Dynamic Care Plan System for Personalized Patient Monitoring
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Solution Overview
Problem
Current care plans are often generic and not tailored to individual patients, lacking the ability for healthcare providers to customize them beyond annotations, and there is no efficient way to monitor patient adherence until follow-up appointments, leading to potential conflicts and poor compliance.
Innovation Solution
A care platform system that allows healthcare providers to design customized care plans for patients with multiple conditions, incorporating specific tasks, metrics, and thresholds, using monitoring devices and mobile devices to collect data and adjust the plan dynamically based on patient adherence and health metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a generic care plan is used for all patients with a particular condition, then the care plan can be easily implemented and standardized, but it cannot be tailored to individual patient needs and preferences
Solution Approach 1:
The care plan system transitions from static generic plans to dynamic personalized plans that adapt based on patient data, preferences, and real-time monitoring. The system automatically generates customized care plans by integrating electronic health records, patient preferences, and clinical guidelines, allowing each patient to receive a tailored plan while maintaining systematic management.
Solution Approach 2:
The system changes key parameters of care plans including task selection, frequency, duration, and alerts based on individual patient characteristics. By modifying these parameters dynamically according to patient-specific data, the system achieves both customization and systematic implementation.
2Loss of information
If healthcare providers manually monitor patient adherence through follow-up appointments and call centers, then they can obtain adherence information, but the process is costly and time-consuming
Solution Approach 1:
The system enables automatic self-monitoring of patient adherence through electronic logging and mobile device integration. Patients automatically track and report their care plan completion, eliminating the need for manual provider monitoring. The system automatically detects non-adherence patterns and triggers appropriate responses.
Solution Approach 2:
The system implements continuous feedback loops where patient adherence data is automatically collected, analyzed, and used to adjust care plans in real-time. Automated alerts notify providers of adherence issues, enabling timely interventions without requiring manual monitoring efforts.
3Speed
If a simple event classification is performed by remote devices, then the processing is fast and efficient, but the classification accuracy and detail are limited
Solution Approach 1:
The event classification process is segmented into two stages: initial rapid classification by remote devices for immediate response, and subsequent detailed analysis by the central care plan management system for accurate categorization. This division allows both speed and precision to be achieved at different levels of the system.
Solution Approach 2:
The system uses an intermediary classification layer where remote devices perform preliminary event detection and transmission, and the central system performs detailed classification. This intermediary approach enables fast initial processing while maintaining high classification accuracy through centralized analysis.
Data Source
AI summary
Techniques for administering a care plan. Biometric data collected by a monitoring device is received at a care plan management system using a communication network. The biometric data includes a first event classified by a remote device as a first type of event. A relative processing priority for the first event is determined, at the care plan management system, based on the first type. The first event is processed, at the care plan management system, based on the relative processing priority. The processing includes re-classifying the first event as a second type of event. The re-classification is more computationally intensive than the classification by the remote device. At least one treatment task specified in the care plan is initiated, using a computer processor, and based on the re-classified first event.