Hybrid Predictive Model for Patient Alertness and Visit Timing
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Solution Overview
Problem
Existing patient monitoring systems fail to predict patient alertness accurately, as they do not integrate medication data, scheduling events, and rely solely on wearable devices, leading to missed visit opportunities and inefficient visitor coordination.
Innovation Solution
A predictive model that integrates patient sensor data, medication scheduling, and calendar events to forecast wakefulness and alertness, using machine learning and cognitive agents like IBM Watson®, providing customized alerts to authorized visitors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a predictive model integrates multiple data sources (sensor data, medication scheduling, calendar events), then prediction accuracy of patient alertness is improved, but system complexity increases
Solution Approach 1:
The patent combines multiple data sources including sensor data from wearable devices, medication scheduling information, and calendar events into a unified predictive model. This integration allows the system to correlate various factors affecting patient alertness, thereby improving prediction accuracy while managing complexity through systematic data fusion
Solution Approach 2:
The predictive model serves multiple functions: it processes diverse data types (sensor readings, scheduling data, event information), generates alertness predictions, and provides customized notifications to different user groups. This multi-functionality consolidates what would otherwise require separate systems into a single comprehensive platform
2Productivity
If the system provides customized alerts to multiple authorized users, then visitor coordination efficiency is improved, but information management complexity increases
Solution Approach 1:
The system segments information delivery by user type, providing customized alerts to different authorized users (family members, friends, healthcare providers) based on their specific interests and relationships with the patient. This segmentation allows efficient targeted communication while managing information complexity through role-based notification strategies
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions with alerts and visitation patterns are used to refine and update the predictive model. This continuous feedback loop improves prediction accuracy over time while automating information management, reducing the burden of manual coordination
Data Source
AI summary
A method, computer program product, and a system where a processor(s) obtains data related to medical treatment and a current physical state of a given patient. The processor(s) cognitively analyzes the data to identify potential patient behavioral patterns of the given patient. The processor(s) obtains general patient data and correlates the general patient data with a portion of the data to identify elements in the general patient data with a potential impact on the identified potential patient behavioral patterns. The processor(s) determines impacts of the elements on the identified potential behavioral patterns and applies the impacts to generate a data structure comprising baseline behavioral patterns, i.e., a predictive model to utilize in determining probabilities that the given patient will exhibit behaviors comprising the baseline behavioral patterns during a defined future time interval.