SQL Cohort Data Comparison Procedures
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Solution Overview
Problem
Current methods for processing large datasets, such as those used in producing National Commissioning for Value packs, are inefficient and resource-intensive, leading to outdated information and difficulty in understanding clinical variations across England due to the complexity and size of the data sets.
Innovation Solution
The use of Structured Query Language (SQL) procedures to aggregate data on group levels, generating comparison data values by standardizing and statistically analyzing attributes within a database environment, thereby reducing computational complexity and enabling efficient data comparisons across cohorts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to process large datasets for cohort-wise comparisons, then comprehensive data analysis can be performed, but processing time and resource consumption increase significantly
Solution Approach 1:
The patent segments the large dataset into cohorts based on shared characteristics (age, gender, diagnosis, etc.), allowing parallel processing of each cohort independently. This segmentation enables the system to analyze subsets of data simultaneously, reducing overall processing time while maintaining comprehensive analysis capabilities across all cohorts.
Solution Approach 2:
The patent performs preliminary actions by pre-calculating and storing cohort membership information, aggregate statistics, and comparison metrics in a database. This preliminary processing allows subsequent queries to retrieve pre-computed results without reprocessing the entire dataset, significantly reducing processing time for repeated analyses.
2Loss of information
If detailed cohort-wise data comparisons are performed, then clinical variations can be identified, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the computational task into distinct modules: cohort identification, data aggregation, statistical comparison, and result generation. Each module handles a specific aspect of the analysis, reducing overall computational complexity while enabling detailed cohort-wise comparisons that preserve clinical variation information.
Solution Approach 2:
The patent introduces an intermediary database layer that stores pre-computed cohort statistics and comparison results. This intermediary structure acts as a mediator between the raw data and the analysis queries, reducing computational complexity by avoiding repeated full-data processing while preserving detailed clinical variation information.
3Measurement precision
If large datasets are processed in detail, then accurate statistical comparisons can be generated, but resource consumption and processing overhead increase
Solution Approach 1:
The patent performs preliminary actions by pre-computing and storing aggregate statistics (means, standard deviations, counts) for each cohort in the database. This preliminary calculation enables accurate statistical comparisons to be generated by retrieving and comparing pre-computed values rather than reprocessing raw data, significantly reducing resource consumption while maintaining statistical accuracy.
Solution Approach 2:
The patent creates copies of aggregated cohort data and stores them in the database for efficient retrieval. These copies contain the essential statistical information needed for comparisons without requiring access to the full raw dataset, reducing resource consumption while preserving the ability to generate accurate statistical comparisons.
4Reliability
If comprehensive data aggregation is performed across multiple primary data groups, then meaningful comparisons can be made, but data processing time and system load increase
Solution Approach 1:
The patent segments the data aggregation process by primary data groups (e.g., practice, CCG, ICS) and computes aggregates for each group independently. This segmentation allows parallel processing of multiple data groups simultaneously, maintaining the validity of comparisons across groups while improving overall processing speed through concurrent execution.
Solution Approach 2:
The patent performs preliminary aggregation of data by primary data groups and stores these intermediate results in the database. This preliminary aggregation enables faster subsequent comparisons by working with pre-computed group-level statistics rather than raw individual records, maintaining comparison validity while improving processing speed.
Data Source
AI summary
Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and computing entities for generating a comparison data value for each attribute of interest for one or more attributes of interest associated with a dataset. In one embodiment, a method is provided comprising: importing the dataset into a database environment; executing a SQL procedure to aggregate records in the dataset into attribute data items, wherein each item comprises an aggregate data value for each attribute of interest; and for each item: executing the SQL procedure to (1) generate a standardized data value for each attribute of interest based on the aggregate data value; (2) identify a cohort; (3) generate a statistical data value for each attribute of interest with respect to the cohort; and (4) generate the comparison data value for each attribute of interest based on a comparison between the standardized data value and the statistical data value.