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How do you accelerate data warehousing to meet the demands of the data-driven economy? Semantic technology provides an agile platform to bring data together, focus on data that matters and ultimately derive a target data model that can be easily extended. This webinar will present a semantically-based data federation case study and highlight the semantic components that facilitate agile data federation in the enterprise.
Citation preview
Using Semantic Technology
to Drive Agile Analytics
Semantics, Analytics and
Data Unleashed™
May 14, 2014
Presenters Overview
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 2
• Architecture
• Data Security
• Innovation
David Read
• Database Design
• Warehousing
• Semantics
Scott Van Buren
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
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 3
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!
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 4
Data Federation Agility: Iterative Process
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 5
Define Iteration Goal
Identify Data
Federate Explore
Result
SEMANTIC TECHNOLOGY
A brief look at the semantic technology underpinnings
that agile data federation leverages
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 6
• 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)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 7
Subject
Predicate
Object
What’s Different About Semantic Technology?
• Structure is (mostly) logical not physical
– Triple
– Directed Graph
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 8
Lisa
bioMotherOf
Michael
friendOf
Carl
favoriteSport
Bowling
Lisa
bioMotherOf
Michael
The Physical Structure is Flexible by Design
• Relationships can be added or removed
as they are found, explored, accepted or
discredited
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 9
friendOf
Carl
Iced Tea
favoriteDrink
Clm…
Bridging And Federating Data
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 10
Source_Pvdr Source_Clm
Clm_1 Pv1
name id dos lamt
ClaimHeader ClaimLine Provider
NPI
ClaimId
ch1 ch… prv1
PatNm BillAmt ServDt
Pv…
NatId
“Jones” “AB12” 2/3/14 405.00 “G403”
cl1 cl… prv…
Clm… Pv… Pv… Clm…
Line
Pvdr
Prov
Directed Graph: Domain Class & Individuals
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 11
Drilling Into Individuals
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 12
Federating Example (merged individuals)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 13
Source 1 Source 2
CASE STUDY
Semantically-enabled analytic agility
at a healthcare plan
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 14
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
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 15
Infrastructure
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 16
Part A Claims • Headers • Lines
Part B Claims • Headers • Lines
General Info • Members • Providers Medical Review • Checklists • Denials
Database Schemas
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 17
Part A Part B
Providers and Beneficiaries Medical Review
ITERATION 1
Validate the hypothesis
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 18
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)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 19
I1: Relevant System Data
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 20
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
Pa
rt A
Co
st ($
)
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
Pa
rt B
Co
st ($
)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
Analytic Tools
I1: Federated Data Sample
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 21
I1: EOC Aggregated Costs by DRG and LOS
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 22
280 281 282
40
00
10
00
0Total EOC Cost by DRG
DRG
To
tal E
OC
Co
st ($
)
2 3 4 5 6
40
00
10
00
0
Total EOC Cost by Inpatient LOS
LOS (Days)
To
tal E
OC
Co
st ($
)
I1: Patient EOC Costs by DRG
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 23
2500
5000
7500
10000
12500
15000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
To
tal E
OC
Co
st ($
)
DRG
280
281
282
Total EOC Cost by DRG
I1: Patient EOC Costs by DRG (Part A, Part B)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 24
2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
Pa
rt A
Co
st ($
)
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
Pa
rt B
Co
st ($
)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
I1: Patient EOC Costs by LOS
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 25
2500
5000
7500
10000
12500
15000
2 3 4 5 6
LOS (Days)
To
tal E
OC
Co
st ($
)
DRG
280
281
282
Total EOC Cost by LOS
2000
3000
4000
2 3 4 5 6
LOS (Days)
Pa
rt A
Co
st ($
)
DRG
280
281
282
Part A Cost by LOS
2500
5000
7500
10000
2 3 4 5 6
LOS (Days)
Pa
rt B
Co
st ($
)
DRG
280
281
282
Part B Cost by LOS
Costs Categorized by LOS
ITERATION 2
Investigate the Part B stratifications
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 26
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
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 27
I2: Relevant System Data
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 28
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
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 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
I2: Federated Data Sample
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 29
pcp
car
inp
phm
car
nut
phm
gpt
cpt
nut
pcp
cpt
hom
car
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
hom
car
pcp
cpt
gpt
cpt
car
pcp
car
Flow by Patient Count (DRG 282)
pcp
car
inp
phm
car
nut
phm
gpt
cpt
nut
pcp
cpt
hom
car
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
hom
car
pcp
cpt
gpt
cpt
car
pcp
car
Flow by Patient Count (DRG 281)
pcp
car
inp
phm
car
nut
phm
gpt
cpt
nut
pcp
cpt
hom
car
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
hom
car
pcp
cpt
gpt
cpt
car
pcp
car
Flow by Patient Count (DRG 280)
I2: Part B Facility Flow (by DRG)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 30
1
1
2
3
2 3
pcp
car
inp
phm
car
nut
phm
gpt
cpt
nut
pcp
cpt
hom
car
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
hom
car
pcp
cpt
gpt
cpt
car
pcp
car
Flow by Patient Count
pcp
car
inp
phm
car
nut
phm
gpt
cpt
nut
pcp
cpt
hom
car
pcp
edu
gpt
hom
edu
cpt
pcp
cpt
hom
car
pcp
cpt
gpt
cpt
car
pcp
car
Flow by Mean Aggregated $ per Patient
I2: Part B Facility Flow
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 31
I2: Average EOC Costs By DRG and Flow
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 32
280-PCP 280-CDO 280-REH 281-PCP 281-CDO 281-REH 282-PCP 282-CDO 282-REH
Average Claim Costs
DRG and Flow
Do
lla
rs
0
2000
4000
6000
8000
10000
12000
Claim Type
Part B
Part A
280 281 282
40
00
80
00
12
00
0Total EOC Cost by DRG with Rehab Hosp
DRG
To
tal E
OC
Co
st ($
)
280 281 282
30
00
50
00
70
00
90
00
Total EOC Cost by DRG without Rehab Hosp
DRG
To
tal E
OC
Co
st ($
)I2:EOC Agg by DRG (Rehab Hosp Difference)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 33
I2: Patient EOC by DRG (w/o Rehab Hosp)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 34
4000
6000
8000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
To
tal E
OC
Co
st ($
)
DRG
280
281
282
Total EOC Cost by DRG
I2: Patient EOC by DRG (w/o Rehab Hosp)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 35
2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
Pa
rt A
Co
st ($
)
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
Pa
rt B
Co
st ($
)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
pcp
car
phm nut
gpt
cpt
pcp
car
cpt
hom
edu
car
pcp
hom
edu
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
edu
car
pcp
hom
edu
gpt
cpt
hom
edu
cpt
pcp
car
cpt
hom
cpt
car
pcp
car
cpt
gpt
cpt
pcp
car
Flow by Patient Count (DRG 281)
pcp
car
phm nut
gpt
cpt
pcp
car
cpt
hom
edu
car
pcp
hom
edu
gpt
cpt
hom
edu
cpt
pcp
car
cpt
hom
cpt
car
pcp
car
cpt
gpt
cpt
pcp
car
Flow by Patient Count (DRG 280)
I2: Part B Facility Flow (w/o Rehab Hosp)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 36
pcp
car
phm nut
gpt
cpt
pcp
car
cpt
hom
edu
car
pcp
hom
edu
gpt
cpt
hom
edu
cpt
pcp
car
cpt
hom
cpt
car
pcp
car
cpt
gpt
cpt
pcp
car
Flow by Patient Count
pcp
car
phm nut gpt
cpt
pcp
car
cpt
hom
edu
car
pcp
hom
edu
gpt
cpt
hom
edu
cpt
pcp
car
cpt
hom
cpt
car
pcp
car
cpt
gpt
cpt
pcp
car
Flow by Mean Aggregated $ per Patient
I2: Part B Facility Flow (w/o Rehab Hosp)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 37
280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO
Average Claim Costs
DRG and Flow
Do
lla
rs
0
2000
4000
6000
8000
Claim Type
Part B
Part A
I2: Average EOC Costs By DRG and Flow
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 38
ITERATION 3
Refine the data set
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 39
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
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 40
I3: Relevant System Data
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 41
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
Do
lla
rs
0
1000
2000
3000
4000
5000
6000
Claim Type
Part B
Part A
I3:EOC Agg by DRG (Denied Claims Diff)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 42
280 281 282
30
00
50
00
70
00
90
00
Total EOC Cost by DRG with Denied Claims
DRG
To
tal E
OC
Co
st ($
)
280 281 282
30
00
50
00
70
00
Total EOC Cost by DRG without Denied Claims
DRG
To
tal E
OC
Co
st ($
)
I3: Patient EOC by DRG (w/o Denied Claims)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 43
3000
4000
5000
6000
7000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
To
tal E
OC
Co
st ($
)
DRG
280
281
282
Total EOC Cost by DRG
2000
3000
4000
1.0 1.5 2.0 2.5 3.0
DRG Grouping
Pa
rt A
Co
st ($
)
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
Pa
rt B
Co
st ($
)
DRG
280
281
282
Part B Cost by DRG
Costs Categorized by DRG
I3: Patient EOC by DRG (w/o Denied Claims)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 44
pcp
car
phm nut
gpt
cpt
hom
cpt
edu
pcp
edu
cpt
gpt
cpt
edu
car
pcp hom pcp gpt pcp
Flow by Patient Count
pcp
car
phm nutgpt
cpt
hom
cpt
edu
pcp
edu
cpt
gpt
cpt
edu
car
pcp hompcp gpt pcp
Flow by Mean Aggregated $ per Patient
I3: Part B Facility Flow (w/o Denied Claims)
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 45
I3: Average EOC Costs By DRG and Flow
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 46
280-PCP 280-CDO 281-PCP 281-CDO 282-PCP 282-CDO
Average Claim Costs
DRG and Flow
Do
lla
rs
0
1000
2000
3000
4000
5000
6000
Claim Type
Part B
Part A
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
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 47
Thank You
(c) 2014 Blue Slate Solutions Semantics Underpins Analytic Agility 48
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:
[email protected] [email protected]
www.blueslate.net
www.dataunleashed.com