emdeon analytics nhcaa_2012_11_15_masters
TRANSCRIPT
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
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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
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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.
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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.
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Terminology
CategoryIdentified
as FWAContains
FWA
True Positive Yes Yes
True Negative No No
False Negative No Yes
False Positive Yes No
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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
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To run an efficient and profitable SIU, we must actively work to reduce false
positives in identifying FWA.
False Positives
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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
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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
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• 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
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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
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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
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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)
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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
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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
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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
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• 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
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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
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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
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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
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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%
99214 OFFICE/OUTPATIENT VISIT EST 20.0%
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%
99212 OFFICE/OUTPATIENT VISIT EST 1.5%
99204 OFFICE/OUTPATIENT VISIT NEW 1.4%
99215 OFFICE/OUTPATIENT VISIT EST 1.3%
80061 LIPID PANEL 1.2%
36415 ROUTINE VENIPUNCTURE 1.0%
93000 ELECTROCARDIOGRAM COMPLETE 1.0%
Develop an Effective Peer GroupExample
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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
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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
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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
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Reducing False PositivesSummary
Don’t let these be your approach to fraud detection!
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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