Dynamic Coaching Plan Configurator for Remote Patient Management
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Solution Overview
Problem
Current Remote Patient Management (RPM) systems lack the ability to personalize coaching plans based on individual patient needs and local clinical practices, as they only offer predefined, static care plans that cannot be dynamically changed during execution.
Innovation Solution
A computer-implemented method using a coaching plan configurator, comprising an authoring tool, compiler, and linker, to create personalized and optimized coaching plans from clinical guidelines, local standards, and patient-specific factors, allowing medical professionals to define and execute tailored interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predefined static care plans are used, then guideline compliance is ensured, but adaptability to individual patient needs and local clinical practices is lost
Solution Approach 1:
The system transforms static care plans into dynamic, configurable plans that can be automatically adjusted based on patient characteristics, local clinical practices, and provider preferences. The care plan generator allows runtime configuration of plan parameters while maintaining guideline compliance through structured templates and constraints.
Solution Approach 2:
The system changes parameters of care plans by allowing configuration of plan parameters such as intervention frequency, duration, and specific actions based on patient characteristics and local practices. This enables customization without departing from guideline-compliant structures.
2Ease of manufacture
If static care plans are used, then implementation simplicity is maintained, but ability to personalize coaching plans based on patient determinants is lost
Solution Approach 1:
The care plan is segmented into configurable components and parameters that can be independently adjusted. The structured format allows separation of guideline requirements from patient-specific configurations, enabling personalization while maintaining implementation simplicity through automated generation.
Solution Approach 2:
The system creates a universal care plan generator that can produce multiple personalized plans from a single template framework. The same guided configuration process works for different patient populations and clinical settings, providing multi-functionality without increasing implementation complexity.
3Loss of time
If predefined care plans are used, then development time is reduced, but ability to dynamically change plans during execution phase is lost
Solution Approach 1:
The care plans are designed to be dynamically modifiable during execution. The system enables runtime changes to plan parameters and interventions based on evolving patient conditions, while the guided configuration ensures these changes maintain guideline compliance without requiring extensive development time.
4Reliability
If comprehensive personalization options are provided, then patient compliance is improved, but system complexity increases
Solution Approach 1:
The system manages complexity by focusing on configurable parameters rather than comprehensive redesign. Key parameters such as intervention frequency, duration, and specific actions can be adjusted based on patient determinants, achieving personalization while keeping the overall system structure manageable through standardized templates.
Data Source
AI summary
A computer-implemented method of manufacturing a computer-readable storage medium for a remote patient management system with instructions for causing the execution of a care plan comprising a coaching plan. The method comprises: displaying a list of psychological determinants, receiving a selection of a group of psychological determinants chosen from the list of psychological determinants, generating a list of behavioral models using the group of psychological determinants, receiving a selection of at least one selected behavioral model from the list of behavioral models, determining a time line for the global structure of the coaching plan using the at least one selected behavioral model, compiling a coaching object file using the time line, linking the coaching object file to a library of multi-media content to resolve unresolved symbolic links of the time line, integrating the coaching plan into the care plan, and writing the care plan to the computer-readable storage medium.