Dynamic Patient Guidance via Machine Learning
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Solution Overview
Problem
Current healthcare systems lack dynamic patient guidance, relying on static patient care plans that fail to adapt to individual patient behavior and characteristics, leading to inefficient use of care coordinators' time and potential deviations from prescribed plans.
Innovation Solution
A computer network architecture with machine learning and artificial intelligence that continuously updates patient care plans based on adherence data, risk scores, and patient interactions, automatically adjusting guidance and intervention frequency to optimize care plan achievement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static patient care plans are used, then implementation simplicity is maintained, but adaptability to individual patient behavior deteriorates
Solution Approach 1:
The patent implements dynamic patient care plans that automatically adapt to individual patient behavior, characteristics, and progress over time. The system uses machine learning algorithms to continuously learn from patient data and adjust care plan recommendations, transforming static protocols into living, evolving guidance that responds to real-time patient needs while maintaining systematic structure.
Solution Approach 2:
The care plan system performs self-adjustment through automated machine learning processes. The system learns from patient interactions, adherence patterns, and outcomes without requiring manual redesign of care plans by healthcare professionals. This self-service capability allows the system to improve its own adaptability while reducing the complexity burden on human operators.
2Productivity
If manual monitoring of patient adherence is performed, then detection precision is maintained, but loss of time for care coordinators increases
Solution Approach 1:
The patent replaces manual mechanical monitoring of patient adherence with automated electronic systems. Machine learning algorithms continuously analyze patient data, interaction patterns, and care plan completion status, substituting the time-consuming manual checking process with automated digital assessment. This enables precise detection of adherence issues while freeing care coordinators from routine monitoring tasks.
Solution Approach 2:
The system implements continuous feedback loops where patient behavior and adherence data automatically trigger updates to care plans and notifications to care coordinators. The machine learning system processes patient interactions in real-time, providing immediate feedback on adherence status and predicting potential compliance issues before they manifest, thereby reducing the time needed for manual review.
3Reliability
If frequent direct intervention by care coordinators is performed, then reliability of patient care is improved, but loss of time and resources increases
Solution Approach 1:
The machine learning system performs preliminary analysis of patient data to predict which patients are at risk of non-adherence or care plan failure before problems occur. By identifying at-risk patients in advance, the system enables proactive rather than reactive intervention, allowing care coordinators to focus their time on patients who need it most while avoiding unnecessary interventions for stable patients.
Solution Approach 2:
The system applies differentiated intervention strategies based on individual patient characteristics, risk levels, and care plan progress. Rather than uniform frequent intervention for all patients, the machine learning algorithms identify local quality needs - adjusting intervention frequency and type to match each patient's specific situation. This ensures high reliability for at-risk patients while reducing time expenditure on low-risk patients.
Data Source
AI summary
Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and dynamic patient guidance. Embodiments automatically update patient guidance in the patient care plan, based on the effectiveness of the guidance to date, attributes of the patient, other updated information, ongoing experience of the network, and updated predictions of possible patient outcomes and metrics.