Assignment 3 (Hypotheses, Methods, and Measurements) Name ______.docx

Assignment 3 (Hypotheses, Methods, and Measurements)
Name: ________________________________
Research Topic:
_____________________________________________________
_______________________
Research Questions: List your RQs again (to refresh my
memory). If you changed your RQs from last time, please
append your revised Assignment 1 and/or 2 at the end of this
assignment.
Hypotheses: Propose 3-7 hypotheses to answer your RQs.
Identify which hypotheses address which RQs; don’t include
hypotheses that don’t address any of your RQs. Each hypothesis
must have clearly defined and measurable DV, IV, and/or MV.
Propose hypotheses that are insightful and new rather than
tautological or obvious (see examples from class). Explain your
rationale for each hypothesis (separately). Theories provide
logical bases for justifying hypotheses; however theories are not
necessary for this proposal.
Measurement: Create a table listing the different variables in
your hypotheses, and provide conceptual and operational

definition for each variable. Feel free to list specific items or
instruments that you can use to measure each variable. List the
source of each instrument. If no prior measures are available for
some constructs, describe how you will build your own measure
and how will you assess the quality of that measure.
Methods: How will you collect data for your research? If
survey, describe whether it is a cross-sectional or longitudinal
survey, sampling frame, desired sample size, and how you will
maximize response rates. If experiment, describe the
experimental design, desired sample size, treatment, treatment
manipulation checks, and what data will be collected before and
after the experiment. If case research, how many cases, case
site(s), site selection strategy, key respondents, what data you
will gather from each respondent, data triangulation strategy,
etc. How will you analyze data thus collected?
References: List all references cited in this proposal

Assignment 2 (Literature Review)
Research Topic: Small Business Failure
Research Questions: List your RQs again (to refresh my
memory). If you changed your RQs from last time, please
append your revised Assignment 1 at the end of this assignment.
1. What factors causes the high failure rate of microbusiness?
2. What non-monetary interventions would help to minimize
microbusiness failure?
Literature Review Process: Review 10-15 research papers that
informs us of our current state of knowledge in your problem
area. If you find no prior paper in this area, expand your search
terms to extract 10-15 papers in related areas. Describe the
process by which you selected papers for review (e.g., which
databases were used, what keywords, etc.), how you filtered
your search results, actual counts of papers extracted in each
search, etc.
I did an abstract search in the ABI/Inform Global database
using the keywords “microbusiness” and “failure”. This search
resulted in zero articles. Hence, I expanded my search to “small
business” and “failure”, and got 77 articles. I manually screened
the abstracts of these 77 articles based on their relevance to my
research questions to shortlist 12 articles for further review.
These 12 articles were Knotts, Jones and Udell (2003), Urban
and Naidoo (2012), Atamina and VanZante (2010), Rolleri,
Nadan, and Lussier (2016), Yallaprogada and Bhuihan (2010),
Perry (2001), Purves, Niblock, and Sloan (2009), Franco and
Hasse (2009), Beaver and Graham (2005), Rasheed (2005),
Bornstein (2007), and Gerhardt, Hazen, Lewis (2014).

Synthesis: Create tables summarizing what prior literature tells
us about your problem area, similar to the tables in Eppler &
Mengis’s (2004) information overload paper. Notice how Table
2 presents definitions, Table 3 lists causes (of information
overload), Table 4 describes symptoms or effects, and Table 5
shows countermeasures. In each case, they list the issues in the
central column, groups them in the left column, and presents
references in the right column. I’m expecting similar tables
(perhaps smaller, given your 10-15 paper limit).
TABLE 1: Key Concepts
Concepts
Description
References
Small Business
· Fewer than 500 employees
Knotts, Jones, Udell, 2003
Contribution to economy
· Contribute to growth & stability of US economy: 50% of all
sales, employ ½ US workforce, 55% of innovations
· 99% of registered firms are SB
· 39% US GNP; 2 of 3 new jobs
· 27million SB = 99.7% employer business
Knotts, Jones, Udell,2003
Rolleri, Nadim, Lussier, 2016
Yallapragada, Bhuiyan, 2011
Failure
· 90% SBF are management related

· Each year, 1/2million SB start-up and close
· 34% SB do not survive 2 years
· 20% of new ventures fail w/in 1 year and 66% w/in 6 years
Franco, Hasse, 2009 (Port)
TABLE 2: Factors that Cause Small Business Failure
General Factors
Specific Cause
Reference
Management (mgmt) Skills
Accounting Knowledge
Poor data/ Information
Sales
Product
Internal (Environ) Factors

Operational Skills
· Needs improved mgmt skills
· Lack mgmt expertise
· Relationship between planning & SBF
· Inferior mgmt practices
· 90% failures are mgmt related per SBA
· No training in key areas (gave list of 10 areas)
· Lack of – impairs the new business
· Owner-manager does not adhere to the “rules” and
expectations of classical mgmt theory
· Unbalanced experience
· Poor mgmt process in SB is unique – not like processes found
in larger orgs- limted resources. SB does not predict -they react
· Mgmt process in SB is unique. Bears little to no resemblance
to mgmt process found in larger orgs – limited resources. SB
does not predict -the react
· Lack of mgmt. & internal controls
· Reliability & accuracy of accounting info
· Garbage in/garbage out
· Need written planning documents
· Lack of recordkeeping and therefore inability to access info
for decision making
· No ability to recognize a position of crisis (metrics)
· Lack of monthly sales/revenues
· Do not have sales budget/plan
· Inferior Product characteristics
· Products not sufficiently prepared
· Internal vs External Factors are the cause
· Positively assoc w sustainability
· Most significant area of training required
· Failed companies show non-existence of terms
· Inertia of the decision-making process can lead to missed
opportunities

· There are discrepancies to identifying factors that lead to SBF
Bornstein, 2007
Bornstein, 2007
Perry, 2010
Yallagrada, Bhuiyan, 2010
Urban, Naidoo, 2012 (S Africa)
Beaver, Jennings, 2005
Bornstein, 2007
Beaver, Jennings, 2005
Beavers, Jennings, 2005
Purves, Niblock, 2016 (Austral)
Purves, Niblock, 2016 (Austral)
Bornstein, 2007
Perry, 2001
Purves, Niblock, Sloan, 2016
Gerhardt, Hazen, Lewis, 2014
Gerhardt, Hazen, Lewis, 2014
Rolleri, Nadam, Lussier, 2016
Urban, Naidoo, 2015 (S Africa)
Urban, Nadooo, 2012 (S Africa)
Purves, Niblock, Sloan, 2016
TABLE 3: Intervention to Minimize Small Business Failure

Category
Intervention
Reference
Knowledge/Understanding
Financial Literacy Training
Predicting Failure
Resources spending on SB
Strategic Partners
Plan for Success
Enhanced Skills
· Practical Accounting & Analytical Tools
· Financial Stmt Analysis/Ratios (blood test)
· Enhance existing training programs
· Banks are starting programs to assist SB
· Useful info to predict SBF
· Financial stmt analysis can detect SBF trends within critical
first 5 years
· Be proactive in evaluating health of SB-mgmt audits, gap
analysis, check-ups
· Vast amounts are spent on SB – useful?
· Need to measure before & after to know of effect of spending
on viability of SB
· Need to find ways to insure viability
· Member of CoC, webinars, compliance, CPA, Lawyers,
Coaching

· Multiplicity of skills seldom found in one individual
· SB owner’s product should be the business itself – not what
they produce (line vs support activities)
· Support Activities yield decision making
· Ounce of prevention = pound of cure
· Financing, Human Resources, Ops, Marketing/Sales, Customer
Service, Info Mgmt, Admin
Bornstein, 2007
Bornstein, 2007
Bornstein, 2007
Bornstein, 2007
Bornstein, 2007
Bornstein, 2007
Atamian, VanZante, 2010
Purves, Niblock, 2016
Yallapragada, Bhuiam, 2011

Analysis (Gaps): What patterns do you see in your literature
review? What gaps or opportunities do you see (i.e., what is
MISSING from your literature review)? Note that gaps may
NOT be explicitly mentioned in the literature; you may use your
intuition and experience to identify what is not there. Based on
this analysis, do your RQs still seem appropriate? If not, modify
the RQs accordingly.
While there is some research on small business failure, there is
practically no research on microbusiness failure.
Microbusinesses are a unique subset of small businesses with
less than 5 employees (some research indicate 9 employees or
less), while small businesses may have up to 500 employees.
Microbusinesses are often family owned businesses where
employees are typically family members, often working without
compensation.
The literature review reveals that small businesses have a high
failure rate. There seem to some general underlying causes for
these failures, as listed in Table 2, though there is also little
discussion on which of these factors are the most important.
While lack of access to capital is often presumed to be the
primary cause of small-business failure, the literature suggests
that several non-monetary factors also contribute to small
business failure. Most of these non-monetary factors are in the
area of management skills and acumen. Most small businesses
are also unaware of resources available to them that can help
their business succeed and grow. Proper utilization of available
resources and addressing non-monetary factors may help
mitigate many small business failures.
I found only five articles that addressed potential interventions
to minimize small business failure. Most of these interventions
targeted managerial skills development and building networks
of relationships. Given the importance of interventions and the
dearth of research in this area, identifying and testing potential

interventions seem like a particularly promising area of work.
Based on the literature review, I find that microbusiness failure
is an important yet unaddressed area of research. Following a
review of the small business literature, some of the factors that
drive small business failure may also apply to microbusinesses,
while microbusiness may also have unique factors of their own
worth investigation that are not reflected in the small business
literature. My research will attempt to (1) extend our
understanding of small business failures to understand
microbusiness failures, and (2) uncover microbusiness-specific
factors that lead to microbusiness failure, and (3) identify
intervention mechanisms that can minimize microbusiness
failure.
Assignment 2: Literature Review
Research Topic:
Reducing Evaluation and Management Coding Errors in
Physician Practices.
Research Questions:
RQ1: What are the causes/drivers of evaluation and management
coding errors in physician practices.
RQ2: How can we reduce evaluation and management coding
errors in physician group practices.
Literature Review Process: Review 10-15 research papers that
informs us of our current state of knowledge in your problem
area. If you find no prior paper in this area, expand your search
terms to extract 10-15 papers in related areas. Describe the
process by which you selected papers for review (e.g., which
databases were used, what keywords, etc.), how you filtered

your search results, actual counts of papers extracted in each
search, etc.
I performed a search in the OVID Health Journals database
using the following keywords: physician coding, physician
documentation, evaluation and management codes, medical
coding accuracy, and upcoding. The initial search returned 186
articles. I repeated the search limiting the keywords to the
abstract which then returned 16 articles. I selected 4 articles
after a full text review.
Next, using the same keywords from the previous search, I
performed a second search in the ABI/Inform Global, Academic
Search Premier and Science Direct databases for peer reviewed
scholarly journals. The search produced 331 results. I again
refined the relevance by limiting the keyword search to the
abstracts which then resulted in 41 articles. A review of the
abstracts produced 9 relevant articles; I eliminated articles that
either did not focus on physician practices (i.e. focused
exclusively on hospitals) or was not relevant to physician CPT
coding errors (i.e. focused on DRG or ICD-10 coding).
I found 25 relevant sources in the bibliographies of the selected
articles, of which I selected 11 to include in the literature
review, resulting in a final list of 24 articles.
Selection Criteria for Literature Review
TABLE 1: Key Issues and Implications of Evaluation &

Management Coding Errors
Issues & Implications
Description
References
Coding Guidelines
· 57% concordance rate between coding specialist due to
complexity of the coding system.
· 44% concordance rate between auditors due to
vague/ambiguous guidelines
· Produces inconsistent results 43% of the time; inadequately
represents breadth of work of family physicians
King, Lipsky, Sharp, 2002
Zuber, Rhody, Muday, Jackson, Rupke, Franke, Rathkamp, 2000
Kikano, Goodwin, Stange, 2000
Error Rates
· 42% of podiatry residents found to code incorrectly
· Coding compliance among surgical residents as low as 36%
· 33% of physician visits are under-coded based on the quality
of their written documentation
· 57% of evaluation and management claims were incorrectly
coded/lacking documentation in 2010
· 45% error rate in family physicians with discrepancies evenly
divided between undercoding and overcoding
Varacallo, Wolf, Herman, 2017
Howard, Reddy, 2018
Holt, Warsy, Wright, 2010
DHHS, 2014
Chao, Gillanders, FLocke, Goodwin, Kikano, Stange, 1998
Health Policy Implications
· Systemic mis-reporting of medical coding data undermines
national and international comparative population health

analysis.
· Physician concern/worry about audits, negative impact to
patient care
Lorence, Richards, 2002
Kikano, Goodwin, Stange, 2000
Economic Implications
· Over 40% of physician payments are incorrect leading to
billions in losses to Medicare.
· Physician improper payment rate was 10.1% in 2012 costing
CMS $34 billion in incorrect Medicare payments
· CMS experienced $20 billion in overpayments from improper
coding of physician services.
· Government accountability office reported $50 billion in
improper payments from physician documentation and coding
errors.
· 3% to 10% of healthcare spending is lost to healthcare fraud
and abuse.
Zuber, Rhody, Muday, Jackson, Rupke, Franke, Rathkamp, 2000
Varcallo, Wolf, Herman, 2017
Bauder, Khoshgoftae, Seliya, 2015
Rashidian, Joudaki, Vian, 2012
Financial Implications
· Compromises financial viability of physician practices.
· Undercoding errors lead to devastating effects on financial
success of physician practices.
· Risk of fraud detection and persecution is outweighed by the
financial gain of mis-coding.

· Reimbursement can increase by 10 to 30% with proper use of
CPT codes.
· Undercoding is more pervasive than the literature suggests and
threatens the financial viability of physician practices.
Nguyen, O’Mara, Powell, 2017
Cohen, Marculescu, 2001
Legal Implications
· Exclusion from participation in government programs,
financial sanctions and disciplinary actions.
· Small-business providers suffer most from Anti-fraud laws
Andreae M., Dunham K., Freed, G., 2009.
Doan, 2011
TABLE 2: Factors that Cause Evaluation and Management
Coding Errors
Factors
Specific Cause
Tested
Reference
Resource Characteristics
· Knowledge - deficit in basic coding and billing principles
among residents and fellows.
· Knowledge - 81% of generalists and 78% of subspecialist
indicate they could use more training in billing and coding.
Fewer than 20% report their training was adequate.
· Knowledge – 2.27 mean knowledge score on a 10-point scale.
· Attitudes - miscoding is driven by intrinsic profit motives of
the physician/organization.
· Attitudes - physicians do not put forth the effort to understand
coding and billing rules because they are too complex.

· Experience – no statistical difference in coding accuracy based
on years of experience.
Yes
Yes
Yes
No
No
Yes
Andreae, Dunham, Freed, 2009
Brennan, Probe, 2011
Zuber et.al., 2000
Environmental Characteristics
· Time constraints reported to be a barrier to accurate coding
· Negative correlation between time spent coding, volume of
coded charts and accuracy.
· 43.5% of survey respondents reported influences from senior
management to upcode to optimize reimbursement.
No

Yes
Yes
Information Characteristics
· Completeness – physician documentation does not completely
address the patient’s symptoms and problems
· Completeness - Inadequate or incorrect charge documentation
leads to incorrect coding
· Ambiguity - documentation does not adequately or clearly
describe the seriousness of the illness which is critical to
correct coding.
Yes
No
No
Zuber et.al., 2000
Bauder, Khoshgoftaae, Seliya, 2015
TABLE 3: Countermeasures to Reduce Evaluation and

Management Coding Errors
Category
Type
Description
Tested
Reference
Prevention
Educational Interventions
· Study demonstrated significant improvement in coding/billing
concepts after focused/targeted educational sessions.
· Small and large group education discussions did not improve
coding knowledge. Recommends individual learning.
· Formal education programs for resident trainees and medical
students.
· Enhance residency training programs for physician residents.
· Develop a curriculum on financial impact of billing and
coding for pediatric residents.
· Provide CPT code training for all Nurse Practitioners during
graduate education programs.
· Create physician awareness of the financial impact of
additional documentation on proper code selection
· Revise the CMS physician educational materials to focus on
the components of E/M coding and proper documentation.
· Fraud, waste and abuse training during residency and
fellowships, should be prerequisites to Board certifications.
Yes
Yes

No
No
No
No
No
No
No
Nguyen, O’Mara, Powell, 2017
Howard, Reddy, 2018
Andreae, Dunham, Freed, 2009
Ng, Lawless, 2001
DHHS, 2014
Agrawal, Taitsman, Cassel, 2013
Organizational Interventions
· Physician-assigned codes should be reviewed by certified

coders prior to billing.
Yes
Duszak, Blackham, Kusiak, Majchrzak, 2004
Detection
Government audits
· Pre and post-payment audits of highest risk areas.
· Retrospective claim audits from highest coding physicians.
No
No
US GAO, 2001
DHHS, 2014
Information Technology
Interventions
· Descriptive statistics effectively identifies CPT coding fraud.
· Datamining, mixed logit machine learning algorithms
successfully detect and deter upcoding fraud.
· Datamining and neural networking techniques to extract and
analyze data.
Yes
Yes
Yes
Ornstein, Grochowski, 2014
Brunt, 2011

US GAO, 2001
Response
Administrative & Legal Interventions
· Limit punitive enforcement efforts to instances of deliberate
fraud.
· Follow up on improperly paid claims via physician payment
adjustments
No
No
Doan, 2011
DHHS, 2014
TABLE 4: Possible Theories to Explain Coding Errors
Theory
Application to Coding Errors
Reference
Resource Dependency Theory
· Organizations change behavior to maximize profits. Physicians
therefore may intentionally upcode to maximize reimbursement.
Pfeffer & Salancik, 2003
Accounting Control Theory
· Internal controls are critical to preventing errors and fraud by
ensuring information used in transactions are 1) valid, 2)
accurate 3) complete and 4) timely
AICPA, 1980
Availability Heuristics Theory
· Cognitive overload and cognitive limitations, often driven by
time limitations lead medical coders to pick codes they can
recall rather than select the most appropriate code.
Tversky & Kahneman, 1982
Satisficing Theory
· Under time pressures, knowledge and cognitive limitations,

physicians/coders “make do” rather than try to determine the
best code selection.
Simon, 1956
Self-Efficacy Theory
· People take action if two conditions are met 1) the outcome is
desirable i.e. they will reduce coding errors and 2) they are
confident in their ability to achieve the outcome, i.e. they
believe they have necessary knowledge and skill to reduce
coding errors.
Bandura, 1977
Analysis (Gaps):
Medical Coding Scope and Definition
All outpatient encounters are coded using evaluation and
management codes. This literature review focuses on coding
errors for evaluation and management services because they
represent nearly 30% of Medicare Outpatient payments and
according to CMS, they are the most common type of medical
billing error (Department of Health and Human Services, 2014).
There are five levels of evaluation and management codes.
Level 1 codes reflects the least complex outpatient encounter
and correspondingly the lowest level of reimbursement.
Accordingly, level 5 codes represent the most complex patient
encounter and the highest level of reimbursement. Undercoding
is a term used to describe a situation where a physician bills a
lesser code - and therefore receives less reimbursement – for
services provided. Overcoding - which is the subject of medical
coding fraud literature - occurs when a physician overcharges
for the services provided (Bauder, Khoshgoftaar, & Seliya,
2017). Both overcoding and undercoding are considered medical
coding errors (Varacallo, Wolf, & Martin, 2017) and
subsequently are included in the literature review.
Key Issues and Implications
The literature supports a high rate of evaluation and

management coding errors in physician practices. Numerous
studies, spanning almost two decades, cite error rates ranging
from 33% to 57% (Chao et al., 1998; Department of Health and
Human Services, 2014; Holt, Warsy, & Wright, 2010; Varacallo
et al., 2017). The persistent error rates raise concerns about its
effect on health care policy (Lorence & Richards, 2002), the
impact on national healthcare costs (Department of Health and
Human Services, 2014), the financial viability of physician
practices (Nguyen, O'Mara, & Powell, 2017) and the increasing
criminal and civil penalties physicians face (Doan, 2011;
Hyman, 2002).
A key theme in the literature is the apparent subjectivity of the
coding guidelines. Zuber et al. (2000), found a 44%
concordance rate between coding auditors. Kikano et al, (2009)
noted similar results with 43% concordance rate among Family
Physicians. These studies underscore a prevalent concern in the
literature; physicians’ cannot correctly apply coding guidelines
because the coding system is ambiguous and too complex to be
uniformly applied(Kikano, Goodwin, & Stange, 2000; King,
Lipsky, & Sharp, 2002; Zuber et al., 2000).
Causes of Evaluation and Management Errors
There is general agreement a knowledge deficit exists in
physician coding and education. Results from survey
instruments, self-assessments and coding tests, demonstrate a
knowledge gap in understanding of basic coding principles
(Andreae, Dunham, & Freed, 2009; Cohen, Marculescu, & Sa,
2001; Varacallo et al., 2017). Leaning on Bandura’s (1977)
self-efficacy theory, Cohen et al. (2001), and Brennan et al,
(2011) show that when physicians doubt their ability to
accurately learn the system they do not put forth the effort thus
they continue to make coding errors. Other causes of coding
errors involve environmental factors such as time constraints
and management pressures. Simon’s satisficing theory (Simon,
1956), and the theory of availability heuristics (Tversky &
Kahnerman, 1982), form complementary conceptual

frameworks for arguments that time pressures, limited
knowledge and cognitive abilities are key contributing factors
to coding errors (Cohen et al., 2001; King et al., 2002). Finally,
the quality of the information used in the coding process is also
a key contributor to coding errors. Since the physicians’
documentation forms the basis of selecting the medical codes,
quality of the information documented is critical to accurate
coding. Documentation which is incomplete, inaccurate or
ambiguous will inevitably result in incorrect coding (Howard &
Reddy, 2018; Varacallo et al., 2017).
Countermeasures Against Evaluation and Management Coding
Errors
Prevention – There is widespread agreement that educational
intervention is the best strategy to reduce coding errors.
However, these recommendations are not scientifically tested.
One study found significant improvement in knowledge after
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. Nonetheless, the literature
calls for formal graduate education programs during
residencies, fellowships and as a prerequisite to board
certifications (Agrawal, Taitsman, & Cassel, 2013; Howard &
Reddy, 2018). I did not find any studies that tested training
program characteristics proven to improve coding accuracy or
strategies physicians may employ (such as accounting control
theory) to improve the quality of the information in their
documentation. Further, the literature is also silent the use of
information technology as a tool to prevent coding errors.
Detection – The government primarily relies on post and pre-
payment audits to identify incidences of medical coding errors
and improper payments (Department of Health and Human
Services, 2014). This investigative approach is manual, resource
intensive and therefore not cost effective. Datamining

techniques, focused on the highest risk areas, are more accurate
and effective in identifying patterns of coding errors which can
then be handed over to investigative agencies to research and
respond.
Response – While most concede regulatory enforcement is a
necessary response to government fraud, others argue deterrence
does nothing to address human and information errors (Doan,
2011; King et al., 2002). Undercoding occurs with equal
frequency as overcoding, which suggest lack of training as a
root cause and not motivations of financial profit (Chao et al.,
1998; Holt et al., 2010; Kikano et al., 2000). Further, there is
limited evidence that the legal interventions, ushered in by the
Health Insurance Portability and Accountability Act of 1996,
have been effective in deterring coding errors (Hyman, 2002;
Rashidian, Joudaki, & Vian, 2012). Nonetheless, there remains
concern the current system of reimbursement gives physicians
financial incentives to upcode (Lorence & Richards, 2002).
Resource dependency theory (Pfeffer & Salancik, 2003) for
example, postulates that external influences, may lead
physicians to intentionally upcode to maximize reimbursement.
It has been shown that the primary drivers of evaluation and
management coding errors are inadequate knowledge and
awareness. It follows then preventative measures such as
physician documentation improvement, and coding education
programs, are vital yet missing countermeasures against
evaluation and management errors. Considering the dearth of
research on the economic, financial and legal implications of
medical coding errors, identifying and testing the effectiveness
of educational and information technology interventions seem
like logical extensions of the current body of work. My research
will attempt to 1) test the effectiveness of training and
education interventions to minimize evaluation and management
coding errors 2) identify information technology interventions
that are proven to improve information quality in physicians’
documentation and 3) further our understanding of evaluation
and management coding errors.

References
Agrawal, S., Taitsman, J., & Cassel, C. (2013). Educating
physicians about responsible management of finite resources.
Jama, 309(11), 1115-1116. doi:10.1001/jama.2013.1013
Andreae, M. C., Dunham, K., & Freed, G. L. (2009). Inadequate
training in billing and coding as perceived by recent pediatric
graduates. Clinical Pediatrics, 48(9), 939-944.
doi:10.1177/0009922809337622
Bauder, R., Khoshgoftaar, T., & Seliya, N. (2017). A survey on
the state of healthcare upcoding fraud analysis and detection.
Health Services and Outcomes Research Methodology, 17(1),
31-55. doi:10.1007/s10742-016-0154-8
Becker, D., Kessler, D., & McClellan, M. (2005). Detecting
Medicare abuse. Journal of Health Economics, 24(1), 189-210.
doi:10.1016/j.jhealeco.2004.07.002
Brennan, M., & Probe, R. (2011). Common errors in billing and
coding for orthopaedic trauma care. Current Orthopaedic
Practice, 22(1), 12-16. doi:10.1097/BCO.0b013e31820598bd
Brunt, C. S. (2011). CPT fee differentials and visit upcoding
under Medicare part B. Health Economics, 20(7), 831-841.
doi:10.1002/hec.1649
Chao, J., Gillanders, W. G., Flocke, S. A., Goodwin, M. A.,
Kikano, G. E., & Stange, K. C. (1998). Billing for physician
services: A comparison of actual billing with CPT codes
assigned by direct observation. The Journal of Family Practice,
47(1), 28. Retrieved from
https://www.ncbi.nlm.nih.gov/pubmed/9673605
Cohen, J., Marculescu, G., & Sa, T. L. (2001). Nurse
practitioners' attitudes and knowledge toward current procedural
terminology (CPT) coding. Nursing Economics, 19(3), 100.
Retrieved from https://search.proquest.com/docview/236964527
Department of Health and Human Services. (2014). Improper
payments for evaluation and management services cost medicare
billions in 2010. (). Washington, D.C.: Office of Inspector
General. Retrieved from http://purl.fdlp.gov/GPO/gpo68476

Doan, R. (2011). The false claims act and the eroding scienter
in healthcare fraud litigation. Annals of Health Law, 20(1), 49.
Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/21639018
Duszak, R., Blackham, W. C., Kusiak, G. M., & Majchrzak, J.
(2004). CPT coding by interventional radiologists: A multi-
institutional evaluation of accuracy and its economic
implications. Journal of the American College of Radiology,
1(10), 734-740. doi:10.1016/j.jacr.2004.05.003
Holt, J., Warsy, A., & Wright, P. (2010). Medical decision
making: Guide to improved CPT coding. Southern Medical
Journal, 103(4), 316-322. doi:10.1097/SMJ.0b013e3181d2f19b
Howard, R., & Reddy, R. M. (2018). Coding discrepancies
between medical student and physician documentation. Journal
of Surgical Education, doi:10.1016/j.jsurg.2018.02.008
Hyman, D. A. (2002). HIPAA and health care fraud: An
empirical perspective. Cato Journal, 22(1), 151-178. Retrieved
from https://search.proquest.com/docview/195575577
Kikano, G. E., Goodwin, M. A., & Stange, K. C. (2000).
Evaluation and management services. A comparison of medical
record documentation with actual billing in community family
practice. Archives of Family Medicine, 9(1), 68-71.
doi:10.1001/archfami.9.1.68
King, M. S., Lipsky, M. S., & Sharp, L. (2002). Expert
agreement in current procedural terminology evaluation and
management coding. Archives of Internal Medicine, 162(3),
316-320. doi:10.1001/archinte.162.3.316
Lorence, D. P., & Richards, M. (2002). Variation in coding
influence across the USA. risk and reward in reimbursement
optimization. Journal of Management in Medicine, 16(6), 422-
435. doi:10.1108/02689230210450981
Ng, M., & Lawless, S. T. (2001). What if pediatric residents
could bill for their outpatient services? Pediatrics, 108(4), 827-
834. doi:10.1542/peds.108.4.827
Nguyen, D., O'Mara, H., & Powell, R. (2017). Improving coding
accuracy in an academic practice. U.S. Army Medical
Department Journal, (2-17), 95. Retrieved from

Ornstein, C,. Grochowski, R.J., : Top billing: meet the docs who
charge Medicare top dollar for office visits,
https://www.propublica,org/articles/billing-to-the-max-docs-
charge-medicare-top-rate-for-offive-visits (2014)
Rashidian, A., Joudaki, H., & Vian, T. (2012). No evidence of
the effect of the interventions to combat health care fraud and
abuse: A systematic review of literature. PLoS One, 7(8),
e41988. doi:10.1371/journal.pone.0041988
Strategies to manage improper payments: Learning from public
and private sector organizations. (2001). U.S. Government
Accountability Office. Retrieved from Social Science Premium
Collection Retrieved from
https://search.proquest.com/docview/1820808290
Varacallo, Matthew A., MD|Wolf, Michael, MD|Herman, Martin
J., MD. (2017). Improving orthopedic resident knowledge of
documentation, 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 office services. health care
financing administration. The Journal of Family Practice, 49(7),
642. Retrieved from
References retrieved from Health Databases
186
References with keywords in the "abstract"
16

References reamining after review of abstract
4
Peer reviewed References from Scopus &ABI/Global
331
References after removing "peer" review restriction
41
References remaining after review of abstract
9
References selected from Bibliographies
25
References remaining after full text review
11
Final List of References
24
2 | Page

Assignment 3 (Hypotheses, Methods, and Measurements) Name ______.docx

Assignment 3 (Hypotheses, Methods, and Measurements) Name ______.docx

Recommended

Recommended

More Related Content

Similar to Assignment 3 (Hypotheses, Methods, and Measurements) Name ______.docx

Similar to Assignment 3 (Hypotheses, Methods, and Measurements) Name ______.docx (14)

More from ursabrooks36447

More from ursabrooks36447 (20)

Recently uploaded

Recently uploaded (20)

Assignment 3 (Hypotheses, Methods, and Measurements) Name ______.docx