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Chapter 3 • Data Warehousing 119

septic patients. Adult patients presenting with sepsis code neuro, code STEMI), code sepsis at MultiCare
receive the same care, no matter at which MultiCare is designed to bring together essential caregivers in
hospital they present. order to efficiently deliver time-sensitive, life-saving
treatments to the patient presenting with severe
early identification: modified early sepsis.
Warning system (meWs) In just 12 months, MultiCare was able to
reduce septicemia mortality rates by an average of
MultiCare developed a modified early warning sys- 22 percent, leading to more than $1.3 million in
tem (MEWS) dashboard that leveraged the cohort validated cost savings during that same period. The
definition and the clinical EMR to quickly identify sepsis cost reductions and quality of care improve-
patients who were trending toward a sudden down- ments have raised the expectation that similar
turn. Hospital staff constantly monitor MEWS, which results can be realized in other areas of MultiCare,
serves as an early detection tool for caregivers to including heart failure, emergency department
provide preemptive interventions.
performance, and inpatient throughput.
efficient Delivery: code sepsis
(“time is tissue”) Questions for Discussion
1. What do you think is the role of data warehous-
The final key piece of clinical work undertaken by ing in healthcare systems?
the Collaborative was to ensure timely implementa- 2. How did MultiCare use data warehousing to
tion of the defined standard of care to patients who improve health outcomes?
are more efficiently identified. That model already
exists in healthcare and is known as the “code” pro- Source: healthcatalyst.com/success_stories/multicare-2 (ac-
cess. Similar to other “code” processes (code trauma, cessed February 2013).

Many organizations need to create data warehouses—massive data stores of time-
series data for decision support. Data are imported from various external and internal
resources and are cleansed and organized in a manner consistent with the organization’s
needs. After the data are populated in the data warehouse, data marts can be loaded for a
specific area or department. Alternatively, data marts can be created first, as needed, and
then integrated into an EDW. Often, though, data marts are not developed, but data are
simply loaded onto PCs or left in their original state for direct manipulation using BI tools.
In Figure 3.3, we show the data warehouse concept. The following are the major
components of the data warehousing process:
• Data sources. Data are sourced from multiple independent operational “legacy”
systems and possibly from external data providers (such as the U.S. Census). Data
may also come from an OLTP or ERP system. Web data in the form of Web logs may
also feed a data warehouse.
• Data extraction and transformation. Data are extracted and properly trans-
formed using custom-written or commercial software called ETL.
• Data loading. Data are loaded into a staging area, where they are transformed
and cleansed. The data are then ready to load into the data warehouse and/or data
marts.
• Comprehensive database. Essentially, this is the EDW to support all decision
analysis by providing relevant summarized and detailed information originating
from many different sources.
• Metadata. Metadata are maintained so that they can be assessed by IT personnel
and users. Metadata include software programs about data and rules for organizing
data summaries that are easy to index and search, especially with Web tools.

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