and is required information including a plurality of problem areas, that is, specific measures comprehensively and efficiently covering policies for health promotion. The purpose is to provide a health promotion plan formulation device that can formulate while efficiently acquiring only.

  The present invention is a health promotion plan formulation device configured by a computer, and includes a policy definition comprising definitions for determining the degree of applicability of each health / life state to each policy for each health promotion policy, A database storing a specific measure definition that defines the degree of relevance of each policy to the specific measure for each specific measure for promotion, and a user input for each policy based on the policy definition The policy selection means that obtains the degree of appropriateness to the overall health / life state input from the appropriateness of each health / life state, and guides the policy list indicating the appropriateness of each policy, and each specific based on the above specific measure definition For each measure, a health promotion plan formulating device comprising: a measure selection means for obtaining a measure against the policy from the relevance of the policy in the policy list and deriving a measure list showing the measure of each measure It is in.

  In the present invention, specific measures comprehensively covering policies for promoting health are formulated while efficiently obtaining necessary information.

  The current concept of health promotion is to prevent worsening of symptoms of people who have developed health disorders (called secondary prevention), with medical insurance as the fund, doctors, public health nurses, dietitians, and exercise instructors. A vertical division system has been established in which individual know-how (for elderly people in the region, patients admitted to hospitals, home care / nursing subjects, etc.) can be handled. For this reason, the know-how is also vertically divided (second stage is independent for each job type) as seen in Non-Patent Document 2.

  On the other hand, it is difficult to systematically organize the know-how of doctors and public health nurses who are responsible for comprehensive judgment on how to respond to those who are found (assignment to specialized occupations). It is positioned as a job that professionals (people) should do. Therefore, the awareness of the problem that the user handled this work with the support of the information system by himself / herself was sparse (or disappointed).

  With the recent increase in medical cost reduction demand, health disorders (especially lifestyle-related diseases) have not yet developed, but considering health status trends and lifestyles, the reserve army is likely to develop in the near future. On the other hand, there has been an increasing social need for reviewing lifestyle habits and preventing onset (called primary prevention). On the other hand, taking into account the breadth of the corresponding person, it is extremely difficult economically and temporally for professionals to deal with all applicable persons. For this reason, in the category of providing information to healthy persons (preliminaries) outside the scope of medical insurance, the need for automation by information systems or substantial semi-automation under the supervision of professionals and the feasibility of public insurance systems have increased. (Preventive benefit legislation is being considered).

  This invention develops a comprehensive health promotion policy that integrates the know-how of individual specialists and the know-how of public health nurses who have previously been a vertical division of labor due to such changes in social trends. Yes, in response to the demand to find a combination of specific measures according to the combination of a plurality of policies due to the above-mentioned social background in the health promotion field.

Embodiment 1 FIG.
FIG. 1 is a block diagram showing an example of the configuration of a health promotion plan formulation apparatus according to the present invention. The health promotion plan formulation device is composed of, for example, a health promotion plan formulation unit 1 composed of a computer and a database 2 storing various information. The health promotion plan formulation unit 1 is a network terminal via a network NET such as the Internet, for example. T and an external information system ES can be communicated. The network terminal T and the external information system ES may be configured to be connected to the health promotion plan formulation unit 1 via another LAN or the like.

  The health promotion plan formulation unit 1 includes various means indicated by functional blocks. However, illustrations of functions not directly related to the health promotion plan are omitted. The policy selection means 11 is based on a policy definition 21 stored in the database 2 by inputting data such as a user's health condition and living condition (health / living condition), and a policy list 3 consisting of a pair of a policy and its corresponding degree ( 2) is generated. The policy selection means 12 presents the policy list 3 to the user, and obtains the policy list 4 (see FIG. 2) selected by the user. The specific measure selection means 13 receives the selected policy list 4 as an input, and generates a specific measure list 5 (see FIG. 2) composed of a specific measure and its corresponding degree based on the specific measure definition 22. The network communication means 19 communicates with the network terminal T and the external information system ES via the network NET. The other parts will be described in order.

  FIG. 2 is a flowchart for explaining the operation of the health promotion plan formulation apparatus according to the present invention. The operation will be described below with reference to the drawings. When the user's health / life state data is input from the user's network terminal T or the external information system ES, a policy list 3 consisting of pairs of policies and their corresponding degrees based on the policy definition 21 stored in the database 2. (Step S1: Policy selection means 11).

  FIG. 3 shows an example of the policy definition 21 stored in the database 2, and is a correspondence table of policies and health / life conditions used when selecting a policy. In addition, an example of a procedure for calculating the degree of policy suitability using the correspondence table of FIG. 3 is shown below.

(1) The following (2) to (4) are executed for each of the policies in FIG. 3 (FIG. 3 shows “exercise to increase endurance” as an example).
(2) For each condition line of the policy selected in (1), calculate the appropriateness of each line using the appropriateness weight function shown as an example in FIG. 4 while referring to the health / life state. .
(3) If there are multiple lines that refer to the same health / life condition, select the one with the highest degree of appropriateness, and set it as the relevant degree for that health / life condition.
(4) The corresponding degree of health / life state calculated in (2) and (3) is integrated, divided by the number of different health / life state, and averaged to obtain the corresponding degree of policy.
(5) The policy list 3 is a pair of the policy degree calculated in (4) and the policy selected in (1).

  This will be described with an example. The row 101 in the sixth row in FIG. 3 indicates that when “LDL cholesterol” takes a value of 0 or more and 30 or less, the corresponding degree of this row 101 is 1.0 (completely applicable). . Since the boundary area in FIG. 4 is 10 in the row 101 in FIG. 3, the degree of correspondence is linearly decreased to 0 (not applicable at all) when it is 0 or less −10 or more and 30 or more and 40 or less. Similarly, in the row 102 portion, the degree of correspondence is 1.0 when the value of the inquiry “use daily stairs” is “No: 2”, but the degree of correspondence is otherwise (“Yes: 1”). 0.

  The degree of appropriateness for each row calculated in this way is averaged for those having different health / life conditions 103 and is regarded as the degree of appropriateness of the policy of “exercising to increase endurance”. When there are a plurality of rows related to the same health / life state 103, the one corresponding to the health / life state is determined by the one having the maximum value.

  Next, the policy list 3 is presented (displayed) to the user, enabling selection input by the user, and the selected policy list 4 is obtained as a result (step S2: policy selection means 12). Then, using the selected policy list 4 as an input, based on the specific measure definition 22 stored in the database 2, a specific measure list 5 including pairs of specific measures and their corresponding degrees is generated and presented to the user as necessary. (Step S3: Specific measure selection means 13).

  FIG. 5 shows an example of the specific measure definition 22 stored in the database 2, and is a correspondence table of policies and specific measures used when selecting specific measures. In addition, an example of a procedure for calculating the appropriateness of the specific measure using the correspondence table of FIG. 5 is shown below.

(a) For each selected policy, the points are assigned to specific measures by the following methods (b) to (f).
(b) When the degree of relevance between the policy and the specific measure is ◎, assign 1.2 points to the specific measure by the following methods (d) to (e).
(c) When the degree of association between the policy and the condition is ○, 1.0 points are assigned to the specific measures by the following methods (d) to (e).
(d) If a score is not assigned to a specific measure, the score for that specific measure is set to 1.0.
(e) The product of the score already assigned and the score to be assigned is taken as the new score.
(f) Of the relevance levels (◎ or ○) specified in the row, those that do not appear in the policy selected by the user are (number of ◎ x 0.2) + (number of ◯ x 0.1) And subtract from the score.
(g) The score at the time of assigning scores for all policies is the appropriateness of the specific measure.
(h) The specific measure selected in (a) and the corresponding degree obtained in (g) are defined as a specific measure list 5.

  This will be described with an example. In the first row 104 in FIG. 5, the specific measure “every day” “walk from home to the nearest station” is related to the policy of “exercising to increase endurance” (the degree of association, that is, the intersection of the matrices is ◎ This means that it is expensive and is accompanied by [Constraint 1]. Similarly, line 105 shows that the specific measures of “2 to 3 times a week” and “shopping while walking for more than 1 hour” are both “exercise to increase endurance” and “provide mental margin” policy Is related (relevance is ◯).

  In order to explain the procedure for calculating the degree of appropriation of specific measures using the correspondence table of FIG. 5 above, the policy “exercise to increase endurance” and the policy “execute exercise to increase muscular strength” are selected in step S2. If so, the corresponding degree of the row 104 is 1.2 (1 corresponds to ◎), and the corresponding degree of the row 105 is 0.9 (1 corresponds to ○, but there is also 1 non-applicable ○. 0-0.1), the corresponding degree of the row 106 is 1.44 (1.2 × 1.2 because two ◎ correspond).

  Then, the specific plan list 5 obtained in this way is presented to the user. In addition, it has a two-stage configuration comprising a first-stage policy selection means 11 for determining a policy (basic policy for solving a problem area) and a second-stage specific policy selection means 13 for determining a specific policy from a combination of policies. Therefore, the policy is a structure that is compatible with the method of searching for effective effective measures related to each medium and short term while maintaining the policy over the medium to long term. However, it is only necessary to execute the second-stage specific measure selecting means 13, and the number of times of policy re-development by the first-stage policy selecting means 11 (and the number of times of reacquiring information necessary for it) is unnecessary.

  In addition, if the policy list 3 obtained in step S1 is managed by, for example, a user ID and stored and stored as the personal policy list information 21a, the next time policy selection is performed even when planning is made after a long time. Step S1 can be omitted.

  In addition, the association procedure and the degree-of-applicability calculation parameters shown in FIGS. 3 to 5 are for explaining one mode of realization, and adaptation of various methods related to similarity search, change of parameter values, and the like. Is possible. Further, the score assigned in (d) may be the degree of corresponding policy calculated by the above-described processing (1) to (5) executed for each of the policies in FIG. 3 instead of 1.0.

Embodiment 2. FIG.
Next, a second embodiment of the present invention will be described. In FIG. 1, the constraint definition 23 stores the contents of the constraints illustrated in FIG. 6 used in the second embodiment, and the additional information extracting unit 14 is a specific measure selected by the specific measure selecting unit 13. Extract the constraint list for the list. The additional information input means 15 obtains additional information related to the restriction from the user based on the restriction definition 23 and accumulates it as individual additional information 24. The constraint evaluation unit 16 re-extracts a specific measure list that satisfies the constraints from the specific measure list selected by the specific measure selection unit 13.

  The operation will be described below with reference to the flowchart of FIG. As described above, FIG. 6 is an example of the constraint definition 23. Based on the constraint definition 23, the constraint list 6 related to the specific measure list 5 selected in step S3 is extracted (step S4: additional information extracting means 14). Next, the restriction list 6 is presented to the user, and additional information 7 related to the restriction is acquired from the user (step S5: additional information input means 15). Then, the specific measure list 8 satisfying the constraints is re-extracted from the specific measure list 5 selected in step S3 (step S6: constraint evaluation means 16).

  This will be described with an example. In step S3, a specific measure “stop the train or bus and commute by bicycle” and a specific measure (see FIG. 5) “stop commuting by train and car” refer to the specific measure list 5 according to the relevance with the policy list 3 in step S3. The case where it is selected will be described. In step S4, "constraint [2] and constraint [3]" are generated as the constraint list 6 from the constraint definition 23 shown in FIG. In step S5, the restriction list 6 is presented to the user, and input of required additional information “commuting method” based on the restriction list 6 is requested. If the user inputs “train / bus” as additional information 7, in step S 6, constraint 2 is satisfied and constraint 3 fails. Only the specific measures to “commute” will be left. Then, the specific measure list 8 obtained in this way is presented to the user.

  With such a configuration, for the constraints related to the specific measures not selected by step S3, that is, the specific measure selecting means 13, the user is required to confirm the additional information (additional information other than [commuting method] in the above example). In addition to the omission, it is possible to select a more specific concrete measure (in the above example, “stop commuting by bicycle and commuting by bicycle”) using the success / failure information of the related constraints.

  Further, in step S5 (additional information input means 15), if the additional information obtained in this way is managed by a user ID, for example, and stored and stored as personal additional information 24 for future use, In the input process, when there is the stored personal additional information 24, it may be configured to be presented to the user as a default value and to be corrected as necessary. Thereby, the effort which re-inputs the additional information which the user input in the past can be reduced.

Embodiment 3 FIG.
Next, a third embodiment of the present invention will be described. When the policy list 3, the specific measure list 5, and the specific measure list 8 are presented to the user, they may be sorted in descending order of the degree of appropriateness. Further, the list length may be limited by an upper limit value (for example, m) of the number of policies (candidates) set in advance by the user or the like. Further, the policies are classified into several categories (for example, c), and n policies having a high degree of appropriateness for each category are selected from each category, and a total of n obtained from each category, c You may sort xn items in descending order. When m is given and c × n <m, the policy not included in c × n is ignored and the category is ignored, and mc × n is selected for each policy. The selected c × n items may be combined and sorted in descending order of the degree of correspondence to select m policies. Further, the value of n may be automatically set to the maximum integer that does not exceed c × n.

  Due to either or both of the ordering and numbering of such policies and measures, when a user browses them, only similar policies belonging to a specific category are selected at the top, and policies of other categories are selected. It is possible to avoid the problem that the cognition is impaired or the number of policies or specific measures that the user can easily list on the screen of the terminal T is exceeded.

Embodiment 4 FIG.
Next, a fourth embodiment of the present invention will be described. In step S2 (policy selection means 12), restrictions (lower limit and upper limit) may be provided on the number of policies that can be selected. In addition, it is necessary to restrict the user's selection based on the policy combination definition that cannot be selected at the same time, or to select the user based on the definition of the policy combination that should not be selected at the same time or the policy combination definition that should be selected at the same time. Information (pop-up screen) for requesting the determination of NO may be presented. These definitions are stored in accordance with any policy definition 21.

Embodiment 5. FIG.
Next, a fifth embodiment of the present invention will be described. In step S3 (specific measure selection means 13), in addition to the items to be implemented as specific measures (such as “walk to the station”) in the specific measure definition 22, as shown in FIG. In the same way as explained so far, the frequency should be selected at the same time by defining a specific measure including “once a week” etc. and associating it with the selected policy list 4 Anyway. Further, specific measures (with different frequency portions) may be selected based on the constraints described in the second embodiment. That is, a frequency definition 23a having the same format as the constraint definition 23 shown in FIG.

  In step S 5 corresponding to the additional information input means 15, additional information related to the constraint and the frequency is obtained from the user based on the constraint definition 23 and the frequency definition 23 a and stored as the individual additional information 24. In step S6 corresponding to the constraint evaluation means 16, the specific measure list 8 that satisfies the constraints and frequency is re-extracted from the specific measure list 5 selected in step S3. This frequency can be treated similarly as another type of constraint described above.

Embodiment 6 FIG.
Next, a sixth embodiment of the present invention will be described. In this embodiment, the specific measure adding means 17 for the user to add the specific measure definition 22 (FIG. 5) related to the specific measure selected by the specific measure selecting means 9, and the content of the specific measure input by the user is valid. By checking the characteristics, the correspondence between specific measures and policies (specific measure definition 22, individual specific measure definition 22a) and restrictions (constraint definition 23, frequency definition 23a) can be changed, edited by editing And specific measure constraint editing means 18 for redefining that it can be used by other users.

  The operation will be described below with reference to the flowchart of FIG. For example, in response to a request from the user via the network NET, the user can add the specific measure definition 22 (FIG. 5) regarding the specific measure selected in step S3 (step S7: specific measure adding means 17). At this time, the selected policy list 4 when the user inputs a new specific measure is acquired and managed in a correspondence table corresponding to FIG. You may make it add as a concrete measure (individual concrete measure definition 22a) which only the user can utilize. As a result, when the user selects the same policy from the next time onward, it is possible to select a specific measure defined by himself / herself.

  In addition, by confirming the validity of the content of the specific measures input by the user, the correspondence between the specific measures and policies of the specific measure definition 22, the personal specific measure definition 22a, the constraint definition 23, and the frequency definition 23a. Editing is performed by changing, adding, or deleting constraints and frequencies, and redefined as those that can be used by other users (step S8: specific measure constraint editing means 18). With this configuration, the service provides specific measures (examples) that are difficult for experts to complete in advance because they vary (large variations) depending on the user (place of residence, occupation, age, etc.) However, it is possible to collect a wide range from all users, and the experts can select and select them so that other users can use them. In general, people are more motivated to protect what they have decided by themselves than others have thought, so we can expect an improvement in the implementation rate of the specific measures they have defined.

Embodiment 7 FIG.
Next, a seventh embodiment of the present invention will be described. In this embodiment, in step S3 by the specific measure selection means 13 of the specific measure, at the stage where one specific measure with the highest degree of correspondence is selected, there is a corresponding relationship with the specific measure (marked with ○ or ◎). The policy may be removed from the selected policy list, and a specific measure may be recursively selected for a policy that does not have a corresponding relationship with the specific measure (no ○ or ◎). In addition, the specific measures to be selected first are selected in descending order of the degree of relevance, and the above processing is repeated several times, so that a plurality of combinations of specific measures are presented to the user as the specific measure list 5. It may be configured. This makes it possible to formulate a combination of specific measures that efficiently covers all the selected policies.

  In the present invention, a first stage for determining a policy (basic policy for solving a problem area) and a second stage for determining a specific measure from a combination of policies (not one or three stages), As a result, the policy should be maintained over the medium and long term, and the relevant effective measures should be explored by trying each medium and short term (this is an effective methodology usually used in carrying out health promotion plans). For example, when changing specific measures, only the second stage needs to be executed, and the number of steps for re-developing the first stage policy (and necessary for it) No need to re-acquire new information).

  Furthermore, it is basically based on medical / healthological knowledge, and the first stage consists of processing related to policies that can be shared by many users. Because it depends on the others, it is divided into a two-stage configuration called the second stage that includes specific measures that are difficult to complete in advance. It becomes easy (because it is possible only with the additional correction of the stage).

  In the past, the second stage processing means corresponding to the number of policies has been switched (alternatively) according to the selection result of the first stage. By adopting a method that processes in parallel based on the selection information of all policies in the first stage, the second stage processing means that makes judgments on the degree to which it falls are covered comprehensively and efficiently. Specific measures can be selected.

It is a block diagram which shows an example of a structure of the health promotion plan formulation apparatus by this invention. It is a flowchart for demonstrating operation | movement of the health promotion plan formulation apparatus by this invention. It is a figure which shows an example of the policy definition stored in the database by this invention. It is a figure which shows an example of the weight function of the applicable degree in the policy definition by this invention. It is a figure which shows an example of the specific measure definition stored in the database by this invention. It is a figure which shows an example of the constraint definition stored in the database by this invention.

Explanation of symbols

  1 health promotion plan formulation department, 2 database, 11 policy selection means, 12 policy selection means, 13 specific measure selection means, 14 additional information extraction means, 15 additional information input means, 16 constraint evaluation means, 17 specific measure addition means, 18 Specific policy constraint editing means, 19 Network communication means, 21 Policy definition, 21a Personal policy list information, 22 Specific policy definition, 22a Personal specific policy definition, 23 Constraint definition, 23a Frequency definition, 24 Individual additional information.

Claims (7)

A health promotion plan formulation device comprising a computer,
To each policy for health promotion, policy definition consisting of definitions that define the degree of applicability of each health / life state to the policy, and to the specific policy of each policy above for each specific policy for health promotion A database that stores the specific measure definition that defines the degree of relevance of
Based on the above policy definition, for each policy, obtain the appropriateness to the whole health / life state input from the appropriateness of each user's health / life state, and create a policy list showing the appropriateness of each policy Policy selection means to guide,
Based on the above specific measure definition, for each specific measure, a specific measure selection means for obtaining the appropriate level for the policy from the relevance level for the policy in the policy list and deriving a specific plan list indicating the appropriate level of each specific measure; ,
A health promotion plan development device characterized by comprising   It further comprises policy selection means for presenting the policy list to the user and prompting the user to select and input a policy. The specific measure selection means is a positive element indicating the degree of relevance of the policy selected and input by the user, and the relation of the policy not selected. The health promotion plan formulation apparatus according to claim 1, wherein the degree of matching is obtained by using degree as a negative element. The specific measure definition includes constraints for narrowing down specific measures for each specific measure,
The database further stores a constraint definition that stores additional information on various constraints of the specific measure definition,
Based on the constraint definition, additional information extraction means for deriving a constraint list indicating additional information of the constraint of the specific measure appearing in the specific measure plan list,
An additional information input means for presenting the constraint list to the user, prompting input of additional information regarding the constraint, and obtaining additional information;
Constraint evaluation means for evaluating constraints based on the obtained additional information, narrowing down the specific measures in the specific measure list, and leading the specific measures list;
The health promotion plan formulation device according to claim 1 or 2, characterized by comprising:   The health promotion plan formulation device according to any one of claims 1 to 3, further comprising specific measure adding means for adding a specific measure of the specific measure definition in accordance with an input from a user.   4. The health promotion plan formulation apparatus according to claim 3, further comprising specific measure constraint editing means for performing editing by changing, adding, or deleting the specific measure definition and the constraint definition in accordance with an external input.   A plurality of policies are selected by the user in the policy selection means, and a policy related to the specific measure is selected by the user at the stage where one specific measure having the highest degree of association is selected in the specific measure selection means. The health promotion plan formulation device according to any one of claims 2 to 5, wherein a specific measure is recursively selected for a policy that is removed from the policy and has no relation to the specific measure.   In the policy selection means, a plurality of policies are selected by the user, and in the specific measure selection means, the specific measures to be selected first are selected in descending order of relevance, and by repeating this, a plurality of combinations of specific measures are selected. The health promotion plan formulation apparatus according to any one of claims 2 to 5, wherein the example is guided as a specific measure list.

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