Pseudonymized Medical Data Retrieval for Longitudinal Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The handling of multi-time point medical data analysis in cloud-based systems is complicated by data privacy concerns, as persistently storing patient data outside the hospital is restricted, and transferring large medical data files is time-consuming and occupies significant storage space, while local hospital storage may be unreachable for querying/retrieving data due to security considerations.
Innovation Solution
The use of pseudonymized identifiers to associate medical data with patients, allowing retrieval of prior medical result information from a long-term data repository, reducing the need to transfer and store large medical data files by processing medical data based on pseudonymized identifiers, ensuring data privacy and reducing storage burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical data is stored in a cloud-based system outside the hospital, then longitudinal analysis capability is improved, but data privacy security deteriorates
Solution Approach 1:
The patent extracts personal health information (PHI) from medical data, storing only de-identified data in the cloud repository. This separates the identifiable elements (kept locally) from the analytical data (stored externally), enabling longitudinal analysis while preserving patient privacy security.
Solution Approach 2:
The patent introduces a de-identification layer as an intermediary between the hospital's local system and the cloud repository. This intermediary process removes PHI before external storage, allowing the system to bridge local security requirements with external analytical capabilities.
2Adaptability or versatility
If large medical data files are transferred to the cloud repository, then external data access capability is improved, but data transfer time increases
Solution Approach 1:
The patent extracts and stores only essential metadata and de-identified data elements in the cloud repository, rather than transferring complete large-scale medical data files. This selective extraction reduces transfer time while maintaining external access capability for longitudinal analysis.
3Loss of information
If complete medical data files are stored in the cloud repository, then data completeness is improved, but storage space consumption increases
Solution Approach 1:
The patent extracts and retains only the essential data elements needed for longitudinal analysis in the cloud repository, removing redundant and non-essential information. This extraction approach maintains analytical completeness while significantly reducing storage space requirements.
4Object-affected harmful factors
If local hospital storage is used for medical data, then data security is improved, but external querying capability deteriorates
Solution Approach 1:
The patent segments data into two categories: PHI (stored locally for security) and de-identified data (stored externally for accessibility). This segmentation allows the system to simultaneously achieve local security requirements and external querying capability through distributed storage architecture.
Solution Approach 2:
The patent introduces a de-identification process as an intermediary that enables data to move from the secure local environment to the accessible external repository. This intermediary transformation preserves security constraints while enabling external querying functionality.
Data Source
AI summary
Techniques of storing and retrieving medical data and information are provided. Medical result information can be stored on a long-term data repository in a shared network, e.g., the Internet. Pseudonymized identifiers of patients can be used to retrieve such data and information.