AI Storage Policy Selection for Healthcare Data
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Solution Overview
Problem
Healthcare providers face challenges in optimizing the storage and retrieval of imaging data due to outdated storage policies that vary across facilities, leading to inefficiencies in storage costs and retrieval costs, which can hinder the adoption of industry best practices.
Innovation Solution
An AI-assisted data storage policy selection system that defines and evaluates data storage policies based on costs and access frequency, using machine learning techniques to determine the optimal storage location for healthcare data items, such as imaging data, by analyzing patient characteristics and historical data usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different storage policies are used across health care facilities, then each facility can have customized storage rules, but the policies become outdated and fail to incorporate industry best practices
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring storage policy performance metrics (access frequency, storage costs, retrieval costs) and using this information to automatically update and refine storage policies. The AI engine learns from historical data and adjusts policies to incorporate industry best practices while maintaining facility-specific customization.
Solution Approach 2:
Storage policies are transformed from static, manually-defined rules into dynamic, adaptive policies that automatically adjust based on changing data usage patterns, access frequencies, and cost structures. The system continuously evolves policies to remain current with industry best practices while adapting to local facility needs.
2Ease of operation
If image data sets are stored in online storage for extended periods, then data accessibility is improved, but storage costs increase significantly
Solution Approach 1:
The system dynamically adjusts storage location assignments based on real-time and historical access frequency data. Images that are accessed frequently remain in online storage, while those with declining access patterns are automatically moved to offline storage, optimizing the balance between accessibility and cost.
Solution Approach 2:
The system changes the storage state parameter (online vs. offline) based on evaluated metrics including access frequency, patient characteristics, and cost considerations. This parameter adjustment allows the system to optimize both accessibility and storage costs by placing data in the most appropriate storage location at any given time.
3Productivity
If storage policies are standardized across all health care facilities, then industry best practices can be adopted, but facility-specific data usage patterns cannot be optimized
Solution Approach 1:
The system segments the storage policy decision-making process into standardized components (industry best practices framework) and facility-specific components (local data usage patterns). This allows simultaneous adoption of proven industry practices while maintaining customization for individual facility needs through the AI engine's ability to process facility-specific historical data.
Solution Approach 2:
The AI-based storage policy system serves multiple functions: it implements industry best practices universally while simultaneously adapting to facility-specific patterns. The single system handles both standardized optimization and localized customization, making it universally applicable across different health care facilities with varying needs.
4Reliability
If manual policy updates are performed regularly, then policies can be kept current, but the complexity and time required for policy management increases
Solution Approach 1:
The system performs self-service by automatically monitoring performance metrics, evaluating storage policy effectiveness, and updating policies without human intervention. The AI engine continuously learns from data and autonomously refines storage strategies, eliminating the need for manual policy updates while maintaining current, optimized policies.
Solution Approach 2:
Automatic feedback loops continuously monitor storage policy performance and trigger updates when improvements are identified. This self-correcting mechanism keeps policies current without requiring manual review or management, reducing complexity while maintaining reliability.
Data Source
AI summary
A method includes defining a plurality of data storage policies, each of the plurality of data storage policies providing rules for storing data among a plurality of data storage locations, each of the plurality of data storage locations having a data storage cost and a data retrieval cost associated therewith; determining a baseline policy distribution among the plurality of data storage policies for an entity; receiving new data items corresponding to the entity; storing the new data items in the plurality of data storage locations using the plurality of data storage policies based on the baseline policy distribution; and determining, using the artificial intelligence engine, a selected one of the plurality of data storage policies to use in storing the new data items corresponding to the entity based on the data storage cost for each of the plurality of data storage locations, and the data retrieval cost for each of the plurality of storage locations.