The document describes a webinar that demonstrates how semantic technology enables better decision making by providing the right data at the right time. It introduces semantic terminology and how semantic technology simplifies data federation. A case study is presented that analyzes post-discharge costs for heart attack patients through iterative analysis. The analysis uncovered unexpected results regarding rehabilitation hospital utilization and costs.
2. Presenters Overview
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• Architecture
• Data Security
• Innovation
David Read
• Database Design
• Warehousing
• Semantics
Scott Van Buren
3. Webinar Goal
• Demonstrate how semantic technology
enables you to make better decisions by
providing:
– the right data
– in the right form
– at the right time
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4. Agenda
• Introduce semantic terminology
• Describe how semantic technology
simplifies the data federation process
• Present a case study analyzing post-
discharge cost of care for heart attack
patients to illustrate how semantic
technology and agile analytics lead us to
an unexpected and surprising result!
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5. Data Federation Agility: Iterative Process
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Define Iteration Goal
Identify Data
FederateExplore
Result
6. SEMANTIC TECHNOLOGY
A brief look at the semantic technology underpinnings
that agile data federation leverages
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7. • Standards → RDF/RDFS/OWL/SPARQL
• Definitions → Ontologies
• Storage → Triple Stores
• Data Access → SPARQL
– Federation is assumed
• Inferencing → Reasoners
What Is Semantic Technology? (in this case)
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8. Subject
Predicate
Object
What’s Different About Semantic Technology?
• Structure is (mostly) logical not physical
– Triple
– Directed Graph
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Lisa
bioMotherOf
Michael
friendOf
Carl
favoriteSport
Bowling
9. Lisa
bioMotherOf
Michael
The Physical Structure is Flexible by Design
• Relationships can be added or removed
as they are found, explored, accepted or
discredited
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friendOf
Carl
Iced Tea
favoriteDrink
10. Clm…
Bridging And Federating Data
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Source_PvdrSource_Clm
Clm_1 Pv1
nameid dos lamt
ClaimHeader ClaimLine Provider
NPI
ClaimId
ch1ch… prv1
PatNm BillAmtServDt
Pv…
NatId
“Jones”“AB12” 2/3/14 405.00 “G403”
cl1 cl… prv…
Clm… Pv…Pv…Clm…
Line
Pvdr
Prov
11. Directed Graph: Domain Class & Individuals
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15. Case Study Description
• Healthcare Plan
– Medicare Administrative Contractor (MAC)
• Hypothesis
– Inconsistent treatment plans for heart attack
patients leads to varying costs and outcomes
• Opportunity
– Determine optimal post-discharge plans to
improve outcome and reduce costs to the
Medicare Trust Fund
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16. Infrastructure
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Part A Claims
• Headers
• Lines
Part B Claims
• Headers
• Lines
General Info
• Members
• Providers
Medical Review
• Checklists
• Denials
17. Database Schemas
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Part A Part B
Providers and Beneficiaries Medical Review
18. ITERATION 1
Validate the hypothesis
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19. I1: Business Definition and Ontology
• Iteration Goal (Problem Statement)
– Determine the overall cost of Part B claims
directly related to patients discharged with
DRG 280, 281 and 282 coded Part A claims
• Identify Data (Terms and Relationships)
– Interested in Part A and B claim headers
• Part A: Patient Id, DRG, admit date, discharge
date, paid amount
• Part B: total paid across episode of care [EOC]
(based on dates and prior history)
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20. I1: Relevant System Data
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Part A Claims
• Headers
Part B Claims
• Headers
Semantic Environment
• Ontologies (narrow)
• Triple Store (in-memory/physical)
• Reasoner
• SPARQL Endpoint
2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartACost($)
DRG
280
281
282
Part A Cost by DRG
2500
5000
7500
10000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartBCost($)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
Analytic Tools
21. I1: Federated Data Sample
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22. I1: EOC Aggregated Costs by DRG and LOS
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280 281 282
400010000 Total EOC Cost by DRG
DRG
TotalEOCCost($)
2 3 4 5 6
400010000
Total EOC Cost by Inpatient LOS
LOS (Days)
TotalEOCCost($)
23. I1: Patient EOC Costs by DRG
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2500
5000
7500
10000
12500
15000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
TotalEOCCost($)
DRG
280
281
282
Total EOC Cost by DRG
24. I1: Patient EOC Costs by DRG (Part A, Part B)
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2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartACost($)
DRG
280
281
282
Part A Cost by DRG
2500
5000
7500
10000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartBCost($)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
25. I1: Patient EOC Costs by LOS
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2500
5000
7500
10000
12500
15000
2 3 4 5 6
LOS (Days)
TotalEOCCost($)
DRG
280
281
282
Total EOC Cost by LOS
2000
3000
4000
2 3 4 5 6
LOS (Days)
PartACost($)
DRG
280
281
282
Part A Cost by LOS
2500
5000
7500
10000
2 3 4 5 6
LOS (Days)
PartBCost($)
DRG
280
281
282
Part B Cost by LOS
Costs Categorized by LOS
26. ITERATION 2
Investigate the Part B stratifications
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27. I2: Business Definition and Ontology
• Iteration Goal
– Understand the Part B cost stratification. Start
by looking at the types of providers and
facilities within the relative order of visits
• Identify Additional Data
– Interested in Part B claim headers, lines and
providers (facilities)
• Part B: total paid for each claim, provider
associated with lines, provider specialty or facility
type
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28. I2: Relevant System Data
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Part A Claims
• Headers
Part B Claims
• Headers
• Lines
Semantic Environment
• Ontologies (broadened)
• Triple Store (in-memory/physical)
• Reasoner
• SPARQL Endpoint
General Info
• Providers
pcp
car
inp
car
phm
nut
phm
cpt
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nut
pcp
cpt
car
hom
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
car
hom
pcp
cpt
cpt
gpt
car
pcp
car
Flow by Patient Count
pcp
car
inp
car
phm
nut
phm
cpt
gpt
nut
pcp
cpt
car
hom
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
car
hom
pcp
cpt
cpt
gpt
car
pcp
car
Flow by Mean $ per Patient
Analytic Tools
29. I2: Federated Data Sample
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30. pcp
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inp
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gpt
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pcp
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pcp
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pcp
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Flow by Patient Count (DRG 282)
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inp
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Flow by Patient Count (DRG 281)
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Flow by Patient Count (DRG 280)
I2: Part B Facility Flow (by DRG)
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1
1
2
3
2 3
32. I2: Average EOC Costs By DRG and Flow
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280-PCP 280-CDO 280-REH 281-PCP 281-CDO 281-REH 282-PCP 282-CDO 282-REH
Average Claim Costs
DRG and Flow
Dollars
0
2000
4000
6000
8000
10000
12000
Claim Type
Part B
Part A
33. 280 281 282
4000800012000
Total EOC Cost by DRG with Rehab Hosp
DRG
TotalEOCCost($)
280 281 282
3000500070009000
Total EOC Cost by DRG without Rehab Hosp
DRG
TotalEOCCost($)
I2:EOC Agg by DRG (Rehab Hosp Difference)
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34. I2: Patient EOC by DRG (w/o Rehab Hosp)
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4000
6000
8000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
TotalEOCCost($)
DRG
280
281
282
Total EOC Cost by DRG
35. I2: Patient EOC by DRG (w/o Rehab Hosp)
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2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartACost($)
DRG
280
281
282
Part A Cost by DRG
2000
3000
4000
5000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartBCost($)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
36. pcp
car
phm nut
gpt
cpt
pcp
car
cpt
hom
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car
pcp
hom
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gpt
cpt
hom
edu
cpt
pcp
car
cpt
hom
cpt
car
pcp
car
cpt
gpt
cpt
pcp
car
Flow by Patient Count (DRG 282)
pcp
car
phm nut
gpt
cpt
pcp
car
cpt
hom
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car
pcp
hom
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gpt
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hom
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cpt
pcp
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cpt
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pcp
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cpt
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pcp
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Flow by Patient Count (DRG 281)
pcp
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car
pcp
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cpt
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cpt
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pcp
car
cpt
gpt
cpt
pcp
car
Flow by Patient Count (DRG 280)
I2: Part B Facility Flow (w/o Rehab Hosp)
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38. 280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO
Average Claim Costs
DRG and Flow
Dollars
0
2000
4000
6000
8000
Claim Type
Part B
Part A
I2: Average EOC Costs By DRG and Flow
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39. ITERATION 3
Refine the data set
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40. I3: Business Definition and Ontology
• Iteration Goal
– Remove claims that would be denied based
on claim review history
• Define Additional Data
– Interested in claim review denials and
relationship to claims in the study’s data set
• Medical Review: adjudication status, claim
information such as provider specialty and facility
type
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41. I3: Relevant System Data
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Part A Claims
• Headers
Part B Claims
• Headers
• Lines
Semantic Environment
• Ontologies (broadened)
• Triple Store (in-memory/physical)
• Reasoner
• SPARQL Endpoint
General Info
• Providers
Medical Review
• Adjudication
• Claim Details
Analytic Tools
280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO
Average Claim Costs
DRG and Flow
Dollars
0
1000
2000
3000
4000
5000
6000
Claim Type
Part B
Part A
42. I3:EOC Agg by DRG (Denied Claims Diff)
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280 281 282
3000500070009000 Total EOC Cost by DRG with Denied Claims
DRG
TotalEOCCost($)
280 281 282
300050007000
Total EOC Cost by DRG without Denied Claims
DRG
TotalEOCCost($)
44. 2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartACost($)
DRG
280
281
282
Part A Cost by DRG
1500
2000
2500
3000
3500
1.0 1.5 2.0 2.5 3.0
DRG Grouping
PartBCost($)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
I3: Patient EOC by DRG (w/o Denied Claims)
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45. pcp
car
phm nut
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cpt
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cpt
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pcp
edu
cpt
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cpt
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car
pcp hom pcp gpt pcp
Flow by Patient Count
pcp
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cpt
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pcp
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cpt
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cpt
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pcp hom
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Flow by Mean Aggregated $ per Patient
I3: Part B Facility Flow (w/o Denied Claims)
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46. I3: Average EOC Costs By DRG and Flow
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280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO
Average Claim Costs
DRG and Flow
Dollars
0
1000
2000
3000
4000
5000
6000
Claim Type
Part B
Part A
47. Conclusions
• Data federation agility
accelerates data analysis
& reporting
– Experts follow unexpected
paths as they work with data
– Predicting up-front what data will be useful
• Error prone (too little missed)
• Heavyweight (too much wasted effort)
• Targeting federation at specific questions
reduces the scope of data integration
• Multiple iterations inform a broader data
warehousing need
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48. Thank You
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We appreciate your spending time with us.
If there are questions we do not cover during
the Q&A time in the webinar, feel free to
contact us at your convenience:
David.Read@blueslate.net
Scott.VanBuren@blueslate.net
www.blueslate.net
www.dataunleashed.com