Parallel Processing Architecture for Population Health Data Analysis
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Solution Overview
Problem
Traditional healthcare systems are ill-equipped to manage large volumes of health data for population segments, lacking scalability and flexibility, and are often tied to specific data formats, making it difficult to identify and stratify patients for health intervention programs effectively.
Innovation Solution
A distributed storage system coupled with parallel processors executes simple, descriptive clinical logic to identify and stratify patients for health intervention programs, allowing for the management of large patient populations by converting human-readable documents into computer-readable code and processing patient data in varying formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional systems are used for population health management, then system simplicity is maintained, but the system cannot scale to process large volumes of health data for populations of 2,000,000 or more patients
Solution Approach 1:
The system divides the population health management functionality into separate modular components: a data reception module that handles incoming health data in various formats, a translation module that converts human-readable documents to computer-readable code, and a processing module that executes clinical logic. This segmentation allows each component to be optimized independently, enabling the system to scale to process large populations while maintaining manageable complexity through clear separation of concerns.
Solution Approach 2:
The system implements universal processing logic that can handle multiple data formats and structures through a common translation layer. The translation module converts various human-readable document formats into standardized computer-readable code, allowing the same clinical logic to process diverse data sources. This multi-functionality enables the system to scale across different data types without requiring separate processing systems for each format.
2Adaptability or versatility
If traditional processing logic is used, then implementation is straightforward, but the logic lacks extensibility to execute against data in varying formats
Solution Approach 1:
The system introduces a translation module as an intermediary between the diverse data formats and the clinical processing logic. This mediator converts human-readable documents in various formats into standardized computer-readable code, allowing the clinical logic to remain simple and extensible while handling varying data formats. The translation layer absorbs the complexity of format variations, enabling the processing logic to adapt to different formats without becoming inherently complex.
Solution Approach 2:
The system changes the parameter representation of data by translating various data formats into a unified computer-readable code structure. This parameter transformation allows the same clinical logic to process different data formats by changing how the data is represented rather than changing the logic itself. The translation module adjusts data parameters and structure to match the expected input format for clinical processing.
3Productivity
If healthcare facilities manually manage population health data, then data accuracy can be maintained, but the facilities lack the processing power to effectively manage populations of 2,000,000 or more patients
Solution Approach 1:
The system replaces manual mechanical data processing with automated computational processing. The parallel processing architecture uses multiple processors working simultaneously to execute clinical logic against population data, substituting human manual review with machine-based automated analysis. This substitution enables facilities to manage populations of 2,000,000 or more patients by leveraging computational power rather than human resources, dramatically increasing population management capacity.
Solution Approach 2:
The system performs preliminary translation of human-readable documents into computer-readable code before the main processing occurs. This preliminary action prepares the data in advance, allowing parallel processors to efficiently execute clinical logic without delay. By pre-processing and standardizing data formats before population-level analysis, the system reduces the computational burden during actual processing, enabling scalable management of large populations.
Data Source
AI summary
Methods, systems, and computer-storage media are provided for facilitating the management of population health. A parallel processing architecture receives patient population health data from healthcare facilities along with one or more documents that are in human-readable form. From the human-readable document, computer-readable code is generated that includes high-level clinical logic is executed against the population health data to identify, among other things, patients in the population who qualify for health intervention programs. Using this information, healthcare facilities can implement management programs to help care for these patients.