This document discusses how big data analytics can be used to fight fraud, waste and abuse (FWA) in the payer industry. It outlines traditional and new FWA challenges such as doctor shopping and provides examples of how big data can help through pattern analysis, geo-mapping, social media analysis, and statistical modeling. The key takeaway is that big data platforms can efficiently handle healthcare's high volume, variety and velocity of data to detect FWA patterns across multiple sources and prevent rising insurance costs.

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CitiusTech Thought
Leadership
2nd July, 2018 | Author: Harshit Raikwal| Healthcare Consultant
Fighting FWA in the Payer
Industry Using Big Data
CitiusTech Thought
Leadership

2. 2
Objective
 This document gives a brief introduction on Fraud, Waste and Abuse (FWA) and lists down
traditional as well as modern FWA challenges
 It also gives an introduction to Big Data analytics and how it can be used to solve these
challenges
 Readers will have a better understanding on why and how Big Data should be used to identify
occurrences and patterns of FWA in Payer industry

3. 3
Agenda
 Why Payers Should Avoid FWA
 Payers: FWA Challenges
 FWA Prediction Scorecard: Using Big Data to Control FWA
 Big Data Analytics in FWA: Use Cases
 Big Data Analytics in FWA: Solution Approach
 Key Takeaways
 References

4. 4
Why Payers Should Avoid FWA?
Shrinkage in Customer Segment
 Health insurance fraud translates into
higher premiums and out-of-pocket
expenses for consumers, as well as
reduced benefits or coverage, so much
that fraud costs the average U.S. family
between $400 and $700 per year in the
form of increased premiums.
 Some people opt out of insurance and
some, less fortunate, can’t afford it
anymore
Impact on Individual Customers
Increased Investment in Technology
 As Payers are usually the first line of
defense in identifying and averting
FWA, they have to invest heavily in
technology, not only to safeguard
patient info against malicious attacks,
but also to detect instances of FWA
and take appropriate actions
 False patient diagnosis, treatment, and
medical histories
 Theft from patients' finite health
insurance (lifetime upper capped)
benefits
 Medical identity theft
 Physical risk to patients
Increased Cost
 Every instance of FWA will almost
always add an overhead cost in Payer’s
ability to provide appropriate level of
insurance benefits to its members
 To maintain their bottom line, payers
sometimes have to renegotiate lower
rates with providers. This might lead to
a shrink in their networks, impacting
their business in a negative way

5. 5
Payers: Traditional FWA Challenges
Challenge Details
Up-Coding  Billing for more expensive services or procedures than actually provided or performed
services which often requires the accompanying "inflation" of the patient's diagnosis code to
a more serious condition consistently with the false procedure code
Double Billing  Provider attempts to bill Medicare/Medicaid and either a private insurance company or the
patient for the same treatment
Unbundling  Billing each step of a procedure as if it is a separate procedure
Non-Covered
Item
 Misrepresenting non-covered treatments as covered service like cosmetic surgeries as
necessary repairs; for example, non-covered cosmetic procedures such as "nose jobs" are
billed to patients' insurers as deviated-septum repairs
Phantom Billing  Billed for procedures which are not performed at all
Duplicate Claims  Claims submitted for the same service with minor modifications in fields like date, time, etc.
but the service is rendered only once
Fake Billing  Billing for services that were never rendered – either by using genuine patient information,
sometimes obtained through identity theft, to fabricate entire claim or by padding claims
with charges for procedures or services that never happened
Excessive Billing  Billing for excessive services that have no correlation with the diagnosis of the patient

6. 6
Payers: New FWA Challenges
Challenge Details
Manipulation in ‘Pay For
Performance’
environment
 The ‘Pay For Performance’ model, can be tricked when providers manipulate the
documentation to indicate that they service a sicker set of people than they
actually do, in order to increase their risk adjustment payments
Doctor Shopping  Patients visiting multiple doctors to obtain multiple doses of otherwise illegal
drugs – for personal use or to give to others illegally
 As per Centers of Disease Control and Prevention, overdose deaths involving
opioid prescription is 5 times higher in 2016 than that of 1999. From 1999 to
2016, more than 630,000 people have died in the U.S. from a drug overdose.
Around 66% of the drug overdose deaths in 2016 involved an opioid, with an
average of 115 people dying every day

7. 7
Need for Data Collaboration Across Payers
In order to better prepare against FWA, private payers, state (Medicaid), and federal government
(Medicare and Medicaid) have to work together towards collaboration of aggregation of member
data in one place from multiple entities.
This approach will benefit the healthcare market in following ways:
 Detecting FWA instances where multiple payers are involved. For example:
• A provider billing 5 hours of service per day to 6 different payers on the same day.
• If one looks at one payer’s data at a time, nothing emerges as alarming.
• But, if the data from all the payers can be collected and research upon, it will be a clear case
of FWA
 Easier and faster to find FWA patterns when complete data for patients, providers, pharmacies,
medical equipment suppliers, and claims are present at one place
 Sharing of best practices across different entities to remain one step ahead against FWA

8. 8
FWA Prediction Scorecard: Using Big Data to Control FWA
 The traditional FWA Audit Rules method comes with limitations as it is slow, labour intensive,
prone to auditor’s judgmental error, and outcome can be inconsistency between two auditors.
 An more efficient way to control health insurance frauds is to use an FWA Prediction Scorecard,
which is much better than using FWA Audit Rules.
 Highlights of FWA Prediction Scorecard:
• Uses computer-based statistical analysis on large historical data sets (Big Data).
• The quality of the outcome of the predictive analysis depends on the quality of historical
data. So a data set which covers wide range of use cases can make better predictions.
• Analytical processing with more influencing factors (statistically speaking - explanatory
variables) results in higher confidence.
 Specialized Big Data processing platforms (focused on healthcare data) can efficiently handle
high volume, velocity, variety, and veracity of healthcare data. Key benefits include:
• Ability to ingest and normalize data from disparate sources such as providers, clearing
houses, TPAs, government organizations such as CMS, Pharmacies, etc.
• Ability to store, process and analyze huge volumes of real-time data (structured, semi
structured and unstructured data)
• Ability to develop and run powerful algorithms and statistical models to detect instances
and patterns of FWA.

9. 9
Big Data Analytics in FWA: Pattern Analysis
Use Case 1: Identifying Abuse Trends in
Clinical Procedures
 Mismatch between diagnosis and
medications / procedures
 Recurrent high value claims by provider
Use Case 2: Tracking Member Visit Patterns
 Indicators such as drug prescription refills
before time
 Multiple prescriptions for similar condition
from geographically distant locations

10. 10
Big Data Analytics in FWA: Geo-Mapping Analytics
Use Case: Tracking Member Movement Across
Provider Locations
 Multiple member visits to various providers
or a single provider
 Specific providers / facilities which have
exceptionally high or exceptionally low visits
 Average distance travelled by members in a
region
 Noticeably long distance traversed by a
member for an office visit
 Correlation between visit frequency /
adherence and distance travelled by
members

11. 11
Big Data Analytics in FWA: Social Media Analytics
Use Case 1: Validating Claims Data with Social
Media Analytics
 Member information mismatch
 Abnormalities in member’s demo data vs actuals
from social media
 Deviations in claims data using unstructured
data (audio and text messages)
Use Case 2: Validating Member Data with Social
Media Analytics
 Checking for historical, hospitalization, and
medication data
 Tracking changes to patient demographic data

12. 12
Big Data Analytics in FWA: Risk Score Analytics
Use Case: Assessing Member Risk Score vs. Claim
Costs
 Cluster cases of high claim amount and
corresponding low risk scores
 Deviations / outliers in claim patterns
 Claim amount mismatch– actual vs. expected
claim amount based on the risk score
 Enable stratification of providers based on their
potential fraud appetite, tempered by overhead
of possible appeals
$Value

13. 13
Big Data Analytics in FWA: Provider Analytics
Use Case: Generating and Analyzing Provider Reports
 Providing multiple care services with pre-established code pair violation
 Prescribing certain drugs at higher rate than others
 High number of treatments for a type of injury
 Abnormally long treatment time off for the type of injury
 Accident severity not correlating with severity of injury
 High average cost per patient (medications, procedures, tests, etc.)
 Billing for services not provided
 Multiple billing for services rendered
 Unnecessary or medically unrelated procedures, tests, treatments or equipment
 More expensive tests and equipment (up-coding)
 Unbundling or billing separately for laboratory tests performed together to get higher
reimbursements

14. 14
Big Data Analytics in FWA: Statistical Analysis
Use Case: Analyzing Data from Provider and Other Sources
 Detect the patterns of healthcare FWA from bills provided
 Identify outliers that could indicate FWA
 Profile and segment claimants to identify those who are likely to commit FWA using unsupervised
learning methods
 Identify connections among fraudsters via social network analysis
 Detect abnormal medical event sequences or unexpected occurrences, e.g., suspicious time of
data entry
 Define the similarity between claims to identify hidden claim duplicates
 Detect FWA by using a combination of anomaly detection, business rules, and predictive models
 Reveal fraudulent activities by analyzing unstructured data (for example, tweets, e-mails, etc.)
using advanced text analytics
 Detect anomalies in patients’ physician office visits and pharmacy usage

15. 15
Big Data Analytics in FWA: Solution Approach
Big Data analytics will be more
efficient and effective if it uses
following key data streams
 Claims data
 Member eligibility
 Provider profiles, ratings,
and history
 Preauthorization info
 Lab requests
 Prescription and
pharmacy info
 Data from Medical
Practice Management
System
 Social Media Integration
Data RequirementsIdentify Data Sources
Use Analytic Insights to develop Big Data
Analytic Models
Use Big Data techniques to improve modeling
capabilities in order to handle variations
Use Machine Learning to enhance modeling
capabilities
Enable interactive and dynamic data
visualizations using industry leading tools like
Spotfire, Tableau, D3, Pentaho, Jasper

16. 16
Key Takeaways
 Because of FWA, payers experience rise in premiums and out-of-pocket costs, leading to a
shrinkage of the member base. FWA also results in higher technology overheads in terms of data
security and access management.
 Payers need to identify, prevent, and stop not only traditional FWA challenges such as up-coding,
unbundling, double billing, but also new FWA challenges like doctor shopping and manipulating
‘Pay For Performance’ models
 Traditional predictive models lack the ability to store and process huge volumes of real-time data
(structured, semi-structured and unstructured).
 Healthcare data can come from disparate sources such as providers, clearing houses, TPAs,
government organizations such as CMS, pharmacies, etc.
 Specialized platforms for healthcare Big Data processing and analytics can efficiently handle high
volume, velocity, variety, and veracity of healthcare data
 Different types of Healthcare Big Data Analytics – Pattern, Geo Mapping, Social Media, Risk
Scores, etc., can be used to detect and prevent FWA effectively

17. 17
References
 www.govhealthit.com
 www.nhcaa.org
 www.fraud-magazine.com
 www.insurancefraud.org
 http://www.businessofgovernment.org/
 https://www.smartbrief.com/original/2017/10/how-physician-education-and-engagement-can-
stem-waste-and-abuse
 https://enterprise.microsoft.com/en-us/articles/industries/health/the-most-common-types-of-
healthcare-fraud/

18. 18
Keywords
 Fraud
 Waste
 Abuse
 FWA
 Payer
 Big Data
 Analytics
 Billing
 Member Risk

19. 19
Thank You
Authors:
Harshit Raikwal
thoughtleaders@citiustech.com
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