Patient Data Processing Location Under Legal Constraints
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Solution Overview
Problem
The manual review and static configuration of patient-specific data processing locations fail to account for varying legal constraints and contractual obligations, making it difficult to plan data processing effectively.
Innovation Solution
A system utilizing a pretrained large language model to parse legal constraints and determine appropriate processing locations for patient-specific data, ensuring compliance with these constraints by configuring applications to run either in the cloud or on-premises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual review and static configuration are used to determine data processing locations, then the system is simple to operate, but it cannot adapt to varying legal constraints and contractual obligations
Solution Approach 1:
The system automatically determines processing locations by analyzing changing legal and contractual parameters, replacing static configuration with dynamic parameter-based decision-making. The LLM processes updated legal constraints and contractual obligations to determine appropriate processing locations, allowing the system to adapt to parameter changes without manual reconfiguration.
Solution Approach 2:
The system performs self-service by automatically reviewing legal constraints, contractual obligations, and determining processing locations without requiring manual intervention. The LLM autonomously processes input data, applies relevant rules, and outputs appropriate processing location recommendations, eliminating the need for manual review and configuration.
2Productivity
If static configuration is performed on data processing locations, then the system is easy to implement, but planning of data processing becomes difficult due to multiple processing rules
Solution Approach 1:
The system incorporates feedback mechanisms where the LLM processes legal constraints and contractual obligations to determine processing locations, then this information feeds back into the data processing planning. This feedback loop enables efficient planning by automatically considering multiple processing rules and their implications, rather than requiring manual analysis of each rule.
Solution Approach 2:
The LLM acts as an intermediary between the complex set of processing rules and the data processing planning process. It receives legal constraints and contractual obligations as input, processes them through its knowledge base, and outputs simplified processing location recommendations, mediating between complexity and ease of operation.
3Adaptability or versatility
If applications are configured to run in specific locations based on fixed rules, then the system is stable and predictable, but it cannot respond to changes in legal and contractual conditions
Solution Approach 1:
The system transitions from static configuration to dynamic processing location determination. The LLM continuously processes updated legal constraints and contractual obligations to determine appropriate processing locations, allowing the system to respond to changes in legal and contractual conditions while maintaining compliance reliability through automated rule application.
Data Source
AI summary
The application relates to a system configured to manage processing of patient-specific data. The system includes a pretrained large language model configured to: receive, as an input, processing rules including legal constraints as to which location a processing of the patient-specific data is allowed; process the input to determine a processing location where an application configured to process the patient-specific data can process the patient-specific data meeting the legal constraints; and provide, as output, the processing location for further use.