Healthcare Resource Demand Estimation via Multi-Source Data Integration
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Solution Overview
Problem
Current healthcare systems lack reliable tools to estimate short-term or long-term demand for healthcare resources, such as medical equipment and staff, due to factors like seasonal variations, geographic differences, and unforeseen events, leading to supply shortages that can endanger patients and increase costs.
Innovation Solution
A system and method that collect and integrate data from various sources, including wearable devices, social media, and electronic health records, to generate labeled records and train a resource demand estimation model, which correlates and combines data to predict healthcare resource demand accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional healthcare resource allocation methods are used, then simplicity and ease of operation are maintained, but measurement precision and reliability of demand estimation deteriorate
Solution Approach 1:
The system segments the demand estimation problem into multiple components: short-term demand prediction using time-series analysis, long-term demand prediction using trend analysis, and separate modeling for different resource types (staff, equipment, supplies). This segmentation allows each component to be optimized independently while maintaining overall system manageability
Solution Approach 2:
The patent introduces an intermediary computational layer that processes raw healthcare data through multiple algorithms (ARIMA, exponential smoothing, regression analysis) to generate refined demand estimates. This intermediary layer acts as a buffer between raw data and final decisions, improving precision while isolating the complexity within a dedicated processing module
2Reliability
If comprehensive data collection from multiple sources is implemented, then reliability and measurement precision of demand estimates improve, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The system implements a universal data collection framework that handles multiple data sources (electronic health records, insurance claims, wearable devices, social media) through a single integrated architecture. This multi-functional platform standardizes data ingestion, cleaning, and validation processes, making the system capable of processing diverse data types through common procedures
Solution Approach 2:
The patent incorporates automated data quality assessment and cleaning mechanisms that self-correct common data issues without manual intervention. The system automatically detects missing values, outliers, and inconsistencies, applying appropriate imputation or correction algorithms, thereby reducing the operational burden of managing comprehensive data collections
3Productivity
If advanced predictive models are used, then productivity and timeliness of resource allocation improve, but device complexity and cost increase
Solution Approach 1:
The system employs dynamic model selection that adapts the complexity of predictive algorithms based on the specific forecasting scenario. For short-term predictions, simpler time-series models are used; for long-term trends, more complex regression and machine learning models are applied. This dynamic approach optimizes computational resources while maintaining high productivity across different time horizons
Solution Approach 2:
The patent implements a tiered modeling approach where basic predictive capabilities are always available, and advanced models are activated only when additional accuracy is needed and computational resources are available. This partial application of complex models allows the system to maintain high productivity for routine predictions while reserving complex analysis for critical decision points
Data Source
AI summary
Presented are systems and methods that allow healthcare providers and governments to infer demand for healthcare resources to ensure effective and timely healthcare services to patients by reducing healthcare supply shortages, emergencies, and healthcare costs. In embodiments, this is accomplished by gathering data from a number of sources to generate labeled records from which entity features and relationships between entities are extracted, correlates, and/or combined with other external healthcare data. In embodiments, this information is used to train a model that predicts healthcare resource demands given a set of input conditions or factors.