Integration of Web-based and PC-based Clinical Research Databases

Summary

We have created a Web-based repository or data library of information about measurement instruments used in studies of multi-factorial geriatric health conditions (the Geriatrics Research Instrument Library – GRIL) based upon existing features of two separate clinical study data management systems. GRIL allows browsing, searching, and selecting measurement instruments based upon criteria such as keywords and areas of applicability. Measurement instruments selected can be printed and/or included in an automatically generated standalone microcomputer database application, which can be downloaded by investigators for use in data collection and data management.

Methods: Integration of database applications requires the creation of a common semantic model, and mapping from each system to this model. Various database schema conflicts at the table and attribute level must be identified and resolved prior to integration. Using a conflict taxonomy and a mapping schema facilitates this process.

Results: Critical conflicts at the table level that required resolution included name and relationship differences.

Conclusions: A major benefit of integration efforts is the sharing of features and cross-fertilization of applications created for similar purposes in different operating environments. Integration of applications mandates some degree of metadata model unification.

Keywords

Database interoperation - clinical research database management systems

• References

• 1 Nadkarni PM, Brandt C, Frawley S, Sayward F, Einbinder R, Zelterman D. et al. Managing attribute-value clinical trials data using the ACT/DB client-server database system. JAMIA 1998; 5 (02) 139-51.
• 2 Chueh HC, Barnett GO. Client-server, distributed database strategies in a health-care record system for a homeless population. JAMIA 1994; 1 (02) 186-98.
• 3 Friedman C, Hripcsak G, Johnson S, Cimino J, Clayton P. A Generalized Relational Schema for an Integrated Clinical Patient Database. In: Proc. 14th Symposium on Computer Applications in Medical Care. Washington, D.C: IEEE Computer Press, Los Alamitos, CA; 1990: 335-9.
• 4 Huff SM, Haug DJ, Stevens LE, Dupont CC, Pryor TA. HELP the next generation: a new client-server architecture. In: Proc. 18th Symposium on Computer Applications in Medical Care. Washington, D. C: IEEE Computer Press, Los Alamitos, CA; 1994: 271-5.
• 5 Niedner C. Use of SQL with an entity-attribute-value database. MUG Quarterly 1991; 21 (03) 40-45.
• 6 Oracle Corporation. Oracle Clinical Version 3.0: User’s Guide. Redwood Shores, CA: Oracle Corporation; 1996
• 7 Brandt CA, Nadkarni PM, Marenco L, Karras BT, Lu C, Schacter L. et al. Re-engineering a Database for Clinical Trials Management: Lessons for System Architects. Controlled Clinical Trials 2000; 21 (05) 441-61.
• 8 Nadkarni PM, Brandt CA, Marenco L. Web-EAV: automatic metadata-driven generation of web interfaces to entity-attribute-value databases. JAMIA 2000; 7 (04) 343-56.
• 9 Kashyap V, Seth A. Semantic and schematic similarities between database objects: a context-based approach. VLDB Journal 1996; 5: 276-304.
• 10 Elmagarmid A, Rusinkiewicz M, Sheth A. (eds) Management of Heterogeneous and Autonomous Database Systems. San Fancisco: Morgan Kaufmann; 1999
• 11 Kim W, Seo J. Classifying Schematic and Data Heterogeneity in Multidatabase Systems. IEEE Computer 1991; 24 (12) 12-8.
• 12 UMLS. Unified Medical Language System. UMLS Knowledge Sources: U.S. Department of Health and Human Services, National Institutes of Health, National Library of Medicine; 2000. Report No.11th Edition.