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5
Anticipating a Data-Centric Future
C
MS’s role in the U.S. health care mosaic will be pivotal as the nation
shifts to improved approaches for organization, payment, con-
sumer engagement, understanding of the bioscience foundations
of health, and data management for the provision of quality, equitable
health care. This transition will take place over many years, but some key
shifts are already underway, with some dimensions being planned while
others evolve on their own. At every stage, the capacity to improve deci-
sion making throughout the entire system will depend not only on having
timely access to data but also on the capacity to transform the raw data
first into information and ultimately into intelligence to support future
planning and action.
Data warehouses, business intelligence, and data analysis have
existed for more than 30 years and have a long history of use in the sci-
ences. The explosive growth of data in all areas of business, science, medi-
cine, and life in general has opened ever greater potential for discovery
and understanding through analysis of data. Over the past 20 years the
advent of virtual or data-driven science has meant that in some areas it is
possible to experiment or discover for the cost of database searches and
analysis. Data-driven techniques can have application in medicine and
health care in addition to sciences such as astrophysics. For example, if
data from longitudinal studies, clinical observations, and other health care
activities were made available to researchers, studies of the comparative
effectiveness of alternative medicines could be conducted in a fraction of
the time and cost required for clinical trials, which are often extremely
107
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108 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
limited in terms of the population studied, are expensive, and can incor-
porate biases.1
Another health care trend that has implications for CMS’s data-related
efforts is the ongoing shift of individual practitioners from solo and/or
small group practices into care systems and networks. This realignment of
providers and institutions could result in far more payments for bundled
services intended to achieve defined outcomes. The efficiency and effec -
tiveness of such an approach will depend on the collection and transfer
of a great deal of data.
CMS is in the process of transforming itself to enable a focus on
information collection and data management while still fulfilling its tra -
ditional mandates, including retrospective payment for health services
for segments of the population. Several trends in health care illustrate
the broad need for a more data-centric approach, including the diffusion
of electronic health records (EHRs), practitioner positioning into care
networks, and increased consumer access to and demand for health and
medical information.
In aggregate, these trends in health care regarding data will inter-
act in ways to produce both additional work and new requirements for
CMS. Although the ultimate result is currently unknowable, the drive to
achieve great value for health care for both individuals and populations
is not likely to abate anytime soon, especially in light of the demographic
pressures and the size of the financial investment the nation is making in
health care services.
Data are essential to and underpin nearly all of the efforts CMS is
undertaking—and data are an essential driver for the strategic technology
plan advocated in Chapter 2, motivate the meta-methodology outlined in
Chapter 3, and are a key impetus for the organizational changes discussed
in Chapter 4. Gathering these data and sorting out how to make data
available and to whom cannot be envisaged adequately until all stake-
holders have been engaged and are contributing to an ongoing discussion.
Doing that incremental engagement is part of the committee’s recom-
mended approach and is essential to the development of mechanisms for
future data management as an aspect of CMS’s new and changing rela -
tionship to data and information. In addition, CMS has to grapple with
ensuring that only authorized users have access to data such as personal
health information or other individual-level information.2
1 Sharon Begley, 2011, “The Best Medicine: A Quiet Revolution in Comparative Effec-
tiveness Research Just Might Save Us from Soaring Medical Costs,” Scientific American
305(July):50-55.
2 While posing technical challenges, the question of who is authorized to access what sorts
of data is a policy matter distinct from the technical challenges.
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109
ANTICIPATING A DATA-CENTRIC FUTURE
This chapter discusses several ways in which the data and infor-
mation collected by CMS are used extensively within the agency for
purposes such as quality-related efforts, policy analysis, and combating
fraud, as well as for informing consumers and managing payments. It
also addresses recent legislative mandates for CMS—such as use of CMS
information to analyze racial and ethnic disparities so as to contribute to
their reduction.
IMPROVING QUALITY
Heightened since the publication of a 2000 Institute of Medicine
report,3 the effort to improve the quality and safety of health care has
been fostered by CMS and many other key actors in health care, including
insurers, professional associations, accreditation and review groups such
as the Joint Commission,4 the National Committee for Quality Assurance
(NCQA),5 and the National Quality Forum (NQF),6 and care providers
such as physicians and hospitals. Although this effort has been uneven
and at a slower pace than hoped for by safety champions,7 the cumulative
effect of several factors—policy maker and academic attention to quality
and safety improvement,8 Medicare payment and reporting incentives
such as pay for performance9 and value-based purchasing, and the pro-
liferation of EHRs, aided by the financial incentives in HITECH—have
3 Institute of Medicine, 2000, To Err Is Human: Building a Safer Health System, Washington,
D.C.: National Academy Press.
4 Joint Commission Center for Transforming Healthcare, 2011, “About the Center,” web -
site, available at http://www.centerfortransforminghealthcare.org/about/about.aspx, last
accessed August 8, 2011.
5 National Committee for Quality Assurance, 2011, “State of Health Care Quality Reports,”
website, available at http://www.ncqa.org/tabid/836/Default.aspx, last accessed August
8, 2011.
6 National Quality Forum, 2011, “ABC’s of Measurement,” website, available at http://
www.qualityforum.org/Measuring_Performance/ABCs_of_Measurement.aspx, last ac-
cessed August 8, 2011.
7 Mark R. Chassin, Robert W. Galvin, and the National Roundtable on Healthcare Quality,
1998, “The Urgent Need to Improve Health Care Quality,” Journal of the American Medical
Association 280(11):1000-1005.
8 The Dartmouth Institute for Health Policy and Clinical Practice, 2011, “Dartmouth Atlas
of Health Care,” website, available at http://www.dartmouthatlas.org/, last accessed Au -
gust 8, 2011.
9 CMS, 2011, “IPPS Regulations and Notices,” website, available at http://www.cms.gov/
acuteinpatientpps/ipps/itemdetail.asp?filterType=none&filterByDID=-99&sortByDID=4&
sortOrder=ascending&itemID=CMS1229138&intNumPerPage=10, last accessed August 8,
2011.
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110 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
created an environment in which the measurement and improvement of
health care10 are better understood now than 10 years ago.
The budgetary imperatives to “bend the cost curve”11 and the move-
ment for improved quality come together in the Patient Protection and
Affordable Care Act (PPACA), which contains numerous initiatives (for
example, the promotion of accountable care organizations and the cre -
ation of the CMS Innovation Center) intended to result in better, more
cost-effective care for Medicare beneficiaries. CMS has substantial respon-
sibility in implementing parts of the PPACA and therefore in creating
the standards, reporting mechanisms, payment processes, and data and
measurement requirements that will foster these hoped-for improvements
in health care for beneficiaries and more broadly for the U.S. population.
Nationwide investment in EHRs and EHR systems—enabled through
HITECH—is occurring on such a broad scale that it has the potential to
accomplish some sea changes in the information infrastructure of health
care delivery across the country and to generate the fine-grain data needed
for improved health care delivery. For example, although the focus today
is heavily on clinical transactions and data capture, insurance recipi-
ents will increasingly have the ability to access information about their
health directly through secure patient portals. The day is coming when
evidence-based care protocols will support not only decision making by
clinicians and patients but also direct enhancements of health care quality
and safety for diverse populations and subpopulations through IT-based
applications that help ensure quality as care is being delivered rather
than focusing on measurement after the fact. The HITECH approach to
meaningful use of health information technology also has the potential
to allow measurement of the quality of performance without engaging
data abstractors, thereby making such assessment much less expensive.
However, there are also some limitations that will have to be overcome—
including those related to the connectivity of interoperable systems, to
systems themselves, to users, and to potential barriers resulting from pub-
lic policy and regulation. The shift will have clear implications for CMS.
The data provided by the expansion of EHRs deserves special notice.
Although policy analysts (in, for example, the Medicare Payment Advi-
sory Commission (MedPAC), the Congressional Budget Office, the Office
for Management and Budget, and research-oriented “think tanks”) have
used Medicare claims data for decades in proposals for improving the
10 Atul Gawande, 2007, Better: A Surgeon’s Notes on Performance, New York: Metropolitan
Books.
11 “Obama Talks Health Care with Fred Hiatt,” 2009, Washington Post, July 22,
available at http://www.washingtonpost.com/wp-dyn/content/article/2009/07/22/
AR2009072202522.html, last accessed August 1, 2011.
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ANTICIPATING A DATA-CENTRIC FUTURE
health care system, the more robust data contained in EHRs now allow for
richer analyses of the health care provided to populations than those anal-
yses based on data previously available. Because the results of medical
procedures, medications, and treatments can be measured and analyzed
by using the information contained in EHRs, providers such as integrated
medical groups and major hospital systems, which have led the way in
implementing EHRs, are now better able to improve care processes by,
for example, introducing evidence-based “alerts” and guidance for physi-
cians and nurses during the actual provision of medical care. In addition,
having electronic access to real-time health and medical data can advance
people’s capacity to manage health care.
CMS’s role in the context of today’s data explosion is multifaceted.
CMS has responsibility for establishing and evaluating the meaningful-
use standards and incentive payments legislated in HITECH. It establishes
standards for quality reporting, such as the “core measures” required in
the value-based purchasing mandated by the PPACA. Through accredi-
tation processes, CMS can measure how well providers meet the “con -
ditions of participation” in Medicare. It can set the criteria by which
quality improvement efforts (such as “medical homes”) are evaluated by
patient-specific clinical data. The possible ways that such data in elec -
tronic records can be used to improve measurement and payment pro-
cesses are numerous, and CMS will have to determine the preferred
options within its broader implementation of PPACA.
This transition will induce heavy demands for the capture of accurate,
meaningful data that account fully for the health status of those served
by health care delivery organizations. To manage this accounting, CMS
is likely to focus increasingly on the full range of social determinants
of health status, moving beyond those that relate solely to health care
technology and medical interventions. An example of the complexity of
this effort can be seen with respect to the use of billing data for quality
measurement; although some relevant information can be extracted, that
data alone is not enough. Future efforts are likely to require ongoing
attention to high-resolution information in the form of natural language
or formalized data flows realized through an evolution of ontologies,
terminologies, and, ultimately, relevant standards that can help to ensure
that meaning is not lost in the translation of data to understanding.
Clinical outcomes data are currently used throughout the health care
system to monitor, improve, and report on the quality of care in a wide
variety of settings. Increasing use of EHRs, and the potential associated
increase in available clinical data, offer both great potential benefits in
terms of measuring and monitoring quality—and potential risks in terms
of cost, acceptability, and protection of patient privacy. CMS will be tasked
not only with using outcomes data to evaluate the care of its own benefi -
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112 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
ciaries but also with building links to information systems with data on
the care given to others so that comprehensive evaluations of the quality
of care provided for individuals and groups can be developed, both inside
and outside government programs.
There are a number of measures of basic quality for which data could
be collected on all providers. Additional information from certain geo -
graphic regions or practice settings might then be collected, if problems
arise, to help illuminate the source of a problem. The committee is aware
of the assumption of some that CMS should plan to collect information
on patients following what Diamond and colleagues call the “dominant
paradigm” for handling population health data: “gather copies of all
the detailed information one needs, normalize the information once one
has it, and then run queries against that data storehouse.”12 Such an
approach—if applied to CMS’s role in analyzing and monitoring health
care quality, equity, and safety—has drawbacks in terms of cost and the
potential for violations of privacy, and may reduce the acceptability of
EHRs for many practitioners. In addition, such an approach is relatively
rigid, requiring advance knowledge of what data are needed and imply-
ing a single national approach to improvement of quality.
Others argue for a distributed analytic system that, once in place,
could be used to increase monitoring in settings where problems have
been identified while maintaining only minimum information on practice
groups that are functioning well. A distributed system could be deployed
rapidly in support of local efforts at containment of public health emer-
gencies such as the Severe Acute Respiratory Syndrome epidemic. Such
an approach, in which information remains at the source where it was
collected, is being used increasingly for purposes as disparate as public
health surveillance and cancer research. Of particular interest for CMS’s
purposes is the Distributed Research Network (DRN) supported by the
Agency for Health Care Research and Quality (AHRQ), which is designed
to support composite data analysis on the safety and effectiveness of
health care.13 Distributed approaches have disadvantages as well, such
as decreased access to data for some stakeholders and potentially less
comprehensive analytic capabilities.
Separate from how data are collected and stored are the many oppor-
tunities clearly afforded in the area of quality and safety by the develop -
ment of effective analytics. Information from payers other than Medicare
12 CarolC. Diamond, Farzad Mostashari, and Clay Shirky, 2009, “Collecting and Sharing
Data for Population Health: A New Paradigm,” Health Affairs 28(2):454-466.
13 Andrew J. McMurry, Clint A. Gilbert, Ben Y. Reis, Henry C. Chueh, Isaac S. Kohane, and
Kenneth D. Mandl, 2007, “A Self Scaling, Distributed Architecture for Public Health, Re -
search and Clinical Care,” Journal of the American Medical Informatics Association 14(4):527-533.
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ANTICIPATING A DATA-CENTRIC FUTURE
and Medicaid could be analyzed in combination with CMS information
to provide a much more comprehensive view of the performance of a
practitioner, group, or system. Information from sources other than indi-
vidual clinical records (such as registries with data on the incidence of
exposure to disease) can, when appropriate, be included in the analysis
to give a clearer picture of trends in uses of medical care. In planning its
future quality management strategies, CMS will have to resolve for itself
and in collaboration with its stakeholders what strategies for handling
such data it will adopt.
Outside researchers, many of whom are investigating quality-related
research questions, currently make extensive use of the data sets gener-
ated by CMS. Although in comments received by the committee the cost
of obtaining CMS data was raised as a concern, the chief complaint was
that currently almost 2 years elapse before the data can be accessed. For
example, the most current data available on the frequently used MEDpar
file (hospital discharges) is from 2009, with the release of data from 2010
expected in October 2011 as of this writing. Data in the “access to care”
section of the Medicare Current Beneficiary Survey, another area of par-
ticular interest to many, is also available only through 2009, with the 2010
update scheduled for the summer of 2012.14
Among the reasons for these delays are that claims data from any
insurance program are not complete until sometime after the date of ser-
vice. In Medicare’s case this “claims lag” is fairly short, with 98 percent
of claims submitted within 3 months,15 although even so, a data set for
any given year still lacks some claims at the end of the first quarter of
the following year. To permit earlier release of claims, CMS could use a
variety of strategies, such as providing an interim data set of the most-
used information on a quarterly basis with the limitations clearly spelled
out. Many of the modernization steps discussed throughout this report
will make data integration easier (for example, integrating the reports
from Medicare Managed Care with those from fee for service), leading
toward earlier release. Much earlier release of survey data, which should
be technically possible even now, will support the best use of this impor-
tant information.
14 See http://www.resdac.org/Tools/TBs/TN_015_CMS%20Data%20Availability_508%20.
pdf, last accessed June 14, 2011.
15 Department of Health and Human Services, CMS, 2011, “Medicare Program; Medicare
Shared Saving Program: Accountable Care Organizations,” Proposed Rule, Federal Register
76(67; April 7):19554.
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114 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
CONSUMER ACCESS TO CMS INFORMATION
CMS’s first effort at consumer-oriented, public reports on the quality
of care first began approximately 15 years ago with dialysis units. Those
results are available at “Dialysis Compare”16 (with individual data sets
available on data.gov), and the effort is widely regarded as successful. It
is not clear that consumers have used the information extensively—but
providers pay attention and work to meet the standards. Dialysis, how-
ever, has two characteristics that make it unique: (1) Medicare is, for all
practical purposes, the only payer, and so data on Medicare beneficiaries
reflect the full experience of dialysis centers, and (2) dialysis has a limited
number of easily measurable objective outcomes.
CMS’s efforts have expanded to other consumer-oriented “report
cards,” and the CMS website now also has sections, known as “Hospital
Compare”17 and “Nursing Home Compare,”18 that make use of informa-
tion collected in surveys as well as reporting of quality measures and
“never events” (adverse outcomes that ought not to have happened,
such as wrong-site surgery). There are limitations to the information’s
utility, because hospitals and nursing homes serve many non-Medicare
beneficiaries, and so even the most precise analysis of care received under
Medicare may not reflect overall performance. However, as CMS’s ability
to use more granular data from sources outside the organization becomes
more robust, these reports have the potential to become more accurate
and, consequently, more useful.
Although the mechanisms for consumer engagement are somewhat
unclear, more groups and individuals will seek greater access to CMS data
and information. It is reasonable to assume that the equivalent of citizen
engagement in health services and health policy research will increase,
analogous to social networking in other domains, as will online dialogue
relating to the output of such efforts.19
POLICY ANALYSIS
One of the most important secondary uses of CMS data on health care
encounters is the analysis of current spending patterns and projections of
future spending. The number of reports that make use of CMS encounter
data is vast; two of the most important are the annual trustees’ report
16 See http://www.medicare.gov/Dialysis/.
17 See http://www.hospitalcompare.hhs.gov.
18 See http://www.medicare.gov/NHCompare/.
19 Schumpeter, 2011, “Saving Britain’s Health Service: The NHS Needs to Learn from In -
novations in the Rest of the World,” The Economist, June 16.
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ANTICIPATING A DATA-CENTRIC FUTURE
(which evaluates the current status of the trust funds20) and the “data
book” (published at least annually by the MedPAC21). The trustees’ report
focuses on projections of future costs of the Medicare program, and the
data book is a more detailed analysis of changes in patterns of use and
spending over time.
These documents use information from a variety of sources; the most
significant of these are (1) the Medicare Current Beneficiary Survey, which
is a continuous, multipurpose survey of a nationally representative sam-
ple of aged, disabled, and institutionalized Medicare beneficiaries, and
(2) the various “market baskets” developed by an economic forecasting
firm22 to serve as the basis for the annual updates of payments to hospitals
and other providers.
CMS and MedPAC are not the only government and quasi-govern-
ment agencies using encounter data—other groups, such as the Congres -
sional Budget Office, the Government Accountability Office, and HHS’s
Office of the Inspector General, also depend on encounter data for their
analyses and predictions. The Independent Payment Advisory Board,
established by the PPACA, will also require Medicare data in order to ful-
fill its mission to help reduce the rate of growth in Medicare costs without
affecting coverage or quality.
As the Medicare actuaries note in their discussion of the data in the
trustees’ report, there are elements in the information, such as delayed
decisions on the exact amounts paid to specific hospitals, which lead to
small error rates, which are multiplied when extended projections are
developed.23 It is therefore particularly important that the same sources of
information be available to independent researchers in order to facilitate
well-informed debate regarding the future of the Medicare and Medicaid
programs.
20 The Boards of Trustees of the Federal Hospital Insurance and Federal Supplementary
Medical Insurance Trust Funds, 2011, 2011 Annual Report of the Boards of Trustees of the Fed-
eral Hospital Insurance and Federal Supplementary Medical Insurance Trust Funds , available at
https://www.cms.gov/ReportsTrustFunds/downloads/tr2011.pdf, last accessed July 21,
2011.
21 Medicare Payment Advisory Commission (MedPac), 2010, A Data Book: Health Care
Spending and the Medicare Program, June, available at http://www.medpac.gov/documents/
Jun10DataBookEntireReport.pdf, last accessed July 21, 2011.
22 Currently Global Insights, headquartered in Lexington, Massachusetts.
23 Boards of Trustees, 2011, “Actuarial Methodology,” 2011 Annual Report, p. 150.
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116 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
REDUCING HEALTH DISPARITIES
Evidence confirms the reality of health disparities experienced by
minority Medicare, Medicaid, and other beneficiaries served by CMS. 24
These individuals constitute an ever-increasing percentage of the total—
for example, nearly 20 percent of Medicare beneficiaries and 60 percent of
all individuals receiving assistance through Medicaid.25 Persistent health
inequities26 among population groups in the United States are not only
unacceptable as characterized by the Institute of Medicine in its landmark
2003 report,27 but also costly,28 contributing substantially to the nation’s
spiraling health care costs.
CMS’s key role in the transformation of the nation’s health care sys-
tem has been noted throughout this report. The significance of that role
in addressing health disparities is also critical. It will not be possible
for CMS, and the nation as a whole, to cross the “quality chasm”29 and
achieve the transformation of the nation’s health system if the needs of all
populations are not addressed in an equitable manner.
The committee is aware that strategies to reduce health disparities are
receiving high-priority attention by CMS, HHS, and the U.S. Congress.30
24 See, for example, David C. Goodman, Dhannon Brownlee, Chaing-Hua Chang, and
Elliott S. Fischer, 2010, “Regional and Racial Variation in Primary Care and the Quality
of Care Among Medicare Beneficiaries,” from the Dartmouth Atlas Project, available at
http://www.dartmouthatlas.org/downloads/reports/Primary_care_report_090910.pdf,
last accessed August 1, 2011; and Tracy Onega, Eric J. Duell, Xun Shi, et al., 2010, “Race
Versus Place of Service in Mortality Among Medicare Beneficiaries with Cancer,” Cancer
116(11):2698-2706.
25 Kaiser Family Foundation, “Distribution of Medicare Enrollees by Race/Ethnicity,
States (2008-2009), U.S. (2009),” available at http://www.statehealthfacts.org/comparebar.
jsp?ind=297&cat=6, accessed August 1, 2011; and Kaiser Family Foundation, “Distribution of
the Nonelderly with Medicaid by Race/Ethnicity, States (2008-2009), U.S. (2009),” available
at http://www.statehealthfacts.org/comparebar.jsp?ind=158&cat=3&sub=42, last accessed
August 1, 2011.
26 Agency for Healthcare Research and Quality, 2010, “Disparities in Healthcare Qual -
ity Among Racial and Ethnic Minority Groups: Selected Findings from the 2010 National
Healthcare Quality and Disparities Reports,” available at http://www.ahrq.gov/qual/
nhqrdr10/nhqrdrminority10.pdf, last accessed August 1, 2011.
27 Institute of Medicine, 2003, Unequal Treatment: Confronting Racial and Ethnic Disparities in
Health Care, Washington, D.C.: The National Academies Press.
28 Thomas A. LaVeist, Darrell J. Gaskin, and Patrick Richard, 2009, The Economic Burden of
Health Inequalities in the United States, Report by the Joint Center for Political and Economic
Studies, available at http://www.jointcenter.org/sites/default/files/upload/research/
files/The%20Economic%20Burden%20of%20Health%20Inequalities%20in%20the%20Unit-
ed%20States.pdf, last accessed August 1, 2011.
29 Institute of Medicine, 2001, Crossing the Quality Chasm: A New Health System for the 21st
Century, Washington, D.C.: National Academy Press.
30 U.S. Congress, 2009, “Addressing Health Care Disparities,” Congressional Record, Sec.
1946, November 19, S11734.
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ANTICIPATING A DATA-CENTRIC FUTURE
It has also noted the findings of agencies and other observers, both within
and outside government, that the availability of data, disaggregated by
race, ethnicity, primary language, and other factors, is essential for the
accomplishment of this goal. Available evidence indicates, however, that
adequately categorized, complete, and comprehensive data, collected by
systematic and effective means, currently are not readily available at CMS.
CMS race/ethnicity data are of uneven quality with respect to accu-
racy and completeness, as documented by reports produced by HHS,
IOM, and other agencies, as well as testimony received by the com-
mittee. For example, although the Social Security Administration (SSA)
modified its data collection practices in 2008 to follow the categorization
standards promulgated by the Office of Management and Budget (OMB),
the updated procedures apply only to new Social Security and Supple -
mental Security Income claims and replacement number and lost card
applications. These revised OMB standards, which provide for detailed
race/ethnicity categories, do not apply to applications filed before 2008
or to applications received under SSA’s Enumeration at Birth process,
which precludes the collection of race and ethnicity data because of states’
restrictions.
Recent developments, however, are encouraging. Although CMS’s
report to Congress as required under the Medicare Improvements for
Patients and Providers Act (MIPPA) of 2008 was not available for the com-
mittee’s review, it is expected to reflect new and innovative “approaches
. . . for identifying and collecting and evaluating data on health care
disparities on the basis of race, ethnicity, and gender” as mandated by
the MIPPA.31 CMS’s requirement of those receiving EHR meaningful-use
incentives to collect data on race, ethnicity, primary language, and other
factors is an opportunity to “connect the dots,” revealing and tracking
health care patterns and trends by population and subpopulation in rela -
tion to the quality of services received—not just for CMS beneficiaries but
also for a much wider population of health care consumers.
CMS leadership also gave presentations to the committee on the
primacy of equity in advancing a health quality agenda.32 And indeed,
the value of these data has been succinctly described by HHS Secretary
Kathleen Sebelius, who stated in her March report to Congress: “Improve-
ments in the way data is collected help to pinpoint and address where
31 Medicare Improvements for Patients and Providers Act of 2008, HR 6331, 110th Con -
gress, 2nd session.
32 Terris King, CMS Office of Minority Health, 2011, “Health Disparities,” presentation to
the committee, April 18, Baltimore, Md.
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118 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
health disparities exist.”33 CMS’s role in collecting and reporting race/
ethnicity, gender, and other disaggregated health data is critical to the
equitable delivery of quality health services to all CMS beneficiaries. Thus
CMS’s vision, strategies, and priorities for the use of information technol -
ogy as well as its organizational and strategic technology plans will have
to take this role and its requirements into account.
FIGHTING FRAUD
CMS’s Center for Program Integrity faces a formidable challenge in
dealing with fraud, waste, and abuse within the Medicare and Medicaid
programs. The following major elements of criminal fraud were identified
for the committee:
• Registration of fraudulent providers and/or suppliers, most com-
monly involving providers of durable medical equipment;
• Fraudulent use of an existing provider number;34
• Fraudulent, duplicative, or excessive billings by an existing pro-
vider who is also delivering legitimate services;35
• Theft of beneficiary identification; and
• Fraud in which the beneficiary participates (for example, billings
for expensive services not rendered, with profits split with the beneficiary).
At present, only a tiny minority of claims are reviewed prior to pay-
ment. But as has been noted by both the current administrator of CMS
and the secretary of HHS,36 and as industry experience demonstrates,37 it
is materially more productive and efficient to identify questionable bill-
ings in advance of payment. An ability to analyze all claims prior to pay -
ment as a basic element in CMS’s fraud detection would enable unusual
patterns to be identified and holds to be placed on those that are most
33 Department of Health and Human Services, Office of the Secretary, Office of the Assis -
tant Secretary of Health, Office of Minority Health, 2011, Report on Minority Health Activities
as Required by the Patient Protection and Affordable Care Act, P.L. 111-148, available at http://
www.healthcare.gov/center/reports/minorities03252011a.pdf, last accessed August 1, 2011.
34 For example, use of a retired physician’s identity.
35 See, for example, Mark Schoofs and Maurice Tamman, 2010, “In Medicare’s Data Trove,
Clues to Curing Cost Crisis,” Wall Street Journal, October 25, available at http://online.wsj.
com/article/SB10001424052748704696304575538112856615900.html, last accessed August
1, 2011.
36 See Healthcare Fraud Prevention Summit video, December 16, 2010, available at http://
www.stopmedicarefraud.gov/videos/fraudprevention_boston.html, last accessed August
1, 2011.
37 Bob Shiflet, 2011, “Fraud Detection and Prevention in Large Scale Systems,” presentation
to the committee, February 17, Irvine, Calif.
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suspicious. This identification necessarily depends on techniques of pat -
tern recognition across multiple data sets, including data sets related to
claims, providers, patients, and third parties and including government
and private data sources. CMS’s current information infrastructure was
not designed to provide the ability to mine data in a timely fashion, even
within a single system, much less across all of them.
Moreover, as certain patterns of fraud or abuse become identifiable
and bad actors modify their strategies, CMS will need to continuously
modify its approach and responses. As it is notoriously complex to retrofit
legacy systems to deliver the agility and flexibility to meet these chal-
lenges, new information management and analysis solutions will have to
be designed to provide this agility.
To prevent the registration of false providers and suppliers, CMS
will have to develop strategies to deal with the 18,000 Part A and B
provider enrollment applications and 900 curable medical equipment
supplier applications received each month.38 At present the steps taken
to ensure that only genuine providers and suppliers are enrolled include
surprise site visits as well as a focus on high fraud areas. External data
such as data on location are available on new business concerns. Using a
modernized claims payment system, however, it would also be possible to
conduct intensely focused pattern analysis of claims submitted by newly
enrolled providers to detect outliers with high billing rates that can in
turn be subject to on-site inspection. Similarly, pattern analysis of claims
submitted by all providers for beneficiaries who have reached retirement
age is likely to be productive in terms of identifying unusual and suspi-
cious changes in billing behavior.
The current separation of the Medicare and Medicaid programs
allows duplicate billings by the same provider for the same service. Merg-
ing that information in ways that allow detection of this sort of duplica-
tion would be useful. Using insurance exchanges to correlate data across
all plans can also yield information about patterns of fraudulent activity
that should make fraud detection more rapid and efficient. The committee
heard arguments that reduction of fraud in the Medicare and Medicaid
systems might be more easily accomplished through a preventive stance
rather than an emphasis on detection and enforcement after fraudulent
claims have been submitted.39 For example, with the increasing adoption
of electronic health records, there are improved opportunities for detect-
ing fraud when a patient is being scheduled or seen, or as a fee-for-service
38 CMS, 2010, “Partner with CMS,” website, available at https://www.cms.gov/
Partnerships/Downloads/72010NMEPFraudandAbuse508.pdf, last accessed August 8, 2011.
39 Donald W. Simborg, 2011, “CMS IT and Fraud,” presentation to the committee, February
17, Irvine, Calif.
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120 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
bill is being generated.40 Similarly, some of the metadata in EHRs could
serve to identify patterns that suggest improper billings, such as notes
written before the official date of service.
Although it is CMS that faces the onus of dealing with fraudulent
claims when they are submitted, and the cost to the nation is generally
judged to be enormous, partnerships with other agencies, and particularly
the Office of the National Coordinator, may be required to effect some of
the needed innovation. Some have argued that EHR vendors should be
required to address these issues in their products, which raises the pos-
sibility of anti-fraud capabilities being incorporated into future criteria for
meaningful-use payments. The Small Business Jobs Act of 2010, passed
in September 2010,41 directs CMS to use predictive modeling and other
techniques to identify improper claims and prevent the payment of such
claims.42 CMS began using a new fraud management platform in July
2011.43 At present, however, the primary focus on fraud and abuse at
CMS continues to be in the post-billing payment arena, where CMS has
greater control but still faces significant challenges in recognizing fraud
before bills are paid, after which funds can be recovered only with great
difficulty.
In addition to criminal fraud, The CMS Center for Program Integrity
must also monitor a number of complex rules about physician behavior.
The anti-kickback statute44 and the physician self-referral statute45 forbid
activities that may appear innocent to a new provider, such as the offer
of a “medical directorship” or other position that involves generous pay -
40 D.W. Simborg, 2008, “Healthcare Fraud: Whose Problem Is It Anyway?” Journal of the
American Medical Informatics Association 15(3):278-280; D.W. Simborg, 2011, “There Is No Neu-
tral Position on Fraud!” Journal of the American Medical Informatics Association 18(5):675-677.
41 Public Law 111-240, Small Business Jobs Act of 2010, 124 Stat. 2504, September 27, 2010.
42 As stated by CMS,
“The Small Business Lending Act, which was signed into law on September 27, 2010,
included an anti-fraud provision requiring that CMS implement new software with “predic-
tive modeling,” a type of analytical technology that already has been adopted in the credit
card industry to identify potentially fraudulent bills. The provision requires CMS to launch
a competitive bidding process by January 2011 for predictive modeling software contrac -
tors and to begin implementing the technology by July in the ten states with the highest
Medicare fraud rates. A key driver to the success of Program Integrity (PI) at CMS is data
integration—across programs and across patient, provider, and plan domains.” See CMS,
2010, “Modernizing CMS Computer and Data Systems to Support Improvements in Care
Delivery,” December 23, available at https://www.cms.gov/InfoTechGenInfo/Downloads/
CMSSection10330Plan.pdf, last accessed October 21, 2011.
43 CMS, 2011, “New Technology to Help Fight Medicare Fraud,” press release, June 17,
available at http://www.cms.gov/apps/media/press/release.asp?Counter=3983, last ac -
cessed September 12, 2011.
44 Within the Medicare and Medicaid Patient Protection Act of 1987, 42 U.S.C. §1320a-7b.
45 Section 1877 of the Social Security Act, enacted in 1989, also referred to as the Stark law.
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ment for little work. There is every reason to believe that good pattern
analysis will be productive here as well.
DATA GOVERNANCE
Chapter 4 discusses issues related to internal data governance in
CMS, and previous sections of this chapter discuss the potential utility
that comes with the enormous amount of data that CMS already has, as
well as data that will be generated in the future and will involve specific
governance issues. As CMS prepares for a data-centric future, a number
of questions will merit careful consideration. This section describes some
of them, but the list not intended to be exhaustive.
• What is the scope of the data that will be available? The scope can range
from national-level summary data to data with granularity at the level of
states, counties, cities, individual institutions, specific providers, or even
individual patients.
• What is the nature of the data to be provided? The data could be billing
codes or could include overview-level clinical summaries. There might
even be such specificity as clinical details and short-term outcomes. Even
more specific would be data on long-term outcomes and the follow-up
regarding patient status in the months or years after care, or long-range
data on lifetime cumulative health status.
• Who will have access to the data? Although CMS itself and other pay-
ers or their proxies (for example, insurance companies, state Medicaid
agencies, and so on) are among those that are likely to have first-order
access, there will also be interest on the part of the providers and others
from whom the data on quality and cost-effectiveness data are collected.
In addition, as discussed in Chapter 1, an even broader range of potential
data users includes academic researchers, public interest groups, certifi -
cation bodies, and disease-focused societies that have an interest in CMS
data. CMS might even choose to post some data sets (with suitable pri-
vacy protections) on data.gov and make them accessible to anyone.
• Different access and use models will have very different gover-
nance models. Access to the data of CMS and other payers would require
relatively simple agreements and access authorization and authentication.
Opening up data to broader groups such as researchers might require
institutional review board approval to examine limited data sets. Wide
public access has the potential to be exciting—opening up the possibility
of a health information economy by allowing anyone to develop inno-
vative analytic measures from the data—but would also raise concerns
about such things as the residual identifiability of individual-level data,
biased competitive use of the data, and so on. Such broad disclosure
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122 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
would also allow CMS to share the responsibility to define the “right”
derived data with other analysts. This could reduce CMS’s own admin -
istrative costs, reduce the cost of data to potential users, and permit the
growth of profitable businesses to do useful analyses.
• How are the data organized? Data could be stored centrally within
a CMS repository or distributed in some federated manner that keeps
the data closer to their source. There are of course tradeoffs involved in
whether and how CMS collects and stores detailed clinical data. An in-
depth discussion of this question is beyond the scope of this report—but
such choices have clear implications for CMS’s business and information
ecosystems, and so the committee outlines some of these issues briefly.
Benefits to a centralized approach could include:
—The comprehensive ability to measure detailed outcomes;
—Support of large-scale research on comparative effectiveness;
—Public and institutional access to unbiased summary data; and
—Potential for consumers to have direct access to their own inte-
grated data, experience with cases similar to their own, alternative treat -
ments, and so on.
Offsetting these benefits are:
—Technical challenges of operating a national data warehouse for
all clinical data;
—Creation of a prime target for security threats; and
—Political challenges related to the role of government with regard
to such data.
There are more issues to be sorted out than these, but such tradeoffs
will have to be considered carefully.
Other organizational issues include whether the data are left in het -
erogeneous forms or transformed into a common form on the basis of a
consensus set of standards or ontologies, and whether any standards or
ontologies extend to the representation of metadata.46 Finally, the desired
timeliness of data releases may have to be balanced against the desire
for increased utility that will come with transformation into a common
format.
• Who is in charge of providing the data? This responsibility could be
located within CMS or organized along the lines of a consortium. The
responsibility for privacy protections—which may be the largest public
46 A nexample of the rationale for the use of metadata would be what was recommended
by the President’s Council of Advisors on Science and Technology in its December 2010
report to the President, Realizing the Full Potential of Health Information Technology to Improve
Healthcare for Americans: The Path Forward, available at http://www.whitehouse.gov/sites/
default/files/microsites/ostp/pcast-health-it-report.pdf, last accessed August 8, 2011.
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concern—also bears consideration. Another option is a repository, struc -
tured similarly to the National Center for Biotechnology Information. 47
• What does CMS expect to gain from providing the data? In the short
term, CMS’s provision of data may lead to faster, more efficient versions
of today’s payment systems, the opportunity for increased scrutiny of the
data, and the possibility of innovative analyses by allowing many stake-
holders to analyze both their own and others’ experiences as reflected in
the data. In the longer term, broader access to data may lead to the con-
struction of better models of clinical outcomes and subsequent improved
guidelines for the delivery of high-quality and cost-effective care. It could
also foster greater competition among providers and provider organiza -
tions on price and quality, by making measures available and by allowing
organizations to manage care processes to improve their performance in
relation to accepted measures.48
The increasing use of partnerships may allow CMS to see progress
in these various arenas of change without being directly and primarily
engaged in them. For example, CMS could engage through contracts
with consortia of medical and surgical specialty societies that have valu -
able registries of data and that can, working with CMS, reduce the direct
burden on CMS of doing such work alone. These collaborations will also
put a premium on data security.
CONCLUSION
Recent pieces of legislation—including the Medicare Improvements
for Patients and Providers Act of 2008, the HITECH provision of the
American Reinvestment and Recovery Act, and the Patient Protection
and Affordable Care Act—have the potential to improve health care in the
United States, and much of that change is data-dependent.
The effective analysis and management of data have the potential to
reduce costs, by giving providers the information necessary to choose
effective treatments and also by allowing CMS to identify improper pay-
ments and prevent fraud; improve overall public health by reducing
disparities in treatment and also by rewarding effective outcomes; and
empower consumers by providing them with information to manage their
own health and also by providing them with information on the quality
47 This model would have an effect on the organization of the data and involve direct
interaction with those that fund these capabilities.
48 Other possible transformations are outlined in previous sections of this chapter as
well as in Institute of Medicine, 2001, Digital Infrastructure for a Learning Health System: The
Foundation for Continuous Improvement in Health and Health Care, Washington, D.C.: National
Academy Press.
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124 STRATEGIES AND PRIORITIES FOR INFORMATION TECHNOLOGY AT CMS
of providers. A learning health care system should emerge over time,
improving the quality, equity, and safety of care for both individuals and
populations.
Achieving these goals will be neither easy nor automatic, but with
careful attention to the development of a robust, data-driven environment
and culture as described in the previous chapters, it is possible.
