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Use of Health InformationUse o ea t o at oนพ.นวนรรน ธีระอัมพรพันธุ์
หลักสูตร Healthcare CIO
โรงเรียนบริหารงานโรงพยาบาล คณะแพทยศาสตร์ รพ.รามาธิบดี
17 ธันวาคม 2553
Except where citing other works
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Uses of Health Information• Patient Care• AdministrationAdministration
• Claims & Reimbursements, Inventory Management’ k• Management’s Decision Making
• Quality Improvement & Organizational Learning
• Public Health• Surveillance monitoring disease reporting• Surveillance, monitoring, disease reporting
• Education• Research• Etc• Etc.
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Types of Uses• Primary• SecondarySecondary
• Use of clinical data for researchf l l d f l• Use of clinical data for quality improvement
• Use of claims data for research• Use of clinical/administrative data for performance evaluation or strategic planningg p g
• Etc.
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Issues for Secondary Data Uses• Subject coverage• Data definition & measurement (context)• Data quality
• Accuracy (validity) precision (reliability)Accuracy (validity), precision (reliability)• Timeliness• Relevance• Relevance• Completeness (vs. missing data)
• Data format/uniformity (standards)• Data format/uniformity (standards)• Data integration from disparate sources• Privacy• Unintended consequences of secondary data usesq y
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Sample Problems with Secondary Data• High number of “cystic fibrosis” cases in a Thai hospital’s monthly/annual statisticsp 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
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Informatics Tools• Data warehouse & Data marts
• Dimensional modelingimensional modeling• Facts, dimensions, cubes/hypercubes• Star schema Sno flake schema• Star schema Snowflake schema
• Extract Transform Load• Extract‐Transform‐Load
Image Source: SqlPac via http://en wikipedia org/wiki/Star schema andImage Source: SqlPac via http://en.wikipedia.org/wiki/Star_schema andhttp://en.wikipedia.org/wiki/Snowflake_schema
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Informatics Tools• Business intelligence
• OLAPO 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)
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Data Mining Methods: Classification• Example of classification using decision tree
MarStMarried
Single, Divorced
ID Home Marital Annual Defaulted
Home Owner
NOYes No
ID Owner Status Income Borrower
1 Yes Single 125K No
2 No Married 100K No
IncomeNO< 80K > 80K
3 No Single 70K No
4 Yes Married 120K No
5 No Divorced 95K YesYESNO
5 No Divorced 95K 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 From a teaching slide by Tan, Steinbach & Kumar at UMN (2007)
10
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Data Mining Methods: Clustering• Clustering
Image Source: hellisp via http://en wikipedia org/wiki/Cluster analysisImage Source: hellisp via http://en.wikipedia.org/wiki/Cluster_analysis
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Data Mining Methods: Association Rules• Association Rules
Market-Basket transactions
TID ItemsExample of Association Rules
TID Items
1 Bread, Milk
2 Bread, Butter, Milk, Eggs
{Bread} → {Milk},{Bread, Milk} → {Butter},{Bread Butter} → {Milk}2 Bread, Butter, Milk, Eggs
3 Milk, Eggs, Coke
4 Bread, Milk, Butter
{Bread, Butter} → {Milk},{Milk} → {Eggs}
5 Milk
Adapted from a teaching slide by Tan, Steinbach & Kumar at UMN (2007)
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Data Mining Methods: Anomaly Detection• Anomaly/Outlier Detection
3 5
4
3
3.5
2.5
1.5
2
1
0.5
From a teaching slide by Tan, Steinbach & Kumar at UMN (2007) Outlier Score
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CLASS EXERCISECLASS EXERCISE
Suggest 1 “question” related to gg qhealth care that can be answered with each of the data mining methodseach of the data mining methods
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Take‐Home Message• Data are “gold”• Tools are useful but we need to ask good questionsTools 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 analysisy