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A strategy for statistical Master Person Index linking.

M K Martin¹, I P Shevchenko, L L Reed-Fourquet

¹Connecticut Healthcare Research and Education Foundation, Wallingford, USA.

Proceedings. AMIA Symposium

|November 24, 1999

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A new patient linking program is being developed to manage Connecticut's growing healthcare data. This system uses parallel processing for better performance and scalability in linking diverse patient information.

Area of Science:

Health Informatics
Computer Science

Background:

The existing patient linking program for Connecticut's statewide health data repository is struggling with database growth.
The Master Person Index (MPI) is critical for managing large, complex patient datasets.

Purpose of the Study:

To present the development of a second-generation patient linking program.
To improve the performance, scalability, and sophistication of patient data linking.

Main Methods:

Developing a new linking program utilizing a unique multi-step process.
Implementing parallel multi-processing for enhanced performance.
Utilizing inexpensive PC-based systems, Linux OS, a free database engine, and Java.

Main Results:

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The new linker is designed for effective data linking from numerous dissimilar sources.
Parallel multi-processing is expected to significantly improve performance and scalability.
The system architecture allows for more sophisticated statistical methods for link confidence.

Conclusions:

The second-generation linker aims to overcome the limitations of the current system.
The proposed system offers a scalable and cost-effective solution for managing large patient data repositories.
This work lays the foundation for advanced patient data integration and management in healthcare.