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Database Systems and Data Warehouses

Healthcare Information Systems and the CATCH Data Warehouse

The measurement and assessment of health status in communities throughout the world is a massive information technology challenge.  The Comprehensive Assessment for Tracking Community Health (CATCH) data warehouse provides systematic methods for community-level assessment that is invaluable for resource allocation and health care policy formulation.  CATCH is based on health status indicators from multiple data sources, using an innovative comparative framework and weighted evaluation process to produce a rank-ordered list of critical community health care challenges.  The community-level focus is intended to empower local decision-makers by providing a clear methodology for organizing and interpreting relevant health care data.  Extensive field experience with the CATCH methods, in combination with expertise in data warehousing technology, has led to an innovative application of information technology in the health care arena.  The data warehouse allows a core set of reports to be produced at a reasonable cost for community use.  In addition, online analytic processing (OLAP) functionality can be used to gain a deeper understanding of specific health care issues.  The data warehouse in conjunction with Web-enabled dissemination methods allows the information to be presented in a variety of formats and to be distributed more widely in the decision-making community. 

Journal Publications and Book Chapters

  1. J. Studnicki, A. Hevner, and D. Berndt, “Using Data to Meet a Policy Objective: Community Health Assessment Practice with the CATCH Data Warehouse,” Chapter 30 in Public Health Informatics and Information Systems, Edited by P. O’Carroll, W. Yasnoff, M. Ward, L. Ripp, and E. Martin, Spring-Verlag, New York, 2003, pp. 661-680.
  2. D. Berndt, A. Hevner, and J. Studnicki, “The CATCH Data Warehouse: Support for Community Health Care Decision Making,” Decision Support Systems, Vol. 35, June 2003, pp. 367-384. (Available at http://dx.doi.org/10.1016/S0167-9236(02)00114-8 )
  3. D. Berndt, J. Fisher, A. Hevner, and J. Studnicki, “Healthcare Data Warehousing and Quality Assurance,” IEEE Computer, Vol. 34, No. 12, December 2001, pp. 33-42. (Available at http://doi.ieeecomputersociety.org/10.1109/2.970578 )
  4. J. Studnicki, A. Hevner, D. Berndt, and S. Luther, “Rating the Health Status of U.S. Communities,” Managed Care Interface, Vol. 14, No. 11, November 2001, pp. 43-51. (Available at http://www.medicomint.com/Search/SubjectDetails.asp?SUBJECT=Rating+the+Health+Status+of+U%2ES%2E+Communities )
  5. J. Studnicki, A. Hevner, D. Berndt, and S. Luther, “Comparing Alternative Methods for Composing Community Peer Groups: A Data Warehouse Application," Journal of Public Health Management and Practice, Vol. 7, No. 6, November 2001, pp. 87-94.
  6. D. Berndt, A. Hevner, and J. Studnicki, “Data Warehouse Dissemination Strategies for Community Health Assessments,” Informatik/Informatique, Journal of the Swiss Informatics Society, No. 1, February 2001, pp. 27-33. (Available at http://www.svifsi.ch/revue/pages/issues/n011/no011.html )
  7. J. Studnicki, B. Steverson, B. Myers, A. Hevner, and D. Berndt, “Comprehensive Assessment for Tracking Community Health (CATCH),” Best Practices and Benchmarking in Healthcare, Vol. 2, No. 5, September/October 1997, pp. 196-207.

Refereed Conference Publications

  1. D. Berndt, A. Hevner, and J. Studnicki, “Community Health Assessments: A Data Warehousing Approach,” Proceedings of the Eighth European Conference on Information Systems, Vienna, July 2000, pp. 1276-1283.
  2. D. Berndt, A. Hevner, and J. Studnicki, “Hospital Discharge Transactions: A Data Warehouse Component,” Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Hawaii, January 2000. (Available at http://doi.ieeecomputersociety.org/10.1109/HICSS.2000.926791 )
  3. D. Berndt, A. Hevner, and J. Studnicki, “CATCH/IT: A Data Warehouse to Support Comprehensive Assessment for Tracking Community Health,” Proceedings of the American Medical Informatics Association (AMIA) Annual Symposium, Orlando, November 1998.