Time-Series Data Structure for Prescription Complexity Analysis
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Solution Overview
Problem
Existing electronic data storage and manipulation methodologies are incompatible with the organic complexities of patient medication regimens, providing rigid, metadata-driven mechanisms that fail to accurately combine data structures, leading to inaccuracies in determining the effective length of prescriptions and creating downstream errors.
Innovation Solution
Systems and methods are configured to combine time-series based data records associated with individual prescriptions based on substantive and characteristic content, accurately reflecting changes in dosages and residual quantities, while efficiently generating member-specific timeframes to calculate prescription complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rigid, metadata-driven mechanisms are used to combine data structures, then the storage and manipulation process is simplified, but the accuracy in determining effective prescription length deteriorates
Solution Approach 1:
The patent changes the approach from using fixed metadata parameters to dynamically calculating time-series duration based on substantive content characteristics. The system computes actual time overlaps and gaps between prescriptions, rather than relying on predetermined metadata fields, thereby improving measurement precision while maintaining computational efficiency through algorithmic optimization.
Solution Approach 2:
The patent replaces rigid, rule-based metadata combining mechanisms with a more flexible computational approach that analyzes substantive content. Instead of mechanically merging records based on predefined fields, the system uses algorithmic processing to determine actual time overlaps and prescription effectiveness, achieving higher accuracy without sacrificing ease of implementation.
2Quantity of substance
If existing storage databases are used without underlying metadata, then storage capacity is maximized, but the ability to reorganize and consolidate data structures deteriorates
Solution Approach 1:
The patent performs preliminary calculations of time-series duration and time overlaps when data records are first processed and stored. By pre-computing these characteristics and storing them alongside the substantive content, the system enables efficient reorganization and consolidation operations later without requiring complex real-time calculations, thus maintaining both storage efficiency and operational versatility.
Solution Approach 2:
The patent segments the data structure into distinct components: substantive content, calculated time characteristics, and metadata. This segmentation allows the system to store maximum quantities of prescription data while maintaining the ability to efficiently query, reorganize, and consolidate records based on time overlaps and other characteristics without processing the entire dataset.
3Speed
If simple combining methods are used for data records, then processing speed is improved, but the accuracy in capturing dosage changes and overlaps deteriorates
Solution Approach 1:
The patent changes the processing approach by pre-calculating and storing time-series duration and overlap characteristics as derived parameters. This allows the system to quickly retrieve and compare prescription effectiveness data without performing complex temporal analyses during query operations, thereby maintaining high processing speed while ensuring accurate capture of dosage changes and time overlaps.
Data Source
AI summary
Generating a data structure based on time-series data stored within a data store comprises: generating a plurality of time frame nodes within a data structure having a member node associated with a particular member, each time frame node reflecting a particular period of time during which there are no changes to the content of drug nodes associated with the particular period of time. At least a portion of those drug nodes at least partially reflect merged data records reflecting the combined duration of a plurality of matching data records, wherein the duration of the merged data records are determined based at least in part on characteristic data stored within one or more of the identified matching data records. The time frame nodes of the data structure are generated based on determined changes in content of the drug nodes based on the merged data records.