Data Warehouse Committee
Membership in this committee is designated by Article IX, Section 2 of the ACHA Bylaws.
If you would like to learn more about this group or have a question related to this topic, contact the chairperson listed below.
About the Committee
ACHA has accumulated thousands of data elements throughout its research initiatives, with potentially thousands more down the line and another large number stored in our members’ computer systems but currently inaccessible via direct linkage.
The future of college health care (and health care in general) depends on our ability to use these massive amounts of data (currently available and potentially collected in the future) to achieve better patient outcomes at a lower cost. Our current limited ability to perform analytics to make sense of the data will lead to serious challenges to improving quality and costs and uncovering valuable information necessary to achieve the association’s strategic objectives.
A data warehouse (DW) is a large, centralized data repository accumulated from a wide range of sources within an organization or through external links and used to guide business decisions through benchmarking, identifying best practices, and monitoring emerging risks. It is designed for query and analysis rather than for transaction processing. It separates analysis workload from transaction workload and enables an organization to consolidate data from several sources.
In addition to a relational database, a data warehouse environment includes an extraction, transportation, transformation, and loading (ETL) solution, an online analytical processing (OLAP) engine, user analysis tools, and other applications that manage the process of gathering data and delivering it to business users. Typically, a data warehouse is housed on an enterprise mainframe server or, increasingly, in the cloud.
A “database,” on the other hand, is designed to make transactional systems run efficiently. A database handles transactions. An electronic health record (EHR) system, for example, is an application that runs on an online transaction processing (OLTP) database. A data warehouse is structured to analyze those transactions.
The Data Warehouse Committee is charged with:
- Developing a product concept to be tested with the membership
- Refining and/or expanding the goals of the initiative
- Providing a guide to staff and consultants in determining costs and establishing a comprehensive and realistic phased timeline
- Determining user requirements and resources for implementation/participation
- Developing business intelligence tools (reports)
- Developing a strategy for monetization
- Developing a business plan based on the recommendations of the Data Warehouse Committee
- Establishing regular and direct conversations with EHR vendors, engaging them in the design and development process
- Sharing the plan and seeking input from all possible stakeholders (including non-members, government, affinity organizations, etc.)
Sarah Van Orman, MD, MMM, FACHA
University of Southern California
Kevin Readdean, MSEd, LMHC
Rensselaer Polytechnic Institute
Mary Hoban, PhD, CHES