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Chapter 8:
Databases: Creating Information
From Data
Copyright © 2016 Wolters Kluwer Health | Lippincott Williams & Wilkins
Nursing and Databases
• Collecting data organized and structured in such a way
that selections from them are retrievable, singly or as a
group.
• Relational database: data from more than one table can
be integrated as if it were in one table.
• The electronic patient record allows us to use patient
documentation data.
– Although a paper medical record provides a
wonderful individualized record for one patient, the
lack of data structure makes it difficult to retrieve the
data.
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Anatomy of Databases
• Tables
– Contain all the data.
– Foundation for the database.
– Each table has a relationship with other database
tables.
• Record requirements
– Data should be organized by categories (structured
format).
– Field names.
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Anatomy of Databases (cont.)
• Query: one characteristic giving databases power
• Boolean logic querying
– Based on algebra; data in field either are or are not a
match
– “And”: all specified terms returned
– “OR”: at least one of the words
– “Not”: one term but not the other
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Anatomy of Databases (cont.)
• Forms
– Data stored in table
– Form view showing all fields related to the record for
which data must be entered
– Useful for data entry and for viewing data for one
record
• Reports
– Another view of data
– Information from more than one table and from
queries
– Possible inclusion of graphs and calculations on data
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Concepts and Terminology
• Database is best used to analyze data that may include
numbers in records (rows) and fields (columns).
• Spreadsheet helpful when needing to “crunch numbers”
associated with data located in cells.
• Steps in the database design process:
– Identifying the purpose of the database
– Identifying the questions or queries that aggregated
data can answer
– Making a list of all data requirements necessary to
draw conclusions from the questions asked of the
data
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Concepts and Terminology (cont.)
• Relationship of tables:
– “one-to-many” and “parent/child”
• Tables are identified with a primary key that is then
identified as a foreign key in a related table.
• Normalization rules:
– Facilitating the organization, aggregation, and
display of data
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Database Models
• Flat database: all data in one table, that is, spreadsheet
worksheet
• Hierarchical database: tables organized in the shape of
an inverted tree
• Network model: similar to hierarchical model, but tree
can share branches
• Relational database model: two or more tables connected
by identical information in fields in each table (key fields)
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Database Models (cont.)
• Relational database model
– Habit of “scope creep”: when those who want
information see what they can learn from the data,
they ask for more information; designer will
anticipate this reaction and provide for expansion of
original design.
– Atomic-level data for each field (cannot be reduced
any more)
• Object/relational model (refinement of relational model)
• Object-oriented model
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Creating a Simple Database
• Identifying the question data needs to answer
• Identifying pieces of data needed to answer question
• Determining column or field for each item
• Creating table (naming each field, designating type and
description of data field will contain)
• Entering data into the table to test design
• Adjusting, creating another table “related” to master table
• Design table:
– Data type column
– Key field
– Foreign key
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Saving Data
• Data saving
– Data entry saved as soon as insertion point moved
from the record where it was entered.
– Possible to undo one change, but only the last
change made.
– Deletion of record is permanent.
– Save objects such as table or form after creation or
change in design.
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Data Manipulation
• Sorting
– Rearrangement of records in a table based on the data
in a field or fields
– Primary sort: simplest sort; other types: secondary,
tertiary, and further levels based on groupings provided
by the sort one level above it
• Queries
– Most powerful tool in a database; many ways (in
addition to Boolean query)
– Limiting factors: data available, user’s imagination,
ability to use criterion selectors of Boolean algebra and
symbols of mathematical operators
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Knowledge Discovery in Large Databases
• Data mining
– Extracting from data potentially useful information
previously unknown
– Most effective when done on a “data warehouse”
(collection of diverse data from sources that one
would not think of relating that is specifically
structured for query and analysis)
• Online analytical processing (OLAP): real-time analysis of
data stored in databases
• Structured query language (SQL): name of coding used
for querying in many databases; American National
Standards Institute (ANSI) standard language
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Question
• Which of the following terms would be least appropriate
to use when using Boolean logic for querying?
– A. And
– B. Or
– C. But
– D. Not
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Answer
• C. But
• Rationale: Boolean logic uses and, or, or not as key
terms. But is not used.
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Question
• Is the following statement true or false?
• A hierarchical database is the simplest type of database
model.
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Answer
• False
• Rationale: A flat database is the simplest type of
database model because all data are in one table.
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Question
• Is the following statement true or false?
• The first step in creating a database is to identify the
pieces of data needed.
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Answer
• False
• Rationale: When creating a database, the first step is to
identify the question the data need to answer.
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