Extraction method, information processing device, and extraction program

Abstract

In this extraction method, a computer executes processing wherein, when the computer acquires a first measure, i.e. a measure including a conditional branch and a node coupled by a directed edge, and a first node condition set for the first measure, i.e. a node condition set in which the node and a condition assigned to the node are associated with each other, the computer references a storage unit that stores a second measure and a second node condition set for the second measure, and extracts the differences between the first measure and the second measure for nodes associated with the same condition .

Description

Extraction method, information processing device, and extraction program

[0001] The present invention relates to an extraction method, an information processing device, and an extraction program.

[0002] One type of workflow is the policy flow graph, which diagrams the process of assigning policy target objects, such as people, to services to achieve the policy's objectives in various fields such as medical care, nursing care, and administration.

[0003] When drafting such measures, it is important to consider whether similar measures have been implemented in the past, from the perspective of administrative (political) ease of implementation. For this reason, it is becoming increasingly important to compare the flow graph of the proposed measure with the flow graph of an existing measure to be used as reference.

[0004] For example, as one technique for supporting comparison of flow graphs of measures, the following prediction device has been proposed (see, for example, Patent Document 1). For example, in a graph consisting of nodes and multiple links that connect nodes based on the similarity between the nodes, the prediction device propagates the degree of effectiveness of a measure from a first node to a second node. As a result, even if only a few targets have actually implemented the measure, the prediction device predicts and recommends effective measures for other targets that have not implemented the measure.

[0005] International Publication No. 2019 / 208319

[0006] However, the above-mentioned prediction device only predicts the effect of a policy when it is introduced. Therefore, even if the policy recommended by the prediction device is highly effective, it is difficult for the entity receiving the recommendation, such as a local government, to determine whether it will be easy or difficult to introduce the policy.

[0007] In one aspect, the present invention aims to provide an extraction method, an information processing device, and an extraction program that can present the ease of introducing measures.

[0008] In one embodiment of the extraction method, when a computer acquires a first measure, which is a measure including conditional branches and nodes connected by directed edges, and a first node condition set for the first measure, which is a node condition set in which nodes are associated with conditions assigned to the nodes, the computer references a memory unit that stores a second measure and a second node condition set for the second measure, and executes a process to extract differences in nodes associated with the same conditions between the first measure and the second measure.

[0009] According to one embodiment, the ease of introducing a measure can be presented.

[0010] FIG. 1 is a block diagram illustrating an example of the functional configuration of a server device. FIG. 2 is a diagram illustrating an example of a flow graph of a policy. FIG. 3 is a diagram illustrating a specific example of a flow graph of a policy. FIG. 4 is a diagram illustrating an example of referencing existing policies in a data base. FIG. 5 is a diagram illustrating an example of a usage scenario for policy planning. FIG. 6 is a schematic diagram illustrating an example of registering policy information in a policy DB. FIG. 7 is a diagram illustrating a specific example of a policy flow. FIG. 8 is a diagram illustrating an example of an intervention condition set. FIG. 9 is a diagram illustrating a specific example of a draft flow. FIG. 10 is a diagram illustrating an example of an intervention condition set. FIG. 11 is a diagram illustrating an example of matching an intervention condition set. FIG. 12 is a diagram illustrating an example of extracting a resource difference. FIG. 13 is a diagram illustrating an example of output items by an output unit. FIG. 14 is a diagram illustrating an example of a display on a client terminal. FIG. 15 is a flowchart illustrating the procedure of a registration process. FIG. 16 is a flowchart illustrating the procedure of a first extraction process. FIG. 17 is a flowchart illustrating the procedure of a second extraction process. FIG. 18 is a diagram illustrating an example of a hardware configuration.

[0011] Hereinafter, with reference to the accompanying drawings, a description will be given of an embodiment (hereinafter referred to as "embodiment") for carrying out an extraction method, an information processing device, and an extraction program according to the present application. Each embodiment merely illustrates examples and aspects, and does not limit the range of values, functions, or usage scenarios. Each embodiment can be adaptively combined within a range that does not cause inconsistencies in the processing content.

[0012] <First Embodiment> <System Configuration> Fig. 1 is a block diagram showing an example of the functional configuration of a server device 10. The server device 10 shown in Fig. 1 provides a data-based platform that enables sharing, cross-referencing, and updating of policy flow data.

[0013] For example, the server device 10 can provide the functions of the data infrastructure platform as a cloud service by executing PaaS (Platform as a Service) type middleware or SaaS (Software as a Service) type applications.

[0014] As shown in Fig. 1, the server device 10 can be communicatively connected to a client terminal 30 via a network NW. For example, the network NW may be any type of communication network, whether wired or wireless, such as the Internet or a local area network (LAN). Note that Fig. 1 shows an example in which one client terminal 30 is connected to one server device 10, but any number of client terminals 30 may be connected.

[0015] The client terminal 30 is a terminal device that receives the above-mentioned data infrastructure. For example, the client terminal 30 may be used by a policy planner as an example of an entity that implements a policy, such as a local government or other related party. Note that the client terminal 30 may be realized by any computer, such as a personal computer, a smartphone, a tablet terminal, or a wearable terminal.

[0016] <Flow Graph of Policy> An example of a flow graph of the above policy is shown in Figure 2. Figure 2 is a diagram illustrating an example of a flow graph of a policy. Z1, Z2, Z3, and Z4 in Figure 2 indicate, for example, services that an administrator provides to a user. These may also be referred to as "service implementation components." Specific examples of services include, in the medical field, "interventions" to which the target object of the policy, such as a resident, is assigned, such as undergoing a health checkup or being examined by a specialist, as well as "no intervention" such as follow-up observation, but are not limited to policies in the medical field.

[0017] H1 and H2 indicate, for example, conditional branches including conditions. These may also be referred to as "conditional branch components." Specific examples of conditions, in the medical field, include an estimated glomerular filtration rate (eGFR) below a threshold, a hemoglobin A1c value (HbA1c) below a threshold, and a urinary protein value equal to or greater than a threshold, but are not limited to conditions in the medical field.

[0018] Z1, Z2, Z3, Z4, H1, and H2 may each be referred to as a "component." From the perspective of graph data, such a "component" may correspond to an example of a "node." Furthermore, the connection between nodes may correspond to an example of an "edge," including a "directed edge."

[0019] In this embodiment, policy planning in the medical field will be described as an example, but the present invention is not limited to this. The above-described embodiment may be used for various policy planning such as work with conditional branching, tests, and questionnaires. In this case, the same effects as those of the above-described embodiment can be obtained.

[0020] Figure 3 shows a specific example of a policy flow graph. As shown in Figure 3, a policy is modeled as a workflow consisting of a combination of components such as conditional branching and service implementation. Then, the number of people receiving each service is output from a model that has been trained by accumulating information and parameters on the flow of people based on the actual values ​​when each conditional branching component is used.

[0021] In the example shown in FIG. 3 , the number of people N=1000 is input at S0. At S1, component #1 as service execution component A is set to "health check." At S2, component #2 as conditional branch component B is set to "eGFR<α." If "eGFR<α" is not satisfied (see the NO route at S2), it is determined that "no intervention" by a specialist is required for the citizen, as shown at S5.

[0022] On the other hand, if "eGFR<α" is satisfied (see the YES route at S2), then component #3 as conditional branch component C is set to "HbA1c<β" as shown at S3. If "HbA1c<β" is satisfied (see the YES route at S3), then component #4 as conditional branch component D is set to "nephrologist" as shown at S6, and it is determined that the citizen requires intervention by a "nephrologist." On the other hand, if "HbA1c<β" is not satisfied (see the NO route at S3), then it is determined that the citizen requires intervention by a "diabetes specialist" as shown at S7.

[0023] In the example shown in Figure 3, the number of people who will flow through part #1, part #2, part #3, and part #4 in that order is predicted, as indicated by the arrows. For example, in the policy flow graph shown in Figure 3, the results of assigning the number of people N = 1000 to interventions Z2 to Z4 are as follows: 50 people are assigned to intervention Z2. 150 people are assigned to intervention Z3. Furthermore, 800 people are assigned to intervention Z4.

[0024] Hereinafter, the flow graph of a policy may be abbreviated as "policy flow." Furthermore, among policy flows, a policy flow corresponding to a draft may be referred to as an "original policy flow," while a policy flow corresponding to an existing policy may be referred to as an "existing policy flow." Note that the "original policy" here refers to a policy designated as a draft when the policy is formulated. For example, it may be possible to designate one of the existing policies as is, or it may be possible to designate a modified policy in which a portion of an existing policy has been modified, or it may be possible to designate a newly created policy.

[0025] <Data Infrastructure> In the above data infrastructure, policy flows may be shared in any framework. As just one example, the above data infrastructure allows policy flows to be shared among organizations around the world, for example, public organizations such as local governments.

[0026] FIG. 4 is a diagram showing an example of referencing existing policies in a data infrastructure. As shown in FIG. 4, a policy planner can refer to templates of existing policies from around the world collected in the data infrastructure via a client terminal 30. For example, a policy planner can update a policy plan by incorporating all or part of an existing policy similar to the original policy from the templates collected in the data infrastructure. In the example shown in FIG. 4, the prevention and screening policy of City A can be introduced as is, or part of the prevention and screening policy of City B can be incorporated.

[0027] Thus, when drafting a policy, it is important to consider whether similar policies have been implemented in the past, from the perspective of administrative (political) ease of implementation. For this reason, it is becoming increasingly important to compare the flow graph of the proposed policy with the flow graph of existing policies that serve as reference.

[0028] <One Aspect of the Problem> As explained in the Background Art section above, conventional technologies such as the prediction device merely predict the effects of introducing a policy. Therefore, even if the policy recommended by the prediction device is highly effective, it is difficult for the entity receiving the recommendation, such as a local government, to determine whether it will be easy or difficult to introduce the policy.

[0029] That is, according to the above-described conventional technology, when a policy is referred to during policy formulation, the determination of which parts of the policy flow differ between the original plan and the referenced policy is left to the user of the data platform, such as the policy planner. For this reason, it may be difficult to determine the differences in the policy flow between the original plan and the referenced policy in the first place. In such a situation where the differences in the policy flow are unclear, it is difficult to determine whether the implementation will be easy or difficult. Note that the "referenced policy" referred to here does not necessarily have to be an existing policy that has already been implemented, but may be a policy other than an existing policy.

[0030] <One aspect of the problem-solving approach> Therefore, the server device 10 according to this embodiment provides an extraction function that extracts differences in interventions for common conditions between the original plan flow and the reference plan flow, or differences in conditions for common interventions, from the perspective of presenting the ease of introducing a policy. This extraction function extracts differences in the policy flow between the original plan and similar policies, making it possible to present information for determining whether the implementation is easy or difficult. Therefore, the server device 10 according to this embodiment can present the ease of introducing a policy.

[0031] Although the above-described examples have been given in which the functions of the data infrastructure platform and the extraction function are provided as cloud services, the present invention is not limited to this. For example, the functions of the data infrastructure platform and the extraction function may be provided on-premise. Furthermore, the above-described examples have been given in which the functions of the data infrastructure platform and the extraction function are provided in a client-server system, but the present invention is not limited to this. For example, the above-described extraction function may be provided standalone by causing an application running on the client terminal 30 to cause the client terminal 30 to execute processing corresponding to the extraction function.

[0032] <Configuration of Server Device 10> Fig. 1 shows a schematic diagram of blocks related to the data base and extraction function of the server device 10. As shown in Fig. 1, the server device 10 has a communication control unit 11, a storage unit 13, and a control unit 15. Note that Fig. 1 only shows an excerpt of functional units related to the above data base and the above extraction function, and the server device 10 may also be provided with functional units other than those shown.

[0033] The communication control unit 11 is a functional unit that controls communication with other devices such as the client terminal 30. As just one example, the communication control unit 11 can be realized by a network interface card such as a LAN card. In one aspect, the communication control unit 11 receives, from the client terminal 30, a request to register policy information including a policy flow or a request to extract differences between original plans and existing policies, or outputs the results of extracting differences between original plans and existing policies to the client terminal 30.

[0034] The storage unit 13 is a functional unit that stores various types of data. As an example, the storage unit 13 is realized by internal, external, or auxiliary storage of the server device 10. For example, the storage unit 13 stores a policy DB (DataBase) 13A. The policy DB will be described together with the situation where reference, generation, or registration is performed.

[0035] The control unit 15 is a functional unit that performs overall control of the server device 10. For example, the control unit 15 can be realized by a hardware processor. Alternatively, the control unit 15 can be realized by hardwired logic. As shown in FIG. 1 , the control unit 15 includes a receiving unit 15A, a registering unit 15B, an extracting unit 15C, and an output unit 15D.

[0036] The reception unit 15A is a processing unit that receives various requests from the client terminal 30. In one aspect, the reception unit 15A can receive a request to register policy information including a policy flow from the client terminal 30. In another aspect, the reception unit 15A can receive a request to extract the difference between a draft and an existing policy.

[0037] The registration unit 15B is a processing unit that registers the policy information in the policy DB 13A of the storage unit 13. As an example, when a registration request for policy information is accepted by the acceptance unit 15A, the registration unit 15B registers the policy information in the policy DB 13A.

[0038] As an example of a scenario for policy planning, the following example illustrates the planning of a policy flow for medical follow-up for chronic kidney disease (CKD). Figure 5 illustrates an example of a scenario for policy planning. As shown in Figure 5, a CKD medical follow-up system can be created by a specialist committee based on a project to prevent the progression of nephropathy. Such a specialist committee corresponds to an example of a policy planner and may include personnel such as nephrologists, diabetologists, public health nurses, and government health promotion and health policy departments. For example, the specialist committee periodically, e.g., annually, creates and revises a policy flow based on past project results and policies of other municipalities. Based on the health checkup result conditions set in the created or revised policy flow, CKD medical follow-up is implemented, assigning residents targeted by the policy to interventions for which measures corresponding to their health checkup results have been developed.

[0039] The policy information received in the registration request may include the policy flow created or revised by the expert committee, as well as resource information related to the organization to which the policy planner of the policy flow belongs, such as a local government. In addition, the policy information may include, for each intervention node in the policy flow, an evaluation value of the intervention node, such as the actual value of indicators such as effectiveness and cost.

[0040] FIG. 6 is a schematic diagram showing an example of registration of policy information in the policy DB 13A. FIG. 6 shows an example in which a registration request for policy information is accepted from a policy planner in City Z. As shown in FIG. 6, the policy information includes resource information for City Z as well as a policy flow related to CKD medical follow-up in City Z. The resource information includes information such as a total population of "500,000," "10 nephrologists," and "10 diabetes specialists." The policy information accepted in this way by the registration request is additionally registered in the policy DB 13A.

[0041] When a policy flow is registered in the policy DB 13A in this way, the registration unit 15B can extract conditions to be assigned to each intervention node corresponding to a service execution component, for example, "intervention," in the policy flow, and register the intervention node and a set of its assignment conditions. Such intervention nodes may be identified by referring to meta-information in which the type of each node included in the policy flow is associated with the node, or the node located at the end of the policy flow, which is a directed graph, may be identified as the intervention node.

[0042] Hereinafter, a set of intervention nodes and allocation conditions may be referred to as an “intervention condition set.” As an example only, an example will be given in which an intervention condition set is extracted from the policy flow f1 shown in FIG.

[0043] Figure 7 is a diagram showing a specific example of a policy flow. In Figure 7, policy flow f1 for City Z in Country Z is shown as an example of a policy flow registered in the policy DB 13A. As shown in Figure 7, policy flow f1 includes a total of seven nodes, node n1 to node n7. Of these seven nodes, node n1 and five nodes n4 to n7 correspond to service implementation components provided to residents.

[0044] For example, node n1 "Specific health checkup" is set to a service of conducting specific health checkups for residents. Node n4 "Doctor (nephrologist) referral" is set to a service of referring residents to nephrologists. Node n5 "Lifestyle guidance" is set to a service of providing residents with health guidance to improve their lifestyle habits. Node n6 "Doctor (family doctor) referral" is set to a service of referring residents to family doctors. Node n7 "Doctor (diabetes specialist) referral" is set to a service of referring residents to diabetes specialists. Of these five nodes, four nodes, node n4 "Doctor (nephrologist) referral", node n5 "Lifestyle guidance", node n6 "Doctor (family doctor) referral", and node n7 "Doctor (diabetes specialist) referral", correspond to intervention nodes.

[0045] Node n2 "Laboratory Value Judgment 1" and node n3 "Laboratory Value Judgment 2" are conditional branching nodes that determine which intervention in the policy flow a resident is assigned to. For example, conditional branching node n2 "Laboratory Value Judgment 1" determines whether the resident meets condition A, in which the laboratory value "eGFR" in the health checkup results obtained in node n1 "Specific Health Checkup" is less than 45, or condition B, in which the laboratory value "eGFR" is 45 or greater. Conditional branching node n3 "Laboratory Value Judgment 2" determines whether the resident meets condition D, in which the laboratory value "urine protein" is negative (-), condition E, in which the laboratory value "urine protein" is false positive (±) or positive (+), or condition F, in which the laboratory value "urine protein" is positive (2+) or positive (3+).

[0046] For each intervention node included in such a policy flow f1, an intervention condition set can be extracted by starting from the intervention node and tracing back in the opposite direction of the edge to search for consecutive condition branch nodes.

[0047] For example, in the case of intervention node n4 "Referral to doctor (nephrologist)," condition B "eGFE≧45" is extracted by tracing back from intervention node n4 "Referral to doctor (nephrologist)" to condition branch node n2 "Test Value Judgment 1." However, no condition branch exists even when tracing back further than condition branch node n2 "Test Value Judgment 1." As a result, condition B "eGFE≧45" is extracted as the allocation condition.

[0048] In the example of intervention node n5 "Lifestyle Guidance," condition D "Urine Protein = -" is extracted by tracing back from intervention node n5 "Lifestyle Guidance" to condition branch node n3 "Laboratory Value Judgment 2." Furthermore, condition A "eGFE < 45" is found by tracing back from condition branch node n3 "Laboratory Value Judgment 2" to condition branch node n2 "Laboratory Value Judgment 1." However, no condition branch exists even when tracing back beyond condition branch node n2 "Laboratory Value Judgment 1." As a result, the AND condition of condition A and condition D, "eGFE < 45 AND Urinary Protein = -," is extracted as the assignment condition.

[0049] Furthermore, in the example of intervention node n6 "Doctor (Primary Physician) Referral," condition E "Urine Protein = ± OR Urine Protein = +" is extracted by tracing back from intervention node n6 "Doctor (Primary Physician) Referral" to condition branch node n3 "Laboratory Value Judgment 2." Furthermore, condition A "eGFE < 45" is found by tracing back from condition branch node n3 "Laboratory Value Judgment 2" to condition branch node n2 "Laboratory Value Judgment 1." However, no condition branch exists even when tracing back beyond condition branch node n2 "Laboratory Value Judgment 1." As a result, the AND condition of condition A and condition E, "eGFE < 45 AND (Urine Protein = ± OR Urine Protein = +)," is extracted as the assignment condition.

[0050] In the example of intervention node n7 "Referral to doctor (diabetes specialist)," condition F "urine protein = 2+ OR urine protein = 3+" is extracted by tracing back from intervention node n7 "Referral to doctor (diabetes specialist)" to condition branch node n3 "Laboratory value judgment 2." Furthermore, condition A "eGFE < 45" is found by tracing back from condition branch node n3 "Laboratory value judgment 2" to condition branch node n2 "Laboratory value judgment 1." However, no condition branch exists even when tracing back beyond condition branch node n2 "Laboratory value judgment 1." As a result, the AND condition of condition A and condition F, "eGFE < 45 AND (urine protein = 2+ OR urinary protein = 3+)," is extracted as the assignment condition.

[0051] As a result, four intervention condition sets c1 to c4 shown in FIG. 8 are extracted from the policy flow f1 shown in FIG. 7 . FIG. 8 is a diagram showing an example of intervention condition sets. As shown in FIG. 8 , the intervention condition set c1 is associated with an intervention node "lifestyle guidance" and an allocation condition "eGFE<45 AND urinary protein=-". The intervention condition set c2 is associated with an intervention node "referral to doctor (nephrologist)" and an allocation condition "eGFE≧45". The intervention condition set c3 is associated with an intervention node "referral to doctor (primary physician)" and an allocation condition "eGFE<45 AND (urinary protein=± OR urinary protein=+)". The intervention condition set c4 is associated with an intervention node "referral to doctor (diabetes specialist)" and an allocation condition "eGFE<45 AND (urinary protein=2+ OR urinary protein=3+)". In this way, the intervention condition set extracted from the policy flow f1 can be included in the policy information and registered in the policy DB 13A.

[0052] Returning to the explanation of Figure 1, the extraction unit 15C is a processing unit that extracts differences in interventions associated with the same allocation conditions, as well as differences in allocation conditions associated with the same intervention, between an intervention condition set corresponding to the original plan and an intervention condition set corresponding to an existing measure.

[0053] As just one example, the extraction unit 15C can start processing when the receiving unit 15A receives a request to extract the difference between an original plan and an existing plan. Here, the original plan α specified in the extraction request can be specified from among the existing plans registered in the plan DB 13A, or a plan not registered in the plan DB 13A can be specified from the client terminal 30. In this case, if a plan not registered in the plan DB 13A is received as the original plan α, it is possible to register plan information including the plan flow, resource information, and intervention condition set of the original plan α in the plan DB 13A, and then extract the difference between the original plan and the existing plan.

[0054] The following description will be given, as an example only, of a usage scenario in which a policy planner in N city makes an extraction request via the client terminal 30 specifying the policy flow F1 shown in FIG. 9 as the original plan α.

[0055] FIG. 9 is a diagram showing a specific example of a draft flow. FIG. 9 shows policy flow F1 for City N as an example of draft flow α. As shown in FIG. 9, policy flow F1 includes a total of eight nodes, nodes N1 to N7. Of these eight nodes, node N1 and six nodes N4 to N8 correspond to service implementation components provided to residents. Of these six nodes, five nodes correspond to intervention nodes: nodes N4 and N7 "Doctor (Primary Physician) Referral," nodes N5 and N8 "Doctor (Nephrologist) Referral," and node N6 "Lifestyle Guidance."

[0056] Furthermore, node N2 "Laboratory Value Judgment 1" and node N3 "Laboratory Value Judgment 2" are conditional branching nodes that determine which intervention in the policy flow a resident is assigned to based on a condition. For example, conditional branching node N2 "Laboratory Value Judgment 1" determines whether the health check results obtained in node N1 "Specific Health Checkup" fall under condition A, condition B, or condition C. Condition A corresponds to the case where the laboratory value "eGFR" is less than 45. Condition B corresponds to the case where the laboratory value "eGFR" is between 45 and 59, and the age is 40 or greater. Condition C corresponds to the case where the laboratory value "eGFR" is between 45 and 59, and the age is less than 40. Furthermore, conditional branching node N3 "Laboratory Value Judgment 2" determines whether the health check results obtained in node N1 "Specific Health Checkup" fall under condition D, condition E, or condition F. Condition D corresponds to the case where the laboratory value "Urine Protein" is negative (-). Condition E corresponds to a case where the test value "urine protein" is false positive "±" or positive "+". Condition F corresponds to a case where the test value "urine protein" is positive "2+" or positive "3+".

[0057] For each intervention node included in such a policy flow F1, an intervention condition set can be extracted by starting from the intervention node and tracing back in the opposite direction of the edge to search for consecutive condition branch nodes.

[0058] For example, "Doctor (Personal Physician) Referral" has two intervention nodes, N4 and N7. Taking the former example of intervention node N4 "Doctor (Personal Physician) Referral," searching back from intervention node N4 "Doctor (Personal Physician) Referral" to condition branch node N2 "Laboratory Value Judgment 1" extracts condition B "eGFR = 45-59 AND Age ≥ 40." Furthermore, no condition branch exists even when tracing back beyond condition branch node N2 "Laboratory Value Judgment 1." As a result, condition B "eGFR = 45-59 AND Age ≥ 40" is extracted as the assignment condition. Meanwhile, taking the latter example of intervention node N7 "Doctor (Personal Physician) Referral," searching back from intervention node N7 "Doctor (Personal Physician) Referral" to condition branch node N3 "Laboratory Value Judgment 2" extracts condition E "Urine Protein = ± OR Urine Protein = +." Furthermore, by tracing back from the conditional branch node N3 "Test Value Judgment 2" to the conditional branch node N2 "Test Value Judgment 1," the condition A "eGFE<45" is found. No conditional branch exists even when tracing back further than the conditional branch node N2 "Test Value Judgment 1." As a result, the AND condition of condition A and condition E, "eGFE<45 AND (urinary protein = ± OR urinary protein = +)," is extracted as the assignment condition.

[0059] Furthermore, "Doctor (Nephrologist) Referral" has two intervention nodes, N5 and N8. Taking the former intervention node N5 "Doctor (Nephrologist) Referral" as an example, searching back from intervention node N5 "Doctor (Nephrologist) Referral" to condition branch node N2 "Laboratory Value Judgment 1" extracts condition C "eGFR = 45-59 AND Age < 40." Furthermore, no condition branch exists even when tracing back beyond condition branch node N2 "Laboratory Value Judgment 1." As a result, condition C "eGFR = 45-59 AND Age < 40" is extracted as the assignment condition. Meanwhile, taking the latter intervention node N8 "Doctor (Nephrologist) Referral" as an example, searching back from intervention node N8 "Doctor (Nephrologist) Referral" to condition branch node N3 "Laboratory Value Judgment 2" extracts condition F "Urine Protein = 2+ OR Urine Protein = 3+." Furthermore, by tracing back from the conditional branch node N3 "Test Value Judgment 2" to the conditional branch node N2 "Test Value Judgment 1," the condition A "eGFE<45" is found. No conditional branch exists even when tracing back further than the conditional branch node N2 "Test Value Judgment 1." As a result, the AND condition of condition A and condition F, "eGFE<45 AND (urinary protein=2+ OR urinary protein=3+)," is extracted as the assignment condition.

[0060] Furthermore, in the example of intervention node N6 "Lifestyle Guidance," condition D "Urine Protein = -" is extracted by tracing back from intervention node N6 "Lifestyle Guidance" to condition branch node N3 "Laboratory Value Judgment 2." Furthermore, condition A "eGFE < 45" is found by tracing back from condition branch node N3 "Laboratory Value Judgment 2" to condition branch node N2 "Laboratory Value Judgment 1." However, no condition branch exists even when tracing back further than condition branch node N2 "Laboratory Value Judgment 1." As a result, the AND condition of condition A and condition D, "eGFE < 45 AND Urinary Protein = -," is extracted as the assignment condition.

[0061] As a result, three intervention condition sets C1 to C3 shown in FIG. 10 are extracted from the policy flow F1 shown in FIG. 9. FIG. 10 is a diagram showing an example of an intervention condition set. As shown in FIG. 8, the intervention condition set C1 is associated with an intervention node "lifestyle guidance" and an allocation condition "eGFE<45 AND urinary protein=-". The intervention condition set C2 is associated with an intervention node "referral from doctor (attending physician)" and an allocation condition "(eGFE=45-59 AND age≧40) OR {eGFE<45 AND (urinary protein=± OR urinary protein=+)}". The intervention condition set C3 is associated with an intervention node "referral from doctor (nephrologist)" and an allocation condition "(eGFE=45-59 AND age<40) OR {eGFE<45 AND (urinary protein=2+ OR urinary protein=3+)}". The intervention condition sets C1 to C3 thus extracted from the policy flow F1 are included in the policy information of the original plan α and registered in the policy DB 13A.

[0062] When the policy information of the original plan α has already been registered in the policy DB 13A, the intervention condition sets C1 to C3 of the original plan α can be acquired by reading them from the policy DB 13A. Furthermore, when the policy information of the original plan α has not yet been registered in the policy DB 13A, the intervention condition sets C1 to C3 of the original plan α can be extracted, and the policy information including the intervention condition sets C1 to C3 can be registered in the policy DB 13A, after which extraction of the differences between the original plan and the existing policies can begin.

[0063] When the intervention condition set for original plan α is obtained in this manner, the extraction unit 15C executes the following processing. In one aspect, the extraction unit 15C compares the intervention condition set for original plan α with the intervention condition sets of existing policies stored in the policy DB 13A to extract differences in intervention nodes associated with common allocation conditions. In another aspect, the extraction unit 15C compares the intervention condition set for original plan α with the intervention condition sets of existing policies stored in the policy DB 13A to extract differences in allocation conditions associated with common intervention nodes.

[0064] Fig. 11 shows an example of matching intervention condition sets. Fig. 11 shows an example of matching intervention condition sets C1 to C3 (see Fig. 10) of policy flow F1 of City N, which is a draft α, with intervention condition sets c1 to c4 (see Fig. 8) of policy flow f1 of City Z, which is an existing policy.

[0065] As shown in Figure 11, intervention condition set C3 and intervention condition set c4 share the same allocation condition "eGFE < 45 AND (urinary protein = 2+ OR urinary protein = 3+)." Meanwhile, the allocation condition of intervention condition set C3 is associated with the intervention node "Doctor (nephrologist) referral," while the allocation condition of intervention condition set c4 is associated with the intervention node "Doctor (diabetes specialist) referral." Because the intervention nodes associated with the common allocation condition differ in this way, the intervention of "diabetes specialist" is extracted as the difference in the intervention nodes of the existing measures for draft α.

[0066] Furthermore, the intervention node "Doctor (Primary Physician) Referral" is common between intervention condition set C2 and intervention condition set c3. On the other hand, the intervention node of intervention condition set C2 is associated with the allocation condition "eGFR = 45-59 AND age ≥ 40 OR {eGFR < 45 AND (Urine Protein = ± OR Urine Protein = +)}", while the intervention node of intervention condition set c3 is associated with the allocation condition "eGFR < 45 AND (Urine Protein = ± OR Urine Protein = +)". In this way, the allocation conditions associated with the common intervention nodes are different. Therefore, "eGFR = 45-59 AND age ≥ 40" is extracted as the difference in allocation conditions between the existing measures and the original plan α.

[0067] In addition to the difference in the intervention nodes associated with these common allocation conditions, the extraction unit 15C can further extract the difference in resources resulting from the difference in the intervention nodes. The "difference in resources" here refers to the difference in resources resulting from introducing intervention nodes of existing policies that are associated with allocation conditions common to the original plan α, instead of intervention nodes of the original plan α that are associated with allocation conditions common to the existing policies.

[0068] 12 is a diagram illustrating an example of extracting a difference in resources. Fig. 12 illustrates an example of extracting a difference in resources when the intervention node of the original plan α is replaced from "referral to doctor (nephrologist)" to "referral to doctor (diabetes specialist)" based on the difference between the intervention node of the original plan α, "referral to doctor (nephrologist)" and the intervention node of the existing measure, "referral to doctor (diabetes specialist)."

[0069] In this case, as shown in FIG. 12 , the number of "diabetes specialists" resources ("0") corresponding to the intervention node "Doctor (diabetes specialist) referral" to be introduced in the resource information for City N corresponding to draft α is referenced. Meanwhile, the number of "diabetes specialists" resources ("10") corresponding to the intervention node "Doctor (diabetes specialist) referral" to be introduced in the resource information for City Z corresponding to the existing policy is referenced. Then, the difference between the number of "diabetes specialists" resources ("0") in City N and the number of "diabetes specialists" resources ("10") in City Z is calculated. For example, the number of "diabetes specialists" resources ("10") in City Z is subtracted from the number of "diabetes specialists" resources ("0") in City N to calculate a resource difference of "-10."

[0070] The resource difference between City N and City Z calculated in this way, "-10 people," could be presented as is, but the population sizes of City N and City Z are not necessarily the same. For this reason, the population ratio of City N and City Z can be used to convert the amount of resources required at the time of implementation in City Z into the amount of resources required at the time of implementation in City N. For example, the resource difference between City N and City Z, "-10 people," can be converted to "-20 people" by multiplying it by the ratio of City N's population to City Z's population, "2 = (1,000,000 people / 500,000 people)."

[0071] Returning to the description of FIG. 1 , the output unit 15D is a processing unit that outputs various types of information to the client terminal 30. In one aspect, the output unit 15D can output, to the client terminal 30, differences in interventions associated with the same allocation conditions extracted by the extraction unit 15C as a response to an extraction request from the client terminal 30. In another aspect, the output unit 15D can output, to the client terminal 30, differences in allocation conditions associated with the same interventions extracted by the extraction unit 15C as a response to an extraction request from the client terminal 30. At least one of the differences in interventions associated with the same allocation conditions and the differences in allocation conditions associated with the same interventions may be output, or both may be output.

[0072] In addition to the differences in interventions associated with the same allocation conditions and the differences in allocation conditions associated with the same interventions, the output unit 15D can also output the differences in resources extracted by the extraction unit 15C. In this case, the output unit 15D may narrow down the existing policies to those in which the resource differences extracted by the extraction unit 15C satisfy the constraint conditions set when the extraction request is received, and output information about the existing policies, such as differences in intervention nodes, differences in allocation conditions, and differences in resources. This makes it possible to output information about existing policies in cases in which it is possible to introduce intervention nodes of existing policies within the scope of resource changes permitted by the organization to which the original policy planner belongs, such as a local government.

[0073] Furthermore, the output unit 15D can further output the difference in evaluation value when an intervention node of an existing policy that is associated with an allocation condition common to the original plan α is introduced instead of an intervention node of the original plan α that is associated with an allocation condition common to the existing policy. For example, the output unit 15D refers to the evaluation value stored for each intervention node in the policy DB 13A, and outputs the difference between the evaluation value of the intervention node of the original plan α that is associated with the same allocation condition and the evaluation value of the intervention node of the existing policy that is associated with the same allocation condition.

[0074] Furthermore, the output unit 15D can further output the difference in evaluation value when, instead of the allocation conditions of the original plan α associated with the intervention node common to the existing plan, the allocation conditions of the existing plan that are associated with the intervention node common to the original plan α are introduced. For example, the output unit 15D refers to the evaluation values ​​stored for each allocation condition in the policy DB 13A, and outputs the difference between the evaluation value of the allocation conditions of the original plan α associated with the same intervention node and the evaluation value of the intervention node of the existing policy associated with the same intervention node.

[0075] Fig. 13 is a diagram showing an example of output items by the output unit 15D. Fig. 13 shows an example in which the difference in intervention nodes shown in Fig. 11, the difference in allocation conditions shown in Fig. 11, and the difference in resources shown in Fig. 12 are output items.

[0076] As shown in FIG. 13 , the output unit 15D can output the intervention of a "diabetes specialist" for residents who meet the common allocation condition "eGFE<45 AND (urinary protein=2+ OR urinary protein=3+)" as the difference in the intervention node of the existing measures relative to the original plan α. In this case, the output unit 15D can output 20 diabetes specialists as the resources required for the intervention of the "diabetes specialist." Furthermore, the output unit 15D can output the effect of "+2%," the cost of "+3%," and the resource of "+1%" as the difference in evaluation value at the time of introducing the intervention of the "diabetes specialist."

[0077] Furthermore, the output unit 15D can output the condition "eGFR=45-59 AND age≧40" for implementing the intervention of the common intervention node "Physician (Primary Physician) Referral" as the difference between the allocation conditions of the existing measures and the original plan α. In this case, the output unit 15D can output "none" as the resources required for introducing the allocation conditions of the existing measures. Furthermore, the output unit 15D can output the effect "+0%", cost "+0%", and resource "+0%" as the difference in evaluation value when the allocation conditions of the existing measures are introduced.

[0078] FIG. 14 is a diagram showing an example of a display on the client terminal 30. The window 200 shown in FIG. 14 shows an example in which CKD follow-up in City Z is specified as the policy flow for original plan α. Furthermore, the window 200 shown in FIG. 14 shows an example in which the existing policies to be compared with the original plan α are sorted in descending order of effectiveness among the order of effectiveness, cost, and resources. Furthermore, the window 200 shown in FIG. 14 shows an example in which CKD follow-up in City a in Country A is specified among the existing policies sorted in descending order of effectiveness. Furthermore, the window 200 shown in FIG. 14 shows an example in which difference b of intervention content is specified among two differences, difference a of intervention conditions and difference b of intervention content.

[0079] As shown in FIG. 14 , window 200 displays, as differences in intervention content b, the intervention nodes "nephrologist intervention" for City N and "diabetes specialist intervention" for City a in Country A, which are associated with the allocation condition "Condition A" common to the CKD follow-up in City Z and the CKD follow-up in City A in Country A. At this time, window 200 further displays the resource "diabetes specialists + 20 people" required when the intervention node in City N is replaced from "nephrologist intervention" to "diabetes specialist intervention." Furthermore, window 200 displays the difference in treatment effectiveness of "+30%," the difference in cost of "+10%, and the difference in resources of "+20%" when the intervention node in City N is replaced from "nephrologist intervention" to "diabetes specialist intervention."

[0080] <Processing Flow> Next, the processing flow of the server device 10 according to this embodiment will be described. Here, the processing flow of each of the following processes executed by the server device 10 will be described in this order: (1) registration processing, (2) first extraction processing, and (3) second extraction processing.

[0081] (1) Registration Processing Fig. 15 is a flowchart showing the procedure of the registration processing. This processing is merely an example and may be started when the reception unit 15A receives a registration request for policy information.

[0082] As shown in FIG. 15, when the reception unit 15A receives a request to register policy information (step S101), the registration unit 15B executes loop processing 1, which repeats the processing from step S102 to step S105 described below a number of times corresponding to the number I of policy flows for which registration requests have been received.

[0083] That is, the registration unit 15B extracts an intervention node included in the i-th policy flow (step S102). Then, the registration unit 15B executes loop process 2, which repeats the processes of step S103 and step S104 described below a number of times corresponding to the number J of intervention nodes extracted in step S102.

[0084] For example, the registration unit 15B extracts an allocation condition k to be assigned to the j-th intervention node by searching for conditional branch nodes starting from the j-th intervention node and tracing back in the reverse direction of the edge (step S103).The registration unit 15B then associates the allocation condition k extracted in step S103 with the j-th intervention node (step S104).

[0085] By repeating this loop process 2, an assignment condition k to be assigned to each of the J intervention nodes is associated with the J intervention nodes, resulting in J intervention condition sets.

[0086] Thereafter, the registration unit 15B registers the i-th policy information including the i-th policy flow and the J intervention condition sets in the policy DB 13A (step S105).

[0087] By repeating this loop process 1, I pieces of policy information are registered in the policy DB 13A.

[0088] 16 is a flowchart showing the procedure of the first extraction process. This process may be started when the receiving unit 15A receives a request to extract the difference between the original plan and the existing measures.

[0089] As shown in Figure 16, when the reception unit 15A receives a request to extract the difference between the original plan and the existing measures (step S301), the extraction unit 15C executes loop processing 1, which repeats the processing from step S302 to step S306 below a number of times corresponding to the number N of allocation conditions included in the intervention condition set of original plan α.

[0090] Such loop processing 1 may include loop processing 2 that repeats the processing from step S302 to step S306 below for the nth allocation condition included in the intervention condition set of original plan α a number of times corresponding to the number I-1 of existing policies excluding original plan α among the existing policies included in policy DB13A.

[0091] Furthermore, loop processing 2 may include loop processing 3, which repeats the processing from step S302 to step S306 below a number of times corresponding to the number M of allocation conditions included in the intervention condition set of the i-th existing policy.

[0092] That is, the extraction unit 15C determines whether the nth allocation condition included in the intervention condition set of the original plan α is the same as the mth allocation condition included in the intervention condition set of the i-th existing policy (step S302).

[0093] In this case, if the nth allocation condition and the mth allocation condition are identical (Yes in step S302), the extraction unit 15C further determines whether the intervention node of the original plan α corresponding to the nth allocation condition and the intervention node of the ith existing policy corresponding to the mth allocation condition are not identical (step S303).

[0094] If the intervention node of the original plan α corresponding to the n-th allocation condition is not identical to the intervention node of the i-th existing policy corresponding to the m-th allocation condition (Yes in step S303), the extraction unit 15C executes the following process: That is, the extraction unit 15C extracts the intervention node of the original plan α corresponding to the n-th allocation condition and the intervention node of the i-th existing policy corresponding to the m-th allocation condition in association with each other (step S304).

[0095] Next, the extraction unit 15C further extracts a resource difference caused by the introduction of the intervention node of the i-th existing policy corresponding to the m-th allocation condition (step S305). Furthermore, the extraction unit 15C calculates an evaluation value for the introduction of the intervention node of the i-th existing policy corresponding to the m-th allocation condition (step S306).

[0096] Then, the output unit 15D outputs the intervention node difference, resource difference, and evaluation value obtained as a result of the loop processing 1 to the loop processing 3 to the client terminal 30 (step S307), and ends the processing.

[0097] 17 is a flowchart showing the procedure of the second extraction process. This process may be started when the receiving unit 15A receives a request to extract the difference between the original plan and the existing policy.

[0098] As shown in Figure 17, when the receiving unit 15A receives a request to extract the difference between the original plan and the existing policy (step S501), the extraction unit 15C executes loop processing 1, which repeats the processing from step S502 to step S506 below a number of times corresponding to the number N of intervention nodes included in the intervention condition set of original plan α.

[0099] Such loop processing 1 may include loop processing 2 that repeats the processing from step S302 to step S306 below for the nth intervention node included in the intervention condition set of original plan α a number of times corresponding to the number I-1 of existing policies excluding original plan α among the existing policies included in policy DB13A.

[0100] Furthermore, loop processing 2 may include loop processing 3, which repeats the processing from step S302 to step S306 below a number of times corresponding to the number M of intervention nodes included in the intervention condition set of the i-th existing policy.

[0101] That is, the extraction unit 15C determines whether the nth intervention node included in the intervention condition set of the original plan α is the same as the mth intervention node included in the intervention condition set of the i-th existing measure (step S502).

[0102] In this case, if the nth intervention node and the mth intervention node are the same (Yes in step S502), the extraction unit 15C further determines whether the allocation conditions of the original plan α corresponding to the nth intervention node and the allocation conditions of the i-th existing policy corresponding to the mth intervention node are not identical (step S503).

[0103] If the allocation conditions of the original plan α corresponding to the nth intervention node and the allocation conditions of the i-th existing policy corresponding to the m-th intervention node are not identical (Yes in step S503), the extraction unit 15C executes the following process: That is, the extraction unit 15C extracts the allocation conditions of the original plan α corresponding to the n-th intervention node and the allocation conditions of the i-th existing policy corresponding to the m-th intervention node in association with each other (step S504).

[0104] Next, the extraction unit 15C further extracts a resource difference caused by the introduction of the allocation condition of the i-th existing policy corresponding to the m-th intervention node (step S505).Furthermore, the extraction unit 15C calculates an evaluation value for the introduction of the allocation condition of the i-th existing policy corresponding to the m-th intervention node (step S506).

[0105] Then, the output unit 15D outputs the difference in allocation conditions, the difference in resources, and the evaluation value obtained as a result of the loop processes 1 to 3 to the client terminal 30 (step S507), and ends the process.

[0106] <One Aspect of Effect> As described above, the server device 10 according to this embodiment compares the intervention nodes and sets of conditions assigned to the intervention nodes between the original policy flow and the reference policy flow, and extracts differences in the intervention nodes associated with common conditions. This extracts differences in the policy flow between the original policy and similar policies, making it possible to present information on whether the policy will be easy or difficult to implement. Therefore, the server device 10 according to this embodiment can present the ease of implementing a policy.

[0107] Although the embodiments relating to the disclosed device have been described above, the present invention may be embodied in various different forms other than the above-described embodiments. Therefore, other embodiments included in the present invention will be described below.

[0108] <Distribution and Integration> Furthermore, the components of each device shown in the figure do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of the devices can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. For example, the reception unit 15A, registration unit 15B, extraction unit 15C, or output unit 15D may be connected via a network as an external device to the server device 10. Furthermore, the reception unit 15A, registration unit 15B, extraction unit 15C, or output unit 15D may each be included in a separate device, and the functions of the server device 10 may be realized by the devices being connected to a network and operating together.

[0109] <Hardware Configuration> The various processes described in the above embodiments can be realized by executing a prepared program on a computer such as a personal computer, a workstation, etc. Therefore, an example of a computer that executes an extraction program having the same functions as those in the first and second embodiments will be described below with reference to FIG.

[0110] Fig. 18 is a diagram showing an example of a hardware configuration. As shown in Fig. 18, a computer 100 has an operation unit 110a, a speaker 110b, a camera 110c, a display 120, and a communication unit 130. The computer 100 also has a CPU 150, a ROM 160, an HDD 170, and a RAM 180. These units 110 to 180 are connected via a bus 140.

[0111] As shown in Fig. 18, the HDD 170 stores an extraction program 170a that performs the same functions as the reception unit 15A, registration unit 15B, extraction unit 15C, or output unit 15D shown in the first embodiment. This extraction program 170a may be integrated or separated, similar to the components of the reception unit 15A, registration unit 15B, extraction unit 15C, or output unit 15D shown in Fig. 1. In other words, the HDD 170 does not necessarily have to store all of the data shown in the first embodiment, as long as the data used for processing is stored in the HDD 170.

[0112] Under such an environment, the CPU 150 reads the extraction program 170a from the HDD 170 and loads it into the RAM 180. As a result, the extraction program 170a functions as an extraction process 180a, as shown in FIG. 18 . This extraction process 180a loads various data read from the HDD 170 into an area of ​​the storage area of ​​the RAM 180 allocated to the extraction process 180a, and executes various processes using the loaded data. For example, examples of processes executed by the extraction process 180a include the processes shown in FIGS. 15 to 17. Note that the CPU 150 does not necessarily need to operate all of the processing units described in the first embodiment above; it is sufficient that the processing units corresponding to the processes to be executed are virtually implemented.

[0113] The extraction program 170a does not necessarily have to be stored in the HDD 170 or the ROM 160 from the beginning. For example, each program may be stored on a "portable physical medium" such as a flexible disk, a so-called FD, a CD-ROM, a DVD disk, a magneto-optical disk, or an IC card that is inserted into the computer 100. The computer 100 may then retrieve and execute each program from such a portable physical medium. Alternatively, each program may be stored in another computer or server device connected to the computer 100 via a public line, the Internet, a LAN, a WAN, or the like, and the computer 100 may retrieve and execute each program from such a computer or server device.

[0114] REFERENCE SIGNS LIST 10 Server device 11 Communication control unit 13 Storage unit 13A Policy DB 15 Control unit 15A Reception unit 15B Registration unit 15C Extraction unit 15D Output unit 30 Client terminal

Claims

1. The computer When a first measure is acquired, the first measure being a measure including a conditional branch and a node connected by a directed edge, and a first node condition set for the first measure being a node condition set in which a node and a condition assigned to the node are associated with each other, a storage unit that stores a second measure and a second node condition set for the second measure is referenced, and differences between nodes associated with the same condition between the first measure and the second measure are extracted. An extraction method comprising:

2. The extraction method described in claim 1, characterized in that the extraction process includes, when the first measure and the first node condition set are obtained, referring to the memory unit that further stores the resources of the second measure, and extracting the differences in nodes associated with the same conditions between the first measure and the second measure and the differences in resources resulting from the differences in the nodes.

3. When the constraint conditions for the resources of the first measure are acquired, it is determined whether or not the difference in the resources extracted by the extraction process satisfies the constraint conditions; If it is determined that the constraint condition is satisfied, output the resource difference and the second measure from which the resource difference is extracted. The extraction method according to claim 2, further comprising the steps of:

4. The extraction method described in claim 1, characterized in that the extraction process includes, when the first measure and the first node condition set are obtained, referring to the memory unit and extracting differences in nodes associated with the same conditions between the first measure and the second measure, and differences in conditions associated with the same nodes between the first measure and the second measure.

5. The extraction method described in claim 1, characterized in that when the first measure and the first node condition set are obtained, the computer further executes a process of referring to the memory unit that further stores the evaluation value for each node, and outputting the evaluation value of the node of the second measure for which a difference has been extracted by the extraction process.

6. The computer When a first measure, which is a measure including a conditional branch and a node connected by a directed edge, and a first node condition set for the first measure, which is a node condition set in which a node and a condition assigned to the node are associated with each other, are acquired, a storage unit that stores a second measure and a second node condition set for the second measure is referenced, and differences in conditions associated with the same node between the first measure and the second measure are extracted. An extraction method comprising:

7. When a first measure is acquired, the first measure being a measure including a conditional branch and a node connected by a directed edge, and a first node condition set for the first measure being a node condition set in which a node and a condition assigned to the node are associated with each other, a storage unit that stores a second measure and a second node condition set for the second measure is referenced, and differences between nodes associated with the same condition between the first measure and the second measure are extracted. An information processing device comprising a control unit that executes processing.

8. On the computer, When a first measure is acquired, the first measure being a measure including a conditional branch and a node connected by a directed edge, and a first node condition set for the first measure being a node condition set in which a node and a condition assigned to the node are associated with each other, a storage unit that stores a second measure and a second node condition set for the second measure is referenced, and differences between nodes associated with the same condition between the first measure and the second measure are extracted. An extraction program characterized by executing a process.