boost the efficiency and effectiveness of audits with data ......decreased reliance on sampling 5....
TRANSCRIPT
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Boost the efficiency and effectiveness of audits with data analytics
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Chris Magee
Performance Data Analytics Manager, Louisiana Legislative Auditor
Meet the speakers
Brent McDougall
Performance Audit Senior Data Analyst, Louisiana Legislative Auditor
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Agenda
• Introduction• What is data analytics?• Using data to measure compliance• The ROI of data analytics• Using data analytics to identify possible FWA• Data issues becoming their own projects• Lessons learned• Q&A
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About Louisiana Legislative State Auditor
• 250 employees; 31 in performance audit section
• Staff from all different backgrounds
Better Information, Better Louisiana!To foster accountability and transparency in Louisiana government by providing
the Legislature and others with audit services, fiscal advice, and other useful information
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What is data analytics?
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What is data analytics?• The science of examining
raw data in order to draw conclusions about that information.
• The goal is to improve the business or program by gaining knowledge which can be used to make improvements or changes.
• Insights can be historical, real-time, or predictive, and risk-focused or performance-focused
• Provides the “how?” and “why?” answers to the initial “what?” questions.
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1. Increased efficiency
2. Decreased costs
3. Stronger evidence
4. Decreased reliance on sampling
5. Ability to replicate
6. Easier documentation
6 benefits of using data analytics
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Tools available
• Auditing software• Databases• Visualization• Others
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Using data to measure compliance
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Using data to measure compliance
Is the agency meeting the requirements established in law, regulation, and policy?
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Measuring compliance using single datasets
State Board of Dentistry
Did not conduct required inspections on 35.5% of dental offices
Alcohol and
Tobacco Control
Did not conduct inspections on
24.1% of alcoholic beverage outlets
Office of Public Health
Did not conduct timely inspections on 80.7% of restaurants
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Another way to identify cause is visualizing the data using a geographic information system
Inspections of Alcohol Beverage Outlets in Pierre Part, Louisiana
Using Data to Identify Cause
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Measuring compliance using two datasets
Medicaid Benefits to Deceased Individuals• $1.85 million in participant fees for 1,727 deceased
individuals
SNAP Benefits to Deceased Individuals• $1.3 million in SNAP benefits for 3,939 single person
households were spent after the participant’s deathAre your tax dollars being spent on dead people?
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Measuring compliance using multiple datasets
What issue were we trying to solve?
Are Foster Parents receiving their required criminal background check prior to caring for children?
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Measuring compliance using multiple datasets
What data did we look at?
• Foster Care (Name, SSN, Date cared for foster children)
• State Police (Name, DOB, Date of background check)
• Driver’s License (Name, SSN, DOB)
What issue were we trying to solve?
Are Foster Parents receiving their required criminal background check prior to caring for children?
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Measuring compliance using multiple datasets
What data did we look at?
• Foster Care (Name, SSN, Date cared for foster children)
• State Police (Name, DOB, Date of background check)
• Driver’s License (Name, SSN, DOB)
What issue were we trying to solve?
Are Foster Parents receiving their required criminal background check prior to caring for children?
What did we find?
• 34.0% of foster parents did not receive a timely criminal background check
• 28.9% with NO background check
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Thinking outside the Box with Galvanize
Inspirations Hub
Community & User Groups
ScriptHub & Machine Learning
…and much more
• Removing Duplicates• Joining Data• Data Quality• Clustering• Train & Predict • …and much more
• Do Not Pay• Medicare/Medicaid• SNAP• Unemployment
Insurance
Pre-built Fraud Scripts
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The ROI of Data Analytics
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Case Study: Medicaid Eligibility
The issue:
Is LDH’s current process of using wage data to determine the eligibility of Medicaid recipients sufficient?
The Analysis:
• Conducted a targeted selection of 100 individuals with the highest incomes and performed case reviews.
• Conducted a random sample of 100 recipients and performed case reviews.
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Case Study: Medicaid Eligibility
Background:• 2016 – Medicaid expansion
was implemented• LDH pays PMPM fee to
Managed care organizations
• LDH uses wage data to verify income of applicants & recipients
• LDH verifies income once a year
• LDH relies on recipients to self-report changes in their income
The issue:
Is LDH’s current process of using wage data to determine the eligibility of Medicaid recipients sufficient?
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Case Study: Medicaid Eligibility
The issue:
Is LDH’s current process of using wage data to determine the eligibility of Medicaid recipients sufficient?
The Analysis:• Conducted a targeted
selection of 100 individuals with the highest incomes and performed case reviews
• Conducted a random sample of 100 recipients and performed case reviews
Background:• 2016 – Medicaid
expansion was implemented
• LDG pays PMPM fee to Managed care organizations
• LDH uses wage data to verify income of applicants & recipients
• LDH verifies income once a year
• LDH relies on recipients to self-report changes in their income
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How was the sufficiency tested?
195,306
Single Person Household Medicaid
Recipients
Louisiana Workforce
Quarterly Wage Data
LouisianaOMV
ID & License Data
19,226
Recipients with average
quarterly income
exceeding 138% of FPL
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Targeted vs Random sample
93 of 100Recipients in the
targeted selection did not qualify for PMPMs paid on
their behalf.
93%
$538,795Of $812,662
PMPMs were paid on behalf of
recipients who did not qualify.
66.3%
82%82 of 100
Recipients in the random sample did
not qualify for PMPMs paid on
their behalf.
47.3%
$382,420Of $808,498
million PMPMs were paid on
behalf of recipients who did not qualify
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LDH may have paid between $61.5 and $85.4 million in PMPMs for recipients who did not qualify at some point during their Medicaid coverage
Cost Analysis
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Implemented quarterly wage verifications First quarterly wage verification resulted in the termination of
coverage for 30,051 recipients A LLA follow-up report found that the PMPMs paid on behalf of
these 30,051 recipients for the month of January 2019 totaled approximately $14.7 million
Response from LDH and LLA follow-up report:
The outcome
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Attendance Check
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Using Data Analytics to Efficiently Identify Possible Fraud, Waste & Abuse
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Using data to identify “red flags” and audit more efficiently and effectively
• Think of new ways to analyze your data
• Use data to audit more efficiently and effectively
• Identify new sources of data
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Case Study: New Horizon Counseling Agency, LLC
LLA issued multiple reports on behavioral health services identifying various issues within Louisiana’s Medicaid Behavioral Health Program.
These analyses were later combined to create a risk-matrix that was used to identify possible high-risk providers.
During previous audits we developed multiple analyses which identified “red flags” associated with the billing practices of Medicaid behavioral health providers.
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Case Study: New Horizon Counseling Agency, LLC
LLA issued multiple reports on behavioral health services identifying various issues within Louisiana’s Medicaid Behavioral Health Program.
These analyses were later combined to create a risk-matrix that was used to identify possible high-risk providers.
During previous audits we developed multiple analyses which identified “red flags” associated with the billing practices of Medicaid behavioral health providers.
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Case Study: New Horizon Counseling Agency, LLC
LLA issued multiple reports on
behavioral health services identifying
various issues within Louisiana’s
Medicaid Behavioral Health Program.
These analyses were later combined to
create a risk-matrix that was used to
identify possible high-risk providers.
During previous audits we developed multiple
analyses which identified “red flags” associated with the billing practices of
Medicaid behavioral health providers.
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Case Study: New Horizon Counseling Agency, LLC
• The risk-matrix identified New Horizon Counseling Agency, LLC (New Horizon) as having multiple “red flags”.
• At the time that we generated our risk-matrix, New Horizon was not being investigated by law enforcement, LDH or any of Louisiana’s MCOs.
• As a result we initiated and audit to evaluate New Horizon’s compliance with certain provisions of the Medicaid program.
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77,092 Medicaid
Claims
Totaling $8,083,709
85,346Progress
Notes
Added the time of day that each
service was provided
Case Study: New Horizon Counseling Agency, LLC• By joining Medicaid claims data, Progress Notes data and the Progress
Note data a second time, we identified $6,430 of services which appear to overlap with other one-on-one services provided during the same times.
85,346Progress
Notes
Added the time of day that each
service was provided
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77,092 Medicaid
Claims
Totaling $8,083,709
85,346Progress
Notes
Added the time of day that each
service was provided
Case Study: New Horizon Counseling Agency, LLC• By joining Medicaid claims data, Progress Notes data and Progress
Note creation time data, we identified $542,801 of services which appeared to have been improperly billed.
Creation date and
time for each of the
85,346Progress
Notes
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77,092 Medicaid
Claims
Totaling $8,083,709
85,346Progress
Notes
Added the time of day that each
service was provided
Facebook GPS Data
Bank Transaction
Data
Case Study: New Horizon Counseling Agency, LLC• By joining Medicaid claims data, Progress Notes data, GPS data from
Facebook and bank transaction data, we identified $30,862 of services that do not appear to have been provided due to the provider’s physical location when the services were allegedly provided.
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77,092 Medicaid
Claims
Totaling $8,083,709
85,346Progress
Notes
Added the time of day that each
service was provided
Louisiana Workforce
Commission Wage Data
Secondary Employer
Timesheet Data
Case Study: New Horizon Counseling Agency, LLC• By joining Medicaid claims data, Progress Notes data, Louisiana Workforce
Commission employer wage data and timesheets from individual providers’ secondary employers, we identified $172,717 of services which do not appear to have been provided due to the provider working at another job when the service was allegedly provided.
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New Horizon Employee
List & Payroll Data
Medicaid Recipient
List
Medicaid Claims Data
Louisiana Workforce
Commission Wage Data
Case Study: New Horizon Counseling Agency, LLC• By joining New Horizon’s Employee List and Payroll data, Medicaid Recipient
data, Medicaid Claims data and LWC Employer Wage data, we identified $114,089 of Medicaid premiums and services paid for health care services for the owner of New Horizon’s four dependent children. $49,125 of this amount was paid to New Horizon for services provided to the owner’s children.
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Data Issues Becoming a Project
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What to do when you discover data issues
Complete the original plan and write a
finding
Chase the issue, which can become another data
project
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Case Study: Department of Corrections offender tracking system
• Data fields missing
• Inconsistencies in dates, such as release date taking place prior to sentencing date • Offender locations were not always accurate
• Procedures for monitoring offender data entry were not sufficient to identify all errors
• Process for calculating release dates was inconsistent
• Former employees still had write access to the system
• DOC spent $3.6M on a new tracking system that failed due to inadequate planning and testing and was taken offline after less than two months
Follow-up audit identified
Data issues
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Lessons Learned
Fill in the gaps – ask others to share their data, use what you already have, communicate with co-workers
Fix fields so data can “communicate”
Build off old methodologies
Don’t be afraid to ask for the data
Think outside the box
Show something new from a different angle
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Q&AChristopher Magee: [email protected]
Brent McDougall: [email protected]
Galvanize: [email protected]