Patent Application
20230420119
This application is based upon and claims the benefit of priority of the prior Japanese Patent Application No. 2022-100887, filed on Jun. 23, 2022, the entire contents of which are incorporated herein by reference.
The embodiments discussed herein are related to a display method and a display program.
A technique of supporting to plan a measure flow in various fields, such as medicine, nursing, and administration, as a workflow has been available. For example, for selection of an energy saving measure, an energy-saving-evaluation supporting device to search for measures that can be introduced to a building from input information of a target building and facilities is proposed (for example, Japanese Laid-open Patent Publication No. 2012-27536).
However, because conventional techniques represented by the energy-saving-evaluation supporting device described above and the like are to only search for related measures that can be related to a measure to be planned, there is an aspect that it is not necessarily possible to provide information contributing to judgement on excess and deficiency of resources.
For example, a measure flow has an aspect that persons subject to measures are distributed to a service or the like to achieve a goal of the measures through conditional branches defined in the measure flow. However, the related measures described above only resemble in scale and kind of facilities of a building to the measure to be planned. Therefore, it is unclear whether the measure flow of the related measure described above is appropriate as an index to judge whether resources, such as service providers, can be distributed appropriately to the number of people to be distributed to a service of the measure flow to be planned. Even if a measure flow of such a related measure is presented, it is difficult to judge excess and deficiency of resources.
A measure flow is used as an example of workflow and person is used as an example of an object to be distributed by the workflow herein, but also in a case in which an object other than person is distributed in an entire workflow, a similar problem can occur.
According to an aspect of an embodiment, a display method includes accepting designation of a first workflow, first counting, by applying a plurality of objects to the first workflow, a number of object distributed by an element of a conditional branch of the first workflow for each of element at an end out of elements included in the first workflow, second counting, by applying the plurality of objects to a second workflow that is different from the first workflow, a number of objects distributed by an element of a conditional branch of the second workflow for each of element at an end out of elements included in the second workflow, and displaying the number of objects distributed to an element at the end, associating with the element at the end, for each of the first workflow and the second workflow, by a processor.
The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention.
Preferred embodiments will be explained with reference to accompanying drawings. The respective embodiments only illustrate one example and aspect, and numeric values, the scope of functions, and usage scenes are not limited to these exemplifications. The respective embodiments can be combined within a range not causing contradiction in processing.
The server device 10 is one example of a computer that provides the creation supporting function described above. For example, the server device 10 can be implemented as a server that provides the creation supporting function described above in a on-premise. Besides, the server device 10 can provide the creation supporting function described above as a cloud service by implementing in a form of platform as a service (PaaS) type or software as a service (Saas) type application.
The server device 10 can be connected to a client terminal 30 through a network NW in a communication-enabled manner as illustrated in
The client terminal 30 corresponds to an example of a computer that is provided with the creation supporting function described above. For example, the client terminal 30 may be implemented by a portable terminal device, such as a personal computer, a smartphone, a tablet terminal, and a wearable terminal.
In
In the following, a measure flow is exemplified just as an example of a workflow, and a person is exemplified just as an example of an object to be distributed by the workflow.
The “measure flow” herein is to assign persons subject to the measure in various fields, such as medicine, nursing, and administration, to a service to achieve a goal of the measure or the like. For example, the measure flow includes elements, such as “conditional branch” and “intervention”.
Out of these, “conditional branch” corresponds to an element of distributing persons subject to the measure. Moreover, “intervention” corresponds to an element in which a service or an action to achieve a goal of the measure are defined.
The service that is thus distributed in the measure flow can be provided by a medical service provider. For example, the medical service provider may include medical workers, such as doctor and nurse, and also government service providers, such as public health nurse and public health promotion department.
Accordingly, when a measure flow is to be planned, a point of view whether human resources such as medical service providers can be appropriately distributed to services to which persons are distributed by the measure flow is important.
A measure planning process includes [A] Discovery and Review of Problems, [B] Planning and Operation of Measure, [C] Verification and Assessment of Effect, and the like. Out of these, planning of a measure corresponds to [B] Planning and Operation of Measure. Furthermore, [B] Planning and Operation of Measure includes processes, such as formulation of measure candidates, analysis, comparison, and determination of measures, and the like.
Planning of such measure candidates include four requirements. For example, as the first one, using an existing measure substitute plan as it is exemplified. As the second one, diversion of a measure that has taken in another region as a measure substitute plan can be exemplified. As the third one, planning by referring to a list of general policy measures can be exemplified. As the fourth one, planning a completely new policy substitute can be exemplified.
In addition to these four requirements, the importance of comparing an originally planned measure flow and a measure flow of an existing measure has been increasing because it is obvious that importance is given to whether a similar measure has been taken in past for planning a measure from a viewpoint of administrative implementability.
Hereinafter, the originally planned measure flow can be denoted as “original flow”, and the measure flow of an existing measure can be denoted as “comparative flow”.
As explained in the above section of problems, because conventional techniques represented by the energy-saving-evaluation supporting device described above and the like are to only search for related measures that can be related to a measure to be planned, there is an aspect that it is not necessarily possible to provide information contributing to judgement on excess and deficiency of resources.
For example, a measure flow has an aspect that
persons subject to measures are distributed to a service or the like to achieve a goal of the measures through conditional branches defined in the measure flow. However, the related measures described above only resemble in scale and kind of facilities of a building to the measure to be planned. Even if a measure flow of such a related measure is presented, it is difficult to judge excess and deficiency of resources.
Accordingly, as a part of the creation supporting function according to the present embodiment, a display function of visualizing and displaying a distribution state of persons that are distributed to respective services in an original flow and a comparison flow is provided.
It is found that the services Z1, Z2, and Z4 are common between the original flow f1 and the comparison flow m1. Furthermore, the number of persons distributed to the respective services Z1 and Z4 is common, and 10 persons are common in the number of persons distributed to the service Z2 also. Thus, the number of persons that is affected by a difference between the original flow f1 and the comparison flow m1 is 10 persons out of total 100 persons and, therefore, it is found that the comparison flow m1 is similar measure flow to the original flow f1.
It can be said that the comparison flow m1 like this is appropriate as an index to judge whether human resources can be distributed appropriately to the number of persons distributed to the services Z1, Z2, and Z4 of the original flow f1.
By comparing the original flow f1 and the comparison flow m1, just as one example, it is possible to grasp excess and deficiency of human resources to be distributed to a service from a difference in the number of persons distributed to the same service.
For example, in a service in which the number of persons distributed in the original flow f1 is too large compared to the number of persons distributed in the comparative flow m1, a possibility that a load on a medical service provider assigned to the service increases or exceeds the capacity can be grasped. Moreover, in a service in which the number of persons distributed in the original flow f1 is too small compared to the number of persons distributed in the comparative flow m1, it is possible to grasp that there is surplus human resources to be assigned to the service.
Therefore, according to the display function according to the present embodiment, information contributing to judgment on excess and deficiency of resources can be provided.
Next, a functional configuration example of the server device 10 according to the present embodiment will be explained.
The communication control unit 11 is a functional unit that controls communication of the client terminal 30 and the like with other devices. Just as one example, the communication control unit 11 can be implemented by a network interface card, such as LAN card. As one aspect, the communication control unit 11 accepts a search request for a comparison flow from the client terminal 30, or outputs display data in which a state in which persons are distributed to a service in the original flow and the comparison flow and the like to the client terminal 30.
The storage unit 13 is a functional unit that stores various kinds of data. Just as one example, the storage unit 13 is implemented by an internal, external, or auxiliary storage of the server device 10. For example, the storage unit 13 stores resident data 13A and flow data 13B. Explanation of the resident data 13A and the flow data 13B will be given together with a stage at which reference, creation, or registration is performed.
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 implemented by a hardware processor. Other than this, the control unit 15 may be implemented by hardwired logic. As illustrated in
The accepting unit 16 is a processing unit that accepts various kinds of request from the client terminal 30. Just as one example, the accepting unit 16 can accept a search request for the comparison flow from the client terminal 30.
When such a request is to be accepted, the accepting unit 16 can accept designation of the original flow used for the search for the comparison flow also. As one aspect, the accepting unit 16 can accept the original flow from the client terminal 30 through the network NW. As another aspect, the accepting unit 16 can receive designation of the original flow from a measure flow included in the flow data 13B stored in the storage unit 13. As still another aspect, the accepting unit 16 can accept designation from a measure flow stored in a database server or a file system not illustrated.
Such a measure flow can be created by a special committee based on a nephropathy worsening-prevention measure. For example, the special committee may include a kidney specialist, a diabetes specialist, a public health nurse, and a public health promotion department/health policy department of a city.
As illustrated in
For example, when judgment of the conditional branch L1 is YES and the judgment of the conditional branch L2 is NO (non-diabetic), a resident is distributed to the service by a kidney specialist. Moreover, when judgment of the conditional branch L1 is YES, judgment of the conditional branch L2 is YES (diabetic), and judgment of the conditional branch L3 is NO, a resident is distributed to the service by a diabetes specialist. Furthermore, when judgment of the conditional branch L1 is YES, judgment of the conditional branch L2 is YES (diabetic), judgment of the conditional branch L3 is YES, and judgment of the conditional branch L4 is YES, a resident is distributed to the service of health instructions. Moreover, when judgment of the conditional branch L1 is YES, judgment of the conditional branch L2 is YES (diabetic), judgment of the conditional branch L3 is YES, and judgment of the conditional branch L4 is NO, it is determined as no intervention, and distribution to a specific service is not performed.
The measure flow f11 as described has a problem that a load of a kidney specialist increases or that a resident does not come even though health instructions are provided. On the other hand, in planning of the measure flow f1, from the viewpoint of implementability, it would be better to implement by a measure as close as possible to an existing measure by a trouble-saving method.
The searching unit 17 is a processing unit that
searches for a comparison flow that is similar to the original flow. Just as one example, when the search request described above is accepted by the accepting unit 16, the searching unit 17 acquires resident data, an original flow, and M pieces of comparison flows.
More specifically, the searching unit 17 acquires
the resident data 13A stored in the storage unit 13. The resident data 13A is data in which personal information relating to a resident is associated with each resident.
Furthermore, the searching unit 17 acquires an original flow that is designated by the search request described above. Moreover, the searching unit 17 acquires M (positive integer) pieces of comparison flows from among measure flows included in the flow data 13B stored in the storage unit 13. The flow data 13B may be a database in which measure flows are collected. In the flow data 13B as described, measure flows that have planned in past can be collected. For example, the flow data 13B may include a measure flow that has been created by a department other than a department to which a user of the client terminal 30, that is, a measure planner being an example of a user of the display function described above belongs, a measure flow that has been created in a region other than the region to which the measure planner belongs, and the like. When a comparison flow is acquired from the flow data 13B as described, the comparison flow can be acquired by acquiring the entire measure flows included in the flow data 13B, or by narrowing down to a measure flow of a similar measure that resembles to the measure of the original flow by using an existing technique.
Having acquired the resident data, the original flow and M pieces of the comparison flows, the searching unit 17 performs following processing for the number of times corresponding to K people of residents, for each of the M pieces of the comparison flows. That is, the searching unit 17 identifies a route on a flow to which a resident k is distributed by conditional branches for each of the original flow f and the comparison flow m, by collating an entry of the resident k in the resident data 13A with conditional branches of the original flow f and the comparison flow m.
As illustrated in
After the element of the original flow f is thus converted into a symbol, the elements of the original flow f are searched sequentially from an upstream side of the original flow f. When the element is a conditional branch, a direction of a branch destination is identified based on whether information relating to a condition set to the conditional branch, for example, a numerical value or a flag, satisfies the condition of the conditional branch out of a result of health examination of the resident k. By repeating the search as described until it reaches an end point of the original flow f, a sequence of elements along which the resident k flows on the original flow f is acquired as a symbol string.
For example, in a case of the resident A, a symbol string “L1L2L3Z1” is acquired. Furthermore, in the example of the resident B, a symbol string “L1L2Z3” is acquired. Moreover, in the example of the resident K, a symbol string “L1L2L3Z1” is acquired.
In
Returning back to explanation of
The counting unit 17A is a processing unit that counts the number of residents distributed to respective routes in the original flow and the comparison flow. For example, in the example illustrated in
The calculating unit 17B is a processing unit that calculates a degree of difference between the original flow f and the comparison flow m. Just as one example, the calculating unit 17B calculates a distance between symbol strings corresponding to routes to which the resident k is distributed in the original flow f and the comparison flow m. By thus calculating a statistical value, for example, an average value, a total value, and the like, of the distance calculated for each of K people of residents, a degree of difference between the original flow f and the comparison flow m can be calculated.
For example, in the case of the resident A, a distance between the symbol string “L1L2L3Z1” corresponding to the route to which A is distributed by the original flow f and a symbol string “L1L2L4Z5” corresponding to a route to which A is distributed by the comparison flow m1 is calculated.
For calculation of a distance between these symbol strings, just as one example, the Levenshtein distance that defines a distance between two character strings can be used. That is, by repeating editing processes, such as “insertion”, “deletion”, and “replacement”, with respect to one of character strings, the minimum number of times until it is converted into the other character string is to be the Levenshtein distance.
Just as one example, when “insertion” and “deletion” are permitted but “replacement” is prohibited out of the editing processes, a distance of symbol strings between the original flow f and the comparison flow m of the resident A is acquired as follows.
That is, as the first editing process, a symbol “L4” is inserted to the symbol string “L1L2L3Z1”. Thus, it is edited to a symbol string “L1L2L3Z1L4”. Next, as the second editing process, a symbol “Z1” is deleted from the symbol string “L1L2L3Z1L4”. Thus, it is edited to a symbol string “L1L2L3L4”. Next, as the third editing process, a symbol “L3” is deleted from the symbol string “L1L2L3L4”. Thus, it is edited to a symbol string “L1L2L4”. Finally, as the fourth editing process, a symbol “Z5” is inserted to the symbol string “L1L2L4”. Thus, it is edited to the symbol string “L1L2L4Z5”. Because the symbol string “L1L2L3Z1” is edited to the symbol string “L1L2L4Z5” by the four times of editing process as described above, the Levenshtein distance is calculated as “4.0”. If the editing process of “replacement” is permitted, the minimum editing distance is to be “2”.
As described, for each of the K people of residents, a distance between a symbol string of a distributed route by the original flow f and a symbol string of a distributed route by the comparison flow mi is calculated, Besides, by calculating a statistic value, for example, an average value, of the distance of symbol strings calculated for each of the K people of residents, a degree of difference “2.34” is acquired.
According to such a degree of difference, a flow of persons in the original flow f and the comparison flow m is expressed in a symbol string, and a degree of difference is calculated from a distance of the symbol string. Therefore, not only a similarity in a graph structure between the original flow f and the comparison flow m, but also a similarity in a flow of persons between the original flow f and the comparison flow m can be reflected in a value of the degree of difference.
As illustrated in
Meanwhile, routes of all 100 people of the residents are compared between the original flow f and the comparison flow m3. In this case, between the original flow f and the comparison flow m3, it is duplicated in a point that 50 people of the residents take the symbol string “L1L2L3Z1” and the symbol string “L1L2L3Z2” as the route. Because the distance of these 50 people is to be 0, the distance of the remaining 50 people affects the degree of difference. That is, a total value “10” of distance “1.0” between the symbol string “L1L2Z4” of the original flow f and a symbol string “L1L2L4Z3” of the comparison flow m3 of 10 people is acquired. Furthermore, a total value “40” of distance “2.0” between the symbol string “L1L2Z4” of the original flow f and a symbol string “L1L2L4L5Z4” of the comparison flow m3 of 20 people is acquired. Moreover, a total value “40” of distance “2.0” between the symbol string “L1L2Z4” of the original flow f and a symbol string “L1L2L4L5Z5” of the comparison flow m3 of 20 people is acquired. A total value “90” of the distances of these 50 people is divided by 100 people, to acquire the degree of difference “0.9”.
As described, because the original flow f and the comparison flow m2 have most part of the flow of person, that is, 90% in common, the degree of difference can be calculated low. On the other hand, between the original flow f and the comparison flow m3, because a flow of 50 people, which is a half of the people, is different, the degree of difference can be calculated high.
Furthermore, in the routes included in the comparison flow m, if a route, a frequency of a flow of person of which is equal to or lower than a threshold, or a route, a frequency of a flow of person of which exceeds a capacity set to a service is included, the comparison flow m can be excluded from objects to be displayed.
Moreover, a flow of persons in the original flow f and the comparison flow m is expressed in a symbol string, and a degree of difference is calculated from a distance of the symbol string. Accordingly, the degree of difference is calculated high as a degree of difference in structure between the original flow f and the comparison flow m becomes high and, consequently, a possibility that the comparison flow m in which the degree of difference in structure is high is selected as an object to be displayed can be reduced.
After the degree of difference between the original flow f and the comparison flow m is thus calculated for each of the M pieces of comparison flows, the searching unit 17 extracts, just as one example, a comparison flow, the degree of difference of which is the lowest among the M pieces of comparison flows as an object to be displayed.
Herein, just as one example, a case in which a comparison flow having the lowest degree of difference is extracted as an object to be displayed is exemplified, it is not limited thereto. For example, the predetermined number of comparison flows can be extracted as an object to be displayed sequentially from the lowest degree of difference. Other than this, comparison flows can be sorted in ascending order of the degree of difference, and designation of a comparison flow to be an object to be displayed can be accepted from a list of the sorted Comparison flows.
The display unit 18 is a processing unit that performs various kinds of displays to the client terminal 30. Just as one example, the display unit 18 generates display data in which a distribution state of residents to be distributed to respective services is visualized for each of the original flow f designated in the search request described above and the comparison flow m extracted by the searching unit 17, and displays it on the client terminal 30.
Moreover, to the conditional branch 12 illustrated in
Furthermore, to the conditional branch 13 illustrated in
As illustrated in
Furthermore, as illustrated in
Moreover, as illustrated in
Furthermore, as illustrated in
Subsequently, the searching unit 17 acquires the resident data 13A stored in the storage unit 13 (step S102). Furthermore, the searching unit 17 acquires the original flow designated in the search request described above (step S103). Moreover, the searching unit 17 acquires M pieces of the comparison flow from among measure flows included in the flow data 13B that is stored in the storage unit 13 (step S104).
The processing at these step S102 to step S104 are not always to be performed in order of step numbers, and may be performed irrespectively of order or in parallel.
The searching unit 17 performs loop processing 1 and loop processing 2 in which processing from step S105 described below to step S108 described below is repeated for the number of times corresponding to K people of residents for each of the M pieces of comparison flow. The processing from step S105 to step S108 described below is not necessarily required to be performed in repetition, but may be performed in parallel.
That is, the searching unit 17 collates an entry of the resident k in the resident data 13A acquired at step S102 with a conditional branch of the original flow f and the comparison flow m. Thus, the searching unit 17 identifies a route on a flow to which the resident k is distributed by the conditional branch for each of the original flow f and the comparison flow m (step S105).
Subsequently, the counting unit 17A increments a counter of the number of residents corresponding to the route on the flow tow which the resident k is distributed at step S105 for each of the original flow f and the comparison flow m (step S106).
Thereafter, the calculating unit 178 calculates a distance of a symbol string corresponding to the route to which the resident k is distributed for each of the original flow f and the comparison flow m (step S107). Besides, the calculating unit 17B accumulates the distance of a symbol string of the resident k calculated at step S107 to a total value to this point (step S108).
By repeating the loop processing 2 as described, a route on the flow to which the resident k is distributed is acquired for each of the resident k for each of the original flow f and the comparison flow m, and the total value of distances of symbol strings of all of K people of residents can be acquired. For example, by dividing the total value by K people, the degree of difference of the original flow f and the comparison flow m can be calculated.
Furthermore, by repeating the loop processing 1, the degree of difference of the original flow f and the comparison flow m is calculated for each of the M pieces of the comparison flow.
Thereafter, the searching unit 17 extracts a comparison flow having the smallest degree of difference from among the M pieces of the comparison flow (step S109). The display unit 18 displays display data in which a distribution state of residents to be distributed to respective services is visualized on the client terminal 30 for each of the original flow f designated in the search request described above and the comparison flow m extracted at step S109 (step S110), and the processing is ended.
As described above, the server device 10 according to the present embodiment visualizes a distribution state in which persons are distributed to respective services in the original flow and the comparison flow, and displays it. Therefore, according to the server device 10 according to the present embodiment, a difference in the number of persons distributed to the same service between the original flow f and the comparison flow m5 can be visually presented and, therefore, information contributing to judgment of excess and deficiency of resources can be provided.
Embodiments relating to the disclosed device has so far been explained, but other than the embodiments described above, the present invention may be implemented in various other forms. In the following, other embodiments included in the present invention will be explained.
In the first embodiment described above, a measure flow has been exemplified as an example of a workflow, but this is only one example, and the processing illustrated in
Moreover, the respective components of the respective devices illustrated are not necessarily required to be configured physically as illustrated. That is, specific forms of distribution and integration of the respective devices are not limited to the ones illustrated, and all or some thereof can be configured to be distributed or integrated functionally or physically in arbitrary units according to various kinds of loads, use conditions, and the like. For example, the accepting unit 16, the searching unit 17, or the display unit 18 may be connected to the server device 10 through a network as an external device. Moreover, the accepting unit 16, the searching unit 17, and the display unit 18 may be arranged respectively in separate devices and connected through a network so as to cooperate with each other, and to implement the functions of the server device 10.
Furthermore, an entirety or a portion of the resident data 13A or the flow data 13B stored in the storage unit 13 may be held respectively in separate devices, and those may be connected through a network to cooperate with each other, to implement the functions of the server device 10.
Furthermore, the respective processing explained in the embodiments described above can be implemented by executing a program prepared in advance by a computer, such as a personal computer and a workstation. In the following, an example of a computer that executes a display program having functions similar to the first embodiment and the second embodiment will be explained by using
The HDD 170 stores, as illustrated in
In such an environment, the CPU 150 reads out the display program 170a from the HDD 170, and expands on the RAM 180. As a result, the display program 170a functions as a display process 180a as illustrated in
The display program 170a described above is not necessarily required to be stored in the HDD 170 or the ROM 160 from the beginning. For example, the display program 170a is stored in a “portable physical medium” such as a flexible disk inserted into the computer 100, a so-called FD, a CD-ROM, a DVD disk, an magneto-optical disk, and an IC card. The display program 170a may be acquired from these portable physical medium by the computer 100, to be executed. Moreover, the display program 170a is stored in another computer or server device that is connected to the computer 100 through a public line, the Internet, a LAN, a wide area network (WAN), or the like. The display program 170a thus stored may be downloaded to the computer 100, to be executed.
Information contributing to judgment of excess and deficiency of resources can be provided.
All examples and conditional language recited herein are intended for pedagogical purposes of aiding the reader in understanding the invention and the concepts contributed by the inventors to further the art, and are not to be construed as limitations to such specifically recited examples and conditions, nor does the organization of such examples in the specification relate to a showing of the superiority and inferiority of the invention. Although the embodiments of the present invention have been described in detail, it should be understood that the various changes, substitutions, and alterations could be made hereto without departing from the spirit and scope of the invention.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2022-100887 | Jun 2022 | JP | national |
