This document discusses tools for organizing, analyzing, and presenting healthcare data. It describes using databases and relational database management systems to structurally organize multidimensional healthcare data. Key concepts covered include tables, fields, records, primary keys, entity relationship diagrams, cardinality, and structured query language. Statistical software packages like SPSS and SAS are presented as tools for manipulating and analyzing stored data. Microsoft Excel, graphs, tables, and infographics are also discussed for presenting analyzed data.

1. © 2016© 2016
A Practical Approach to Analyzing
Healthcare Data
Chapter 3 – Tools for Data
Organization, Analysis, and
Presentation

2. © 2016
Data Organization Using
Databases
• Healthcare data is complex and often multi-
dimensional
– Provider
– Patients
– Insurance companies
– Services
• Providing an organizational structure for the data can
facilitate more efficient analysis and reporting
• Database – self-describing collection of integrated
records.
– Self-describing – contains a description of its own structure
– Integrated – data elements are related to each other

3. © 2016
Database Vocabulary
• Tables- two dimensional arrays of data
– Rows = records
– Columns = variables or attributes
• RDMS – Relational Database Management System
– Software that is used to hold and maintain data tables and
their relationships
• SQL – Structured Query Language
– Programming language used to communicate with a
relational database
• ERD – Entity Relationship Diagram
– Diagram that shows how tables in an RDMS relate

4. © 2016
Small-Scale Databases
• Microsoft Access
– Relational database management system (RDMS)
– Strengths
• Approachable for inexperienced database users
• Graphical user interface (GUI)
• Built in wizards for table, query and report design
• Supports integrated forms for data collection
– Weaknesses
• Multi-user support is difficult to implement
• Database size limited to 2GB
• Most ‘classic’ Database Administrators (DBAs) do not
consider Access to be a full blown RDMS

5. © 2016
Hierarchy of a Relational Database
• Tables are rows and columns of values
– Envision a tab in a spreadsheet
• Fields are the columns in a spreadsheet
– In a patient database, fields may be age, gender, admission date, etc.
• Data elements or records are the rows in a spreadsheet
– In a patient database, row may represent patients or services provided to patients
• A unique row identifier in a table is called the primary key
– Cannot be duplicated within the same table
– Used to link tables together

6. © 2016
Entity Relationship Diagram
• Diagram that displays the relationships between tables within a RDBMS
• The key fields are identified as well as the cardinality of the relationship between the
tables
• Cardinality
– One – to – one
– One – to – many
• In Figure A.1
– PatientID is the primary key in the Patient Info table and a secondary key in the Visits table
– The cardinality of the relationship between the table is one to many.
• Each patient may have many visits

7. © 2016
More About Cardinality

8. © 2016
Data Dictionary
• Details roadmap of the database
• Should include
– Name of computer or software program that
contains the data element
– Type of data in the field
– Length of data in the field
– Edits placed on the data field
– Values allowed to be placed in the data field
– A clear definition of each value

9. © 2016
Structured Query Language
• SQL
• Tool to use and maintain databases
– Select data
– Update data
– Insert rows into a table
– Delete rows from a table

10. © 2016
SQL Example
• Retrieve the records for all patients
from Milwaukee
– SELECT PATIENT_LNAME,
PATIENT_FNAME FROM PATIENT
WHERE PATIENT_CITY = ‘Milwaukee’
• Key words in the query are in red font

11. © 2016
Statistical Software Packages
• Statistical Package for the Social Sciences
(SPSS)
– Menu driven program
– More suited for smaller (<1G) datasets
– May open Excel files for analysis
• Statistical Analysis System (SAS)
– Command line program
– Excellent for manipulating large datasets

12. © 2016
SAS Syntax
• SAS is a programming language much like
SQL
– Key words:
• Data – used to name and create a dataset
• Proc – declare which analytic procedure will be used
• Set – declare which dataset will be the subject of the
analysis
• Run – designates the end of the command and starts
the calculation
– Syntax: always end commands with a ‘;’

13. © 2016
Role of Excel – Data Analysis
• Data analysis
– Data analysis toolpak add-in
– Must be careful importing text data elements
that appear to be numeric
• ICD-9 codes with leading zeroes
• Variables with mixed number and alpha values
• Pivot tables are also an excellent
exploratory data analysis tool

14. © 2016
Graphical Displays of Data
• Types of graphical comparisons
– Group summary
– Trends or changes over time
– Relative size of groups
– Relationships between variables

15. © 2016
Bar Graph or Chart
• Group summary
• Comparison of counts or averages across
groups
• Two variables: admissions, age category.
– One bar for each gender

16. © 2016
Line Graphs or Chart
• Trends or changes over time
• Look for trends/patterns
• Should not be used for connecting unrelated points

17. © 2016
Pie Chart
• Compares relative size of groups
• Used to represent relative proportions of a total
• Note that this is different than a bar chart – in a pie
chart categories must be part of a bigger set or
population

18. © 2016
Scatter Diagrams
• Used to display the relationship between two
continuous variables
• Should not be used if either variable is categorical

19. © 2016
Infographics
• Conveys a message or story using a
combination of graphs and text
• Primary types:
– Cause and effect
– Chronological
– Quantitative
– Directional
– Product

20. © 2016
Tables versus Graphs
• Tables have several advantages over graphs such
as:
– Present more information than a graph
– Display the exact values
– Require less work to create
• Graphs also have advantages over tables such as:
– Catch the attention of the reader
– Show trends easily
– Bring out facts or relationships that stimulate thinking