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Running head: Information Warehouse
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Information Warehouse
Lindy Hilding
CAHS 300
Spring 2013
Information Warehouse
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Abstract
“An information (data) warehouse is a relational database that is designed for
query and analysis rather than for transaction processing” (Kamal, 2010). “It usually contains
historical data derived from transaction data, but it can include data from other sources” (Kamal,
2010). Initially, data warehouses were created and labeled as the main repository of historical
data. As information and technology has expanded, “data warehouses have expanded to
providing an informatics environment that helps facilitate both translational research and
advances in personalized medicine,” (Kamal, 2010). Today, information (data) warehouses have
become a valuable asset in data management. Data management is essential in the functioning of
healthcare and healthcare information systems. Information warehouses are valuable
organizational tools for healthcare information.
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Information Warehouse
“The concept of data warehousing dates back to the late 1980s when IBM researchers
Barry Devlin and Paul Murphy developed what is known as the business data warehouse”
(Rainer, 2012). The data warehousing concept was intended to provide an architectural model
for the flow of data from operational systems to decision support systems.
“This concept
attempted to address the various problems associated with this flow, mainly the high costs
associated with it” (Rainer, 2012). “In the absence of a data warehousing architecture, an
enormous amount of redundancy was required to support multiple decision support
environments” (Rainer, 2012). In larger corporations, it was typical for multiple decision support
environments to operate independently. Though each environment served different users, they
often required much of the same stored data. The process of gathering, cleaning, and integrating
data from various sources, usually from long-term existing operational systems, was typically in
part replicated for each environment. Moreover, the operational systems were frequently
reexamined as new decision support requirements emerged. Often new requirements necessitated
gathering, cleaning, and integrating new data from data marts. Data marts store subsets of data
from an information warehouse (Rainer, 2012).
Do not confuse data warehouse with database.
Data (information) warehouses are
significantly different than a database. Even though both the systems are designed to collect
information for easy access, there are many differences between a data warehouse and a
database.
The main difference is that generally most databases are created for a single
application. Like a data warehouse, a database is also an integrated collection of logically
related records that consolidates records into a common place of data records that provides data
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for specific applications. It is created so that the information can be easily accessed, managed,
and updated. Different database systems would emphasis on one single subject or one system
will not deal with other areas. On the contrary, a data warehouse is the data model where
extracted data from the various operational databases of an organization is stored. This is the
central source of data that boasts screened, edited, and integrated data, making it easier for users
or professionals throughout an organization to have access to the right information at the right
time (May, 2009).
A data warehouse plays an important role in the decision-making process of an
organization. Since it boasts of information from multiple areas of the organization, a data
warehouse helps professionals determine how the company is performing as a whole. A data
warehouse acts as a powerful tool for analysis purpose.
The information stored in data
warehouses does not change as much as it changes in a database. There are basically two types
of data used by these systems-operational data or relational database and decision support data.
Both data types are different from each other is respect of their purpose, format, and structure
(May, 2009).
In relational database, mostly tables are used and they may be normalized. This type of
data is created to deal with transactions that are made on a daily basis. A good example of it is
the record of sold items maintained by a retail shop on a daily basis. This data is updated on a
frequent basis.
Such a system is highly effective for operational database, but not that
contributive to queries or analysis purposes or for data warehousing. Thus, decision support data
is mostly used in data warehousing. While operational data deals with daily transactions,
decision support data gives meaning to the data that is operational in an organization (May,
2009).
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Databases are a very important source of information for organizational success, but data
warehouses have become more important in the Information Age. Today large, as well as
medium, sized enterprises and corporations rely on data warehousing in order to attain their
business objectives on a large scale. They are more elaborate than databases (May, 2009).
Synopsis
“A data warehouse is a relational database that is designed for query and analysis rather
than for transaction processing” (Kamal, 2010). “It usually contains historical data derived from
transaction data, but it can include data from other sources” (Kamal, 2010). “It separates
analysis workload from transaction workload and enables an organization to consolidate data
from several sources” (Kamal, 2010). Initially, data warehouses were created and labeled as the
main repository of historical data. As information and technology has expanded, “data
warehouses have expanded to providing an informatics environment that helps facilitate both
translational research and advances in personalized medicine,” (Kamal, 2010). As more capable
computational hardware and functionally superior software are becoming available, the
frequency as well as volume of the data captured in the warehouse is increasing. Real-time and
near real-time data warehousing has further blurred the line between a data warehouse and
traditional business function specific systems such as an electronic health record (EHR), (Kamal,
2010). Data warehouses in a healthcare organization have begun to show their capabilities not
only in business operations, but also in clinical and translational research activities.
The use of a data warehouse instead of EHR systems in such a capacity presents many
benefits in the healthcare and clinical environment. Since EHRs are not designed nor optimized
to link patients across a disease group, diagnosis, or patient demographic, they are less effective
in clinical research and often off limit for translational research.
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One information warehouse, mentioned in the article written by Kamal, 2010, was
developed in 1997 by leaders of the Ohio State University Medical Center. The initial task of the
information warehouse was to replace the existing Decision Support System hosting data feed
from a home-grown accounting system. The information warehouse not only replaced the old
system, but also provides the physicians, administrators, and analysts with a tool that allows
them to monitor the hospital operations in an effort to gain efficiencies and render better cost
effective clinical care for the patients, as well as plan and monitor the financial health of the
organization. “Today, information warehouses include the combination of four integrated
components: a clinical data repository containing over a million patients; a research data
repository housing various research specific data; an application development platform for
building business and research enabling applications; and a business intelligence environment
assisting in reporting in all function areas” (Kamal, 2010).
Information warehouses have many benefits. “They maintain a copy of information from
the source transaction systems” (Hebda, 2013). “This architectural complexity provides the
opportunity to maintain data history, even if the source transaction systems do not” (Hebda,
2013). “Information warehouses integrate data from multiple source systems, enabling a central
view across the enterprise” (Hebda, 2013). “This benefit is always valuable, but particularly so
when the organization has grown by merger” (Hebda, 2013). “Improvements in data quality by
providing consistent codes and descriptions and by flagging or even fixing data, is definitely an
advantage to using information warehouses” (Hebda, 2013). An information warehouse presents
the organization’s information consistently. Data warehouses provide a single common data
model for all data of interest regardless of the data’s source. “Data warehouses, also, restructure
data so that it makes sense to the business users and restructures data so that it delivers excellent
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query performance without impacting the operational systems” (Hebda, 2013). Finally,
information warehouses add value to operational business applications, especially customer
relationship management systems.
Relation to Course
Health information systems are the core content of the course CAHS 300. Health
information systems are ever changing with the changing world of technology in the healthcare
field. The changing healthcare delivery system provides the driving force for improved data
management. “Data management is the process of controlling the collection, storage, retrieval,
and use of data to optimize accuracy and utility while safeguarding integrity,” (Hebda, 2013, p.
65). Computers have become an essential tool in this process. Good data management is
essential for organizational decision making. Good data management involves knowing who
needs report information, what reports are generated and what they are called, and when reports
are available. “A data warehouse offers a more robust application for data management by
serving as a data repository for storage and retrieval,” (Hebda, 2013, p. 65). A data warehouse is
a repository for storing data from several different databases so that it can be combined and
manipulated to provide answers to various questions.
In this course CAHS 300, all aspects of health information systems are being studied and
evaluated. Electronics and technology are the way of the present and future. Electronic health
information systems and information warehouses have many advantages. However, what would
happen if there was a problem in the electronic system or the system was to crash for some
unknown reason? Would there be a backup system? Most healthcare facilities still have a paper
system available in case of technology failure. Facilities should also have a policy and/or
procedures in place on how to scan the paper system into the electronic system or just leave the
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information on paper that was collected while the electronic system was down. If the
information on paper where to be uploaded into the electronic system, all the information would
be in one system. If the information on paper was not uploaded into the electronic system, the
information collected would essentially be stored in to two different systems. That would not be
an efficient system for data management.
As healthcare and its technology keep changing, automation of healthcare records creates
new issues related to data storage and retrieval. “Recent estimates project that personal
computer, network, and mainframe storage requirements will grow 50% per year” (Hebda, 2013,
p. 65). Along with the increase in volume and types of materials for storage, data storage and
retrieval require special conditions to ensure data integrity. Data warehouses, also known as
information warehouses, were created to help maintain this data integrity.
Significance & Recommendations
Today, information warehouses have become a valuable asset in data management. Data
management is essential in the functioning of healthcare and healthcare information systems.
Information warehouses are valuable organizational tools for healthcare information.
Information warehouses handle information from different operational and/or transactional
systems and internal and external databases. All data loaded into an information warehouse is
verified against standard codes and values to check for its validity. Errors can be checked with
this system. These errors are sent back to the source systems. While the corrections of the errors
often requires time and follow ups, it helps improve the quality of data, both in the source
systems and in the information warehouses. Information warehouses are able to provide
customers with data access tools that are intuitive, easy to use, and cater to the very unique
requirements of administrators, analysts, and clinicians. Information warehouses are available
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for access via web, online analytical processing (OLAP) tool for multidimensional analysis and
an ad hoc query and reporting tool, and a Business Intelligence (BI) tool, Oracle Answers, for
parameterized reporting. Information warehouses have a strong security system in place for the
data it stores. This system secures data in compliance with Health Insurance Portability and
Accountability Act (HIPAA). Therefore, patient information is not accessible for the wrong
purposes.
Utilization of information warehouses can be classified into four categories: business,
clinical, research, and inter-organizational applications. Information warehouses can hold
millions of records for patients. These systems are easily accessible, yet properly secured.
These organizational tools for data management are cost effective.
As healthcare and technology has evolved and become more advanced, information
warehouses appear to be the way of the future. They are feasible, accessible, and cost effective
systems implemented in the management of data in healthcare. Education is the key to
successful usage of these valuable tools. Ensuring proper training for information warehouses is
essential in the success of the data management system.
Conclusion
Information warehouses have developed incrementally over many years. An iterative
process has been adopted to allow users optimize processes that address data availability,
scalability, and user needs. With a methodology to incrementally add data sources successfully
established, and the processes to refresh the information warehouse standardized and automated,
in essence, have an information warehouse implementation template that is propelling an
unprecedented growth in data assimilation, which is both efficient and cost effective. Combined
with an architecture that has scaled up with this growth, and information warehouse refresh
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cycles that have increased from monthly to daily, with some in real time, users have the ability to
retrieve more timely information and carry out complex analysis.
Many new technologies have been adapted for use in industries with significant benefits
in the quality and efficiency with which products are manufactured. Medicine has traditionally
thought of itself as a high-quality, human-centered industry, and very resistant to automation.
The productivity and financial pressures operating in the world today have become great enough
that automation is inevitable. The difficulty will be distinguishing between automation for
automation’s sake and applications that improve the process and safety of care.
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