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International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 13 Issue 1, January 2024
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
Digital Transformation: Impact of Claim
Adjudication on Financial Stability and Operational
Efficiency of Health Services Providers
Shreekant Mandvikar1, Alekhya Achanta2
1Independent Researcher, North Carolina, United States of America
2Senior DataOps Engineer, Continental Properties Company Inc, Wisconsin, United States of America
Abstract: Process automation and artificial intelligence are considered as two of the most important fields for the prediction in any
business now a days. The work focuses on the amount of insurance claims that are made by the clients of the healthcare services
providers. Moreover, the study identifies the address impact of claims adjudication on financial stability and operational efficiency of the
healthcare service providers. An extensive review of the state-of-the-art literature has been conducted which reveals that automation of
the claim processing decision-making process in the insurance business by integrating AI technologies is crucial. Hence it is required to
integrate AI process automation into the insurance claim procedure for policyholders in order to achieve high operational efficiency.
Keywords: Process automation, AI, Insurance Claims, Adjudication, Financial Stability, Operational Efficiency
1. Introduction
In the United States, it is estimated that one in seven health
insurance claims are refused; hospitals nationwide lose over
$262 billion a year as a result of these denials. Patients are
overburdened by this pervasive issue, which also severely
disrupts cash flow. Preventing claim denials prior to
insurance claim submission thereby enhances revenue cycle
acceleration, boosts profitability, and promotes patient well-
being[1]. As illustrated in Fig 1, the US healthcare system is
complicated, with many needless administrative processes,
including exchanging patient records between doctors,
completing redundant paperwork, and handling
correspondence with insurance companies. According to [2,
3], the US spends roughly 17% of its GDP on healthcare,
which is nearly twice as much as the average of other high-
income developed nations. This is thought to be primarily
due to these administrative tasks. In the US, billing and
insurance-related (BIR) expenditures—that is, the costs
associated with filing, processing, and reconciling claims—
account for 13% of this substantial yearly spending on
healthcare [4]. Denials of health insurance claims, referred
to as claim denials from here on, are one of the main causes
of the rising costs of BIR [5].
In the healthcare sector, claim adjudication plays a crucial
role in guaranteeing that medical professionals are paid on
time and accurately for their services. The effectiveness and
precision of claim resolution have a significant impact on
the long-term financial viability of insurance companies as
well as healthcare providers. Artificial intelligence (AI) and
process automation have revolutionized claim adjudication
in recent years, resulting in significant gains in accuracy,
efficiency, and overall financial performance for all
stakeholders involved [6].
Figure 1: US Healthcare System [1]
a) Financial Impact of Claim Adjudication on
Healthcare Providers
Healthcare providers' financial stability depends on timely
and proper payment for their services. Payment delays can
cause cash flow problems, restrict access to resources for
improvements, and ultimately lower the standard of service
delivered. It is essential to have a thorough understanding of
how claim adjudication affects finances. In the United
States, the average administrative costs for a refused claim
were $375, per studies conducted in 2019 by the Healthcare
Financial Management Association (HFMA) [7] and in 2023
by the American Council of Life Insurers (ACLI) [8]. This
emphasizes the need for streamlined and effective
procedures by highlighting the cost consequences of
incorrect or delayed claim adjudication.
b) Operational Impact of Claim Adjudication on
Healthcare Providers
Healthcare providers may face operational issues as a result
of inefficient claim adjudication processes. The manual
processing of claims is labor-intensive and time-consuming,
which increases the possibility of errors. These obstacles
may lead to backlogs, annoyance for providers, and trouble
monitoring the status of claims. In addition, manual review
procedures may cause delays in the timely provision of care
since they may cause clinicians to put off treatment if they
are unsure about payment.
Paper ID: SR24111084444
DOI: https://dx.doi.org/10.21275/SR24111084444
916
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 13 Issue 1, January 2024
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
c) Process Automation and AI in China Adjudication on
Healthcare Providers
AI and process automation have become effective solutions
for addressing the difficulties and inefficiencies that come
with deciding claims for healthcare providers. Processes
could be streamlined by automated technologies, which
would minimize the requirement for human intervention and
mistake risk. Furthermore, complicated medical data can be
analyzed by AI systems, which can spot trends that point to
fake or uncovered claims. This dual feature lowers the
possibility of financial losses for healthcare providers while
also greatly improving accuracy.
Numerous advantages have resulted from the use of AI and
process automation in healthcare provider claim
adjudication, including:
1) Decreased Claim Processing Time and Costs:
Automated solutions drastically cut down on the
amount of time needed to process claims, which
speeds up provider reimbursement.
This expediency also lowers the administrative
expenses linked to protracted processing, which
enhances cash flow.
2) Enhanced Claim Accuracy:
AI systems examine medical data more skillfully
than human reviewers, which lowers the possibility
of overpaying or underpaying.
Facilitates accurate reimbursement to providers,
hence promoting financial stability.
3) Enhanced Provider Satisfaction:
Positive relationships between payers and providers
are critical to the health of the entire healthcare
ecosystem.
Accurate and efficient claim adjudication eases
provider dissatisfaction, which improves
satisfaction with the payer's handling of claims.
4) Enhanced Effectiveness of Operations:
By automating repetitive processes, automated
systems free up staff members to concentrate on
more intricate and valuable work.
This increase in operational efficiency lessens the
workload for administrative staff members and
enhances workflow in general.
Healthcare providers' claim adjudication processes are being
revolutionized by a number of AI applications, such as:
1) Natural Language Processing (NLP):
NLP expedites the claim review procedure by
extracting pertinent data from clinical and medical
records.
By enhancing information extraction accuracy and
efficiency, this technology speeds up the processing
of claims.
2) Fraud Detection:
This program supports the financial integrity of
insurance firms and healthcare providers by using
AI algorithms to find trends in claim data that point
to fraudulent activity.
This enables preventive steps to avert financial
losses.
3) Medical Coding Assistance:
Artificial intelligence helps with medical coding,
guaranteeing correct claim classification and
precise provider payment.
Increase coding precision, lowers mistake rates, and
expedites the reimbursement procedure.
2. Related Work
Numerous efforts have been made by various researcher to
investigate the impact of insurance claim adjudication on
financial and operational stability. These are delineated as
follows;
a) Insurance Claim Management and Fraud Detection
Claims are essential to the management of healthcare since
they are standardized records. However, frequent rejections
and denials put a significant financial and administrative
strain on payers as well as providers. Researchers' study [9]
addresses these issues by introducing an automated method
for identifying rejections and denials of claims using
machine learning (ML). The study suggests a unique method
for categorizing claims subject to denial based on
distinguishing features by employing machine learning
algorithms. This novel method, which uses Claim
Adjustment Reason Codes (CARC) for feature engineering,
made a substantial contribution to the creation of the first
claim risk detection system driven by machine
learning.Similarly, public and private health insurance
systems are impacted by rising global healthcare
expenditures, which can be linked to a number of factors.
Notably, fraudulent activity inside these systems adds to
insurance providers' unnecessary expenses. In response, a
study by researchers [10] suggests a multi-step method for
insurance firms to spot fraudulent activity. The steps include
identifying inconsistencies, gathering information for a
thorough risk analysis, and using a decision tree-based
technique to ascertain the veracity of the claim. When
applied to actual insurance data, the results show satisfactory
outcomes.
b) Global Healthcare Challenges and Ethical AI
Implementation
After ten years of implementation, Taiwan's National Health
Insurance (NHI) system still faces difficulties in providing
effective and reasonably priced medical care. To solve this,
the "medical-claim payment auditing (MCPA) procedure" is
introduced in a researcher's study [11]. MCPA offers
significant benefits to the optimization of healthcare systems
by promoting honest medical-claim payments, lowering
auditing expenses, and promoting adherence to international
standards by taking inspiration from well-established sample
procedures. Researchers' study [12] reveals different
dynamics for conventional and Islamic insurance models by
examining consumer behaviors and claims patterns in
Malaysia's medical and health insurance landscape. Ethical
principles in Islamic finance drive honest behavior
throughout the buying stage, while conventional policies are
rejected with high rates of success. But when claims are
submitted, a contradiction appears that raises questions
about possible moral hazard. The study's conclusions apply
outside of Malaysia and can be used to optimize business
performance in comparable market environments. The
investigation of machine learning technologies is prompted
Paper ID: SR24111084444
DOI: https://dx.doi.org/10.21275/SR24111084444
917
International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 13 Issue 1, January 2024
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
by worries about growing health insurance costs worldwide
as a result of errors made when managing claims. A
proactive method to anticipate claim errors is suggested by
the researcher's work [13], which makes use of Responsible
Artificial Intelligence (RAI). The study highlights the
possible advantages in terms of raising profitability,
streamlining revenue cycles, and eventually boosting patient
welfare.
c) Role of AI in Insurance and its Future
Health insurance firms in Brazil tend to arrange claims data
according to service providers, which may cause them to
miss out on important information about the activities of
physicians and the patterns of patient referrals. A modeling
strategy to find physician referral trends using actual
healthcare insurance claims. The goal of the research is to
influence policy-making initiatives and improve physician
referral systems [14]. A sizable percentage of health
insurance claims in the US are turned down. In order to
anticipate possible claim denials, the study [15] suggests a
Responsible Artificial Intelligence (RAI) approach that
makes use of machine learning techniques. The results point
to possible advantages in terms of lower operating expenses
and more efficient claim procedures.AI-enabled healthcare
claims data integration presents prospects for game-
changing discoveries. The ethical and responsible
application of AI in healthcare is emphasized by a
researcher's work [16], which addresses concerns about
algorithmic bias, data protection, and transparency. The
findings portend a new age in proactive and individualized
healthcare. The health insurance industry's adoption of AI is
influenced by concerns about privacy and trust.
The study [17] examines the effects of both hidden and
apparent AI interfaces and finds that when AI is used in a
visible way, trust is significantly reduced. In order to build
trust, the study highlights crucial user control and
transparency when creating AI interfaces.AI has the power
to completely change the insurance sector by optimizing
customer experiences and releasing the potential of data.
The suggested method by [18] investigates how AI may
affect workforce dynamics, business operations, and social
consequences. The report underlines how important it is to
take a calculated strategy to addressing social inequality and
possible job displacement. According to a study [19], the
insurance industry has integrated automated chatbots,
demonstrating how these technologies can add value for
customers. The conceptual framework clarifies the many
tactics used by chatbots by integrating AI, service logic, and
the reverse use of consumer data. The study highlights how
crucial it is to take end users' viewpoints into account when
evaluating efficacy.
3. Conclusion
Healthcare claim adjudication procedures are entering a
revolutionary era because to process automation and
artificial intelligence (AI), which gives providers powerful
tools to improve overall financial performance, accuracy,
and efficiency. The industry may anticipate even more
advanced applications that further improve the claim
adjudication process as AI technologies develop, which will
eventually benefit patients as well as payers and providers.
A more efficient, economical, and patient-focused healthcare
environment is anticipated as AI and automation continue to
be incorporated into healthcare operations.
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International Journal of Science and Research (IJSR)
ISSN: 2319-7064
SJIF (2022): 7.942
Volume 13 Issue 1, January 2024
Fully Refereed | Open Access | Double Blind Peer Reviewed Journal
www.ijsr.net
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Paper ID: SR24111084444
DOI: https://dx.doi.org/10.21275/SR24111084444
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