Frauds In Health Care Research Paper 2022

Running head: HEALTHCARE FRAUDS
FRAUDS IN HEALTH CARE
Name of the Student
Name of the University
Author note

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FRAUDS IN HEALTH CARE
Executive summary
This following research is based on quantitative analysis of healthcare fraud. The paper discusses
about the health care frauds and the ways it is affecting the organization. The research method
chosen here is quantitative where the author has to go collect the data for analysis. A clear
analysis and the necessary ways taken to conducted this research is mentioned in the
methodology section. The research is designed quantitatively for analysis and ethical measures
are also mentioned that the author need to follow while conducting the research.

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Table of Contents
Introduction......................................................................................................................................3
Background......................................................................................................................................3
Problem Statement...........................................................................................................................4
Purpose Statement and Research Questions....................................................................................5
Theoretical framework.....................................................................................................................6
Research Methodology....................................................................................................................8
Research philosophy....................................................................................................................8
Research design...........................................................................................................................8
Data collection method................................................................................................................8
Sampling method and sampling size...........................................................................................9
Data analysis technique...............................................................................................................9
Research ethical consideration....................................................................................................9
Limitation of the study:.............................................................................................................10
Conclusion.....................................................................................................................................10
References......................................................................................................................................11

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FRAUDS IN HEALTH CARE
Introduction
Health care fraud is white collar crime including filing of deceitful health care privileges
to turn a revenue. Fraudulent health care structures come in several forms. Practitioner systems
include: persons gaining funded or insurance covered prescription pills, which are actually
superfluous as well as trading them in the black market for a turnover (Waghade & Karandikar,
2018). Billing by physician for overhaul that they never condensed; filing replica claims for the
identical provision condensed; fluctuating the dates, explanation of services, and modifying
medical records. The research will be discussing about the background, problem statement along
with the purpose and research questions that are to be dealt in this proposal. The proposal will
also discuss about theoretical framework and quantitative methodologies used.
Background
Health care fraud contrasts, however usually, includes trooping deceitful claims of health
for profit. Commercial in the health sector need to consult federal as well as state principles
intended to avert health fraud, and monitor staff to safeguard association acquiescence (Sullivan
& Hull, 2019). There are numerous miscellaneous health industry events, that is seen as being
duplicitous. Limited subordinate with activities commenced by few patient, others done by
physicians, doctors, as well as medical experts. The manifestations where health providers in
addition to connected people include in deception or involve in mistreatment for sketching
additional profits.
Furthermore, healthcare exploitation plus fraud affects the area to a great amount of
dollars annually. In the nonappearance of actions in place for perceiving in addition to averting
fake acts, healthcare supplier can face an inspection, that cost them the income plus status

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(Carvalho et al., 2015). Yet, developing appropriate healthcare fraud in addition to exploitation
evasion policies in addition to acquiescence agendas could be stiff for supplier organizations.
Providers faces a numeral of healthcare frauds in addition to manipulation guidelines at the
federal, state and local levels. Additionally, ensuing the innumerable of strategies proves to be
problematic for workers laying importance over numerous partialities, taking in repair transfer,
billing, payer acquiescence in addition to, management of revenue cycle (Shi et al., 2016).
The research is highly noteworthy to aid the healthcare sector along with administration
proactively avert fraud on an initial phase, particularly by the big data technology in addition to
ecosystems. The development of the big data technology in investigating enormous information
would subsidize immensely to the achievement of the study work. The delinquent was dented in
the earlier due to absence of noteworthy equipment to excerpt expressive visions from the
amorphous documents like physician notes and clinical documentation (Sarkar & Sana, 2018).
The research permits the practical ration to track healthcare fraud. Upcoming research would
allow the investigation be prolonged to other characteristic in the healthcare as well as
pharmaceutical organization.
Problem Statement
The healthcare sector has knowledge enormous share from the government as well as
private stockholders in the USA. Government agendas like the Medicaid in addition to Medicare
synchronized by the Centers for Medicaid and Medicare Services (CMS) has observed
multibillion-dollar outlay from the US government. Nonetheless, the program has agonized a lot
of obstruction as a result of fraud subsequently opening till recent time (Solanki, Berwal &
Dalal, 2016). Medicaid fraud charges USA billions of dollars each year in addition donates to an
increasing debt. Actually, the cost of opposing in addition to minimalizing fraud events is

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becoming complex. In the year 2004, the Lawyer General started 1,002 new unlawful health care
fraud in addition exploitation searches that involves 1,685 suspected defendants (Stowell,
Schmidt & Wadlinger, 2018).
On April 2017, CMS proclaimed that administrative action was taken over 130 strong
medical apparatus corporations which acquiesced over $1.7 billion in entitlements to the
Medicare program (Sheshasaayee & Thomas, 2018). Research works performed to detect
diverse fraud cases in other to benefit the government in addition health sector establishment to
fight deceitful events, nevertheless many fraud cases remain undetected. Researcher engrossed
on the spotting of the fraud cases linked to the entitlements acquiesced to the CMS. The research
tranquil lacks solution. This research emphasis on proactively avoiding the fraud of
hospitalization by associating the clinical certification notes with the coded events. The problem
statements initially examine the doctor notes, by using the big data technology as well as Natural
Language Processing(NLP). Then the notes are supplementary investigated for an awareness into
the facts of the notes of the provider, the outcome are equated with the coded actions by the
health care evidence upkeep team HIM and the entitlement are sent to the indemnification
(Ahadiat, 2017). Erratic pattern in the contrast could cause to additional examination.
Purpose Statement and Research Questions
The purpose of this research is to define an effective practical fraud deterrence procedure
in healthcare sector and lift the veracity of the healthcare facilities in the USA. The essential
purpose of the research is to improve the fraud defensive device by using the big data technology
in addition to machine learning algorithm to investigate the clinical certification and associate to
the coded evidence that absolutely improves the records and perceive any fake activity prior the
evidence is released for entitlements (Kratcoski, 2018). The research questions for the proposal

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how to develop defensive devices for investigating the certification and associates for the coded
evidence?
The action towards diminishing error in addition to stopping possible fraud is to
safeguard the suppliers are accredited for the meticulous provision that was condensed. In many
healthcare facilities, the coders evaluate the doctor notes besides certification prior conveying to
Current Procedural Terminology (CPT) and International Classification of Disease (ICD) codes
(Mishra, 2016). There is always problem in manual coding for unintended error besides
premeditated error that hypothetically leads into fraud. The faults, if not noticed on time find its
method to the assurance and if not noticed by the insurance, leads to fraud. The determination of
the study is not to substitute any contestant on the other hand to safeguard correctness and
recover the effectiveness of the records.
Theoretical framework
The main drive of the research is fraud risk management that can be achieved by ensuring
program veracity by deliberately in addition to unceasingly justifying the likelihood and outcome
of the fraud. It is achieved by simplifying achievement of the inventiveness’s broader mission
and planned ideas by supporting to make certain that the money is professionally spent, facilities
content their predictable purpose, and the possessions are tenable. Furthermore, the treacherous
control performs for treatment fraud jeopardies come under three main groups such as
deterrence, recognition and response (Abdallah, Maarof & Zainal, 2016). The three categories
are related and equally strengthening. The finding practices, like surprise reviews, work like
restrictions for the motive that they shape the vision of controls in addition to probability of
chastisement for disheartening deceitful activities. Moreover, preventive practices, such as
outcomes of examinations for applicant screenings along with fraud indicators.

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A theoretical framework projected to accomplish the healthcare frauds in operative
manner. The Framework comprises the control applies stated above laterally with conservational
factors in addition to structures, that have an influence upon or help managers in accomplishing
goal of extenuating fraud risks. Additionally, the Framework comprises 4 rudiments for
professionally allocating with healthcare fraud dangers such as design, commit, assess,
implementation, adapt and estimate.
Moving forward in the research showed that the fraud losses are every so understood as
being a commercial externality. An trade and industry externality succeeds when there is one
institute that undertakes activities or withholds from acting in addition to thus, authorizations on,
charges or accelerates expenditures on other business (Sigona, 2018). The good illustration of
interior fraud position would be at the period when any monetary organization makes a
pronouncement of not simplifying law implementation’s detention as well as probationary of an
member of staff involved in unlawful acts. Due to such pronouncements, the ex-employees can
quite well get engagement at few other organization in addition to accomplish the same act
again.
Fig 1: Theoretical framework

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Research Methodology
Research philosophy
Research philosophy is defined to analyse and develop a study in detail. Different
research philosophies, are realism, positivism, and interpretivism. Positivism helps in emerging
in-depth examination of a particular study. Interpretivism deals with the organization actions and
functions and realism is a mixed method of the research philosophies (Forte, 2018). In this
research, positivism research philosophy used to explore the subject in a rational in addition to
critical method. Positivism research philosophy is imitative from the experiences besides the
examination based on facts. The other two philosophies will not be chosen as these philosophies
be contingent on each individual in addition to the awareness might differ from one person to the
other.
Research design
Exploratory, explanatory, descriptive or analytical, are the three types of research
designs. Analytical research design aids to comprehend as well as appraise the study in an
evocative way. The exploratory design comprehends the idea in the initial stage of the research
whereas the explanatory design develops an association amid the two variables under
contemplation (Petrenko, Kyslyi, & Pysmennyi, 2018). Therefore, the analytical research design
will help in the study to gain a clear idea of the prevailing philosophies as well as notions on the
study; but the other two designs will not be chosen.

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Data collection method
Both primary and secondary data will be collected for this research. Primary data will be
gathered from different sources such as interviews, surveys as well as secondary sources
including
different kind of data which are present in the online platform (Sullivan, 2017). Both the data
collected will be used for carrying out this specific study. Data collection need to be done
properly as the next procedure will depend on the data collected. The collected data would be
giving the necessary outputs that is required for the next step in this research.
Sampling method and sampling size
Both probability and non-probability sampling technique will be used for sampling
technique. Random sampling will be required for this research to gain better analysis of the
topic. It is anticipated that almost 50 employees of the health organization will be questioned and
asked to participate in the survey in addition to 3 physicians will be invited to extant their views
on this study.
Data analysis technique
The quantitative data that are collected through survey, will be converted into numeric
and percentages, to gain an inclusive plus contrast data of the collected data (Petrenko, Kyslyi &
Pysmennyi, 2018). These data will then be converted into graphs to have a good graphical
representation of the research.
Research ethical consideration
In conducting the research ethical guidelines must be followed. The primary data
collection participants will have voluntary participation in the survey; along with it a consent
form will be provided and the participant must have signed by every participant to make sure that

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the participation was done willingly. There must not be any kind of symbol of the health sector
in the survey document so that there is no portray of advertisement however it must be implicit
that the research for academic purpose only (Waghade & Karandikar, 2018). While conducting
survey, permission might be required from the authority hence, authorization letter from the
University is highly recommended. Secondary research is necessary to avoid plagiarism activity.
No rules will be violated while conducting the research.
Limitation of the study:
There is numerous limitation for the research to be conducted. Mainly in every case the
time frame is constantly a problematic issue or restraint for every research. Along with the time
line, resource availability is also question. Sometimes it happens that some resources are
unavailable or the participants are not giving actual information about the health sector as they
are scared of losing their jobs (Sullivan & Hull, 2019). In many cases it had been noticed that the
resources cannot be accessed. Moreover, the response received from the participants might not
be enough to come to a solution. It had also been noticed that sometimes getting permission
becomes difficult and the interview dates from physicians becomes difficult to obtain.
Conclusion
It can be concluded that the quantitative research is the best way of analysis the research
conducted on healthcare frauds. The above research is designed so that health fraud can be
prevented and proper analysis can be done from the data received from the people working in
that sector. The data collective measures applied will help in easy and proper diagnosis of the
result.

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References
Abdallah, A., Maarof, M. A., & Zainal, A. (2016). Fraud detection system: A survey. Journal of
Network and Computer Applications, 68, 90-113.
Ahadiat, N. (2017). Public Attitudes Toward Healthcare Fraud: Reasons to Commit Fraud and
Common Schemes. Available at SSRN 3078815.
Carvalho, L. F., Teixeira, C., Dias, E. C., Meira, W., & Carvalho, O. (2015). A simple and
effective method for anomaly detection in healthcare. In Proceedings of the SIAM
International Conference on Data Mining Workshop (Vol. 2015, pp. 16-24).
Forte Jr, G. (2018). Investigating Physicians Billing for Services Not Rendered: Fraud Detection,
Interviewing and Referral to Law Enforcement.
Kratcoski, P. C. (2018). Fraud and Corruption in the Healthcare Sector in the United States. In
Fraud and Corruption (pp. 109-124). Springer, Cham.
Mishra, V. (2016). U.S. Patent Application No. 14/882,699.
Petrenko, A., Kyslyi, R., & Pysmennyi, I. (2018). Designing security of personal data in
distributed health care platform. Technology audit and production reserves, 4(2 (42)), 10-
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Sarkar, B. K., & Sana, S. S. (2018). A conceptual distributed framework for improved and
secured healthcare system. International Journal of Healthcare Management, 1-13.
Sheshasaayee, A., & Thomas, S. S. (2018). A purview of the impact of supervised learning
methodologies on health insurance fraud detection. In Information Systems Design and
Intelligent Applications (pp. 978-984). Springer, Singapore.
Shi, Y., Sun, C., Li, Q., Cui, L., Yu, H., & Miao, C. (2016, March). A fraud resilient medical
insurance claim system. In Thirtieth AAAI Conference on Artificial Intelligence.

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Sigona, F. (2018). Voice Biometrics Technologies and Applications for Healthcare: an overview.
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Solanki, K., Berwal, P., & Dalal, S. (2016). Analysis of application of data mining techniques in
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Stowell, N. F., Schmidt, M., & Wadlinger, N. (2018). Healthcare fraud under the microscope:
improving its prevention. Journal of Financial Crime.
Sullivan, C. (2017). Eradicating Fraud in Healthcare: Possibly a Matter of Life or Death. Journal
of Modern Accounting and Auditing, 13(8), 345-349.
Sullivan, C., & Hull, H. (2019). Preserving life and health by preventing fraud in healthcare.
Journal of Business and Behavioral Sciences, 31(1), 48-58.
Waghade, S. S., & Karandikar, A. M. (2018). A comprehensive study of healthcare fraud
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