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Volume 5, Number 3 Research Question Review 31 DECEMBER 2021
What Factors Inuence Physicians’ Billing
Accuracy ?
By
Samantha J. Champagnie, University of South Florida
Copyright © 2021, Samantha J. Champagnie. This article is published under a Creative Commons BY-NC license. Per-
mission is granted to copy and distribute this article for non-commercial purposes, in both printed and electronic
formats
The pervasiveness of billing errors has
drawn national attention to the cost
of healthcare.
While there is debate
over the exact econom-
ic impact, experts agree
it’s in the billions of dol-
lars. Yet, few strategies
have proven effective in
reducing billing errors.
This research examines
the existing literature
to explore theories and
constructs that explain
causal factors of billing
errors and illuminate
strategies for improving billing accuracy.
This paper leverages Leavitt’s system mod-
el of organization change as an organizing
framework. Each of
Leavitt’s four compo-
nents – task, people,
structure, technology
- is evaluated against
the backdrop of bill-
ing accuracy. The
paper begins with a
brief overview of the
concepts of billing
accuracy, which give
context to the subse-
quent discussion on
contributory factors.
Gaps in the literature are identied and future
areas of focus for research studies are proposed.
Medicare loses billions annually to
physician billing errors. Medicare
Part B evaluation and management
services are among the most prev-
alent errors. Opinions vary on the
causes and appropriate counter-
measures of these errors. As of yet,-
no strategies have proven effective
in reducing these errors.
Keywords: Evaluation and management, coding accuracy, documentation, billing errors, fraud, medi-
cal billing, medical necessity, up-coding, health information technology.
Physicians’ Billing Accuracy
10 Volume 5, Number 3
Physician billing errors continue to cost the U.S.
government billions in improper payments with no
viable solution on the horizon. e Oce of Inspec-
tor General (OIG) for the United States Department
of Health and Human Services denes improper
payments as expenses incorrectly paid because they
did not meet federal rules and guidelines for reim-
bursement (U.S. Department of Health and Human
Services, 2014). According to the OIG, evaluation
and management (E/M) services (oce visits per-
formed by physicians and non-physician practi-
tioners to manage a patient’s health), are 50% more
likely to be paid in error than other Medicare Part
B services (U.S. Department of Health and Human
Services, 2014). E/M services represent an estimated
30% of Medicare Part B services and nearly 42% of
all Part B improper payments. ese alarming statis-
tics mandate a strategic focus on reducing physician
billing errors.
In 2016, the OIG reported the rst reduction in
overall errors since 2010 (See Figure 1). While over-
all error rates declined, E/M billing errors remained
virtually at. (U.S. Department of Health and Hu-
man Services, 2017). As Figure 1 illustrates, in each
subsequent year aer 2016, the overall error rates
(for all medical expenditures) continued to decline
while the Part B error rate, which represents physi-
cian services, consistently exceeded the overall error
rate with only modest declines year over year. is
discrepancy between the performance of the overall
error rates and the Part B error rates, suggests health
care policy makers have not given sucient atten-
tion to physician oce visit coding.
e persistent and pervasive nature of medical bill-
ing errors led to the research question: What factors
inuence physicians’ E/M medical billing accuracy?
e goal of this research study is to synthesize prior
research on medical billing errors in E/M services
and, in doing so, identify potential strategies for im-
proving billing accuracy.
While Medicare only provides about one h of the
healthcare of the US population (QUOTATION) it
is the lead author of billing and reimbursement rules
within the United States. All other commercial pay-
ers follow Medicare’s lead. Consequently, although
this paper focuses on the impact to the Medicare
fund, the lessons and consequences arrived at are
equally applicable across all other payer types.
e remainder of this paper is structured as follows:
First the literature review methodology is discussed.
en a brief background is presented on billing accu-
racy, medical record documentation and evaluation
and management coding. Next, to synthesize the re-
search ndings, the paper leverages Leavitt’s (1965)
system model of organizational change initiatives
as a framework to perform the research analysis. A
discussion follows on each of the contributory fac-
tors of evaluation and management errors within the
Leavitt’s diamond framework. e paper concludes
with recommendations for future research.
BACKGROUND
What are Billing Errors
is study denes billing errors as the act of sub-
mitting an incorrect claim for payment. Billing for
services that were either never performed or over-
charging for medical services provided (upcoding)
Figure 1: Comparison of US Billing Error Rates for all Medical Expenditures to Part B only Billing Er-
ror Rates (2015-2018)
Source: U.S. Department of Health and Human Services Improper Payment Rate Reports 2015 -2018
Muma Business Review 11
Champagnie
are the most common types of billing errors (Doan,
2011). ese types of errors are considered Medicare
fraud and may be punishable by the U.S. govern-
ment under the false claims act (Sage, 1999). Un-
doubtedly, there are unscrupulous physicians and
non-physician practitioners (hereinaer referred to
collectively as physicians) who systematically bill for
services not performed and routinely up-code their
services. One has only to look at the OIG most want-
ed physicians to understand the pervasiveness of the
problem.
Upcoding Evaluation and Management
(E/M) Services
E/M services have varying billing levels which re-
ect the level of complexity of the patient visit (See
Appendix A2 for E/M billing rubric). e physician
must justify the level of billing based on several dif-
ferent parameters, such as the patient’s history of
symptoms, the extent of physical examination per-
formed, and the complexity of the physician’s medi-
cal decision-making (Centers for Medicare and Med-
icaid Services, 2010). According to the CMS (2010),
it would not be medically necessary or appropriate
to bill for a higher level of E/M service when accord-
ing to the billing rubric, a lower level of service is
warranted. Physicians are responsible for ensuring
that the charges they submit to Medicare accurate-
Protocol
According to Hart (2018) a good literature
review justies the inclusion and exclusion
criteria to demonstrate the relevance of se-
lected articles. Given that it was not possible
to nd and review all articles on this subject, a
systematic process was required. To this end,
this paper utilized a systematic, heuristic and
iterative approach to identifying and selecting
the most relevant research papers.
• First, we started the research using Med-
line databases. We found very few aca-
demic articles using this type of search
and so expanded the search to other
general databases like ABI-Inform and
Google Scholar). Finally, to understand
the practitioner viewpoint, we expanded
the search to include practitioner trade
journals.
• The bibliographies of the selected arti-
cles proved to be a rich pool of relevant
sources. Consequently, most of the
referenced articles were sourced from
citations in the articles found in previous
searches. The search protocol is depicted
below:
Figure 2: Literature Review Process
Physicians’ Billing Accuracy
12 Volume 5, Number 3
ly reect the E/M services provided and the billing
levels corresponding to those services. Upcoding,
the most common form of E/M billing error, occurs
when physicians choose to bill for, and therefore are
paid for a higher level of service than was actually
performed (Ornstein, C., Grochowski, R.J.,2014).
Implications of Medical Billing Errors
Medical billing errors are a matter of national con-
cern because they carry negative consequences not
only to the U.S. economy but also to healthcare
policy and the nancial viability of physician or-
ganizations (Doan, 2011; Hyman, 2002; Lorence &
Richards, 2002). e most signicant implications of
billing errors are described further below and sum-
marized in Table 1.
DISCUSSION
A Framework for Understanding Caus-
al Factors Inuencing Billing Accuracy
In his seminal work on organizational change, Harry
Leavitt introduced a framework that is now known
as Leavitt’s Diamond (Leavitt & Bahrami, 1988). Ac-
cording to Leavitt & Bahrami (1988), there are four
components to all organizations: people, technology,
structure, and task. is model proposes that an or-
ganization can approach change through any one of
these four components. is framework is also use-
ful in conceptualizing the factors that inuence med-
ical coding and billing accuracy in physician orga-
nizations. is research organizes and discusses the
concepts found in the literature according to each of
Leavitt’s four components. Appendix A1 contains a
full list of articles used categorized by organizational
component and focus of the publication.
Components of Leavi’s Diamond
People – is component deals with the skills,
knowledge, and abilities of the employees of the or-
ganization. Leavitt and Bahrami (1988) assert that
for any change to be successful, consideration must
be given to whether the current skills, knowledge
and abilities of the employees are adequate for their
tasks and the technology they are expected to use.
Task – is component in the organization has to
do with what things are being done in an organiza-
tion and what the organization is trying to achieve.
A change in an organization may involve new ways
and methods of getting work done. Old methods of
Table 1: Implications of Medical Billing Errors
Legal Implications Economic Implications Health Policy Implica-
tions
Financial Implications
Exclusion from partic-
ipation in government
programs
Over 40% of physician
payments are incorrect
leading to billions in
losses to Medicare.
Systemic misreporting
of medical coding data
undermines national
and international com-
parative population
health analysis
Financial viability of
physician practices
compromised by high
administrative burden
of billing rules and
regulations.
Civil Penalties between
$5,000 and $10,000 per
oce visit plus three
times damages sus-
tained by US govern-
ment.
Physician improp-
er payment rate was
10.1% in 2012 costing
CMS $34 billion in
incorrect federal pay-
ments.
Negative impact to
patient care due to phy-
sician distraction and
concern/worry about
audits and cumber-
some guidelines
Physicians may un-
der-code to avoid
threats to legal penal-
ties which in turn leads
to loss of revenue and
threatens the viability
of physician practices.
Disciplinary action
such as Corporate
Integrity Agreements
mandating strict and
oen invasive remedial
compliance eorts.
Government account-
ability oce reported
$50 billion in 2008 in
improper payments
from physician docu-
mentation and coding
errors; An estimated
3%to 10% of health-
care spending is lost to
healthcare fraud and
abuse.
Poor medical data
quality jeopardizes
quality and continuity
of patient care.
Anti-fraud laws dis-
proportionately impact
small-business provid-
ers who don’t have the
same resources, infra-
structure and training
support as other large
physician organiza-
tions.
Muma Business Review 13
Champagnie
completing work may become obsolete and replaced
with new methods.
Structure - e structure of an organization general-
ly refers to the system of hierarchy and the relation-
ship between the dierent levels of the organization.
Leavitt and Bahrami (1988) posit that it also refers
to how tasks are divided up amongst people – where
the responsibility for getting work done lies - and
standard procedures for getting work done within
an organization.
Technology - Technology is that component of the
organization that aids or facilitates people in per-
forming their tasks. It refers to the processes by
which tasks are completed and the systems used to
facilitate these processes (Leavitt & Bahrami, 1988).
Considering the prevalence of the errors and their
persistence over time, improvements in medical
documentation and billing accuracy will require
signicant change initiatives within any physician
organization. erefore, the prevailing theories and
concepts to improving physician accuracy are ex-
plored through the lens of Leavitt’s organizational
change model. In juxtaposing Leavitt’s Diamond
with concepts present in academic literature, the
processes and relationships Inuencing physicians
coding errors can be depicted in the following con-
cept map (Figure 3).
People
e impact of physicians’ attributes such as
knowledge, attitudes, and experience on medi-
cal billing accuracy emerged as a key theme in
the literature review. Leavitt and Bahrami (1988)
postulate that people are a key consideration of
any change initiative because their skill sets, and
attitudes greatly aect the success of change in
any organization. Accordingly, this paper ex-
plores the impact of each of these attributes on
billing accuracy.
Knowledge
ere is overwhelming agreement in the literature
that a knowledge decit exists in physician coding
and billing. Accurate medical documentation, which
is the foundation for the medical code, must be a
core competency in medical education programs,
yet there is little formal education to help resi-
dent trainees master this process (Howard & Red-
dy, 2018). In their study of the adequacy of billing
and coding training for 1,233 pediatric graduates,
Andreae, Dunham, & Freed (2009) concluded that
pediatric residency programs are failing to prepare
new graduates for legal and nancial responsibilities
of medical billing and coding. Ng & Lawless (2002)
Figure 3. Concept Map: Factors Inuencing Physician Billing Accuracy. (Adapted from Leavitt & Bahrami,
1988)
Physicians’ Billing Accuracy
14 Volume 5, Number 3
noted similar results in their study of 344 outpatient
clinic charts. Another study conducted by Adiga,
Buss & Beasley (2006) demonstrated that second
year internal medicine residents across the country
have a low level of understanding of medical billing,
which they correlated to low test scores in this area.
Yet another study of orthopedic residents yielded the
same conclusions; residency programs lack formal
education and training on basic medical documen-
tation principles (Varacallo, Wolf, & Martin, 2017).
Undeniably, medical billing and coding are critical
components of and medical practice and failure to
prepare medical students for this task has manifest-
ed in billions in improper payments.
Experienced physician practitioners fare no better.
Several studies also found poor documentation prac-
tices from highly experienced physicians (Chao et
al., 1998; Holt, Warsy, & Wright, 2010; King, Sharp &
Lipsky, 2001; Zuber et al., 2000). In these studies, the
authors found under-coding to be more prevalent in
established patients and over-coding more common
in new patients. e theory of availability heuristics
(Tversky & Kahnerman, 1982), provides one possi-
ble explanation for this discrepancy. e coding cri-
teria for new patients is stricter and more complex
than for established patients. However, physicians
may be uncertain about the additional complexity
and resort to the familiarity of the coding guidelines
for established patients. A similar theory is Simon’s
(1956) satiscing theory. is theory suggests under
time pressures and limited knowledge, physicians
apply the guidelines they are most familiar with uni-
formly across all patient types – in other words, they
may guess at the appropriate code rather than search
for the more appropriate code (Zuber et al., 2000).
ese studies suggest, regardless of experience level,
billing errors are common across all physicians.
Despite the consensus that a knowledge decit ex-
ists, there have been only a few studies that investi-
gated the longitudinal impact of training and edu-
cation programs on medical data quality and billing
accuracy. One study found signicant improvement
in knowledge aer educational sessions, but this
study did not measure long term retention and is
not transferable to clinical practice (Varacallo et al.,
2017). Nguyen et al (2017) performed a similar study
but found no impact on coding accuracy. Nonethe-
less, as a preventative measure to billing errors, the
literature calls for formal graduate education pro-
grams during residencies and fellowships and as a
prerequisite to board certications (Agrawal, Taits-
man, & Cassel, 2013; Howard & Reddy, 2018).
Attitudes
While not empirically tested, a few authors have
oered behavioral theories to explain the ongoing
problem of coding and billing errors. Brennan and
Probe (2011) and Cohen and Sa (2001) both conduct-
ed studies using self-ecacy theory as the conceptu-
al framework. According to self-ecacy theory, peo-
ple change their behavior if two conditions are met:
a) they believe that a change in their behavior will
lead to the desired outcomes and b) they hold a per-
sonal conviction that they can successfully perform
those behaviors (Bandura, 1978). In their study of
the coding and billing patterns of orthopedic trauma
surgeons, Brennan et. al (2011) found that although
physicians understood the negative consequences of
billing and coding errors, the system was so complex
that physicians did not put forth the eort to learn
to accurately perform the task. Similarly, Cohen
et al. (2001) posit that strengthening nurse practi-
tioner attitudes and knowledge towards correct cod-
ing would subsequently lead to an improvement in
accuracy. In their investigation, Cohen et. al. (2001)
administered a survey to 69 nurse practitioners eval-
uating attitudes towards medical coding. ey found
that most respondents did not nd this task to be a
rewarding part of their job, believed coding was an
added stressor, and found it dicult to code accu-
rately due to time constraints. ese activities were
typically delegated to clerical billing sta, regardless
of their level of coding and billing experience.
Resource dependency theory is another type of be-
havioral theory found in the literature to explain
physician attitudes and their inuence on billing ac-
curacy, specically up-coding. Resource dependen-
cy theory (Pfeer & Salancik, 2003) postulates that
external inuences (in this case, the U.S. system of
healthcare reimbursement) may lead physicians to
intentionally up-code to maximize reimbursement -
there is a 25% to 30% increase in reimbursement for
each higher level of coding (see Appendix A2). Con-
sequently, some researchers argue that under con-
ditions where the physician has to choose between
the highest possible billable code or the appropriate
code for service provided, the higher billable code
will oen prevail (Adams, Norman, & Burroughs,
2002; Brunt, 2011). In the same vein of prot max-
imizing behavior, Lorence et al. (2002) argue that
the nancial incentive to optimize reimbursement
largely outweighs the risk of government anti-fraud
enforcement eorts.
However, these arguments overlook the fact that the
medical documentation determines the appropriate
code. Howard & Reddy (2018) support this assertion
in their argument that improving physician docu-
mentation, versus punitive measures, should be the
focus of billing and coding improvement eorts. In
their study of 100 patient charts, Howard & Reddy
(2018) found that the documentation in the medical
record only supported the medical code billed 31%
of the time, leading to a high rate of both under-cod-
ing (billing for a lesser service than provided) and
over-coding errors. Further refuting the argument
of motivations of personal gain, in their studies of
Muma Business Review 15
Champagnie
Table 2: How People in Organizations Inuence Billing Accuracy
People
Attribute Findings Source
Knowledge
Study of second year internal medicine residents revealed low test
scores and low level of understanding of Medicare coding and bill-
ing guidelines and documentation requirements.
Adiga et al., 2006
“Despite the enormous resources at stake, physicians receive little
education in how to manage and steward nite resources making
formal education of physicians in “program integrity” an essential
component of medical professionalism” (pg. 1115)
Agrawal et al.,
2013
“Residency programs must enhance this training component to
prepare physicians to maintain a nancially viable practice. (pg.
939).
81% of generalists and 78% of subspecialist indicate they could
use more training in billing and coding. Fewer than 20% of study
participants reported their training was adequate.
Andreae et al.,
2009
Nurse practitioners are ill prepared for the task of medical coding.
Test results show a 2.27 mean knowledge score on a 10-point scale.
Authors assert the development of coding skills must be an ongo-
ing process.
Cohen et al.,
2001
“Resident training programs inadequately prepare medical stu-
dents for medical coding and documentation despite it being a
core competency in medical practice.”
Howard et al.,
2018
Pediatric residents are not adequately trained in proper coding
practices which leads to high rates of billing errors and substantial
reimbursement discrepancies.
Ng, Lawless,
2001
Study demonstrated decit in basic coding and billing principles
among residents and fellows. Following a controlled intervention
whereby residents were provided with specic coding and billing
training, participants demonstrated improved understanding and
application of the relevant concepts.
Varcallo et al.,
2017
Experience
Study found no statistical dierence in coding accuracy based on
years of experience. Chao et al.,1998
In an assessment of coding accuracy, study compared coding of
three levels of experience – a professional coder, a family medicine
resident and a residency faculty member. Regardless of level of ex-
perience, study found signicant under-coding of medical record
documentation.
Holt et al., 2010
Using 6 hypothetical progress notes, 300 certied coding special-
ists were asked to assign E/M codes. Study found no statistical
signicance between years of experience and coding accuracy.
King et al., 2002
In one of the largest physician documentation audits conducted,
a study of 1069 patient charts revealed no statistical dierence in
code selection between resident physicians with 2.3 years of coding
experience and attending physicians with 23.3 years of coding
experience.
Zuber et al., 2000
Physicians’ Billing Accuracy
16 Volume 5, Number 3
family physicians, King et.al. (2002) and Kikano et.
al. (2000) both found that the rate of up-coding er-
rors occurred as frequently as under-coding errors.
Similarly, Zuber et.al., (2000) found under-coding
occurred more frequently than over-coding. Each of
these studies contradict the argument that E/M er-
rors result from physicians’ attitudes of gaming the
system for nancial gain.
ough the results are interesting, these studies were
very limited in scope and generalizability across
larger physician populations. However, the theories
are worthy of additional investigation. Education
and other remedial strategies may have little traction
if physicians are not engaged or do not experience
responsibility for improving their coding accuracy.
Factors such as the attitudes and perceptions of the
physician, while heavily discussed, have never been
empirically tested. By examining physician attitudes,
policy makers and healthcare administrators may
develop more eective countermeasures to reduce
medical coding errors.
Experience
It seems reasonable that a physician with several
years of coding and billing experience would also
have a high level of billing accuracy. e literature,
however, does not support this assertion. Although
counter-intuitive, several studies show there is no
relationship between a physician’s years of coding
and billing experience and their accuracy (Holt et
al., 2010; King, Mitchell S., Lipsky, & Sharp, 2002;
Zuber et al., 2000). ese studies suggest that even
though a physician is an expert in determining the
patient’s diagnostic and procedural conditions, their
knowledge and experience in the assignment of the
relevant codes for these conditions may be limited.
In their study of 1,069 patient charts from 10 fam-
ily physician oces, Zuber et. al (2000) found no
statistical dierence in code accuracy between res-
ident physicians with 2.3 years of coding experience
and attending physicians with 23.3 years of coding
experience. ese conclusions add weight to argu-
ments that while hiring for experience may not yield
satisfactory results, change initiatives that address
physicians’ knowledge and attitudes may yield more
desirable outcomes.
Task
Task complexity is a signicant contributor to billing
errors. In healthcare, environmental forces outside
the organization contribute to coding task complex-
ity. Leavitt & Bahrami (1988) assert that organiza-
tions are dynamic entities that interact within the
context of environmental factors. ese environ-
mental factors inuence the functioning of the four
components of an organization. Government-im-
posed billing and coding guidelines are reportedly
very confusing and adds to overall task complex-
ity and therefore increases billing errors (Kikano,
Goodwin, & Stange, 2000; King, Mitchell S. et al.,
2002; Zuber et al., 2000).
Coding Rules and Guidelines
e complexity of the coding and billing system is
commonly cited as a primary factor in the magni-
tude of physician error rates. Coding rules are large-
ly subjective and when applied by multiple users, of-
ten lead to dierent results and a high inherent error
rate (King, Mitchell S. et al., 2002). Several studies
have investigated the concordance rate of various
coding experts by testing inter-rater reliability. Con-
cordance rates across numerous studies showed little
agreement among expert coders in their interpreta-
tion of the E/M coding guidelines. For example, Zu-
ber et al. (2000) found a 44% concordance rate be-
tween coding auditors. Similarly, Kikano et al. (2000)
noted a 43% concordance rate among family physi-
cians. ese studies underscore a prevalent concern
in the literature; physicians’ cannot correctly apply
coding guidelines because the E/M coding system is
ambiguous and too complex to be uniformly applied
(Chao et al., 1998; King, M. S. et al., 2001)
While physicians agree that proper documentation
is essential for good patient care and that well-main-
Attitudes
Study results suggest self-ecacy and outcome expectancy con-
structs - Physicians do not put forth the eort to understand cod-
ing and billing rules because the system is too complex.
Brennan et al.,
2011
e conceptual framework for this study was self-ecacy theory
– if NPs believe they can control the outcome they will put forth
more eort in learning proper coding, documentation and billing
procedures.
Cohen et al.,
2001
Authors discuss changing attitudes and perceptions of physicians
(fraud will not be detected or punished) as an approach to design-
ing interventions to combat healthcare fraud.
Rashidian et al.,
2012
Over-coding is driven by intrinsic prot motives of the physician/
organization. 43.5% of survey respondents reported inuences
from senior management to up-code to optimize reimbursement.
Lorence et al.,
2002
Muma Business Review 17
Champagnie
tained records facilitate communication between
patients and physicians, the coding and billing
guidelines are widely seen as cumbersome and dis-
tracting from patient care (Kikano et. al., 2000).
In this environment of rule complexity, ambiguity,
and time restrictions, the medical documentation
ultimately does not accurately capture the level of
services provided. Proponents of a simpler coding
system have advocated solutions such as a) creating
one set of criteria for new and established patients,
b) decreasing the number of potential codes from
which physicians can choose, and c) decreasing the
number of steps involved in arriving at the correct
code (Kikano et al., 2000; King, et al., 2001; Zuber et
al., 2000) while others declare the coding guidelines
“too awed to be xed” (Kikano et al., 2000).
Ultimately, any changes to the current system of
reimbursement would need to be championed by
constituents outside the organization, such as the
American Medical Association and the Centers for
Medicare and Medicaid Services (2019). In other
words, the current structure of reimbursement, in-
cluding the system of penalties for inadvertent cod-
ing errors would require signicant revision from
actors outside the physician organization.
Structure
Organization structure refers to systems of authori-
ty, the locus of control and hierarchical systems for
getting the work done. How work is grouped within
an organization has an impact on business outcomes
and is a key success factor in any organizational
change initiative (Leavitt & Bahrami, 1988).
U.S. System of Reimbursement
e U.S. Government, namely the Centers for Medi-
care and Medicaid Services (CMS) determines how
Physicians are reimbursed for their services. In
collaboration with the American Medical Associ-
ation, CMS determines the specic amount to pay
Physicians for specic services provided (Centers
for Medicare and Medicaid Services, 2019). Many
researchers argue the fee-for-service reimbursement
system invites the submission of false claims (Brunt,
2011; Lorence & Richards, 2002). is system of
reimbursing a physician’s work for example creates
a perverse incentive to bill for work not actually
performed. e more work produced, regardless of
its quality, the more a provider is paid. Some work
carries higher level of reimbursement and therefore
Physician’s may be motivated to bend the rules to ob-
tain the higher level of reimbursement. For example,
if medical decision making is the only dierentiating
criteria between a level 4 and level 5 E/M code (see
Appendix A2), some researchers argue the Physician
has a compelling incentive to document the requisite
medical decision making criteria to capture the 30%
higher reimbursement that accompanies the higher
code (Lorence & Richards, 2002)
Another criticism of this form of reimbursement is
Physicians’ perceptions of the value of their work is
oen incongruent with the government reimburse-
ment schedules. Physician reimbursement sched-
ules are determined by the value (referred to as
Relative Value Units) placed on each type of service
performed (Brunt, 2011). A physician may bill for
TABLE 3: How the Task Component Inuences Billing Accuracy
Findings Source
Related to self-ecacy theory, Brennan et. al posit the physician compensa-
tion system in the U.S. is so complex that it is prone to errors. Surgeons (the
subject of this study) are forced to learn the nuances of the coding system or
risk penalties or missed reimbursement opportunities.
Brennan et al. 2011
Study highlighted low concordance rates between physicians and
trained experts. Study suggests coding system is too complex to hold
physicians accountable to high standards of accuracy.
Chao et al., 1998
si study found a 44% concordance rate between auditors. e study
attributes these dismal results to vague and ambiguous coding guide-
lines.
Kikano et al., 2000
Study revealed low inter-rater reliability - 57% concordance rate be-
tween coding specialist which suggest that the E/M coding guidelines
are too complex. Study recommends modications to environmental
factors changing the coding criteria, decreasing the number of codes
from which physicians must choose or the number of steps involved in
selecting the appropriate code.
King et al., 2002
Findings showed auditors agreed with medical physicians in 15.2% of
the cases.
Zuber et al., 2000
Physicians’ Billing Accuracy
18 Volume 5, Number 3
a higher level of service than the billing regulations
warrant because the code choices do not adequately
reect their scope and intensity of eort (Kumetz &
Goodson, 2013). In these situations, the physician
bills for his services based on his judgement of the
value of the service provided and not based on the
requisite rules for reimbursement. is scenario is
most frequently evident in the family practitioner
whose cognitive eort to diagnose and treat a chron-
ically ill patient with multiple comorbidities is not
adequately represented in the relative value units of
the appropriate billing codes. Investigators posit that
in these instances, the family practitioner will bill for
a higher code (upcoding his services) than the bill-
ing guidelines warrant.
Criminal/Civil Penalties and Fines
e U.S. government not only has the authority to
dole out payments, but it also doles out penalties for
non-compliance. e federal government spends
billions empowering agencies to seek out upcoding
errors. e False Claims Act of 1863 is the prima-
ry enforcement tool under which physicians found
guilty of upcoding could be subject to penalties of
not less than $5,000 and not more than $10,000 (per
oce visit) plus three times the amount of damages
(Doan, 2011). Several healthcare attorneys zealously
argue the government is overly ambitious in its en-
forcement arsenal. Innocent physicians, guilty only
of participating in a cumbersome healthcare reim-
Table 4: How Structure in Organizations Inuence Billing Accuracy
People
Attribute Findings Source
U.S. System of Reimbursement
Physicians will always be incentivized by nancial motivations
when making coding decisions. Adams et al., 2002
Empirical study found the greater the marginal revenue, the
higher the probability of the physician to select a higher billable
code. Despite education eorts, physicians will be motivated to
select the higher billable code.
Brunt, 2011
Authors argue the U.S. system of reimbursement is awed be-
cause of its over-reliance on the resource-based relative value
scale as a mechanism of determining physician reimbursement.
e RVU scale, dating back to 1980’s does not adequately ad-
dress the full range of oce-based evaluation and management
activities, not does it address intensity for complex patient care.
It overvalues procedural care and undervalues cognitive profes-
sional services.
Kumetz et al., 2013
is study determined that despite the risk of penalty of prose-
cution, physicians will continue to be motivated to increase their
own reimbursement by manipulating the coding guidelines in
their favor.
Lorence et al., 2002
Criminal and Civil
Penalties
Assertion is that upcoding errors, while considered fraud and
punishable by civil and criminal nes, may be the result of a
myriad of contributory factors such as an overly complex billing
system. Author maintains the False Claims Act is overly zealous
and misguided in its eorts
Doan, 2011
“A law originally enacted to combat rampant contractor fraud
during the civil war, is now being used to subject physicians to
harsh penalties for what could be inadvertent billing mistakes.
Maxham, 2014
Certied
Coders vs
Physicians
Authors discuss changing attitudes and perceptions of physi-
cians (fraud will not be detected or punished) as an approach to
designing interventions to combat healthcare fraud.
Duszak et al., 2012
Muma Business Review 19
Champagnie
bursement system, can easily get ensnared in its en-
forcement arsenal (Doan, 2011; Maxham, 2014).
ese enforcement measures while intended to curb
upcoding billing errors oen have the opposite im-
pact of creating under-coding errors. To escape civil
and criminal litigation for innocent billing errors,
physician safeguard their practices by under-coding
their oce visits . While not a cost to the govern-
ment, under-coding can have signicant nancial
implications to the physician practices. By some
health economists’ estimates, undercoding cost phy-
sician practices millions in lost revenue annually
(Brennan & Probe, 2011; Holt et al., 2010).
Certied Coders vs Physicians
One approach to neutralizing perverse incentives to
over-code is centralizing the medical coding func-
tion. While the practitioner literature extensively de-
bates the benets of a centralized versus decentral-
ized approach to coding, the academic literature is
mostly silent on this subject. Our review found one
study whose conclusions recommend changes to or-
ganizational structure to address problems of coding
accuracy. According to this study, “to optimize cod-
ing accuracy, all physician-coded IR cases warrant
review by an experienced individual, preferably a
Radiology Certied Coder, before nal claims sub-
mission” (Duszak, Blackham, Kusiak, & Majchrzak,
2004).
Technology
It was anticipated that electronic health records
would improve patient safety and reduce the cost
of healthcare. A decade aer their emergence, the
anticipated benets are not yet realized while unin-
tended consequences threaten their usability (Ad-
ler-Milstein & Jha, 2014; Bowman, 2013; Ganju,
2016). Technology can be an eective enabler of bill-
ing accuracy, but its adoption, utilization and train-
ing rst must be eectively managed.
Technological Limitations
Practitioners were hopeful that the introduction of
EMRs would improve billing accuracy by alleviat-
ing the inherent task complexity in medical coding
(Bowman, 2013). Until the 20th century, physicians
recorded documentation in abbreviated handwrit-
ten notes, which were stored in the patient’s le, usu-
ally in a ling cabinet (Gruber, Shepherd, & Varner,
2002). In 2009, passage of the American Reinvest-
ment and Recovery Act created nancial incentives
for EMR (Electronic Medical Records) adoption.
HIMSS (Health Information Management Systems
Society) specied an EMR Adoption Model, with
eight levels of EMR adoption (Stages 0 through 7;
About 70.2% of U.S. outpatient clinics achieved
Stage 5 or higher by year-end 2017. Despite evidence
of high rates of EMR adoption, concerns about med-
ical documentation and the related billing accuracy
remain, suggesting a failure in EMR capabilities to
oset coding and billing errors (Shachak & Reis,
2010).
EMRs, if leveraged appropriately, could help ensure
that patient data is complete, accurate, legible, and
readily available. For example, many EMRs have
built-in drop-down lists and templates that aid the
physician in the correct code selection and in cre-
ating more complete documentation (Kabene, 2010;
Kumar & omas, 2011). ese capabilities have
great potential to reduce reliance on the physician
to remember all code choices and all relevant doc-
umentation to support the code choices. However,
several studies have cited concern over unintended
consequences of EMR systems. In her study of med-
ical data quality in EMR systems, Bowman (2013)
argues that with the emergence of EMRs, rather than
an improvement in data quality, there is a greater
quantity of bad data recorded in the medical record
which is attributed to 1) improper system use 2)
poor system design and 3) inappropriate documen-
tation capture. EMR features such as standardized
templates, “point and click”, drop-down lists, and
copy-paste functions, while all helpful in reducing
the physician’s cognitive load, are shown to increase
errors and contribute to poor data quality (Rohr,
2015; Shachak & Reis, 2010). ese errors have seri-
ous ramications for fraud and abuse and are linked
with up-coding errors, specically in evaluation and
management billing codes (Cearnal, 2013). Clearly,
the potential of EMRs is not yet fully realized and
“has not improved the low level of importance most
physicians place on documentation” (Rohr, 2015).
Emerging Technological Advancements
Machine learning and natural language processing is
an emerging area of study in facilitating coding ac-
curacy. Natural language processing, which provides
an ability to read unstructured notes, is gaining
popularity in medical record documentation (Rohr,
2015). Despite their promising capabilities, these
technological advancements may not be the silver
bullet they are advertised to be.
In their 2017 Hype Cycle report (Appendix A3),
Gartner describes adoption of specic IT applica-
tions, with ve major steps: Innovation Trigger, Peak
of Inated Expectations, Trough of Disillusionment,
Slope of Enlightenment and Plateau of Productivi-
ty (Shaer, Mann, & Sachdeva, 2017). e Gartner
report explained that three types of coding made
use of natural language processing. Computer-As-
sisted ICD Coding (CAC) had reached the Slope of
Enlightenment. Computer-Assisted Clinical Docu-
mentation Improvement (CACDI) was nearly out of
the Trough of Disillusionment. In 2017 Gartner pre-
dicted that both CAC and CACDI would be widely
adopted “within two to ve years” (2019-2022). A
Physicians’ Billing Accuracy
20 Volume 5, Number 3
third form of computer-assisted coding, Real-Time
Physician Documentation (RTPD), was dropping
from the Peak of Inated Expectations into the
Trough of Disillusionment; it would take longer to
mature. Some soware in this category attempted to
spot patterns in physician notes to suggest possible
co-morbidities and provide other diagnostic and
procedural guidance. ese promises, however, can-
not be realized if physician documentation is incom-
plete or simply inaccurate. As Rohr (2015) puts it,
“the adage ‘garbage in, garbage out’ indicates the risk
of poor documentation when applied to computer-
ized analysis. In other words, if invalid or imprecise
data are entered into a system, the resulting output
will also be of the same caliber.” In the same vein,
one Gartner report cautioned: “CIOs need to steer
their organizations in distinguishing between bold
dreams and readiness for delivering value.” (Shaer
et al., 2017, p.12). As the reports suggest, while these
technological advancements are promising, they
have not suciently matured to make an immediate
impact on today’s coding accuracy dilemma.
CONCLUSIONS AND FUTURE
RESEARCH
e literature review demonstrates there is little
consensus on the contributory factors of billing ac-
curacy. Some researchers attribute billing errors to
physician knowledge gaps and call for more train-
ing and education during and aer medical school
(Adiga, Buss, & Beasley, 2006; Agrawal et al., 2013;
Howard & Reddy, 2018). Other researchers attribute
billing errors to the complexity of the coding guide-
lines highlighting low concordance rates as evidence
of a awed system (Chao et al., 1998; Kikano et al.,
2000; King, M. S. et al., 2001). Despite anti-fraud
eorts, other investigators insist physician malaise
and greed are the true culprits (Adams et al., 2002;
Brennan & Probe, 2011; Lorence & Richards, 2002).
What is clear is that consensus does not exist and
therefore remedial strategies will have limited suc-
cess.
As the previous discussion revealed, there are plenty
of aws with the way Medicare reimburses doctors.
TABLE 4: How Technology in Organizations Inuence Billing Accuracy
Findings Source
e adoption of EMR created serious unintended consequences which could
lead to fraud and abuse and have legal implications. EHRs have added a level
of complexity to the already-complex healthcare system. System design aws
and inappropriate use created a myriad of patient safety and billing accuracy
concerns. Automatic population of templates, reliance on copy/paste func-
tionality, automatic object insertion and standard phrases and paragraphs,
while designed to reduce the physician’s task complexity, have all resulted in
increased proliferation of poor medical data quality.
Bowman, 2013
EMRs have created new challenges for healthcare professionals. e
increased availability of medical data has resulted in a high volume of
poorly organized information within the records making it dicult
to identify what is relevant to the patient’s care. Copy/pasting texts
from prior dates also obscures notes and may lead to fraud even if not
intended.
Rohr, 2015
EMRs/EHRs are valuable in sharing information across providers
but they also come with a set of disadvantages. e inclusion of drop-
down menus, copy and paste capabilities and documentation tem-
plates increases the risk of “outdated” and “irrelevant” information
to be included in the documentations of the current encounter. ese
errors could have serious consequences, thus blurring the line between
physician eciency and risk of errors.
Shachak et al. 2010
Machine learning and natural language processing are promising
technologies in the advancement of ecient medical documentation
and coding. Technologies like Computer Assisted ICD Coding and
Real Time Documentation Improvement leverage natural language
processing capabilities to read physicians’ unstructured notes and sug-
gest appropriate diagnoses. While these technologies potentially can
transform coding processes, this Gartner report warns that the antici-
pated value should be tempered by the length of time required before
readiness for marketplace adoption.
Shaer et al., 2017
Muma Business Review 21
Champagnie
First, because of the inherent subjectivity of E/M
coding guidelines, physicians have wide latitude to
set their own prices (Brunt, 2011). If a physician per-
ceives the level of care provided (the decision-mak-
ing necessary to treat a patient’s condition) warrants
higher compensation, he can secure this higher
reimbursement by billing a higher code than the
guidelines warrant. e fee-for-service reimburse-
ment method facilitates this type of price-setting by
paying physicians based on the codes they submit.
Any policy initiative aimed to improve billing accu-
racy must address both the subjective nature of the
coding guidelines in tandem with the method of re-
imbursement.
e low concordance rate among auditors is also
problematic. e high rate of disagreement among
coding experts highlights the subjectivity and com-
plexity of the billing guidelines. Physicians should
not be held to criminal and civil sanctions for bill-
ing errors based on guidelines that cannot produce
consistent results. e low concordance rate among
coding experts also underscores the futility of shi-
ing responsibility for coding from physicians to cod-
ers. In our research we only found one article sug-
gesting a reorganization of work from physicians to
coders. Perhaps the low concordance rates do not
justify the added cost of a team of certied coders.
As the research showed, agreement in coding was
low not only among physicians but also among cer-
tied trained coders. We should reiterate that the
scope of this study is oce visit coding in physician
practices. Certied coders are more widely used in
hospital-based setting where reimbursement rates
oer a justiable return on the investment.
Many were hopeful that technology would remove
the subjectivity and therefore produce more con-
sistent and accurate results. Machine learning and
articial intelligence gave hope to the realization of
such promises. To date however, computer assisted
coding has had more success in the hospital setting
than in physician group practices (Skeete & Gogan,
2019) perhaps because hospital costs are signicant-
ly higher than physician costs. While there are sig-
nicant technological advances in computer assisted
coding, to date E/M coding - the largest source of
Part B errors - has received little attention (Shaer
et al., 2017).
Understanding the nature and the scale of medial
billing errors is a pre-requisite in any attempts to
combat physician billing errors. Intervention strat-
egies are useless without a solid understanding of
these factors. Further studies are required to assess
the eectiveness and cost eectiveness of current
strategies to combat billing errors. Further research
is also needed to understand the scale of billing er-
rors due to intentional fraud versus unintentional
billing errors. Nonetheless, it is evident from the
prior research that the current system is vulnera-
ble to abuse – whether intentional or unintentional.
Mandating stronger training programs, requiring
investments in coding technology or certied cod-
ers and investing in greater government oversight
are hardly viable solutions. e problems inherent
in the current reimbursement system which is tied
to an antiquated medical coding model can only be
rectied by a comprehensive redesign of physician
compensation for oce visits. As the literature re-
view reveals, a simpler model of reimbursement is
warranted.
EPILOGUE
In December 2020, the CMS made the most nota-
ble changes to E&M coding and billing guidelines
and reimbursement since 1997. In clear recognition
that the system is too complex to drive consistent
accuracy, the CMS abbreviated the guidelines call-
ing for E/M billing decisions to be based solely on
either medical decision making or time spent with
the patient. In its “Patients over Paperwork initia-
tive, the CMS aims to make these particular billing
guidelines less burdensome to physicians (Centers
for Medicare and Medicaid Services, 2020). Sec-
ond, in recognition of payment inequality for Part
B physicians who bill mostly for E/M oce visits (as
compared to other physicians who perform proce-
dures such as surgeries), the CMS increased reim-
bursement for E&M oce visit codes (Centers for
Medicare and Medicaid Services, 2020). ese two
sweeping changes give evidence that CMS acknowl-
edges the current system of E/M reimbursement is
in need of repair. Only time will tell whether these
changes go far enough. Based on the ndings of
this literature review, we argue it does not. Medical
Decision making is the most subjective portion of
the billing process and as long as it remains the key
factor for determining reimbursement, billing errors
will continue to cost Medicare billions!
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(2019). 2018 medicare FFS supplemental improper
payment report. Retrieved from https://cmc.gov/
cert
Varacallo, M., Wolf, M., & Martin, H. (2017). Im-
proving orthopedic resident knowledge of docu-
mentation, coding, and medicare fraud. Journal of
Surgical Education, 74(5), 794-798. doi:10.1016/j.
jsurg.2017.02.003
Zuber, T. J., Rhody, C. E., Muday, T. A., Jackson, E.
A., Rupke, S. J., Francke, L., & Rathkamp, W. T.
(2000). Variability in code selection using the
1995 and 1998 HCFA documentation guidelines
for oce services. health care nancing adminis-
tration. e Journal of Family Practice, 49(7), 642.
Retrieved from https://www.ncbi.nlm.nih.gov/
pubmed/10923576
Samantha J. Champagnie’s career of nearly two decades has all been in the
health care realm, culminating in her current position of senior director of op-
erations with himagine solutions, the largest provider of outsourced health in-
formation management services in the United States. She oen is called upon
by her company to address some of the complex problems facing all organiza-
tions in the health-care industry today. Prior to her current position, which she
undertook in 2015, Champagnie worked in a variety of executive and direc-
tor roles, including stints at Humana, Mercy Health, Express Scripts and SSM
Healthcare.
Authors
Muma Business Review 25
Champagnie
Appendix A: Literature Review Summary by Causal Factor
Authors Year Leavitt’s
Component Causal
Factor Publication
Adiga et al. 2006 People Knowledge Journal of General Inter-
nal Medicine
Agrawal et al. 2013 People Knowledge Journal of American
Management Association
(JAMA)
Andreae et al. 2009 People Knowledge Clinical Pediatrics
Cohen et al. 2001 People Knowledge Nursing Economics
Howard et al. 2018 People Knowledge Journal of Surgical Edu-
cation
Ng et al. 2001 People Knowledge Pediatrics
Varcallo et al. 2017 People Knowledge Journal of Surgical Edu-
cation
Chao et al. 1998 People Experience Journal of Family Practice
Holt et al. 2010 People Experience Southern Medical Journal
King et al. 2002 People Experience Archives of Internal Med-
icine
Zuber et al. 2000 People Experience Journal of Family Practice
Brennan et al. 2011 People Attitudes Current Orthopaedic
Practice
Cohen et al. 2001 People Attitudes Nursing Economics
Rashidian et al. 2012 People Attitudes PLoS One
Lorence et al. 2002 People Attitudes Journal of Management in
Medicine
Brennan et al. 2011 People Coding Complexity Current Orthopaedic
Practice
Chao et al. 1998 People Coding Complexity Journal of Family Practice
Kikano et al. 2000 Task Coding Complexity Archives of Family Med-
icine
King et al. 2002 Task Coding Complexity Archives of Internal Med-
icine
Zuber et al. 2000 Task Coding Complexity Journal of Family Practice
Adams et al. 2002 Structure U.S. System of Re-
imbursement
Journal of National Medi-
cal Association
Brunt 2011 Structure U.S. System of Re-
imbursement
Health Economics
Kumetz et al. 2013 Structure U.S. System of Re-
imbursement
Chest
Lorence et al. 2002 Structure U.S. System of Re-
imbursement
Journal of Management in
Medicine
Doan 2011 Structure Criminal and Civil
Penalties
Annals of Health Law
Maxham 2014 Structure Criminal and Civil
Penalties
Public Contract Law
Jounal
Appendices: Physicians’ Billing Accuracy
26 Volume #, Number #
Duszak et al. 2004 Structure Centralized Coders Journal of American Col-
lege of Radiology
Adler-Milstein et al. 2014 Technology EMR Health Aairs
Bowman 2013 Technology EMR Perspectives in Health In-
formation Management
Cearnal 2013 Technology EMR Annals of Emergency
Medicine
Kabene 2010 Technology EMR Healthcare and the eect
of Technology
Kumar et al. 2011 Technology EMR Technology and Health-
care
Shaer et al. 2017 Technology Machine Learning Gartner Report
Rohr 2015 Technology Machine Learning Physician Leadership
Journal
Shachak et al. 2010 Technology EMR Healthcare and the eect
of Technology
Muma Business Review 27
Champagnie
E/M
Code Description History Physical Ex-
amination Medical
Decision
Making
2018 Medicare
Reimbursement
99201 Level One -
New Patient
Oce Visit
Problem-fo-
cused
Problem-fo-
cused
Straightfor-
ward
$45.36
99202 Level Two -
New Patient
Oce Visit
Expanded
problem-fo-
cused
Expanded
problem-fo-
cused
Straightfor-
ward
$76.36
99203 Level Three-
New Patient
Oce Visit
Detailed Detailed Low Complex-
ity
$109.80
99204 Level Four -
New Patient
Oce Visit
Comprehen-
sive
Comprehen-
sive
Moderate com-
plexity
$167.40
99205 Level Five -
New Patient
Oce Visit
Comprehen-
sive
Comprehen-
sive
High complex-
ity
$210.60
Source: Adapted from Centers for Medicare and Medicaid Services (2019)
Appendix B: E/M Billing Parameters and Reimbursement
Appendices: Physicians’ Billing Accuracy
28 Volume #, Number #
Appendix C: Gartner’s 2016 Hype Cycle for Healthcare Providers:
CAC and CACDI Placement
Source: Shaer et al. 2017
Reproduced with permission. Gartner. Market guide for healthcare computer-assisted cod-
ing, computer-assisted clinical documentation improvement and real-time physician docu-
mentation improvement. Shaer V, Mann B, Sachdeva M. 29 June 2017. ID G00276035.
Muma Business Review 29
Champagnie
Term Denition
ACO Accountable Care Organization; ties payments to quality metrics and cost of care.
CMS Center for Medicare and Medicaid Services, a federal agency within the U.S. Department of Health and
Human Services, that administers Medicare and works in conjunction with states to administer Medicaid.
CPT Current Procedure Terminology code set; maintained, updated, and copyrighted by the AMA.
EHR Electronic Health Record; a longitudinal collection of an individual patient’s medical history.
E/M Evaluation and Management services represented by a ve-digit numeric code within the CPT code set.
ey represent face-to-face visits between physician and beneciary. Each code and code level represents
the type of clinical setting (hospital, outpatient), the complexity of the visit, and whether the visit is a new
patient visit or a follow-up visit.
EMR Provider-created Electronic Medical Record of specic encounters; the data source for a patient’s EHR.
ICD International Classication of Diseases codes created and maintained by the World Health Organization.
In the U.S., ICD-10 coding was mandated starting in 2015.
Improper
Payment
Payments made by Medicare that should never have been made due to inadvertent billing errors such as
payments for unsupported or inadequately supported claims, payments for services not rendered, dupli-
cate payments, miscalculations, ineligible beneciaries and payments from outright fraud from program
participants.
Medicare National health insurance program, administered by the US Federal government since 1966, for Americans
age 65 and older who have worked and paid into the system through payroll taxes.
Medicare Part B: “e Medicare program is divided into four parts Part A, Part B, Part C and Part D. Part
A and Part B make up the fee-for-service program. Part B visits include physician visits, outpatient care,
preventive services, home health supplies”
Medicare Fraud: knowingly and willfully executing a scheme to defraud the Medicare program.
NLP Natural Language Processing, a form of articial intelligence that helps computers un-
derstand, interpret and manipulate human language.
OIG HHS Oce of the Inspector General; an independent organization commissioned by the federal govern-
ment to ght fraud, waste and abuse. OIG investigations result in criminal convictions and penalties, civil
settlements and administrative actions against those who commit fraud.
Under-cod-
ing
Billing for a level of service that is lower than the actual service provided
Over-coding Billing for a level of service that is higher than the actual service provided
Glossary of Key Terms
