Difference between revisions of "Health data warehouse"

Revision as of 23:02, 12 April 2008

Enterprise Health Data Warehouses for Research and Clinical Improvement With the advent of Electronic Medical Records, clinical data repositories and patients accounting software, most of the information about a patient’s hospital stay is recorded in electronic form, but often are in very separate systems with little to no data querying or analysis tools for real time reports. If real time reports are available, they are geared to only one aspect such as total charges or days in accounts receivable. In most healthcare systems, outpatient or inpatient, there is no accurate measure of physician performance and/or practice patterns. Billing and code data are often grossly inaccurate for judging or improving physician or group performance. One solution to this problem is the development of Enterprise Healthcare data warehousing which collects data from clinical, financial, and ancillary data repositories into a central data warehouse. The key elements are the data warehouse has a different internal indexing and storage structure compared to the transaction based real time databases. A data warehouse is designed to allow complex searching of data with a variety of key indexes. Key parts of a data warehouse include developing the extraction translation and loading of the data warehouse. Data in clinical real time systems must be periodically uploaded to the data warehouse. During this process, all data needed for analysis is extracted in its raw form. Next, the data is translated into a form that is appropriate for searching and indexing. Then the data is loaded into the data warehouse and indexed across many parameters for fast search and retrieval. There are a couple of different structures of the data warehouse from the start scheme to OLAP blocks. Next is building the query and reporting capabilities. The option for this is plentiful. A specific program such as Business Objects can be used which has predefined fields and allows for routine reports and then customizable on the fly reports. This allows for physicians to design performance reports that can compile data from the clinical and billing data to provide a more complete picture of a group and physician’s practice partners allowing for education and practice improvement projects. Other querying tools such as SAS and SPSS can build connect ions that allow for more complex statistical analysis. Health Data Warehouses allows for patient care related research and development of disease and risk stratification models for disease states. With these systems, there is a possibility of multi-institutional data warehouses that increase the research capacity from a larger patient base, possibly allowing for a mixture of academic and community hospitals making the data more generalizable with better mixture of severity levels.

BMI 512 Clinical Information Systems Winter 2008