CQL Query Processing System for Healthcare Data Integration
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Solution Overview
Problem
Existing healthcare systems face inefficiencies and high costs when integrating third-party queries across multiple hospital systems, requiring weeks or months to process queries for large patient populations, and are prone to errors.
Innovation Solution
A dynamic system that processes clinical quality language (CQL) queries in a programmatic manner, transforming CQL queries into modified SQL queries to execute across multiple hospital systems, leveraging metadata from FHIR resources, and automating data management for efficient and cost-effective analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used to integrate third-party queries across multiple hospital systems, then query processing can be performed, but the process is time-consuming (weeks to months), expensive, and prone to errors
Solution Approach 1:
The patent replaces manual mechanical processes with automated computer-based systems. Specifically, it uses natural language processing (NLP) algorithms and machine learning models to automatically parse, understand, and execute clinical queries across multiple EHR systems, eliminating the need for manual data extraction and analysis that previously took weeks or months.
Solution Approach 2:
The patent introduces an intermediary processing layer between third-party queries and EHR systems. This intermediary system translates natural language queries into structured queries, manages data extraction automatically, and handles the complexity of interfacing with multiple different EHR platforms, thereby speeding up the overall process.
2Ease of manufacture
If manual translation of CQL queries is performed, then queries can be executed, but the process requires significant time and resources
Solution Approach 1:
The patent replaces manual translation of Clinical Quality Language (CQL) queries with automated natural language processing systems. The NLP algorithms automatically interpret CQL queries, translate them into executable formats, and map them to the appropriate data structures in various EHR systems, eliminating the need for manual translation efforts.
Solution Approach 2:
The system enables self-service execution of clinical queries by automatically handling the translation and adaptation processes. The automated system independently manages query parsing, data extraction, and result aggregation without requiring manual intervention, making the process both easier and faster.
3Reliability
If third-party queries are processed across multiple hospital systems using conventional methods, then data can be integrated, but the process is expensive and resource-intensive
Solution Approach 1:
The patent replaces resource-intensive manual data integration processes with efficient automated computing systems. By using NLP and machine learning algorithms, the system can process and integrate data across multiple hospital systems more efficiently, reducing both computational costs and human resource requirements while maintaining or improving accuracy.
Solution Approach 2:
The patent creates a universal query processing platform that can handle multiple types of clinical queries across different EHR systems simultaneously. This multi-functional system consolidates what would otherwise require separate manual processes for each query and system, reducing overall resource consumption and costs.
4Quantity of substance
If manual processes are used for integrating third-party information across thousands to millions of patients, then data can be collected, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces manual data collection processes with automated computer-based systems capable of processing large volumes of patient data efficiently. The automated NLP and machine learning systems can analyze and extract relevant information from electronic health records of thousands to millions of patients simultaneously, reducing the time required from months to significantly shorter periods.
Data Source
AI summary
Systems, methods, and storage media useful in a computer healthcare system to consume clinical quality language queries in a programmatic manner are disclosed. Exemplary implementations may: load a CQL query from a third party; transform the CQL query to a modified structured query language query; load medical data elements from a database record store for each patient in one or more defined patient populations; execute the modified SQL query on medical data elements from the database record store for each patient in one or more defined patient populations; and load results of the modified SQL query.