using semantic technology to drive agile analytics - slides

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


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!


Data Federation Agility: Iterative Process


Define Iteration Goal

Identify Data

Federate Explore

Result

SEMANTIC TECHNOLOGY

A brief look at the semantic technology underpinnings

that agile data federation leverages


• 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)


Subject

Predicate

Object

What’s Different About Semantic Technology?

• Structure is (mostly) logical not physical

– Triple

– Directed Graph


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


friendOf

Carl

Iced Tea

favoriteDrink

Clm…

Bridging And Federating Data


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


Drilling Into Individuals


Federating Example (merged individuals)


Source 1 Source 2

CASE STUDY

Semantically-enabled analytic agility

at a healthcare plan


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


Infrastructure


Part A Claims • Headers • Lines

Part B Claims • Headers • Lines

General Info • Members • Providers Medical Review • Checklists • Denials

Database Schemas


Part A Part B

Providers and Beneficiaries Medical Review

ITERATION 1

Validate the hypothesis


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)


I1: Relevant System Data


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


I1: EOC Aggregated Costs by DRG and LOS


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


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)


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


I1: Patient EOC Costs by LOS


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



• 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






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

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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

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cpt

pcp

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car

hom

pcp

cpt

cpt

gpt

car

pcp

car

Flow by Mean $ per Patient

Analytic Tools

I2: Federated Data Sample


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

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gpt

hom

edu

cpt

pcp

cpt

hom

car

pcp

cpt

gpt

cpt

car

pcp

car


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


I2: Part B Facility Flow (by DRG)


1

1

2

3

2 3

pcp

car

inp

phm

car

nut

phm

gpt

cpt

nut

pcp

cpt

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car

pcp

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gpt

hom

edu

cpt

pcp

cpt

hom

car

pcp

cpt

gpt

cpt

car

pcp

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pcp

car

inp

phm

car

nut

phm

gpt

cpt

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pcp

cpt

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car

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gpt

hom

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cpt

pcp

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hom

car

pcp

cpt

gpt

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car

pcp

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Flow by Mean Aggregated $ per Patient

I2: Part B Facility Flow


I2: Average EOC Costs By DRG and Flow


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)


I2: Patient EOC by DRG (w/o Rehab Hosp)


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


I2: Patient EOC by DRG (w/o Rehab Hosp)


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


pcp

car

phm nut

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cpt

pcp

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car

pcp

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pcp

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pcp

car

cpt

gpt

cpt

pcp

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pcp

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pcp

hom

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car

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pcp

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pcp

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I2: Part B Facility Flow (w/o Rehab Hosp)


pcp

car

phm nut

gpt

cpt

pcp

car

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hom

edu

car

pcp

hom

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gpt

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edu

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pcp

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pcp

car

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gpt

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pcp

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pcp

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I2: Part B Facility Flow (w/o Rehab Hosp)


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



ITERATION 3

Refine the data set



• 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






Semantic Environment • Ontologies (broadened) • Triple Store (in-memory/physical) • Reasoner • SPARQL Endpoint

General Info • Providers

Medical Review • Adjudication • Claim Details

Analytic Tools


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)


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)


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


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


I3: Patient EOC by DRG (w/o Denied Claims)


pcp

car

phm nut

gpt

cpt

hom

cpt

edu

pcp

edu

cpt

gpt

cpt

edu

car

pcp hom pcp gpt pcp


pcp

car

phm nutgpt

cpt

hom

cpt

edu

pcp

edu

cpt

gpt

cpt

edu

car

pcp hompcp gpt pcp


I3: Part B Facility Flow (w/o Denied Claims)





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


Thank You


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

http://www.blueslate.net/

http://www.dataunleashed.com/

using semantic technology to drive agile analytics - slides

Technology

analytic agility

blue slate solutions

data federation agility

case c

drg analytic tools

hypothesis c

graph c

federating data c