Emdeon analytics nhcaa_2012_11_15_masters

1Emdeon Proprietary & Confidential 1

D. Bart Masters, M.S.

Kelli Garvanian, A.H.F.I.

NHCAA Annual Training Conference: November 15, 2012

Using Smart Analytics to Eliminate False Positives and Increase the

Efficiency of Your Resources


Fraud, Waste and Abuse (FWA) in Healthcare

2011 US Healthcare Spend - $2.6T

Healthcare Waste - $910B

Fraud

and

Abuse

$177B

1 Donald M. Berwick, MD, MPP; Andrew D. Hackbarth, MPhil Journal of the American Medical Association, Apr 11, 2012, Eliminating Waste in US Health Care

• $0.34 from each $1 spent on US Healthcare classified as Waste1

• Approximately 6% is actual Fraud and Abuse1

http://jama.jamanetwork.com/article.aspx?articleid=1148376


Screening for FWACommon Practices

• Random selection of claims

• For 6% fraud population, this will result in a 94% false positive rate

• Even random selection has 6% true positive rate

• Target High Dollar Claims

• No evidence that high dollar claims are more likely to contain FWA

• Skewed towards institutional billing: core of healthcare system delivery is professional

Common practices in screening for Fraud, Waste, and Abuse:

Hoping the suitcase we select contains a fraudulent provider isn’t good enough.

4Emdeon Proprietary & Confidential

Screening for FWAImprovements to Common Practices

And when we recognize that these methods don’t work, how do we respond?

We throw bodies at it.

• Manual Review by Clinical Experts

• Substantial reduction in false positive rate

• Requires numerous, highly-skilled staff

• Difficult to scale to large healthcare volumes

While more effective at limiting false positives, because of the cost of staff, this approach also won’t be effective.


Terminology

CategoryIdentified

as FWAContains

FWA

True Positive Yes Yes

True Negative No No

False Negative No Yes

False Positive Yes No


Profitability Example

Simplified example of the impact of true positive rate on profitability of a SIU

20% 40% 60% 80% 100%

True Positive Rate

SIU

Pro

fita

bil

ity

• Simple Rules

• Lower Cost, Lower True Positive Rate

• High Levels of Manual Review

• Higher Cost, Higher True Positive Rate

The challenge is to increase true positive rate while increasing SIU profitability

7

To run an efficient and profitable SIU, we must actively work to reduce false

positives in identifying FWA.

False Positives


The primary and most effective approach to increasing profitability while reducing

false positive rate is through the use of Structured Analytics

Reduction in False Positives in Screening for FWA

• Development of Statistical Models effectively identifies outlierproviders based on unusual or abnormal behavior patterns

• Key factors in maximizing the accuracy and completeness of the models include:

• Leveraging the biggest and best data sets available

• Comparing providers to the most accurate peer group possible

• Scoring providers effectively


Structured Analytics

Benefits

• Scalable to large data volumes

• Greater need for control of false positives

• Stay ahead of new schemes

• Identifies abnormal behavior even if part of an unknown scheme

• Gives us tools to lower false positive rate

• Objective mathematical techniques

• Scoring: automatic decision making

Examples

Artificial Neural Networks

Decision Trees

Regression Models

Distribution Analysis


• Stay ahead of schemes and fraud trends

• Objective methods can find FWA even if it is unknown

• Defensible conclusions based on data

• Risk of false positives is higher in unknown schemes

• Greater need for control of false positives for analytical methods

Structured AnalyticsStaying ahead of Fraud


Structured AnalyticsLower and Control False Positives

• Reduce false positives ascompared to random selection

• Incremental reduction in false positive rate by 3-5x

• Gives us tools to lower and/or control the false positive rate

12

Reducing False Positives

How do we effectively reduce false positives?

• Leverage the biggest and best data set possible

• Develop refined peer groups for provider comparison

• Create effective scoring methods

• Automate decision of true or false positive


Use Big DataSize Really Does Matter

• Larger volumes of data yield better analytics

• Easier to “train” models

• Easier to avoid “over-fitting”

• Don’t fit the fraud

• Bigger data makes it easier to avoid modeling bad data

• See the broader provider picture

• Payers

• Geographies

• Claim types (UB-92, CMS-1500, etc)


Use Big DataMulti-Payer Modeling: Fitting the Pieces Together

• Traditional data warehousing is often limited to a single payer, creating a limited picture of a provider’s behavior for even the largest payers

• Medicare, Medicaid, Commercial Insurance, TPAs, etc.

• Applying structured analytics across multiple payers yields the following:

• Increase in FWA Identified

• Lower False Positive Rates

• Incremental reduction to false

positive rate of 10-15%

• Additional model types


Case Study: True Positive IdentifiedExample: Single Payer

147 Patients

2.73 hours worked on June 6, 2001

1.17 hours worked per day for this payer

• Bills many different payers

• Commercial and Government

• Procedures and billing are typical of Internal Medicine Practitioners

• E&M Codes

• Lab Tests

Dr. P: Internal Medicine

• Typical claim volume

June 2011

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

1

0.29 Hrs

2

1.25 Hrs

3

1.38 Hrs

4

5 6

2.73 Hrs

7

0.88 Hrs

8

2.19 Hrs

9 10 11

12 13

1.08 Hrs

14

1.25 Hrs

15

0.83 Hrs

16

0.42 Hrs

17

0.25 Hrs

18

19 20

1.42 Hrs

21

1.25 Hrs

22

0.82 Hrs

23

2.13 Hrs

24 25

26 27

0.25 Hrs

28

0.83 Hrs

29

0.83 Hrs

30

1.25 Hrs


For all payers: 1,654 patients over 26 payers

26.34 hours worked on June 6, 2001

11.84 hours worked per day on averageDr. P: Internal Medicine

• Improbable/ impossible billing didn’t appear until multiple payers’ data was used

• This is a true positive abusive provider

June 2011

Sunday Monday Tuesday Wednesday Thursday Friday Saturday

1

11.16 Hrs

2

11.62 Hrs

3

13.25 Hrs

4

5 6

26.34 Hrs

7

14.35 Hrs

8

10.68 Hrs

9

18.34 Hrs

10

9.08 Hrs

11

12 13

9.21 Hrs

14

14.49 Hrs

15

15.28 Hrs

16

18.23 Hrs

17

11.01 Hrs

18

19 20

20.66 Hrs

21

19.08 Hrs

22

17.76 Hrs

23

19.33 Hrs

24

10.35 Hrs

25

26 27

15.34 Hrs

28

16.50 Hrs

29

11.76 Hrs

30

14.27 Hrs

Case Study: True Positive IdentifiedExample: Multiple Payers


• Specialty on claims for a given payer

• Family practice

• Significant outlier when compared to other family practice providers

• Specialty on claims for other payers

• Dermatologist

Dr. A: False Positive

• False positive using individual payer data

False Positive prevented using Multi-Payer Data

Case Study: False Positive PreventedExample


Develop an Effective Peer Group

Better Peer Groups

Lower False Positive Rate

Increased Profitability

One of the core components of many statistical models is the concept of comparing a provider to his/her peers.

A critical aspect of statistical modeling for healthcare fraud is in ensuring that providers get compared to the most appropriate comparison provider set.

• In healthcare fraud modeling,taxonomy/specialty is the most commonly used peer group

• Only shows part of the picture

• Taxonomy/specialty is often self-reported and unverified

• Data Driven Approach

• Additional provider characteristics should be leveraged to “best fit” like providers


Develop an Effective Peer GroupRefining the Comparison Set

• Provider/Practice Characteristics

• Taxonomy/Specialty

• High-Specialization

• Patient Age Distribution

• Payer Type Distribution

• Provider/Practice Geography

• Zip 5 and Zip 3

• MSA

• State

• Rural/Urban Designation

Peer Group Refinement Elements


Develop an Effective Peer GroupExample: Cardiologists

Internal Medicine:

158,411

Cardiovascular Disease Spec.: 17,593

Average patient age>70:

1,201

California urban zip

codes: 650

Better statistics and analytics with more volume

More specific model

Less false positives

Less specific model: some inappropriate comparisons

Some analytics may not work well on smaller data volume.

Defining a peer group for cardiologists in California urban areas

• Strike a balance

• Enough volume for structured analytics

• Enough specificity to reduce false positives


Dr. Y: Listed as Family Medicine provider in NPPES database

Top procedure codes: Dr. Y

Code Description

TotalDollar

Amount% of Total Practice

97813 ACUPUNCT W/STIMUL 15 MIN $393,795 46%

97814 ACUPUNCT W/STIMUL ADDL 15M $231,800 27%

97026 INFRARED THERAPY $101,606 12%

97124 MASSAGE THERAPY $70,275 8%

99204 OFFICE/OUTPATIENT VISIT NEW $31,820 4%

99214 OFFICE/OUTPATIENT VISIT EST $16,750 2%

99213 OFFICE/OUTPATIENT VISIT EST $2,550 0%

99203 OFFICE/OUTPATIENT VISIT NEW $450 0%

99215 OFFICE/OUTPATIENT VISIT EST $175 0%

97032 ELECTRICAL STIMULATION $90 0%

99212 OFFICE/OUTPATIENT VISIT EST $65 0%

Top procedure codes: Family Medicine Providers

Code Description% of Total Universe

99213 OFFICE/OUTPATIENT VISIT EST 24.9%


99396 PREV VISIT EST AGE 40-64 3.4%

99203 OFFICE/OUTPATIENT VISIT NEW 2.5%

99395 PREV VISIT EST AGE 18-39 1.6%


99204 OFFICE/OUTPATIENT VISIT NEW 1.4%


80061 LIPID PANEL 1.2%

36415 ROUTINE VENIPUNCTURE 1.0%

93000 ELECTROCARDIOGRAM COMPLETE 1.0%

Develop an Effective Peer GroupExample


Develop an Effective Peer GroupExample


Creating ScoresOverview

With large data sets used for modeling, it’s often impossible to review every potentially fraudulent provider.

Scoring System

• Assign scores to providers basedon how egregious an outlier they appear per that model

• Break the scores into tiers

• Review random sample of providers from each tier to develop true positive rate for that tier


Creating ScoresData Driven

When creating your tiers, it is critical to let the data speak for itself.

• Eliminate Personal Bias

• Incorporate potentially unknown patterns

• Clustering methods find natural breaks in data


Model #1

Model #2

Model #3

Combined Score

Category

KARLY KONEDY, DO 80 73 21 174 Tier 2

DUSTIN KYTE, MD 91 83 25 199 Tier 1

SASHA FORGE, DPM 75 25 33 133 Tier 3

FORCASH LABORATORIES, INC. 40 85 50 175 Tier 2

KEN BOWREL 25 25 75 125 Tier 3

Provider Scores

Creating ScoresCombining Multiple Models

Scores allow for the combining of multiple models for a single provider

Combining multiple models into a single score further reduces false positives

• A provider is less likely to be false positive if he/she shows up in multiple models

• Dampens false positives by combining model results


Reducing False PositivesSummary

Don’t let these be your approach to fraud detection!


Reducing False PositivesSummary

The most effective way to reduce false positives is through Structured Analytics.

• Reducing False Positives with Structured Analytics

• Leverage the biggest and broadest data available to you

• Develop more effective peer groups

• Create and understand your provider scores


Questions?

[email protected]

Emdeon analytics nhcaa_2012_11_15_masters

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