HM311

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Introduction to Information Systems for
Health Information Technology
Chapter 3: Databases
© 2020 American Health Information Management Association

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Learning Objectives, 1
Assist in the development of a database
Develop and manage the data dictionary
Develop queries to retrieve data contained in the database
Read and understand an entity-relationship diagram
Identify the primary key contained in an entity

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Learning Objectives, 2
Differentiate between a data repository and a data warehouse
Expound on the ways that data mining can be useful
Complete simple normalization of data
Differentiate between the various types of data

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Introduction, 1
Database: Organized collection of data, text, references, or pictures in a
standardized format, typically stored in a computer system for multiple
applications

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Introduction, 2
Usage of database
• Facilitate data sharing
• Streamline workflow
• Assist in clinical decision making
• Provide information for managerial decision making
• Provide data for data analysis
Database administrator

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Requirements for Establishing a Database
Should meet needs of facility
Data set
• Uniform Hospital Discharge Data Set (U H D D S)
• List of recommended data elements with uniform definitions that are relevant for a particular use

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Database Management System, 1
Manipulates and controls the data stored within the database to meet the
needs of the user
• Parts of database management system (DBMS)
• Data definition language
• Data manipulation language
• Data control language
• Data dictionary

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Database Management System, 2
Natural language queries
Query by example
• Boolean search
• Wildcard search
• Structured query language

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Database Management System, 3
Data dictionary: Descriptive list of the names, definitions, and attributes of
data elements to be collected in an information system or database whose
purpose is to standardize definitions and ensure consistent use
Data dictionary assists improving
• Data quality
• Data integrity
• Documentation
• Data analysis
• Data reuse

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Database Management System, 4
Data dictionary
• Metadata
• Mask

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Database Management System, 5
Data standards
• Allow us to share data in a uniform way
• Standards development organization
Data exchange standards

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Data Modeling, 1
Data modeling is the process of determining the users’ information needs
and identifying relationships among the data
• Conceptual data model
• Physical data model
• Logical data model

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Data Modeling, 2
Entity relationship diagram
• Entity: Person, location, thing, or concept
• Attribute: Facts or data about entity
• Relationships
• One-to-one: Patient has one attending physician
• One-to-many: Patient may have multiple lab tests
• Many-to-many: Lab tests may be performed on multiple patients

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Data Modeling Tools
Computer-aided software engineering
Data flow diagram
Use case

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Common Database Models, 1
Relational database model
• Table
• Types of fields
• Alphabetic
• Numeric
• Alphanumeric
• Time and date
• Autonumbering

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Common Database Models, 2
Relational database
• Normalization
• Query
• Key field
• Primary key
• Foreign key

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Common Database Models, 3
Hierarchical database model
• Node and pointers
• Organizational chart
• Parent-child relationship
Network database model
• Owners and members
• Pointers

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Common Database Models, 4
Object-oriented database model
• Text, images, audio, video, and other objects
• Java
Multidimensional database model
• Data warehouses

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Data Repository and Data Warehouse, 1
Data repository: Open-structure database in which data from multiple
information systems are stored so that an integrated, multidisciplinary
view of the data can be achieved in a single source
• Contains clinical, administrative, and financial data
• Clinical data repository

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Data Repository and Data Warehouse, 2
Data warehouse: Database that makes it possible to access data from
multiple databases and combine the results into a single query and
reporting interface
• Data mart

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Data Mining, 1
Process of extracting and analyzing large volumes of data from a database
for the purpose of identifying hidden and sometimes subtle relationships
that would be unnoticed without the analysis

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Data Mining, 2
Techniques
• Anomaly detection
• Association rule learning
• Cluster analysis
• Classification analysis
• Regression analysis

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Data Mining, 3
Online analytical processing
• Data access architecture that allows the user to retrieve specific information from a
large volume of data
• Methods
• Drilling down into the data
• Consolidation
• “Slicing and dicing”

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Data Mining, 4
Examples
• Best practices in patient care
• Medication adverse effects
• Potential fraud and abuse violations
• Patterns of mortality and morbidity
• Patterns of denials