Reducing Waste, Fraud and Abuse Through Innovation: How AI & Data Can Improve Government Efficiency
Summary
The printed record of a hearing of the Joint Economic Committee of the Congress of the United States titled Reducing Waste, Fraud and Abuse Through Innovation: How AI & Data Can Improve Government Efficiency, on April 9, 2025, S. HRG. 119–79. Chairman David Schweikert presided, and the contents list an opening statement by Ranking Member Maggie Hassan, a Senator from New Hampshire. The witnesses were Dr. Sterling Thomas, Chief Scientist, Government Accountability Office; Dr. Brian J. Miller of the American Enterprise Institute; Neil Chilson of the Abundance Institute; and Andrew Cannarsa of the Council of the Inspectors General on Integrity and Efficiency. The submissions for the record include the prepared statements and questions for the record from Senator Amy Klobuchar to Dr. Thomas and Mr. Cannarsa with responses.
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“Reducing Waste, Fraud and Abuse Through Innovation: How AI & Data Can Improve Government Efficiency.”
Original at www.govinfo.gov. Archived at Pandemic Darlings, https://pandemicdarlings.com/source-documents/chrg-119jhrg60491-de42b33830/
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S. HRG. 119–79
REDUCING WASTE, FRAUD AND ABUSE THROUGH
INNOVATION: HOW AI & DATA CAN IMPROVE
GOVERNMENT EFFICIENCY
HEARING
BEFORE THE
JOINT ECONOMIC COMMITTEE
OF THE
CONGRESS OF THE UNITED STATES
ONE HUNDRED NINETEENTH CONGRESS
FIRST SESSION
APRIL 9, 2025
Printed for the use of the Joint Economic Committee
(
Available via the World Wide Web: http://www.govinfo.gov
U.S. GOVERNMENT PUBLISHING OFFICE
60–491 WASHINGTON : 2026
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JOINT ECONOMIC COMMITTEE
[Created pursuant to Sec. 5(a) of Public Law 304, 79th Congress]
HOUSE OF REPRESENTATIVES SENATE
DAVID SCHWEIKERT, Arizona, Chairman ERIC SCHMITT, Missouri, Vice Chairman
JODEY C. ARRINGTON, Texas TOM COTTON, Arkansas
RON ESTES, Kansas TED BUDD, North Carolina
LLOYD SMUCKER, Pennsylvania DAVID MCCORMICK, Pennsylvania
NICOLE MALLIOTAKIS, New York MARSHA BLACKBURN, Tennessee
VICTORIA SPARTZ, Indiana ASHLEY MOODY, Florida
DONALD S. BEYER JR., Virginia MARGARET WOOD HASSAN, New Hampshire,
GWEN MOORE, Wisconsin Ranking Member
SEAN CASTEN, Illinois AMY KLOBUCHAR, Minnesota
DAVE MIN, California MARTIN HEINRICH, New Mexico
MARK KELLY, Arizona
RON DONADO, Executive Director
LAURA EPSTEIN, Minority Staff Director
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CONTENTS
OPENING STATEMENT OF MEMBER
Page
Ranking Member Maggie Hassan, a Senator from New Hampshire .................. 2
WITNESSES
Dr. Sterling Thomas, Chief Scientist, Government Accountability Office,
Washington, DC ................................................................................................... 3
Dr. Brian J. Miller, Nonresident Fellow, American Enterprise Institute,
Washington, DC ................................................................................................... 5
Mr. Neil Chilson, Head of AI Policy, Abundance Institute, Washington, DC .... 6
Mr. Andrew Cannarsa, Executive Director, Council of the Inspectors General
on Integrity and Efficiency, Washington, DC .................................................... 8
SUBMISSIONS FOR THE RECORD
Statement of Ranking Member Senator Maggie Hassan ..................................... 31
Prepared statement of Dr. Sterling Thomas, Chief Scientist, Government
Accountability Office ............................................................................................ 33
Prepared statement of Dr. Brian J. Miller, Nonresident Fellow, American
Enterprise Institute ............................................................................................. 48
Prepared statement of Mr. Neil Chilson, Head of AI Policy, Abundance Insti-
tute ........................................................................................................................ 58
Prepared statement of Mr. Andrew Cannarsa, Executive Director, Council
of the Inspectors General on Integrity and Efficiency ...................................... 70
Questions for the Record submitted by Senator Amy Klobuchar to Dr. Sterling
Thomas and response .......................................................................................... 73
Question for the Record submitted by Senator Amy Klobuchar to Mr. Andrew
Cannarsa and response ....................................................................................... 75
Letter to the Honorable Russell Vought, OMB Director, submitted by Rep-
resentative Donald S. Beyer Jr. .......................................................................... 77
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REDUCING WASTE, FRAUD AND ABUSE
THROUGH INNOVATION: HOW AI & DATA
CAN IMPROVE GOVERNMENT EFFICIENCY
WEDNESDAY, APRIL 9, 2025
UNITED STATES CONGRESS,
JOINT ECONOMIC COMMITTEE,
Washington, DC.
The committee met, pursuant to call, at 2:33 p.m., in Room 210,
Cannon House Office Building, Hon. David Schweikert [chairman
of the committee] presiding.
Present: Representatives Schweikert, Beyer, Min, Casten,
Malliotakis, Estes, and Spartz.
Senators: Schmitt and Hassan.
Staff Present: Ron Donado, Colleen J. Healy, Alexander
Schunk, Matthew Cernicky, Jaxson Dealy, Rohan Kumar, Garrett
Wilbanks, Jeremy Johnson, Oliver Sibal, Laura Epstein, Sebi
Devlin-Foltz, and Tamara Fucile.
Chairman Schweikert. First off, I want to thank all of you. Ap-
parently, you have all done just a terrific job working with our dif-
ferent staffs in educating us. You’ve seen our idiosyncrasies of
wanting you to fixate on the things where we can actually make
some policy changes that actually make a difference.
So for 6-plus years, because of the quirkiness of our office, we
have been introducing AI bills audit the Pentagon, using tech-
nology, using a crawler to find out where assets are. Healthcare,
if over the next decade interests in healthcare are the primary
drivers of U.S. sovereign debt. Could we actually still protect the
quality of healthcare, maybe even make it better, with using tech-
nology to deal with those cost drivers. And I know sometimes that
makes people very uncomfortable, but there was also a time that
going to Block Buster Video was the family ritual on Fridays and
today you hit a button.
So now I would like to introduce our distinguished witnesses.
You all get to be distinguished. Dr. Sterling Thomas is the chief
scientist at the Government Accountability Office. He leads the
work conducted by GAO’s scientist—Science, Technology Assess-
ment and Analytics team assessment and analytics team, which
provides information to Congress on emerging technologies and
their application in government.
Prior to his appointment at the GAO, Dr. Thomas served as chief
scientist at Noblis.
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Dr. Thomas. Noblis.
(1)
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Chairman Schweikert. Noblis. A nonprofit research institute
where he applied machine learning in artificial intelligence to his
research on synthetic biology. Okay, you already realize you just,
like, doubled the IQ of—never mind.
Dr. Brian Miller is a practicing hospitalist and professor of medi-
cine and business at Johns Hopkins and somewhat of a friend. Dr.
Miller is also a nonresident fellow at the American Enterprise In-
stitute where he researches and focuses on healthcare competition,
FDA policy, public health and integration of AI into the healthcare
system. The doctor is head of AI policy at the Abundance Institute.
Mr. Chilson. That is me.
Chairman Schweikert. Oh, sorry, sorry. Did I just—forgive me.
Is it called the Abundance Institute?
Mr. Chilson. It is, it is.
Chairman Schweikert. His research focuses on how artificial
intelligence can be used to maximize innovation and advance
human interest. Previously, he was the chief technologist at the
Federal Trade Commission, where his worked focused on the eco-
nomics of data privacy and blockchain. We should talk. I worked
on blockchain before coming to this.
Now I would like to turn over to Ranking Member Hassan to in-
troduce the Democrat witness.
Ranking Member Hassan. Well, thank you so much, Mr.
Chairman. And after I introduce the witness, I have a couple of
opening remarks, if that makes sense.
Chairman Schweikert. Why don’t you go straight to opening
remarks because you may get stressed with having the vote on.
Ranking Member Hassan. Okay. And then I will introduce Mr.
Cannarsa.
So I just really want to thank you for calling today’s hearing on
a really important topic. And to your point, a topic that has a lot
of bipartisan interest and a lot of bipartisan potential.
As this is the first JEC hearing this Congress, I also want to say
how much I look forward to working with you, Mr. Chairman, in
your role as chair of the committee and my new role as ranking
member. I really look forward to working together, and I have ap-
preciated our conversations so far.
And I am glad we are starting out with a topic that we can find
some real common ground on: cutting waste, fraud, and abuse
through the use of innovation.
I also want to thank the four witnesses for testifying before the
committee and sharing your expertise on this topic. We need to
keep working together across the aisle to save taxpayer dollars by
reducing waste, fraud, and abuse.
As we will discuss in today’s hearing, there are many ways to do
so by embracing new technologies and better data analytics. These
types of tools can help an investigator identify potential fraud or
a caseworker streamline an application process. I know that all
four of our witnesses today will outline ways that the government
can better deploy. I really look forward to building off these ideas
moving forward.
Before we get to the introduction of our witness, so I do want to
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just note my serious concerns with the reckless actions so far of
Elon Musk and DOGE. Whether the government is providing
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healthcare for veterans, Social Security Administration checks for
seniors or loans for small businesses, we should continually strive
to improve the taxpayers experience. But Mr. Musk’s slash and
burned tactics are doing the opposite.
In addition, I want to bring the Committee’s attention to the fact
that the administration’s decision to fire 17 inspectors general who
investigate fraud, that is what they do, is completely counter-
productive to the goal of improving government efficiency.
I firmly believe that we should be cutting waste, fraud, and
abuse, and that we can do so without making life harder for chil-
dren, families and seniors all across our country. I hope the con-
versations, including the one we will have this afternoon, can help
us chart a better path forward.
So again, I thank you Mr. Chairman and to our witnesses for
agreeing to testify today. And I happy to proceed with the introduc-
tion.
Mr. Andrew Cannarsa is the executive director of the Council of
the Inspectors General on Integrity and Efficiency. That is an inde-
pendent part of the executive branch focused on supporting the
work of inspectors general.
Prior to his current role, Mr. Cannarsa served in the Social Secu-
rity Administration, Office of Inspector General from 2009 to 2023.
Welcome Mr. Cannarsa. I really look forward to your testimony, as
I do the testimony of all our witnesses.
[The statement of Ranking Member Hassan appears in the Sub-
missions for the Record.]
Chairman Schweikert. Thank you, Senator. And please don’t
be discombobulated if you see members popping up and popping
down. It is the nature of being here during a fairly chaotic time.
Dr. Thomas, please 5 minutes. Educate us.
STATEMENT OF DR. STERLING THOMAS, CHIEF SCIENTIST,
GOVERNMENT ACCOUNTABILITY OFFICE, WASHINGTON, DC.
Dr. Thomas. All right. Thank you, Chairman Schweikert, Rank-
ing Member Hassan, and members of the committee. Thank you for
inviting me to participate today, in today’s hearing to discuss how
AI can be used to reduce fraud and improper—waste, fraud and im-
proper payments.
As GAO’s chief scientist and throughout my career in industry in
academia and in the intelligence community, I have seen great ad-
vancements in data science. These innovations offer exciting oppor-
tunities to improve government efficiency. As you know, GAO is a
nonpartisan watchdog for Congress. We have expanded our science
technology team in recent years and it includes a team of data sci-
entists. We know firsthand from their work that AI holds great
promise in furthering GAO’s mission and your goal of safeguarding
taxpayers dollars.
My aim today is to offer three important actions that will help
us reach that goal. First, we must continue and augment our tradi-
tional antifraud efforts. Second, we must apply AI thoughtfully and
ensure that we use quality data to mitigate its well-known risks.
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And third, we must ensure the Federal workforce has the skills
they need to apply new innovations like AI.
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Regarding my first point, GAO has a large body of work on fraud
and improper payments in the Federal Government. Last month
one of my colleagues testified before House Appropriations on this
topic. The Federal Government reported an estimated $162 billion
in payment errors or improper payments during fiscal year 2024.
And that is almost certainly an underestimate.
We have outlined numerous ways that Congress and Federal
agencies can tackle this problem with existing capabilities. For ex-
ample, Congress could make permanent the Social Security Admin-
istration’s authority to share its death list with Treasury’s do not
pay system. These traditional methods are routinely preventing
and detecting fraud. But enhancing them, we can save taxpayer
dollars today without any new technology.
But on my second point, I am optimistic about innovation, but we
must be thoughtful about it. In data science we often say garbage
in, garbage out. Nowhere is that more true than with AI and ma-
chine learning. If we start trying to identify fraud improper pay-
ments with so-so data we are going to get so-so results.
Broad use AI is still in early stages of development and imple-
mentation. And rapid deployment without thoughtful design has
lead already to unintended consequences. Before pouring data
science on a problem we need solid reliable ground truth data and
a human in the loop to ensure data reliability and application of
this technology. GAO has an accountability framework which lays
out these and other principles.
One piece of advice that emerges from such principles is to find
a solution that produces the desired result with the least com-
plexity. For example, in response to our recommendation the Small
Business Administration screened all PPP or paycheck protection
program loans made before September 2020 with a rules-based
tool. It was just looking for indicators, it wasn’t using AI and they
still identified $4.7 billion in loans that went to ineligible recipients
or were used for unauthorized purposes. And those loans were not
forgiven. To build on that success we need more innovation in gov-
ernment. In 2022, we recommended that one way to do that, Con-
gress could establish a permanent analytics center of excellence to
unlock tools that more efficiently identify and prevent fraud and
improper payments.
My final point is that harnessing innovation also requires a Fed-
eral workforce that has the right skills. But agencies continue to
face barriers in hiring, managing and retaining staff with advanced
technical skills. This is another area where GAO has made rec-
ommendations. And we have explored innovative big picture ideas
like establishing a digital services academy.
Rapid advances in AI capabilities hold great promise. GAO be-
lieves that the Federal Government can and must simultaneously
realize those opportunities and do so upon a firm foundation of reli-
able data and a digitally skilled workforce.
Chairman Schweikert and Ranking Member Hassan and mem-
bers of the committee, thank you. And I would be happy to respond
to any questions.
[The statement of Dr. Thomas appears in the Submissions for the
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Record.]
Chairman Schweikert. Thank you, Dr. Thomas.
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Dr. Miller.
STATEMENT OF DR. BRIAN J. MILLER, NONRESIDENT FELLOW
AMERICAN ENTERPRISE INSTITUTE, WASHINGTON DC.
Dr. Miller. Chairman Schweikert, vice chairman Schmitt and
distinguished members of the committee, thank you for allowing
me to join you and share my views on how AI and technology can
improve government efficiency and reduce fraud, waste and abuse.
I am going to focus on operational solutions targeting the Medi-
care and Medicaid programs recognizing that these comprise over
$1.5 trillion in annual spending. I should note that today I’m here
in my personal capacity and the views expressed are my own and
don’t necessarily reflect those of Johns Hopkins University, the
American Enterprise Institute, the Medicare Payment Advisory
Commission or the North Carolina State Health Plan.
That aside, ethos of DOGE asking questions, re-engineering proc-
esses, these are things that we need to do indefinitely in govern-
ment health programs. So there are these areas that I think we
should focus on. The first is Medicaid improper payments.
In 2020, we had the global pandemic. Tens of thousands of peo-
ple were dying everyday and Congress implemented continuous
Medicaid coverage thinking that, you know, if we are going to po-
tentially have COVID, hospitalization coverage shouldn’t be the
issue.
In 2023, States started redetermination for 20 million bene-
ficiaries. CMS noted in 2022 that 76 percent of improper payments
were due to eligibility. That is $61 billion a year. Now I looked this
up and Medicaid eligibility requirements are codified in statute
with little discretion. So if you think about it, redetermination and
then on the other side initial eligibility assessments are two sides
of the same process. We should actually look to automate benefits
eligibility. We might have to have human review first, followed by
an auditing period and then it would move towards fully autono-
mous review. With the simple principle being if you are entitled to
Medicaid benefits, you should be able to get them. And if you are
not entitled to Medicaid benefits, you should not be on Medicaid.
The second area I would focus on is Medicare Advantage diag-
nosis coding, which I think we have all seen the Wall Street Jour-
nal articles and the large plethora of news about this marketplace.
So if you think about it and fee-for-service Medicare it is like run-
ning a pizza shop. You are paid if you make more pizzas. If you
make a slightly fancier pizza, you are also paid a little bit more.
In Medicare Advantage, it’s the difference. You get risk-adjusted
capitated payment or population based payment with risk adjust-
ment from diagnosis coding. That way if plans take care of sick
people, they get more, if they take care of healthier people, they get
paid less. There is no surprise that across these two programs the
coding intensity is different. Coding intensity has three compo-
nents. One fraudulent coding, the DOJ picks you up in a black car
and takes you to jail. Two is upcoding, subject to debate but likely
known. The third is sort of clinically appropriate coding intensity,
better communication about diagnostic specificity or severity. So we
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have sort of studied this problem for 2 decades. You can pick your
favorite health care analyst, economist, actuary, committee or com-
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mission and you can get different numbers. No one has measured
those three components. The research has shown that it varies
across plans. I would argue that if we have predictive typing and
auto correct on our iPhones, we can automate diagnosis coding
across the Medicare program. Do we want doctors spending time
with patients or do we want them doing diagnosis coding for
whether you have CKD, chronic kidney disease 3 or 3b. I think we
can all agree that we want doctors taking care of patients. And so
if we automate that, we will eliminate coding disparities across
both programs, increase trust, eliminate the need for chart reviews
and health risk assessments and eliminate the wastage of both
physician times and government payments. And so I would say
that for Medicare Advantage, we should focus on solving problems
for Americans, instead of measuring them.
And then the last area I want to talk about is prior authoriza-
tions. We spend an estimated $265 billion in the healthcare indus-
try on administrative costs. Depending upon who you ask, prior au-
thorization can be an appropriate tool to steer care or it can be an
inappropriate tool to delay care, increase cost, decrease access. And
you will have to sit around, you will have stakeholders on either
side showing up and telling you one thing or the over.
We have also talked about this for probably longer than I have
been alive. So I would say that we should start with process re-
form, fix the process. So let’s put the fax machine lobby out of their
misery, same for, you know, PDF submission, et cetera. We should
use CMS and ONC to drive automation of data submission and
have it be built directly into electronic health records. So I think
we have a lot of opportunity to are practically make government
more efficient, reduce waste and also make the delivery system bet-
ter.
And thank you for the time.
[The statement of Dr. Miller appears in the Submissions for the
Record.]
Chairman Schweikert. Thank you. Mr. Chilson.
STATEMENT OF MR. NEIL CHILSON, HEAD OF AI POLICY,
ABUNDANCE INSTITUTE, WASHINGTON, DC.
Mr. Chilson. Chairman Schweikert, distinguished members of
the Joint Economic Committee, thanks for the opportunity to be
here to discuss how artificial intelligence can transform govern-
ment efficiency.
America leads the world in advanced AI technologies. Private
companies are rapidly deploying these tools to boost productivity
and eliminate waste. But our government is only beginning to tap
their potential.
Of the 2,100 AI use cases identified in January of this year
across 41 U.S. Federal agencies, I could only identify 17 operational
systems that directly target fraud, waste and abuse. And most of
these are on a small scale. This represents both a challenge and
an extraordinary opportunity. The Trump administration has rec-
ognized this. Just last week the Office of Management and Budget
issued two detailed memos that highlight these benefits and set
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forth the path both for Federal Government use and the procure-
ment of AI technologies.
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Consider the staggering cost of fraud. We have already heard
some of these numbers. You know, the Federal agencies reported
$162 billion in improper payments last year alone across 68 pro-
grams, with Medicare and Medicaid accounting for $85 billion of
that. And GAO has estimated in the past that an annual fraud loss
of potentially as high as $521 billion. That is potentially half a tril-
lion dollars of taxpayer money each year that never reach its in-
tended purpose.
AI technologies offers some powerful solutions. We hear a lot
about large language models and chat bots. But I will talk about
a few specific tools that I think have great application here and
then two specific areas in which two broad areas of government in
which they have some applicability. Again, the point here is that
these are well deployed in the private sector and could be used in
the government.
So anomaly detection is the technology that allows you to quickly
spot unusual activities by learning what constitute normal behav-
ior and then identifying abnormal behavior. Financial institutions
like HSBC have found that this approach identifies two to four
times more financial crime with 60 percent fewer false positives.
Natural language processing, of course LLMs are one type of appli-
cation, can digest mountains of text documents, emails and support
tickets far faster than humans. This technology can streamline
compliance, enhance customer service and an reveal fraud that is
hidden in mass amounts of text. Robotic process automation use
software bots to handle routine tasks. It often is used to connect
legacy systems of which obviously government institutions have
many. And businesses implement this in average efficiency gain of
up to 30 percent while reducing processing errors by 20 to 40 per-
cent.
And then finally the last category of technology, although there
is many others, graph analytics map relationships that entities
have across a bunch of different systems. And so insurers right
now use graph analytics to identify fraud rings and coordinated
abuse that you could not find if you were only looking a single sys-
tem at a time.
So these technologies offer tremendous potential across govern-
ment, but let me give you two critical areas where I think they can
offer some real specific benefits. So in benefits administration, obvi-
ously I mentioned Medicare Medicaid, but many other areas of ben-
efit. AI can swiftly identify fraudulent claims, while speeding serv-
ice to legitimate beneficiaries. They can verify documentation, auto-
mate eligibility verification and help agencies forecast their needs
to optimize resource allocation.
And then in procurement and contracting these tools can flag
high-risk vendors and contracts before the money goes out the door.
They can detect vendor collusion. And they can automate repetitive
procurement tasks, streamlining these functions both for the con-
tractors and the government agencies that rely on them.
Now the path forward here is going to require updating some leg-
acy systems, safeguarding data privacy and addressing some im-
portant policy considerations. But the potential benefits are pro-
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found. And that is why when I look at AI’s capabilities in this
space, I don’t just see a futuristic vision. I see there is practical
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proven technologies that are already transforming private industry
that can immediately strengthen government operations.
Implementing these solutions means ensuring public funds reach
their intended purposes. It means faster, more transparent services
for citizens. And it means substantial savings that could be redi-
rected to higher priority needs or returns to taxpayers.
The question before us is not whether AI can improve govern-
ment efficiency. The evidence from the private sector is over-
whelming that it can. The question is whether we have the vision
and the commitment to embrace these innovations for the benefit
of all Americans.
Thank you. And I look forward to your questions.
[The statement of Mr. Chilson appears in the Submissions for
the Record.]
STATEMENT OF MR. ANDREW CANNARSA, EXECUTIVE DIREC-
TOR, COUNSEL OF THE INSPECTOR GENERAL ON INTEGRITY
AND EFFICIENCY, WASHINGTON, DC.
Mr. Cannarsa. Good afternoon, Chairman Schweikert, Ranking
Member Hassan, and members of the committee. Thank you for in-
viting me to discuss how technology can improve government effi-
ciency and reduce waste, fraud, and abuse.
I serve as the executive director for the Council of the Inspectors
General on Integrity and Efficiency, or CIGIE, a membership orga-
nization for more than 70 Federal IGs. The IG community has a
nearly 50-year history of conducting effective oversight and detect-
ing and preventing waste, fraud, and abuse. The Inspector General
Act of 1978 established IGs and empowered them to provide inde-
pendent, objective, and impartial oversight of agency programs and
operations. IGs have a dual reporting responsibility, reporting to
both their agency head and to Congress. We have a longstanding
relationship with Congress and a shared goal of ensuring that tax-
payers dollars are spent wisely and effectively and that government
benefits are administered correctly. IG findings are based on facts
and applicable law and guided by OIG standards. Trained auditors,
investigators, inspectors, evaluators, IT specialists, and attorneys
work every day to promote efficient and effective government oper-
ations.
In fiscal year 2024, the IG community collectively totaled over
$71 billion in audit and investigative accomplishments, generating
a return of about $18 for every $1 invested in OIGs.
For the government to see the full value of this oversight, agen-
cies must take action to resolve OIG recommendations. There are
currently over 14,000 unimplemented OIG recommendations, many
of them related to improper payments. If implemented, such ac-
tions would improve payment integrity across dozens of Federal
programs. For example, the Department of Labor OIG identified
over $45 billion in potentially fraudulent pandemic unemployment
benefits, and the OIG has engaged the department in resolving
these high-risk payments. However, there are still outstanding rec-
ommendations in this area.
While all fraud is in improper payment, not all improper pay-
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ments are fraudulent. Other common causes include administrative
mistakes, recipient self-reporting issues, and policy limitations. For
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instance, the Social Security Administration OIG estimated over $7
billion in supplemental security income errors because SSA did not
follow policies or use all available tools to prevent, detect, and re-
cover overpayments. SSA OIG has made several recommendations
to reduce and collect the overpayments, but the recommendations
have not yet been resolved.
The use of technology is critical to the IG community’s oversight
work, and IGs have significant experience leveraging data analytics
and artificial intelligence. While these tools do not replace inves-
tigative or audit work, they enhance IGs’ ability to identify anoma-
lies for further review.
Last year the Treasury Inspector General for Tax Administration
leveraged data analytics to help the IRS prevent $3.5 billion in po-
tentially improper Employee Retention Credits and Sick and Fam-
ily Leave credits. TIGTA notified the IRS of the scheme, who then
put controls in place to guard against similar fraudulent claims.
The Postal Service OIG uses data driven insights to analyze mil-
lions of customer complaints to recognize trends and identify poten-
tial mail delivery or theft issues. And the Office of Personnel Man-
agement OIG is using machine learning to inform which health
plans the OIG should audit in the Federal Employee Health Bene-
fits Program.
To continue our effective oversight efforts and improve payment
integrity, sustained access to agency data and analytics resources
are critical. The IG community’s top legislative priority is the es-
tablishment of a permanent, scalable data analytics platform to aid
OIGs in detecting and preventing fraud and improper payments.
In 2021, Congress provided CIGIEs Pandemic Response Account-
ability Committee or the PRAC with the authority and funding to
create a data analytics center. The result was the Pandemic Ana-
lytics Center for Excellence or the PACE. The PACE has proven to
be successful in identifying improper payments and fraud in pan-
demic programs. In one recent case, the PACE helped an OIG to
develop risk models to review some 2,400 pension plans which led
to the recovery of over $165 million. This recovery for taxpayers far
exceeds the $120 million that Congress appropriated for the PRAC
to operate for the last 5 years. However, with the PRAC scheduled
to sunset on September 30th, 2025, the IG community urges Con-
gress to sustain the PRAC and the PACE’s data analytics capabili-
ties.
Last month the House Committee on Oversight and Government
Reform advanced legislation to sustain the PRAC and its capabili-
ties, which we support. We look forward to working with Congress
on this important reform to ensure the IG community is equipped
with advanced technology and data analytics tools.
In conclusion, the Federal IG community remains committed to
conducting Federal program oversight, making recommendations
that can improve government efficiency, and leveraging analytics
and AI supported tools in its efforts.
Thank you again for inviting me to testify. And I am happy to
answer any questions.
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[The statement of Mr. Cannarsa appears in the Submissions for
the Record.]
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10
Chairman Schweikert. Thank you for all of you. Because I
have hours of questions, I am going to go at the very end.
Senator Schmitt, you get to start your questions.
Vice Chairman Schmitt. Thank you all for being here. Thank
you, Mr. Chairman, for holding this hearing.
I think the recent—some of the recent findings from DOGE I
think put this even more up front in the public consciousness and
there is a lot of great work that has happened before. And I am
not going to go through. I was going to talk about, you know, the
money that’s sent overseas for Guatemalan sex changes and DEI
in Burma, but I have done floor speeches about that. I really want
to use the time that I have with asking you some questions that
I think—I can’t imagine there is a ton of disagreement as far as
trying to understand how we are at where we are at.
So Dr. Miller you brought this up, in the healthcare space, I
think one of the great frustrations for policymakers is that the
enormity of some of the problems that exist are so huge that some-
times I think that inhibits peoples’ ability to tackle them effec-
tively. What do you think? So you have had some great rec-
ommendations about some things that could happen and whether
it is coding or finding fraud on the front end or the back end in
a much more meaningful way. It feels like, as you said, you have
been having this discussion longer than being alive. I can relate to
that. What is missing? Like, we know what the problem is, rec-
ommendations are made and I want to ask you this question too,
what is the disconnect? Like, how are the controls not actually put
in place? How does this keep happening? Why isn’t—and DOGE—
I am going to stop talking and let you answer the question.
Dr. Miller. So two reasons. One is whether it is fraud or an im-
proper payment that is someone’s revenue and that person who has
revenue doesn’t like when you take their revenue away. So that is
a political issue. After that, there is the operational issue, which
is often where we measure the problem, we issue a nice report,
write a nice paper and then we don’t get the agency wheels going
to address it. So we need to have oversight, public attention and
energy directed at getting the agencies to execute an and oper-
ational plan. If they don’t have an operational plan, have an oper-
ational plan. For example, we know that there is often fraud in
procedures. And if her procedure takes 2 hours and bill 20 of them
in a day, that is clearly not possible.
We should have prepayment claims editing in Medicare and
Medicaid to prevent that from happening. We don’t. So I would say
aggressive and assertive oversight over agencies for what are the
operational sets that they are going to do. And then continued
oversight to make sure that they execute on those steps.
Vice Chairman Schmitt. Because it is subject to the hearing,
are AI tools being used currently? Outside of DOGE, right, because
they are looking under the hood, are those sort of tools being used
effectively currently in these agencies?
Dr. Miller. I would say no.
Vice Chairman Schmitt. All right. That is I think the right an-
swer.
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I want to ask you, so you have also talked instances where rec-
ommendations were made. In the PACE sort of oversight, you
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11
talked about how that was effective. Maybe that is the model, I
don’t know exactly. But what is the disconnect? Is it that Congress
isn’t calling these people in enough, is it that if it’s not, you know,
there is not a point person within the agencies that are actually
responsible for this? How would you diagnose the problem to be
able to fix?
Mr. Cannarsa. I think Dr. Miller said it well that there is a
plethora of information out there, there are recommendations that
agencies can implement. And where Congress can help is increas-
ing transparency and visibility of those recommendations and talk-
ing directly to the agency. What is prohibiting you from imple-
menting it? Is it a resource issue? Is it a time issue? Is it a need
to continue customer service and it is just trying to implement
these recommendations that is holding us back? I think the infor-
mation is there. It is understanding what is holding the agency
back.
Vice Chairman Schmitt. Are there any other—I mean, obvi-
ously there is the power of the purse and there is the appropria-
tions process—are there any other as you guys have found, any of
you, are there any other hammers that are out there that would
actually hold some of these folks, their feet to the fire to actually
implement these things?
Mr. Chilson. I might flag something quickly from the OMB
guidance that just came out last week. One of the things that they
recommend on AI implementation within agencies is that the re-
sponsibility for addressing and owning the risk be on the person
who is responsible for the mission of the process that AI is being
deployed for. And so that type of personal accountability where
there has to be somebody actually signing off and owning the deci-
sion I think is the sort of thing that keeps new technologies that
are coming in under the right amount of scrutiny and analysis.
And so I don’t know how you retrofit that, but for new technologies,
I think that is a good approach.
Vice Chairman Schmitt. Thank you. Thank you, Mr. Chair-
man.
Chairman Schweikert. Thank you. Senator.
Ranking Member Hassan. Well, thank you Mr. Chairman. And
again, thank you all for your testimony. I caught most of Mr.
Cannarsa’s and I am sorry I missed yours. But I want to start with
a few questions.
Mr. Cannarsa, inspectors general play a key role in identifying
and routing out fraud across the Federal Government. As part of
their efforts, they often encounter barriers when they try to work
together across agencies and share data that could prevent fraud.
For instance, an inspector general in one agency might not have
the staff or time needed to access data on fraud that is stored at
multiple other agencies. I know that your organization wants to es-
tablish some centralized data analytics capacity that the investiga-
tors could use to further detect and prevent fraud across the gov-
ernment. So this is a two-part question. One, what are some of the
most promising examples of how inspectors general are already
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sharing data to prevent fraud, such as by working together to stop
criminals who have multiple shell companies that have been de-
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12
frauding our small business loan programs? Why don’t we start
with that?
Mr. Cannarsa. Sure. Thank you for the question. And Senator
Hassan, I wanted to thank you for working with Senator Joni
Ernst on the IG Senate Caucus. We appreciate your efforts and
look forward to working with you both on that.
To the question, several examples going back to the start of the
pandemic and I want to give credit to the Small Business Adminis-
tration OIG and Department of Labor OIG who had oversight over
the PPP and the EIDL programs and the pandemic unemployment
insurance program, which obviously there was a need for those
funds to go out at the time. But it left it ripe for fraud. And where
the PRAC came in and helped was to identify those—a lot of the
suspicious claims that were clustered and which led to in those
cases groups that were putting in multiple claims, you know, be-
cause they saw some go through so they kept going for them.
Ranking Member Hassan. Right.
Mr. Cannarsa. In both instances. Both SBA, OIG and DOL, OIG
have successful fraud investigations to the tune of millions of dol-
lars that were found and prosecutions continued from.
Ranking Member Hassan. Great. And what can Congress do
to make sure that that sort of cooperation gets easier while also
protecting people’s privacy?
Mr. Cannarsa. I think, as I mentioned, expanding and enhanc-
ing the potential of the PRAC. If we can do what they have shown
in just the pandemic realm programs, if we can do that across Fed-
eral programs, make it easier for—as you mentioned, for agencies
and their OIGs to match in a central location again with all privacy
protections and considerations in place, that just makes it easier to
go to one stop instead of each agency trying to make these agree-
ments one off.
Ranking Member Hassan. And in my experience as a gov-
ernor, one of the hardest things is finding actual staff and re-
sources to focus on this, so that if we can share in the effort, it
makes a big difference.
Dr. Thomas, your position as the chief scientist of the Govern-
ment Accountability Office gives you a good vantage point to see
how the Federal Government deploys technology, especially when
it comes to strengthening government effectiveness and saving tax-
payer dollars. What are areas of the government that have the
highest potential for reducing waste, fraud, and abuse through the
deployment of cutting edge technology, including AI?
Dr. Thomas. Yeah, so if you look at our government wide fraud
report that we just put out, we talk about the different programs—
there is a range that goes with it, so I think earlier it was men-
tioned half a trillion dollars. That is the high range, those are for
high risk programs. You know, think about, like, COVID-style pro-
grams. The lower range is really analyzing the lower risk pro-
grams.
We did a modeling exercise using Monte Carlo to understand
what that range looks like. Essentially what it comes down to addi-
tional transparency, obviously within the payment systems and
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making sure that the agencies are using analytics and sharing the
use of that analytics, as well as what has already been talked
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13
about earlier that we support is setting up a center of excellence
that supports the fraud world so that across the government all of
these agencies can work together, can share data that matters and
share tools that identify those flags of potential fraud.
The important part, though, that I want to make sure that we
don’t miss is that the analysts play a critical role.
Ranking Member Hassan. Yes.
Dr. Thomas. The analytics only identifies a flag, that something
might be associated with fraud. It is the analysts themselves that
actually goes and does the investigation.
Ranking Member Hassan. Right, that is very helpful.
I have lots more questions too, but why don’t I yield back and
then we can keep going.
Representative Malliotakis. Thank you, Mr. Chairman. I want
to thank you all for being here today. I wanted to point out that
in my district we are experiencing a real problem with Treasury
checks being stolen. And obviously, this is not something that is
unique to my district, although it has been heavily impacted. As
you have mentioned, the GAO estimates a total of $162 billion in
improper payments in fiscal year 2024 and more than $2.8 trillion
since 2003. So you have the issue of improper payments and then
you have those of the fraudsters and the folks that are actually
physically stealing these checks, washing them, reissuing them and
cashing them. And in my district alone, we have had roughly 400
constituent cases of stolen Federal checks that total $5.4 million.
This is a big issue, right? If we multiply that by all the members
and the senators in this body, you are talking about a substantial
amount of money. And I would love to hear from any of you on
what you think could be a viable solution. Obviously moving people
to direct deposit is one thing. We are looking at the President did
issue an executive order. We recently passed legislation out of the
House to require IRS to reissue payments if the consumer wants
or the taxpayer wants via direct deposit, maybe tracking checks so
we know what point these checks are being stolen. And then AI,
as you are discussing today, can come in handy as well. So we
would love to hear from any of you on any ideas or solutions you
may have or what you have work potentially in the private sector
as it relates to financial crimes that could be applied here.
Dr. Thomas. So I don’t have an answer for the direct question
of what did you do about washing checks, Congressman, but I ap-
preciate the question, you know.
Within how do you—so we are talking about really fraud, be-
cause fraud is where you get into the point if someone’s purposely
trying to defraud the government, which is what this is about. Ana-
lytics certainly and AI could certainly help this by tracking the ac-
tual payment beyond just the check, making sure that the indi-
vidual is actually receiving it, providing some sort of methodology
to check back, those are the types of things that the private sector
uses that can then be repurposed potentially for the government.
It is not an area that we have looked into, though, at GAO.
Representative Malliotakis. Mr. Chilson.
Mr. Chilson. So obviously taking the middleman—I mean, obvi-
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ously taking the middleman out which is the mailbox, I guess and
doing direct deposit makes a tons of sense. It is not really an AI
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14
approach. It is obviously an established technology, but there you
are relying on the commercial sector which does have a lot of capa-
bilities to identify, you know, when electronic payments are work-
ing and when they are not. Electronic payments are not fraud free,
but the companies have spent a lot of money to identify how to
make sure that money moves properly between accounts. And so I
do think that the more you can move people to that type of ap-
proach better it will be.
Representative Malliotakis. Dr. Thomas, in your testimony
you highlighted a need to extend the Social Security Administra-
tion’s authority to share its full debt data master file with the De-
partment of the Treasury, which is set to expire next year. And I
would like to know your thoughts on how much this has recovered,
how successful it has been. It is my understanding it is about $31
million in payments. Can you highlight any other multi-agency
data sharing that needs to take place?
Dr. Thomas. I would love to, but I want to make sure I give you
exact information so if you don’t mind, I will get back to you on
that.
Representative Malliotakis. Okay, no problem. And then one
more question for you, what is the most significant barrier stopping
the Federal Government from implementing these advanced AI
technologies to eliminate waste, fraud, and abuse?
Dr. Thomas. I think there is two areas that I want to particu-
larly highlight. And, you know, this is one of the biggest questions
out there. And, you know, the first one I think we have already
talked about quite a bit which is just the partitioning of data, try-
ing to make sure the data is available to the antifraud community
so that we can track people across the different parts of the govern-
ment and track the information so that it can actually be used for
large types of analytical models.
The second is actually just as important and probably needs to
happen before the first, which is a foundational data that is vali-
dated, that shows these are actual indicators of fraud for improper
payments. We can’t get into a habit of marking something that is
not fraud and making a guess at it. We do actually need to go
through the entire process with the IGs to make sure that some-
thing has actually been validated and put that into kind of a gold
standard data set. That is going to produce the best outcome for
our analyticals.
Representative Malliotakis. Thank you.
Chairman Schweikert. Mr. Beyer.
Representative Beyer. Mr. Chairman, thank you very much.
And Senator Hassan, thanks for holding this together. And thank
you all you very much for being here. I really, really appreciate
this.
And so many questions I want to ask. Let me just start with, you
know, we have watched Elon Musk and his DOGE team recently
go after waste, fraud, and abuse. And you have laid out the case
very strongly for why that is necessary. There are many of hun-
dreds of millions, of billions of dollars out there. Let me just point
out, though, most of this seems to be improper payments or folks
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stealing checks. I am not quite sure the connection between the
huge amount of waste, fraud and abuse you have identified and fir-
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15
ing half the people at the Department of Education, laying off
20,000 people at HHS, getting rid of the U.S. Institute of Peace, fir-
ing all probationary employees, none of that seems to address the
waste, fraud and abuse that you are trying to find very construc-
tive honest ways to deal with. And I still remember fondly, I just
figured that was 50 years ago that the little family business where
we were doing all our records by hand with a pencil and a 10-key
adder. On December 1st, 1975 I moved it to the internet. It was
a keyboard the size of a electric thing online so it took 5 minutes
between entries and you pushed the key. However, it made possible
a degree of productivity increase that is just unprecedented includ-
ing much, much waste, fraud and abuse. So I just let chairman
Schweikert and I share the vision of what this could do to Federal
Government and to Medicare and to Medicaid, to be able to bring
that technology here.
In the meantime, I am very concerned about DOGE, which I
want to succeed, but the use of underproved technologies without
sufficient oversight, the use in high impact areas, like Federal em-
ployees employment status and the lack of basic security for non-
government data sets. Instead profit sharing off of sharing data
with commercial models. I have prepared a letter with many of my
Democratic colleagues, which we are going to send to OMB. And
Mr. Chair, I would love to submit that letter to the record.
Chairman Schweikert. So ordered.
[The letter from Representative Don Beyer to OMB Director Rus-
sell Vought appears in the Submissions for the Record.]
Representative Beyer. Ask then Mr. Chilson, in your testi-
mony you talked briefly about things like audit standards, training
and investment, which existing laws and specific new policies
would promote to encourage the responsible adoption of AI?
Mr. Chilson. So I am most familiar with regulation in the com-
mercial sector, having spent time at the Federal Trade Commis-
sion. But I am familiar with at the FTC some of the practices of
every privacy officer that is within agencies. And I think the recent
OMB memos, again, if you haven’t seen those, I think they offer
a lot of the safeguards that should be undertaken as an agency
adopts technology into—you know, into use in pursuit of its mis-
sion.
These memorandum replace some Biden-era memorandum,
and—but they cover a lot of the same area, many with a slightly
different focus; one that more encourages the adoption of this tech-
nology as—in pursuit of the mission, but offers the same sorts of
guardrails in some case that will help make sure that this protects
privacy, that it pursues the mission of the agency and that it is ef-
fective.
And so some of those principles in there, I think, are a good
start, although they are guidance, not regulation.
Representative Beyer. Thank you, Mr. Chilson.
And, Dr. Miller, one of my favorite agencies in Congress is
AHRQ, the Agency for Healthcare Research and Quality, because
their mission is all that had to do with healthcare more effectively
and not spending money on costly wasteful treatments. Someone
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once told me that if you want to look at efficiency in Federal Gov-
ernment, AHRQ is the poster child. And it is one of the key agen-
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16
cies that looks at over the $4.5 trillion spent annually on
healthcare.
And it is even more important when we think about artificial in-
telligence applying it. But, again, guess what? Much of AHRQ was
gutted by the reduction in force at HHS last week, and the people
that we need to do HI for healthcare were actually fired. The RIF
apparently eliminated much of the staffing, the office of the chief
information officer, and then they attempted to relocate most of the
executive staff to, like, the Indian Health Service.
I am sure they thought they could outsource the IT services, but
you need people on the ground that understand tech policy and cy-
bersecurity.
What would you do to make sure that we have the folks in the
agencies that can make sure that we have the expertise to advance
AI adoption?
Dr. Miller. Thank you. I think AHRQ is more of a research and
a research contract organization as opposed to a regulations or op-
erations organization. So I would say that CMS, which is func-
tioning as the payer and the regulator of the private payers exe-
cuting the public program needs the digital health, AI machine
learning, expertise built into CMS into each of the centers.
Whether it is the Center for Medicaid, whether it is the Center
for Medicaid and CHIP Services, I don’t think that that is AHRQ’s
primary function, historically.
Representative Beyer. Okay. Well, great. My time is up, but
thank you very much.
Chairman Schweikert. Thank you, Mr. Beyer. Mr. Estes.
Representative Estes. Thank you, Mr. Chairman, for holding
this hearing and for your work on this committee.
Today I am afraid that rooting out waste, fraud, and abuse has
become divisive here in Washington, but it is exactly what Ameri-
cans want us to address. Before this year, rooting out waste, fraud,
and abuse was actually a bipartisan endeavor and talked about,
had broad support. Appears that now there is a number of law-
makers, bureaucrats, and media that actively support the status
quo, which, in essence, is on the side of allowing the waste and
fraud to continue.
We have had decades of Inspector General reports that have out-
lined the reckless misuse of government funds with little or no ap-
petite to address that. And you mentioned that earlier today in
terms of—that there was the status quo or the push on the status
quo was to resist the loss of recipients of the payments that we are
making through that.
The new administration is shattering some of the status quo and
tackling the problem with their directive that they got from the
American people last November. As the chair of the Social Security
subcommittee here in the House, I want to just highlight that—for
everybody in America that every dollar that is saved by eliminating
waste, fraud, and abuse is a dollar that can go towards benefits to
seniors that have earned them or for benefits in other programs as
well.
Typically, things have been slow here in Washington to change
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with just the bureaucracy and the size of the government. If you
contrast that with the private sector, U.S. businesses, if they don’t
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17
innovate and adapt, they die. In D.C., as Ronald Reagan once
quoted, a government bureau is the nearest thing to eternal life we
will ever see on Earth.
Some of the advancements that we are seeing with artificial in-
telligence are already disrupting industries throughout the country,
whether it is in the medical field, manufacturing, or technology.
The Federal Government needs to be looking at ways that we can
actually be more responsible and use AI.
Dr. Thomas, based on data from 2018 to 2022, GAO estimates
that aggregate fraud was between 233 billion to 521 billion each
year. On the low end, that means that governments lost more than
a trillion dollars in a five-year period and maybe as much as 2.6
trillion. It is unfathomable that people will be against finding and
eliminating this blatant theft of taxpayer dollars.
So what, from a GAO’s perspective, are you doing to implement
machine learning and data analytics to help reduce that fraud and
how other Federal agencies can work to get that number closer to
zero?
Dr. Thomas. Yes. So within our data analytics programs, we are
developing tools for our analysts to identify the flags that I men-
tioned earlier of what are potential indicators of fraudulent events,
fraudulent programs, fraudulent payments, and, you know, making
those tools potentially available to the rest of the agencies to use.
And we continue to work with the agencies to help them adopt
the recommendations that we put forth in each of our reports.
When we talk about fraud across the government as an estimate,
that comes from information that we receive from each of the agen-
cies. And as we work with that, we develop a whole bunch of rec-
ommendations, and we follow up with them. And, you know, adopt-
ing those recommendations, we believe, will bring down the risk of
fraud.
Additionally, we want them to use our fraud risk framework that
we have implemented and shared with them that allows them to
look at programs and identify where potential fraud could exist if
that program is implemented in that particular way and make ad-
justments to try to, just as you talked about, stop fraud from hap-
pening before it happens.
Representative Estes. Thank you. Mr. Chilson, I think when
we talk about rooting out waste, fraud, and abuse, sometimes a
false narrative gets promoted that the Federal Government is tak-
ing away benefits from Americans that have—rightfully deserve
those.
And trying to alleviate some of this fearmongering, can you help
dispel some of those fears and explain how AI can more effectively
target those bad actors, target the improper payments to ensure
that our government programs provide the resources to the right
beneficiaries.
Mr. Chilson. Well, accuracy is really important. And what we
have seen in the private sector is that these tools can help both
eliminate fraud, but also serve the people who are rightfully get-
ting benefits more quickly and accurately. So I do think that we
have opportunities in this space.
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The financial sector does this all the time. And what it has found
is that, you know, you may remember you got lots of calls—maybe
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18
five years ago you got lots of calls any time you, like, went on a
trip with your credit card. Well, now they can identify when there
is fraud with a lot fewer of those false positives.
And that is a convenience to the people who are getting benefits,
as well as those who are—as well as addressing the people who are
fraudulent actors.
Representative Estes. Thank you. I have run out of time, like
so many others. We have so many questions we could ask each of
you.
I yield back, Mr. Chairman.
Chairman Schweikert. Mr. Min.
Representative Min. Thank you, Chair Schweikert and Rank-
ing Member Hassan. Really privileged to be on this committee and
to be part of today’s hearing on this very important topic.
Obviously, when we talk about waste, fraud, and abuse, we all
want to reduce this, we all want to eliminate as much as possible.
Ensuring that taxpayer dollars are spent effectively is part of our
mission here in Congress. And so very excited to hear about the
possibility of what we can do to try to rein in some of these waste—
wasteful actions.
Every year the Federal Government disburses trillions of dollars,
as we know, across thousands of different programs, healthcare, in-
frastructure, defense, disaster relief, and on and on and on. And,
of course, with this level of spending comes the inevitable challenge
of effectively managing waste, fraud, and abuse.
Historically, large organizations, including, I just want to note,
private companies, big corporations, have all had to deal with this
problem of waste, fraud, and abuse, and they have done so through
manual audits and control systems. But the rapid development of
technology over the past few years, including AI, as we are talking
about today, give us a really transformative opportunity to try to
run government better. We are talking about some very, very excit-
ing potential here.
That being said, I think it is important to also recognize the tech-
nological advances like AI are only going to be as good as the pa-
rameters we set for them, and these are fundamentally normative
parameters we are talking about. So when we think about effi-
ciency, we need to talk about the goals and timelines that are our
mission here.
Are we talking about just reducing costs, or are there other goals
involved? What is the timeline we are looking at for efficiency or
cost-cutting? Is it immediate; is it a year out; is it ten years out?
All of these things matter.
And I just wanted to give you an example here. When we talk
about Social Security, despite the number of really bad and false
and debunked claims about Social Security fraud recently, we know
that Social Security has been a remarkably effective program and
an efficient program. A very, very low percentage of fraud, always
under one percent under any analysis, a very low percentage of
overhead spent on that; less than .5 percent of the total program
is spent on personnel and overhead.
And until this year, the Social Security Administration was re-
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nowned for its remarkable ability to deliver trillions of dollars of
checks consistently, securely, and on time. Are there ways to cut
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back on Social Security? Certainly, yes, of course. And we have
seen Elon Musk and DOGE do just that, attempting to fire 12 per-
cent of the Social Security workforce.
This has, though, led to lots of reports of delayed and missing
checks, difficulty for seniors, including ones that have called my of-
fice, trying to find personnel that will deal with their problems
with Social Security.
So the question, of course, is what do we mean by efficiency
when it comes to Social Security? Are we talking just about cutting
costs? Are we talking about cutting costs now, or are we talking
about other goals here? I would submit to you that I think what
Social Security has done is get checks out to people; that is its mis-
sion.
And so, again, when we think about AI and how it can improve
efficiency across different agencies, those goals, the time frame that
we are talking about are important. I want to give you another ex-
ample.
I represent a vibrant life sciences industry. And I have heard a
lot of growing concerns about the major cuts that have been made
to staffing at the Food and Drug Administration and that these
will lead to longer approval times for new drugs and devices.
In fact, if you look at the FDA structure, these staff are actually
paid for largely by user fees from the companies seeking approval.
So actually the cost, other than the—if you are looking at the very
immediate time frame is zero. But if your priority is to look for im-
mediate spending cuts, the administration—the terminations of
lots of FDA staff might make some sense.
But beyond just the immediate present, it doesn’t make any
sense. And if you are looking at any other goals, including FDA ap-
proval, rapid approval of drugs and devices, these cuts look very in-
efficient.
So I guess my first question to you all—maybe I will start with
Dr. Thomas. When we talk about efficiency, what time frame
should we be looking at, very short-term or long-term?
Dr. Thomas. We look at both. So we are looking at short-term
efficiencies in the area are rare. We have identified potential areas
of concern and made recommendations of how to improve those
areas of concern.
And then, of course, with the long-term one, that gets into the
system that you are using, a systemic challenge. And there is—we
identify opportunities there as well, sometimes as matters for Con-
gress, sometimes as areas of attention for the executive branch. So
we look at both of them.
Representative Min. I want to ask Mr. Cannarsa that same
question.
Mr. Cannarsa. Yes. That was a good answer. OIGs are in the
same business. You know, there are short-term benefits and then
project out for a longer term for those big efficiencies that you are
talking about.
Representative Min. And I want to follow up with one last
question with you, Mr. Cannarsa. Do you think that Inspector Gen-
eral’s oversight, transparency, disclosure, that these are important
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things that we need to have real efficiency in government whether
we are using AI or not?
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20
Mr. Cannarsa. Absolutely. That is what the IG Act was built
on, was the value of independent, transparent, nonpartisan over-
sight, just so that the facts can be presented as to how to improve
these agencies and their programs.
Representative Min. So just to be clear, firing 19 Inspectors
General would not improve efficiency?
Mr. Cannarsa. I mean, that—I should say that the OIGs them-
selves didn’t go away. Their leadership was removed. So we are
proud that the staff that is there and the leadership that has
stepped up has kept that oversight going.
Representative Min. Thank you.
Chairman Schweikert. Thank you. Ms. Spartz.
Representative Spartz. Thank you, Mr. Chairman.
I think, Dr. Thomas, you are involved with—Government Ac-
countability Office is in charge of audits, right?
Dr. Thomas. I am sorry. Say that again.
Representative Spartz. You are involved with audits of govern-
ment, right?
Dr. Thomas. Yes, we are.
Representative Spartz. Okay. So I am a former auditor, so I
spent over a decade auditing publicly traded and private compa-
nies.
Do you think it is common sense to have authorization of trans-
actions? Like, all transactions that government should be properly
approved and authorized?
Dr. Thomas. Yes, that makes sense.
Representative Spartz. That is right, right? That is what
DOGE is trying to do. They came and say, okay, we need to make
sure that transactions are properly authorized. This is the kind
what we want.
Do you think it is proper to have justification transaction; what
is the reason this transaction was disbursed; is it based on some
invoice, appropriations; and have all of these—this is just common-
sense accounting?
Dr. Thomas. Yes, we support the appropriate documentations.
Representative Spartz. Okay. So that is right. You have docu-
mentation. That is what DOGE is trying to do.
Do you think it is proper to have automated controls and match-
ing of databases to make sure that the things, when we disburse
transactions, have some way of controls and including maybe some
automated controls, do you think that is something that govern-
ment should do more?
Dr. Thomas. That is a much more challenging question. The au-
tomation is entirely dependent upon the algorithms designed.
Representative Spartz. But do you think we should have con-
trols, whether it is manual matching, automated, but matching
controls to match data to have some controls in transaction? Do we
have to have controls? You support having controls?
Dr. Thomas. I thought your question was about automation. I
apologize.
Representative Spartz. Automated controls could be manual. I
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mean——
Dr. Thomas. Manual controls.
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21
Representative Spartz. Right? Do you think—you are in
charge of automation. Do you think it is sufficient technology to
have more automated controls?
Dr. Thomas. I don’t know if we have an answer to that right
now.
Representative Spartz. You don’t know? So we started a lot of
controls, I don’t know, 20 years ago when I was in audits. Private
companies were able to do this. We actually did artificial intel-
ligence and continuous auditing done in private companies. Do you
think government cannot do it?
You are in charge of technology and innovation. How far we need
to go? How many decades behind you think government should be
from the real world?
Dr. Thomas. Within the science domain, which is my area of re-
sponsibility, there is—the science right now does not support artifi-
cial intelligence that is deterministic, and that is the challenge that
exists for using artificial intelligence for deterministic choices like
you are talking about.
Representative Spartz. No. But just for simple transactions,
you know, we are using just to identify fraud; because that is actu-
ally used more efficient.
Don’t you agree that actual continuous auditing and using more
automation would detect and prevent fraud more sufficiently than
manually?
Dr. Thomas. We do, like—a continuous auditing is a practice
that we also enjoy.
Representative Spartz. And I think that is what DOGE is try-
ing to do. So they try to improve control environment to avoid to
have—you know, so I am not sure why there is an argument even
about that if we try to improve. It seems like everyone wants to
have efficiency and better auditing that the things go to proper
causes.
And we need to simplify—don’t you agree, we need to simplify
our billing?
Dr. Thomas. So your first statement was about the actual tech-
nology——
Representative Spartz. Right.
Dr. Thomas [continuing]. And that is what I am here to talk
about.
Representative Spartz. Okay.
Dr. Thomas. We do not agree across everybody that the existing
AI technology can make error-free determination in an entirely
automated fashion.
Representative Spartz. Well, there is—no one can do error-
free. But that could be a tools and mechanism to help us, you
know, to be able to—to detect fraud and have more controls.
Dr. Thomas. Yes.
Representative Spartz. You know, there is no proof, but it is
less likely to have some instead of system. So I think that is ex-
tremely important because we are spending a lot of money in
healthcare, but we need to make sure that it is actually bring
value.
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But it was a different conversation, it is a little bit broader, you
know, not just fraud that we need to make sure it is not hap-
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22
pening, but abuse of the law is because Congress write very broad
laws.
And, unfortunately, this issue became very partisan, where it
used to be overbilling by large hospitals of Medicare was a bipar-
tisan issue or abusers in Medicare advantage was a bipartisan
issue, or in Medicaid. You know, Vice President Biden at the time
was calling Ponzi scheme some of the, you know, gimmicks, you
know, that—that hospitals play with provider tax.
It became very partisan, and now we cannot have a discussion
about the fraud and abuse because everyone wants here to make
sure that people have better benefits and better healthcare. And
because we are spending so much money on healthcare, but we are
not getting out——
Dr. Miller, you are the doctor here. What do you think would be
a key issue that we should address to improve healthcare value?
Dr. Miller. I would say automation to eliminate improper pay-
ments so that way you are either saving money or you are re-
directing that money towards other items and services for bene-
ficiaries.
Representative Spartz. Okay. Well, it seems like we should be
much more of a partisan support and agreement on this issue and
not a partisan issue because we want Americans to be healthy be-
cause a healthy Nation, you know, that is the only Nation can be
strong. But thank you.
And I yield back.
Chairman Schweikert. Thank you, Ms. Spartz.
We have 11 minutes; the votes have been called. Are you com-
fortable—we may have to all run to vote, and I just——
Representative Casten. Thank you, Mr. Chair. Thank you all
for being here. I am delighted to be at my first JEC meeting.
So, Dr. Thomas, I want to nerd out with you a little bit. Before
coming to Congress six and a half years ago, I was in the energy
business, and I built an AI tool for our company because the single
biggest source of profit variance we always had was revenue vari-
ance.
And the reason why a revenue variance was so high was because
our customers, even though they assured us that they knew that
their energy loads would be next year, they actually were really
bad at predicting them. And so I built a model to go in and pull
in economic data and weather data and all sorts of other stuff.
And good news—I am very proud of this—is we cut our revenue
variance by 90 percent. The bad news is I got all of this precision
and no fundamental knowledge of what was going on. I had very
good curve-fitting data, but lost knowledge.
I remember saying this to my head of engineering at the time,
and he said in his wise way, that is the story of every advancing
technology; we get more precision at the expense of knowledge. My
GPS tells me where I am, but I don’t remember how I got here.
I ask that in the context of how you are thinking about how to
design—you know, what do you optimize to? I mean, it is one thing
to say we all want efficiency; of course, that is, you know, mom and
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apple pie. But in a business context, I knew what I was optimizing
to.
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In a political context—and you can take any of these that you
want, but it strikes me that there is four things that I don’t know
how to do the math. How do you deal with political trade-offs?
You know, if we are going to design a tariff policy, let’s say, that
is going to balance the interests of exporters and importers, that
is a political question; that is not a math question. How do you deal
with the fact that the Federal Government has unlimited downside
exposure?
You know, in my company I was protected—my floor was pro-
tected by the bankruptcy code, our insurance protocols. The govern-
ment has to be there in the worst possible case scenario, which
means we need to keep resources around that are not used most
of the time. How do you think about that?
How do you deal with economic impacts outside of the govern-
ment’s balance sheet? We could save a ton of money if we defunded
the police, right? I don’t think anybody would suggest that that
would be societally beneficial, whether it is a cop on the beat or,
you know, someone at the SEC, but we know that we can save
money for the government if we do that.
And then, of course, how do you deal with noneconomic issues?
You know, Fermilab is right outside my district. I don’t know what
the return is on basic investment, but I am sure glad we do it. On
the other side of my brain, Muddy Waters used to live in my dis-
trict. If FDR hadn’t funded the WPA, hadn’t sent Alan Lomax
down south, hadn’t—we wouldn’t have found Muddy Waters, and
we probably wouldn’t have The Rolling Stones. We could have
saved money.
So, like, when you design these algorithms, how do you—do you
have classes of problems that you simply don’t ask, or how do you
think about those when you are trying to optimize for efficiency?
Dr. Thomas. Yeah. That is a very tricky question. So we are
keenly aware—and you will notice in my written testimony—we
talk about if you go too far in one direction, you certainly over-
impact small business and individuals because once you start slow-
ing down the payment systems, people who are dependent upon
timeliness of those payment systems, you are not doing improper
payments or fraudulent. They are just regular folks doing—running
a business or living their lives. It causes significant, you know, fric-
tion.
And so we are trying to optimize when we do analytics to try to
identify the flags we talked about. And that is why I am such an
advocate of high-quality data. You have been through this experi-
ence. High-quality data matters because if you don’t have high-
quality data, it introduces additional error on top of the algorithmic
error that just exists within the process that you are using to do
your modeling.
And so trying to balance those things—and then we actually do
sit down and think through the ethical approach of it. You know,
what is the impact of a—you know, a false negative? What is the
impact of a false positive? Of course, at GAO we are interested in
reducing fraud to zero, in theory.
Representative Casten. So—and I know we are going to be
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tight on time here, but all of that makes perfect sense to me. It
raises the next question.
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What DOGE is doing, are they only using algorithms that have
gone through your, sort of, ethical tests to run traps, or are they
running algorithms outside of your process?
Dr. Thomas. So we have only recently been asked to look at
what DOGE is doing, and so that is——
Representative Casten. So you don’t know?
Dr. Thomas. We just started. And so we are not ready to com-
ment on that, but we are——
Representative Casten. So to the extent DOGE is using AI
tools, no one on this panel knows——
Dr. Thomas. We don’t.
Representative Casten. What they are doing? That is a flag.
To anyone on the panel, is it your view that if we identify a puta-
tive relationship, some opportunity for efficiency—because when I
built these—when I built these algorithms in my company, I didn’t
get to just change our budget. I had to go to our board for approval.
Is it your view that the executive branch can implement these
changes right away without coming to Congress for approval? Does
anyone maintain that position on this committee?
Mr. Chilson. I think there are plenty of tools that——
Representative Casten. I am just asking whether—whether
the Impoundments Act and the Rescission Control Act trump here.
Because I think, to Dr. Thomas’ point, we can learn a lot from
AI. We still need to run ethics tests on the back end. And it is an
important question whether we think the executive branch can act
without congressional approval to implement what they learn with-
out even telling us what they are doing. Because it sounds like we
don’t even know what the hell DOGE is doing.
Chairman Schweikert. You can answer. We are down to five.
Representative Casten. Okay.
Chairman Schweikert. Probably time to head on. If you have
time to hang out with us, we are going to pause the hearing. I do
have one request—because we have some of the joint economic
staff. Be careful. Some of them are freaky smart with Ph.D.s.
Mr. Chilson, Mr. Thomas, you talked about—I am going to stand
up and head towards the door—the AI mapping software. You
called it graphing?
Mr. Chilson. Yeah.
Chairman Schweikert. The ability—if I came to you and said—
because in the old days, we were doing the bolt-on, you know, a
legacy system bolt-on bolt-on. Now some of the AI packages, my
understanding, can take the feed, almost write it to modern code,
analyze it and break it down.
Do you have technology you can hand to him so he can stop writ-
ing me papers and reports, and we can start moving to implemen-
tation?
And then, Dr. Miller, if you talk to one of the two staffs—because
you know we have multiple bills about moving AI in clean claims
and those automations. We are trying to convince our brothers and
sisters, particularly those with relationships at the CMS, that if we
got rid of the chase-and-deny sort of system and did AI match to
here is the benefits, here is the doctor’s notes, AI scrubs, pay it.
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Help us model what the savings and efficiencies are.
And I will see you in a few minutes.
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[Recess].
Chairman Schweikert. You have my questions. My first one
was functioning—okay. Let’s say we have a love connection. You
have dealt with—I really almost ran.
Outside of some of the externalities, is it nice and cuddly? She
always likes to turn the microphone on when I am already loud
enough as it is.
How do I take your technology—your understanding of the tech-
nology, march it over to GAO or actual agencies, have them stop
sending me papers about ideas for implementation and actually im-
plement? Because it is not 20 years ago where it took me a two-
year project, and I had to hire IBM to do a bolt-on on my old bolt-
on—on my AS/400. Seriously, I started on a AS/400 at one time.
Why do you laugh at that?
But I am really serious about this. The ability to take datasets
today and just get going on it; I thought you already had certain
. . . I don’t know if they technically call it the AI programs, but
translational programs that will take my old stuff—and now it is
Java, now it is whatever. How do you help with that?
Mr. Chilson. I don’t know that GAO particularly needs my help
on this. I think Dr. Miller—sorry Dr. Thompson has it well under—
Thomas has it well under hand.
But the agencies face a lot of challenges for a bunch of different
reasons. I mean, they are all their own special creature. And—and
so, you know, whether it is funding—a lot of it is expertise, but a
lot of it is just the status quo of how things have happened in the
past. So they don’t set up a big plan, right?
And so I think demanding a sort of action plan is probably the
first step.
Chairman Schweikert. Is there a way to standardize that ac-
tion plan, where—okay. It has been pointed out the cost of high-
end talent or compensation schedules is one of the reasons we can’t
keep them. We have already learned, whether it be The Wall Street
Journal series in regards to MA or some of the other things, some-
times the one that bathes only on occasion, but is a dropout out of
MIT because he was already bored, has the ability to do things in
hours that would take some of us years. We have all had one of
those in our lives.
I am just trying to figure out how to move away from us having
another hearing, another conversation—to actual implementation.
You know, a decade ago, I was positive blockchains were going to
be my way to build universal databases, access hierarchies of per-
mission, have an expertise in watching node coding and design.
And I am here a decade later, and it didn’t go anywhere. So help
me.
Mr. Chilson. Yeah. I mean, I think it is less of a—I mean, there
is, obviously, the technical problem of how do you provide these
tools, but I think there is very much a social and political challenge
here.
And that is—that is an area that I am not a deep expert on, al-
though I have seen, you know, changes at the Federal Trade Com-
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mission primarily.
Chairman Schweikert. Okay. Dr. Thomas.
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26
Dr. Thomas. In this instance, if you don’t mind, I will prob-
ably—I will talk from my professional experience as opposed to
GAO because we—I don’t think we have a body of work quite on
this topic yet.
You know, there is two areas I think you are getting at. One is
the—just the workforce, right? And there are very bright people
who are interested in working for the government because it makes
a difference right? They are mission-oriented. You know, they
don’t—maybe are not—they are at some point in their career, or
they are not after the big money or they have already done that,
so that happens. Because I am one of them. You know, I am one
of those people who have done that.
But the second part is that getting the new technology into indi-
vidual agencies one by one is a challenge.
Chairman Schweikert. Okay. So can I give you—you are more
than welcome to say I am way over my skis. Okay.
So I have dozens and dozens of different government entities.
They all each have their own databases, their own mechanisms. If
I had them, basically, hosted, you know, with their own sets, their
own permissions, but in a common area——
Dr. Thomas. Yeah.
Chairman Schweikert [continuing]. Could I actually have a
machine learning package that first does this one, then rolls to this
one, then rolls to this one. And you actually do—where it actually
gives your option on your datasets and externalities—you know,
where it is seeing abnormalities and those things.
Dr. Thomas. Yeah.
Chairman Schweikert. And in that case it is a central AI shop.
But because we are actually heading towards a world where we are
going to do some more data sharing to find out so—like, we have
learned with some of the Social Security databases, the death file,
because it didn’t have access to so many things over here, people
would steal grandma’s identity, but grandma hasn’t been with us
for a while. And they were taking out loans! And that’s elementary
school-level coding of just sharing data.
Is the concept of—you’ve talked about professional skill sets and
staff. Could it be in a single island instead of going down around
to the individual agencies? Is that a——
Dr. Thomas. It can be done. Remember, the rules for privacy of
the data is defined by Congress. So that is something you can
change.
Chairman Schweikert. But, look, the Defense Department
keeps much of its data in an encrypted cloud.
Dr. Thomas. So, yeah, international security data is a little bit
different.
Chairman Schweikert. The iPhone has an encryption chip in
it. The fact of the matter is, this is more secure than any HIPAA
database out there.
Dr. Thomas. Yeah. But as a scientist, just what you are describ-
ing is a great environment where the data is there, the analytics
are there. The technology exists to create role-based permissions so
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that you can control who accesses what within a centralized sys-
tem. The technology is there.
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I think there is an—there are barriers within the government
that a lot of it comes down to the rules that have been put into
place.
Chairman Schweikert. Okay. Am I insane? Excuse me. Am I
being too techno-utopian?
Mr. Cannarsa. You know, I think the—the aspiration is worth
pursuing. I think that some good points have been raised, that it
is about empowering the agencies, you know, with maybe—is it
dedicated resources to push forward.
Chairman Schweikert. But in your vision, it is the talent at
the agency. And one of the conversations I have had with certain
folks is saying have the—create the talent, but the agency—but
they capture the agency’s—the data—data more from a central
hub.
Mr. Cannarsa. I think my point is it is priorities, and we were
talking about it briefly here. That a lot of these agencies, they are
in the business of their customer delivery and they are trying to
keep the business going. And whether it is implementing the rec-
ommendations I talked about from the OIGs or it is pursuing these
advanced tools, it is prioritizing for them what is important, what
is going to deliver the greatest return.
Chairman Schweikert. I am going to come back to you on that.
Okay. Forgive me, but Dr. Miller is one of my favorite people,
which, trust me, in this place doesn’t get you much.
If I came to you right now and said, give me the three things
technology can do to change the cost and access to healthcare for
the better for society. Give me the three pieces of legislation some-
one like me should be trying to drive through this process. What
would you do?
Dr. Miller. I would focus on the planned admin processes, which
are the three things that I mentioned. So the automation of Med-
icaid eligibility because it is categorical and financial. There is not
really discretion involved in—a lot of discretion involved in—Med-
icaid eligibility. So move it towards automation. Require CMS to do
that and have a clear timeline.
Chairman Schweikert. Okay, so you would have the Medicaid
eligibility grab the state data and CMS to do the match?
Dr. Miller. Yeah, or the state’s.
Chairman Schweikert. Would you do a match to private data?
Dr. Miller. To what data?
Chairman Schweikert. In Ways and Means we have talked
about certain things where we have certain earned income tax
credit benefits, those things. Instead of putting them on delays for
a couple months to see if you get multiple requests under the same
Social Security number, if you did a match to certain commercial
databases, saying, oh, yeah, consumption matches reported income,
send them their check.
Would you do a data quality bounce of a commercial database
just to see if the eligibility qualifications—to speed it up?
Dr. Miller. I don’t think it is unreasonable. I can’t say if that
would be the right operational answer.
Chairman Schweikert. Okay.
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Dr. Miller. But the idea of making the standard for Medicaid
eligibility, instead of it being a paper or a manual or a fact-based
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process, to have it be an automated technological process, just like
you log on and get into your bank account. It should be simple.
The second thing I would do is I would tell CMS—you could do
it through oversight, you could also do it through legislation—to
move diagnosis coding across the Medicare program, not just MA,
but also in fee-for-service to an automated software.
Chairman Schweikert. Okay. And we actually have—because
that is the one I was hoping you would give me as number three
because that one we have great interest in.
Dr. Miller. Because that—because the issues about diagnosis
coding right there, there is clinically appropriate diagnosis, coding
intensity, there is sort of the gray area, and then there is the—the
DOJ hauls you off to jail.
And then there are—then there are, of course, changes that hap-
pen when beneficiaries—the same beneficiary in fee-for-service and
MA can look differently, but that is because the programs have dif-
ferent incentives for codings. So if you fix coding across the entire
system as opposed to in one of the programs and you fix it at the
point of delivery, that is a huge improvement.
Chairman Schweikert. See, now, I need to talk to you because
I need to make sure. We have been actually having discussion on
MA, what would happen if I could get some of that coding profile
so I know my risk adjustment before a capitated bid.
Dr. Miller. Right.
Chairman Schweikert. You are not bidding—you are not scor-
ing people once a bid is—once services are already out there. But
you do it in your initial bid and, therefore, it changes the incentives
thing. I am going to try to keep you healthier than spend my time
trying to score you as sicker.
Dr. Miller. Yeah. And I would say that the answer is, is because
the beneficiary would be coded the same or very similarly in fee-
for-service versus MA.
Chairman Schweikert. Because——
Dr. Miller. It would eliminate those incentives.
Chairman Schweikert. Okay. And number three?
Dr. Miller. And number three would be making the prior au-
thorization data submission process electronic. It is still like one-
quarter or one—one-third, I think, faxed and a certain significant
share.
Chairman Schweikert. Okay. So, again, universal prior—prior
auth, would you do——
Dr. Miller. Well, I would say prior auth in Medicare and Med-
icaid managed care have the data submissions. So I am not even
talking about the process of whether it is the same specialist re-
viewing that—of the person who is submitting. I am talking about
the submission of data.
Chairman Schweikert. Okay. So that would be different than
a true clean claims model.
Dr. Miller. Right. I am talking first, first order. Like, we haven’t
even gotten——
Chairman Schweikert. All right.
Dr. Miller [continuing]. Submission of data to be purely elec-
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tronic and integrated into the point of care. So it should be—if you
come and see me in clinic or I see someone in clinic or in the hos-
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29
pital and I am typing my note or, you know, my note is being dic-
tated, then at the end of the visit, I click a button and say, yes,
you know, I want to send this to Cigna or whomever. We can’t do
that right now.
And so that is a problem even before—even before the other
issues in prior auth.
Chairman Schweikert. Only because I have people screaming
at me, I am supposed to be in another—will you have a quick con-
versation, Dr. Miller, with this young man behind me? He is freaky
smart, and we have been working on your number two. I was try-
ing to understand, could we get actually the risk adjustment coding
in front instead of doing it over time so we actually from day one
know what helps our brothers and sisters who are receiving par-
ticularly an MA benefit——
Dr. Miller. Happy to.
Chairman Schweikert [continuing]. To keep them healthier.
Gentlemen, I have a dozen questions I want to send you. Forgive
me if you get a couple notes from me. I really do want to think
about—and I wish I could say it is my idea, but I have one of those
people in my life who is truly a genius and bathing is optional, and
that is the one who has always thought, saying, look, you have this
data and hundreds of different concerns through government; you
need to, A, talk to each other; B, if it talks to each other, then you
could actually do much of its mining and set design from a single
location. So it is not genuinely my idea.
And I need to find out, was that just a caffeinated rambling, or
is it a brilliant idea? So—Dr. Thomas, before we throw the gavel.
Dr. Thomas. Yeah. Just as a bug in your ear, a potential model
to start with, something similar to PACE, which is a centralized
program for analytics to fight against fraud. You know, and it is
bringing the data together, it is bringing the analysts together. It
is a starting point, something to consider.
Chairman Schweikert. Okay. You started this. Okay.
But fraud analytics are not only crowdsourced in many ways, but
they are living. They are constant. It’s not like a single match. My
understanding is the best ones, actually, it’s constant. Because
there’s constant—every day there is new inputs, there’s new scams,
there’s new—new bots that—so it is not just a match, good, gone,
done.
It is just, like, how many of you drive with Waze? It’s a living
dataset. And I think that is actually—it was always my blockchain
model. It has to be living. And I think that becomes one of the de-
sign problems so often in government is I would hire some three
people, but they are very smart, they build it; but, about six
months later, everything that is in there is already out of date.
It is—Dr. Miller, I believe I may have actually gotten this from
you; medical knowledge doubles every 72 days.
Dr. Thomas. But there—right. But you create an ecosystem,
similar to what exists in the cyber world where new—new models
and new signatures of those threats are made available to the com-
munity as opposed to—so when someone discovers it—say someone
discovers it in agency A, it can then be communicated to the rest
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of the community through something like a centralized analytics
center.
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Chairman Schweikert. Okay.
Dr. Thomas. Because you are right, the fraudsters are innova-
tive, too. That is what we—we need to build a system for.
Chairman Schweikert. Or the brain trust—excuse me—Con-
gress comes up with another idea, throws out something, and we
will give you absolutely unrealistic limitation times and—in that
we create the fragilities.
I see a white board session coming. Gentlemen, I apologize to
you. You deserve so much more of our attention, but what you do
is important, and thank you for helping us.
And with that, we are adjourned.
[Whereupon, at 4:45 p.m., the committee was adjourned.]
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File and source
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