JP-7855941-B2 - Workflow generation method and workflow generation program

JP7855941B2JP 7855941 B2JP7855941 B2JP 7855941B2JP-7855941-B2

Inventors

堀田真路
猪又明大
倉木健介

Assignees

富士通株式会社

Dates

Publication Date: 20260511
Application Date: 20220628

Claims (4)

We created several candidate workflows by modifying parts of an existing workflow that defines multiple conditional branches and the follow-up actions to be taken at each branch. For each of the multiple candidate workflows created, the KPI change vector from the existing workflow is determined. Multiple combined measures are generated by combining two or more of the KPI fluctuation vectors from among the multiple KPI fluctuation vectors. If a single selection route in the candidate workflow includes multiple changes, the weight parameters are set such that the weight of the higher-level changes in the selection route is higher than the weight of the lower-level changes in the selection route, and the predicted KPI values for each of the multiple composite measures are calculated accordingly. A workflow generation method characterized in that a computer executes a process to output a workflow for the composite measures that satisfy the predicted KPI values as target values.
The workflow generation method according to claim 1, characterized in that the process for calculating the predicted KPI value for each of the multiple composite measures includes a process for calculating the predicted KPI value by weighted summation of the predicted KPI value of an existing workflow and the KPI fluctuation vector.
The workflow generation method according to claim 1 or 2, characterized in that the weight parameter is set according to the flow depth of the candidate workflow.
We created several candidate workflows by modifying parts of an existing workflow that defines multiple conditional branches and the follow-up actions to be taken at each branch. For each of the multiple candidate workflows created, the KPI change vector from the existing workflow is determined. Multiple combined measures are generated by combining two or more of the KPI fluctuation vectors from among the multiple KPI fluctuation vectors. If a single selection route in the candidate workflow includes multiple changes, the weight parameters are set such that the weight of the higher-level changes in the selection route is higher than the weight of the lower-level changes in the selection route, and the predicted KPI values for each of the multiple composite measures are calculated accordingly. A workflow generation program characterized by causing a computer to execute a process that outputs a workflow for the composite measures that satisfy the predicted KPI values as target values.

Description

This invention relates to a workflow generation method and a workflow generation program. In policy planning, proposals are often based on intuition, experience, and assumptions, making it difficult to achieve key performance indicator (KPI) targets. Therefore, there is a demand to generate and present policy proposals that can achieve targets based on evidence-based KPI predictions during the policy planning stage. Conventionally, for example, technologies have been proposed to support policy formulation decision-making by predicting policy KPIs from integrated data consisting of various types for policy candidates. For example, integrated data such as the history of daily health information, diagnostic results, lifestyle habits, and test results for each individual resident of a region is prepared in advance. Furthermore, as a policy KPI prediction, we predict the probability (categorized into high, medium, and low) that each individual in the target area will become obese within a specified period if the proposed policy is implemented. In setting policy candidates, for example, the policy objective might be set as "reduce the obesity rate among adults in the target area to 25%", the policy candidate as "provide exercise and nutrition guidance to young residents of the area by internal medicine physicians", and the KPI (Key Performance Indicator) as "the obesity rate among adults in the area". Japanese Patent Publication No. 2022-14106Japanese Patent Publication No. 2005-332270Japanese Patent Publication No. 2017-208035Japanese Patent Publication No. 2021-72022Patent No. 6799313 specification Konstantinos Moutselos; Dimosthenis Kyriazis; Ilias Maglogiannis, “A web based modular environment for assisting health policy making utilizing big data analytics”, [online], July 23-25, 2018, IEEE, [Retrieved June 15, 2020], Internet <URL: https://ieeexplore.ieee.org/document/8633625> This diagram illustrates the KPI prediction method for synthetic strategies as a related technology.This diagram illustrates the workflow for the CKD (Chronic Kidney Disease) follow-up system in specific health checkups.This figure illustrates the method for generating composite strategies using the KPI forecast shown in Figure 1.This figure illustrates the node allocation results and provisional KPI forecast results for the composite policy generation method exemplified in Figure 3.This figure illustrates the node assignment results and provisional KPI forecast results when the scope of perturbation influences overlap in a method for generating composite measures using KPI forecasting.This block diagram shows an example of a computer hardware (HW) configuration that realizes the functionality of a workflow generation device as an example of an embodiment.This diagram illustrates the functional configuration of a workflow generation device as an example of an embodiment.This is a diagram illustrating a policy workflow in a workflow generation device as an example of an embodiment.This figure illustrates a method for adding perturbations in a workflow generation device as an example of an embodiment.This diagram illustrates a method for generating policy candidates by the policy candidate generation unit of a workflow generation device, as an example of an embodiment.This figure illustrates multiple policy candidates generated by the policy candidate generation unit of a workflow generation device as an example of an embodiment.This diagram illustrates the processing of the policy candidate KPI prediction unit of a workflow generation device as an example of an embodiment.This diagram illustrates the status of each individual and the intervention node assignment results for each candidate measure in a workflow generation device, as an example of an embodiment.This diagram illustrates the predicted KPI results for each individual for each proposed measure in a workflow generation device, as an example of an embodiment.This diagram illustrates the method for calculating KPI prediction values by the policy candidate KPI prediction unit of a workflow generation device, as an example of an embodiment.This figure shows an example of the final KPI prediction values predicted by the policy candidate KPI prediction unit of the workflow generation device, as an example of an embodiment.This figure illustrates a method for generating fluctuation vectors by the policy candidate KPI prediction unit of a workflow generation device, as an example of an embodiment.This figure illustrates the fluctuation vector generated by the policy candidate KPI prediction unit of a workflow generation device as an example of an embodiment.This diagram illustrates the functional configuration of the provisional KPI prediction unit for synthesized measures in a workflow generation device, as an example of an embodiment.This diagram illustrates the processing of the provisional KPI prediction unit for synthesized measures in a workflow generation device as an example o