BSAN 323 – Health Anamatics – Experiential Learning Application

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BSAN 323 – Health Anamatics – Experiential Learning Application 6

Pledged: Name

• Assignment Aligned Learning Outcomes:
BSAN Program Learning Outcomes (PLOs)

1. Problem Solving 
2. Tools and Techniques 
3. Ethics 
4. Communication 
School of Business Learning Outcomes (LOs)

1. Critical and Analytical Thinking 
2. Ethics 
3. Business Principles 
4. Communication 
5. Global 
Stetson University General Learning Outcomes (GLOs)

1. Writing (WE) 
2. Information Fluency (WE) 
3. Speaking (WE)
4. Critical Thinking (WE) 
5. Quantitative Reasoning (Q) 
6. Knowledge of Human Cultures and the Natural World (A,B,H,S,L,P) 
7. Personal and Social Responsibility (R,V,W,D,J) 
8. Integration of Learning 

• Employer-Valued Knowledge, Skills, and Abilities (KSAs) Gained:
o Knowledge of Hospital Data
o Knowledge of Government Data
o Skilled in SEMMA Process
o Skilled in SAS Enterprise Miner Modeling
o Ability to Generate a Neural Network Model
o Ability to Analyze Readmission Data

• Additional Writing Resources
o Stetson Writing Resources
o Stetson Writing Rubric
Experiential Learning Application – Hospital Readmissions
Title:
Chapter: 8
Title: Hospital Readmissions
Dataset: 8_EL2_Readmissions.xlsx
Software: SAS Enterprise Miner (available through vlab.stetson.edu)
Introduction
[SEMMA Process]
Provide an introduction summary
• Provide a summary of the business problem or opportunity and one or more key objectives or goals:

• Create a new SAS Enterprise Miner project. Create a new Diagram.
Sample
Provide an overview following sampling
• Data (sources for exploration and model insights)
• Identify the variables data types, the ID, input and target variable along with the levels during exploration.

Data Set Variable Role Level
RecordID
DischargeDisposition
Cohort
FacilitySize
InsuranceType
Age
ICDCounts
LengthOfStay
Readmission

• Add a FILE IMPORT node
• Provide a results overview following the file import:
o Input / Target Variables
o Level of Variables

• Generate a DATA PARTITION.
• Provide a results overview following the data partition:
Explore • Add a STAT EXPLORE node
• Add a GRAPH EXPLORE node
• Add a MULTIPLOT node
• Provide a results overview following the exploration:
o Summary / descriptive statistics
o Missing data
o Outliers
Modify • Add an IMPUTE node
• Provide a results overview following the modification:

• Add an TRANSFORM VARIABLES node
• Provide a results overview following the modification:
Model Discovery (prototype and test analytical models)
Apply a regression model and provide a results overview following modeling.
• Add NEURAL NETWORK models with 3, 10, 25, and 50 hidden units
o Model description
o Analytics steps
o Results (Lift, Error, Misclassification Rate)
• Add an ENSEMBLE model
o Model description
o Analytics steps
o Results (Lift, Error, Misclassification Rate)
• Selection Model (final model selected)
Assess and Reflection Provide overall recommendations following application completion:
• Overall summary sampling recommendations, findings, insights
o Summary healthcare/clinical recommendations
• Model advantages / disadvantages
o Performance evaluation
• Deployment (operationalization plan: timeline, resources, scope, phases, project plan)
• Value (return on investment, healthcare outcomes)

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