Analytics Specification Compression for Healthcare Data
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Solution Overview
Problem
Healthcare data analytics face inefficiencies due to duplicate and extraneous codes in patient-defined measures, leading to increased processing complexity and memory usage, which hampers the performance of analytics in healthcare networks.
Innovation Solution
A system that optimizes analytics by compressing patient-defined measure specifications through removing duplicates, combining logical conditions, and eliminating unused data, while maintaining compliance with the schema, thereby reducing the memory footprint and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If patient-defined measure specifications are stored with all original codes and conditions, then measurement precision and data completeness are maintained, but device complexity and memory usage increase
Solution Approach 1:
The patent extracts and removes duplicate codes and extraneous data elements from patient-defined measure specifications while retaining the essential analytical logic. This extraction process eliminates redundant information that increases processing complexity without contributing to analytics accuracy, thereby resolving the contradiction between maintaining measurement precision and reducing device complexity
Solution Approach 2:
The patent transforms the structure of measure specifications by changing parameters such as code representation formats and logical condition expressions. By optimizing these parameters, the system maintains the same analytical precision while reducing the overall complexity and size of the stored specifications
2Loss of information
If duplicate codes and extraneous data are retained in specifications, then data completeness is preserved, but processing speed and productivity decrease
Solution Approach 1:
The system extracts and removes duplicate codes and extraneous data elements from specifications before processing. This extraction maintains data completeness by preserving essential information while eliminating redundant elements that slow down analytics processing, thereby improving productivity without losing critical data
Solution Approach 2:
The patent applies preliminary compression and optimization to measure specifications before they are processed by analytics engines. By pre-removing duplicates and extraneous data, the system prepares optimized specifications in advance, which significantly improves processing speed while maintaining data completeness
3Measurement precision
If comprehensive measure specifications are stored without compression, then analytics accuracy is maintained, but memory footprint and resource consumption increase
Solution Approach 1:
The patent merges duplicate codes and redundant logical conditions into single representative elements within compressed specifications. This merging reduces the quantity of stored data and memory footprint while preserving the analytical logic required for accurate measurements, thereby resolving the contradiction between maintaining analytics accuracy and reducing memory usage
Solution Approach 2:
The system changes the parameter representation in specifications by using compressed formats and optimized data structures. This parameter transformation maintains the precision needed for accurate analytics while significantly reducing the memory footprint and resource consumption required to store and process the specifications
Data Source
AI summary
A system optimizes performance of analytics and includes at least one processor. The system analyzes a specification of an analytic produced in accordance with a schema, where the specification indicates a set of conditions for members of a population to determine the analytic. The system compresses the specification by modifying constructs within the specification to produce a compressed specification of a reduced size and complying with the schema, where modifying the constructs within the specification includes removing duplicate portions, combining logical conditions, and removing portions with unused data. The system further performs the analytic based on the compressed specification. Embodiments further include a method and computer program product for optimizing performance of analytics in substantially the same manner as the system.