Patient Projection Methodology Using Intermediary Data Aggregation
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Solution Overview
Problem
Tracking patients with complex medical conditions across multiple treatment entities is challenging due to privacy laws restricting data exchange between healthcare providers, limiting access to comprehensive patient data.
Innovation Solution
A computer-implemented method that aggregates and projects patient data from various sources, including pharmacies, clinics, and hospitals, using anonymous tracking identifiers and pharmaceutical distribution data to provide a comprehensive view of patient treatment and diagnosis, while ensuring compliance with privacy laws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If patient data is aggregated from multiple healthcare entities, then comprehensive patient tracking is improved, but data privacy compliance becomes more difficult
Solution Approach 1:
The patent introduces a data aggregation intermediary system that collects patient data from multiple healthcare entities through standardized interfaces. This intermediary layer enables comprehensive data aggregation while maintaining privacy compliance by acting as a buffer between data sources and analysis systems, allowing data to be aggregated without direct access to sensitive patient information at any single entity.
Solution Approach 2:
The system segments patient data into distinct categories (demographic data, treatment data, outcome data) and aggregates them separately from individual patient records. This segmentation allows comprehensive data collection while maintaining privacy by analyzing data at the population level rather than accessing individual patient information, thus complying with privacy regulations.
2Object-affected harmful factors
If healthcare entities maintain separate data access, then data privacy is protected, but treatment effectiveness monitoring is reduced
Solution Approach 1:
The intermediary data aggregation system enables treatment effectiveness monitoring by collecting data from multiple entities without requiring direct data sharing between them. The intermediary processes and analyzes aggregated data to monitor treatment effectiveness, thus maintaining privacy protection while improving monitoring capability.
Solution Approach 2:
The system merges data from multiple healthcare entities into a unified aggregated dataset that can be analyzed for treatment effectiveness. This merging occurs at the population level through the intermediary system, allowing comprehensive monitoring without combining sensitive individual patient data, thus maintaining privacy while improving reliability of treatment monitoring.
3Ease of operation
If pharmaceutical companies access only distribution data, then data access simplicity is maintained, but patient-level treatment insights are lost
Solution Approach 1:
The data aggregation intermediary provides pharmaceutical companies with patient-level treatment insights by processing and analyzing aggregated data from multiple healthcare entities. The intermediary maintains ease of access through standardized interfaces while uncovering valuable patient-level patterns and insights that would otherwise be unavailable.
Solution Approach 2:
The system transitions from analyzing only distribution data (macro level) to analyzing aggregated patient treatment data (meso level), adding a new dimension of analysis. This dimensional change enables pharmaceutical companies to gain patient-level insights while maintaining operational simplicity through the intermediary's automated processing capabilities.
Data Source
AI summary
The disclosure generally describes computer-implemented methods, software, and systems for projecting patients having a medical condition by utilizing multiple data sources. One computer-implemented method includes accessing patient data related to multiple patient transactions, each patient transaction representing provision of medical care including one or more pharmaceutical products to a patient. One or more factors are derived for use in projecting the accessed patient data to represent all patient transactions for the geographic area. A first projection of the accessed patient data is calculated using the derived one or more factors. The first calculated projection is compared to pharmaceutical distribution data for the geographic area. The derived factors are adjusted by reducing a difference between one or more components of the first calculated projection and one or more components of the pharmaceutical distribution data. A second projection of the accessed patient data is calculated using the adjusted one or more factors.