Data-Driven Predictive Analysis for Healthcare Cost Optimization
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Solution Overview
Problem
Current systems lack sophisticated techniques for accurately matching and modeling vast amounts of data, particularly in predictive analysis, leading to inefficiencies in decision-making processes, especially in healthcare where precise cost analysis is crucial for patient care and treatment planning.
Innovation Solution
A system and method for data-driven predictive analysis that aggregates data from multiple sources, identifies relevant attributes, and applies statistical operations to generate weight values, enabling accurate matching of patient demographics with past treatment outcomes to predict future costs and optimize treatment options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vast amounts of data are collected from multiple sources, then the completeness and accuracy of predictive analysis improves, but the complexity of data processing and matching increases
Solution Approach 1:
The patent segments the complex data processing task into distinct modules: data collection from multiple sources, data cleaning and normalization, attribute identification, statistical operation application, and result generation. Each module handles a specific aspect of the data pipeline, making the overall complex system manageable and maintainable while preserving predictive accuracy.
Solution Approach 2:
The patent introduces intermediary components such as data normalization layers and attribute identification mechanisms that mediate between raw data from multiple sources and the predictive analysis engine. These intermediaries standardize diverse data formats and extract relevant features, reducing processing complexity while maintaining data completeness and analytical accuracy.
2Loss of information
If sophisticated data matching techniques are implemented, then the relevance of information increases, but the computational time and resources required increase
Solution Approach 1:
The patent applies preliminary data cleaning, normalization, and attribute identification before the main predictive analysis. By pre-processing the data and identifying relevant attributes in advance, the system reduces the computational burden during the actual matching and analysis phase, thereby maintaining information relevance while reducing computational time and resource requirements.
3Manufacturing precision
If detailed statistical operations are applied to generate weight values, then the precision of cost predictions improves, but the complexity of the modeling process increases
Solution Approach 1:
The patent transforms complex modeling operations into parameter-based statistical calculations. By applying statistical operations to generate weight values for different data attributes and converting these into standardized parameters, the system achieves precise cost predictions while managing modeling complexity through parameterization rather than complex algorithmic structures.
Data Source
AI summary
Systems and methods are provided for data driven predictive analysis of complex data sets for determining decision outcomes. The systems and methods include obtaining a first set of data associated with individuals and obtaining, a second set of data associated with events, wherein the events are associated with at least one of the individuals. The systems and methods further include determining a subset of the individuals from the first set of data based on the first subset of individuals having common attributes with a target individual and determining a second subset of events from the second set of data based on the second subset of events having common attributes with the target events associated with the target individual. Additionally, the systems and methods include aggregating data associated with the second subset of events based on the target events and providing for display output associated with aggregated data.