A presentation on Dec. 17, 2010 for Hospital Administration School, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Thailand.

1. Use o Health Information
of ea t o at o
นพ.นวนรรน ธีระอัมพรพันธุ์
หลักสูตร Healthcare CIO
โรงเรียนบริหารงานโรงพยาบาล คณะแพทยศาสตร์ รพ.รามาธิบดี
17 ธันวาคม 2553
Except where citing other works

2. Uses of Health Information
• Patient Care
• Administration
• Claims & Reimbursements, Inventory Management
• Management’s Decision Making
’ k
• Quality Improvement & Organizational Learning
• Public Health
• Surveillance monitoring disease reporting
Surveillance, monitoring, disease reporting
• Education
• Research
• Etc
Etc.

3. Types of Uses
• Primary
• Secondary
• Use of clinical data for research
• Use of clinical data for quality improvement
f l ld f l
• Use of claims data for research
• Use of clinical/administrative data for performance
evaluation or strategic planning
g p g
• Etc.

4. Issues for Secondary Data Uses
• Subject coverage
• Data definition & measurement (context)
• Data quality
• Accuracy (validity), precision (reliability)
Accuracy (validity) precision (reliability)
• Timeliness
• Relevance
• Completeness (vs. missing data)
• Data format/uniformity (standards)
Data format/uniformity (standards)
• Data integration from disparate sources
• Privacy
• q y
Unintended consequences of secondary data uses

5. Sample Problems with Secondary Data
• High number of “cystic fibrosis” cases in a Thai
hospital’s monthly/annual statistics
p y/
• A number of female outpatients with benign
prostate hyperplasia (BPH)
prostate hyperplasia (BPH)
• Changes in data definition or collection methods in
HIS, not translated into reports
• “Upcoding” of diagnoses for claims &
Upcoding of diagnoses for claims &
reimbursements
• Adverse selection of patients

6. Informatics Tools
• Data warehouse & Data marts
• Dimensional modeling
imensional modeling
• Facts, dimensions, cubes/hypercubes
• Star schema
Star schema Snowflake schema
Sno flake schema
• Extract Transform Load
Extract‐Transform‐Load
Image Source: SqlPac via http://en wikipedia org/wiki/Star schema and
http://en.wikipedia.org/wiki/Star_schema
http://en.wikipedia.org/wiki/Snowflake_schema

7. Informatics Tools
• Business intelligence
• OLAP
O AP
• Routine & ad‐hoc reporting
• “Slice & Dice” & “Drill‐Down”
Slice & Dice & Drill Down
• Business analytics
• Data mining (knowledge discovery from data)
Data mining (knowledge discovery from data)

8. Data Mining Methods: Classification
• Example of classification using decision tree
MarSt Single,
Married Divorced
Home Marital Annual Defaulted
ID
Owner Status Income Borrower
NO Home
1 Yes Single 125K No
Yes Owner No
2 No Married 100K No
3 No Single 70K No NO Income
4 Yes Married 120K No < 80K > 80K
5 No Divorced 95K Yes
NO YES
6 No Married 60K No
7 Yes Divorced 220K No
8 No Single 85K Yes
9 No Married 75K No
10 No Single 90K Yes
10
From a teaching slide by Tan, Steinbach & Kumar at UMN (2007)

9. Data Mining Methods: Clustering
• Clustering
Image Source: hellisp via http://en wikipedia org/wiki/Cluster analysis
http://en.wikipedia.org/wiki/Cluster_analysis

10. Data Mining Methods: Association Rules
• Association Rules
Market-Basket transactions
Example of Association Rules
TID Items
{Bread} → {Milk},
1 Bread, Milk
{Bread, Milk} → {Butter},
2 Bread, Butter, Milk, Eggs {Bread Butter} → {Milk}
{Bread, {Milk},
3 Milk, Eggs, Coke {Milk} → {Eggs}
4 Bread, Milk, Butter
5 Milk
Adapted from a teaching slide by Tan, Steinbach & Kumar at UMN (2007)

11. Data Mining Methods: Anomaly Detection
• Anomaly/Outlier Detection 4
3.5
35
3
2.5
2
1.5
1
0.5
From a teaching slide by Tan, Steinbach & Kumar at UMN (2007)
Outlier Score

12. CLASS EXERCISE
CLASS EXERCISE
Suggest 1 “question” related to
gg q
health care that can be answered with
each of the data mining methods
each of the data mining methods

13. Take‐Home Message
• Data are “gold”
• Tools are useful but we need to ask good questions
Tools are useful, but we need to ask good questions
• Be aware of data issues
• Data collected for 1 purpose and used for another
• /
Context/definition issues
• Data quality issues
• Data format issues
Data format issues
• Privacy issues
• Needs expertise on best practices in data
warehouse architecture and data analysis
y