Executable Sequence Generation for Adaptive Healthcare Workflows
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Solution Overview
Problem
Current systems lack an efficient and adaptive mechanism for programmatically determining and executing executable action sequences in healthcare contexts, particularly in selecting and adapting actions based on real-time patient data and conditions, leading to delayed responses and suboptimal care.
Innovation Solution
A system that includes memory hardware and processor hardware configured to store and execute instructions for obtaining patient information, determining conditions, identifying states, and scheduling executable sequences based on trigger conditions, allowing for adaptive and responsive action sequences, including communication with patients through various channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated execution of executable sequences is implemented, then productivity and response speed improve, but device complexity increases
Solution Approach 1:
The system segments the healthcare workflow into distinct executable sequences (e.g., patient onboarding, care coordination, follow-up scheduling) that can be independently defined, executed, and modified. Each sequence is broken down into discrete actions with trigger conditions, allowing the complex system to be managed through modular components rather than a monolithic structure.
Solution Approach 2:
The system enables self-service through automated trigger-based execution where defined conditions automatically initiate appropriate sequences without requiring manual intervention. For example, when a patient's condition meets predetermined criteria, the system automatically executes the appropriate care sequence, reducing the need for human decision-making in routine cases and improving response speed.
2Adaptability or versatility
If adaptive selection of executable sequences is implemented, then adaptability improves, but device complexity increases
Solution Approach 1:
The system implements dynamics through its ability to adaptively select and modify executable sequences based on real-time patient data and conditions. The sequences are not static but can be dynamically adjusted based on patient responses, condition changes, and evolving care needs, allowing the system to adapt to individual patient journeys while managing complexity through predefined adaptive rules.
Solution Approach 2:
The system changes parameters such as sequence selection, execution timing, and action details based on patient conditions and responses. By modifying these parameters dynamically rather than requiring complete system redesign, the system achieves high adaptability to different patient scenarios while controlling overall system complexity through parameter-based adjustments.
3Speed
If real-time data processing is implemented, then speed improves, but loss of time in data collection increases
Solution Approach 1:
The system performs preliminary actions by pre-defining executable sequences, trigger conditions, and data requirements before actual processing occurs. This allows the system to be ready for immediate execution when data becomes available, reducing processing delays while minimizing the time needed for data collection by knowing exactly what data is needed and when it will be required.
Data Source
AI summary
A system includes processor hardware configured to execute instructions from memory hardware. The instructions include, in response to designation of an entity within a data store, obtaining information and determining a condition of the designated entity. The instructions include, based on the condition of the designated entity, identifying a set of states. The instructions include obtaining trigger conditions for the selected state, each specifying a set of satisfaction criteria. The instructions include determining whether each trigger condition is satisfied by evaluating the satisfaction criteria based on data corresponding to the designated entity. The instructions include selectively selecting another state based on whether the trigger conditions are satisfied. The instructions include determining, and scheduling for execution, an executable sequence based on the selected state.