The Catch data warehouse: support for community health care decision-making

  • Authors:

  • Donald J. Berndt;Alan R. Hevner;James Studnicki

  • Affiliations:

  • Information Systems and Decision Sciences Department, College of Business Administration, 4202 Fowler Ave., CIS1040, University of South Florida, Tampa, FL;Information Systems and Decision Sciences Department, College of Business Administration, 4202 Fowler Ave., CIS1040, University of South Florida, Tampa, FL;College of Public Health, University of South Florida, Tampa, FL

  • Venue:
  • Decision Support Systems
  • Year:
  • 2003

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Abstract

The measurement and assessment of health status in communities throughout the world is a massive information technology challenge. Comprehensive Assessment for Tracking Community Health (CATCH) 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 decisionmakers 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 tehnology 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. In this paper, we focus on the technical challenges of designing and implementing an effective data warehouse for health care information. Illustrations of actual data designs and reporting formats from the CATCH data warehouse are used throughout the discussion. Ongoing research directions in health care data warehousing and community health care decision-making conclude the paper.