The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Managing Large Healthcare Database for Decision Support System
|
|
Author(s): Bambang Parmanto (University of Pittsburgh, USA), Fei-Ran Guo (National Taiwan University, ROC), Pamela J. Alan (University of Pittsburgh, USA), Syarief A. Ahmad (University of Pittsburgh, USA)and Christopher Lombard (University of Pittsburgh, USA)
Copyright: 2003
Pages: 3
Source title:
Information Technology & Organizations: Trends, Issues, Challenges & Solutions
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-066-0.ch123
ISBN13: 9781616921248
EISBN13: 9781466665330
|
Abstract
Today, the healthcare industry is faced with many tasks including how to improve the quality while reducing costs. Healthcare organizations have accumulated enormous amounts of information related to patients and the medical care they receive. The success of relational database and its query language (SQL) has resulted in a huge amount of data being accumulated in the last thirty years. This type of database management system is latter known as the on-line transaction processing (OLTP). OLTP applications typically automate daily clerical data processing tasks that are the lifeblood of healthcare operations such as patient admission, billing, treatment, and outcome management. OLTP applications are traditionally managed by the relational database systems. While OLTP has been extremely successful in supporting daily operations, it is not designed for supporting fast and complex queries that are needed for data analysis and decision support system. Many of the databases reside in disparate information systems used to manage hospital affairs on a daily basis. These systems are not capable of storing large amounts of historical patient data nor are they built to support complex user queries. Without consolidating all of the information, the data is only of historical significance. Data warehouse is the natural response to the needs for extracting information for decision support from the mountains of data. In contrast with OLTP, data warehouses are targeted for decision support. As such, aggregated and historical data are more important than detailed, individual transactions. To assist analysis and decision modeling, a data warehouse is designed to support intensive, complex, and ad-hoc queries. Query performance is more important than transactional performance. Since decision modeling requires complex analysis and visualization, the data in a data warehouse usually is modeled multidimensionally. In addition to multidimensional data model, OLAP operations also support rollup and drill-down, slice-and-dice, and pivot.
|
|