Workflow Flow Control Method for Dropout Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional workflow generation for measure planning often fails to consider the perspectives of both the implementing and receiving sides, leading to interventions that are not suitable for individuals, resulting in high dropout rates and discrepancies between planned and actual results.
Innovation Solution
A flow control method that extracts subjects who are likely to dropout from the current workflow, determines an insertion point for a new conditional branch, and inserts a new condition and node to guide these subjects to a more suitable intervention, enhancing the workflow's suitability for both sides.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional workflow generation is used, then workflow creation is simple, but dropout rates increase and suitability for individual needs deteriorates
Solution Approach 1:
The system performs preliminary analysis of subject characteristics and workflow paths before finalizing the workflow. It predicts dropout probabilities in advance and pre-determines appropriate conditional branches and nodes to insert, ensuring interventions are tailored to individual needs before execution begins
Solution Approach 2:
The system incorporates feedback loops that analyze actual subject responses and outcomes against predicted dropout patterns. This feedback is used to refine future workflow generation, adjusting conditional branches and node selections to reduce dropout rates while maintaining workflow creation simplicity
2Device complexity
If conventional workflow generation is used, then workflow structure remains simple, but intervention suitability for individual perspectives deteriorates
Solution Approach 1:
The system segments the workflow into distinct conditional branches and nodes based on subject characteristics. Each segment can be independently analyzed and optimized for specific subject groups, enabling tailored interventions without overwhelming complexity in the overall workflow structure
Solution Approach 2:
The system applies local quality by customizing specific portions of the workflow (conditional branches and nodes) based on the characteristics of individual subjects or subject groups. This allows high adaptability in critical areas while maintaining simplicity in other workflow regions
3Ease of operation
If subjects are not separated by conditions, then workflow is simple to execute, but intervention effectiveness deteriorates
Solution Approach 1:
The system introduces dynamic conditional branches that automatically adjust workflow paths based on real-time subject characteristics and predicted dropout probabilities. This dynamic adaptation improves intervention effectiveness while maintaining ease of execution through automated decision-making
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A flow control method executed by a computer, includes: extracting, from a plurality of subjects, one or more first subjects each satisfying a first condition, the one or more first subjects being extracted based on a result of assignment of the plurality of subjects to a node based on a workflow that includes the node connected to a conditional branch by a directed edge; determining an insertion point of a new conditional branch in the workflow, based on distribution of the one or more extracted first subjects in the workflow; inserting a second condition, as the new conditional branch, at the insertion point, the second condition separating, at a lower node that is lower than the determined insertion point, the one or more first subjects from a subject not satisfying the first condition among the plurality of subjects; and inserting a new node at a branch destination of the new conditional branch.