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ARAMIS today: moving toward internationally distributed databank systems for follow-up studies
Clinical Rheumatology
|September 1, 1987
Summary
The American Rheumatism Association Medical Information System (ARAMIS) transitioned from mainframe to distributed computing, improving data management for rheumatic disease research. This shift enhances data quality and cost-efficiency for a growing network of research centers.
Area of Science:
- Rheumatology
- Medical Informatics
- Epidemiology
Background:
- The American Rheumatism Association Medical Information System (ARAMIS) is a large consortium of North American rheumatic disease data banks established in 1974.
- ARAMIS has expanded significantly, encompassing over 16 centers, 22,000 patients, and 80 million observations.
- Historically, data management relied on a centralized mainframe system at Stanford University.
Purpose of the Study:
- To describe the evolution of the ARAMIS data management system.
- To highlight the migration from mainframe to distributed computing.
- To discuss the implications for data quality, cost-efficiency, and international collaboration.
Main Methods:
- The study details the transition from a centralized IBM mainframe system to a distributed system utilizing IBM PC/XT/AT computers and the Medlog software.
- Emphasis is placed on ARAMIS's commitment to data quality, epidemiological soundness, and the establishment of specialized "core" groups for quality control, biostatistics, and other critical areas.
- The migration strategy involved leveraging advances in microcomputer technology and software.
Main Results:
- Substantial cost savings have been achieved through the adoption of distributed processing.
- The new system facilitates easier data and software transfer, laying the groundwork for international data exchange.
- The migration supports ARAMIS's ongoing focus on data quality and epidemiological rigor.
Conclusions:
- The migration to distributed computing represents a significant advancement for the ARAMIS network.
- This technological shift enhances operational efficiency and cost-effectiveness in managing large-scale rheumatic disease data.
- Establishing common vocabulary and quality control procedures is crucial for future international data sharing and collaborative research in rheumatology.