Emergency Surge Prediction via Multi-Source Data Fusion
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Solution Overview
Problem
Current systems for predicting patient surges in emergency departments are inadequate as they rely on historical data and fail to account for real-time events, leading to inefficient staffing and resource allocation, which can result in either overstaffing or understaffing, impacting healthcare costs and patient care.
Innovation Solution
A method and system that analyze real-time data from various sources, including social media, emergency dispatches, and healthcare facilities to identify demand signals for medical resources, and provide notifications to adjust staffing and resource allocation accordingly, ensuring better preparedness for potential surges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If historical data only is used for prediction, then system complexity is low, but prediction accuracy deteriorates
Solution Approach 1:
The patent combines multiple data sources (historical data, real-time social media data, emergency dispatch data, weather data) into a unified prediction system. This merging of diverse data streams enables more accurate surge predictions while the system manages complexity through integrated data processing architecture
Solution Approach 2:
The prediction system is designed to process multiple types of data (structured historical data and unstructured real-time data from various sources) through a single multi-functional platform, allowing it to adapt to different data formats and sources while maintaining prediction accuracy
2Measurement precision
If real-time data from multiple sources is integrated, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system segments data processing into distinct modules: data collection from multiple sources, data validation, feature extraction, model processing, and output generation. This segmentation allows each component to handle specific tasks, reducing overall processing complexity while maintaining high prediction accuracy
Solution Approach 2:
The patent introduces intermediary components including data validation layers, feature extraction modules, and normalization processes that mediate between raw multi-source data and the prediction model. These intermediaries standardize and prepare data, reducing complexity in the core prediction engine
3Productivity
If real-time monitoring and notification systems are implemented, then resource allocation responsiveness improves, but system operational complexity increases
Solution Approach 1:
The system implements continuous feedback loops where prediction results trigger notifications to facility administrators, who then adjust resource allocation. The system monitors the impact of these adjustments and uses this feedback to refine future predictions, creating a responsive closed-loop system that improves resource allocation efficiency
Solution Approach 2:
The system performs preliminary actions by predicting surges before they occur and issuing advance notifications to facilities. This allows facilities to proactively adjust staffing and resource allocation before the actual surge happens, improving responsiveness while automating the planning process to manage operational complexity
Data Source
AI summary
One embodiment provides a method, including: receiving, from a healthcare enterprise system, a notification indicating a need for at least one medical resource during a predetermined time frame at a predetermined facility within the healthcare enterprise system; determining that the indicated need is unavailable during at least one of: the predetermined time frame and the predetermined facility; identifying, based upon the indicated need being unavailable, at least one action to be performed to supply the at least one medical resource; and performing the at least one action. Other aspects are described and claimed.