The Catch data warehouse: support for community health care decision-making
作者:
Highlights:
•
摘要
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 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. 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.
论文关键词:Health care information systems,Data warehousing,Data staging,Online analytic processing (OLAP),Decision support systems,Community decision-making,Data quality
论文评审过程:Accepted 1 April 2002, Available online 2 August 2002.
论文官网地址:https://doi.org/10.1016/S0167-9236(02)00114-8