Literature review Summary
A Framework for Artificial Intelligence Applications in
the Healthcare Revenue Management Cycle
by
Leonard J. Pounds
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in
Information Systems
College of Computing and Engineering
Nova Southeastern University
2021
An Abstract of a Dissertation Submitted to Nova Southeastern University
in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
A Framework for Artificial Intelligence Applications in the
Healthcare Revenue Management Cycle
by
Leonard J. Pounds
September 2021
There is a lack of understanding of specific risks and benefits associated with AI/RPA
implementations in healthcare revenue cycle settings. Healthcare companies are
confronted with stricter regulations and billing requirements, underpayments, and more
significant delays in receiving payments. Despite the continued interest of practitioners,
revenue cycle management has not received much attention in research. Revenue cycle
management is defined as the process of identifying, collecting, and managing the
practice’s revenue from payers based on the services provided.
This dissertation provided contributions to both areas, as mentioned above. To
accomplish this, a semi-structured interview was distributed to healthcare executives. The
semi-structured interview data obtained from each participant underwent a triangulation
process to determine the validity of responses aligned with the extant literature. Data
triangulation ensured further that significant themes found in the interview data answered
the central research questions. The study focused on how the broader issues related to
AI/RPA integration into revenue cycle management will affect individual organizations.
These findings also presented multiple views of the technology’s potential benefits,
limitations, and risk management strategies to address its associative threats. The
triangulation of the responses and current literature helped develop a theoretical
framework that may be applied to a healthcare organization in an effort to migrate from
their current revenue management technique to one that includes the use of AI/ML/RPA
as a means of future cost control and revenue boost.
Acknowledgments
This dissertation would not have been possible without the support of many people.
Many thanks to my chair, Dr. Gregory Simco, who read my numerous revisions and
helped make some sense of the confusion. Also, thanks to my committee members, Dr.
Ling Wang and Dr. Mary Harward, who offered tremendous guidance and support.
I’d also like to extend my gratitude to the numerous staff and administrators at NSU that
assisted me with completing this dissertation. Special thanks to Deans Meline Kevorkian
and Kimberly Durham, who may not know their impact on my completing this program.
As well as my mentor, someone I am so grateful to have had the chance to work with,
Mr. Tom West, for the tireless support and for sharing your knowledge and wisdom
during our frequent conversations. I hope to inspire others as you have inspired me.
And my greatest thanks to my family for all the support you have shown me through this
research, the culmination of the years of the Ph.D. program. For my in-laws, Carmen and
Amy Shick, your entrepreneurial skills, perseverance, and integrity are just a few of your
qualities that continue to inspire me to be a better person every day. And for my wife
Isabelle, who deserves an honorary degree for proofreading every one of my papers, for
her boundless love, support, and for always believing in me, without which I would have
stopped these studies a long time ago.
Finally, I cannot begin to express my thanks to my late parents, Franklin and Suzzann
Pounds. They will not get to share in the joy of this accomplishment, although the
example they set with their work ethic is something I strive to match each day. Thank
you for instilling values in me that I will carry throughout the rest of my life.
v
Table of Contents
Abstract iii
Acknowledgments iv
List of Tables viii
List of Figures ix
Chapters
1. Introduction 1 Background 1
Problem Statement 3
Problem Broader Context 5
Justification of the Study 6
Dissertation Goals 7
Research Questions 7
Relevance and Significance 8
Barriers and Issues 9
Assumptions, Limitations, and Delimitations 11
Assumptions 11
Limitations 12
Delimitations 12
Definition of Terms 13
Summary 14
2. Review of the Literature 16
Literature Review 16
Justification for Inclusion and Exclusion 17
Inclusion 17
Exclusion 18
Previous Work and Strengths and Weaknesses 18
Gaps in the Literature 20
Analysis of Research Methods Used 20
Concept of Artificial Intelligence, Machine Learning, and Robotic Process
Automation 21
Process Definitions of Revenue Cycle Management 23
Potential of Machine Learning for these Processes 24
Application of Machine Learning/Artificial Intelligence to Healthcare Issues 27
Summary and Thematic Analysis 29
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3. Methodology 33
Approach 33
Justification for the Methodology 34
Theoretical Framework and Development 35
Interview Structure and Design 36
Data Collection and Research Questions 39
Sampling 40
Data Analysis Procedures 41
Trustworthiness and Reliability 42
Summary 44
4. Results 45
Data Analysis 45
Thematic Analysis Approach 45
Details of Interviews 47
Word Frequency 47
Analysis Process in NVivo 48
Findings 48
Benefits of AI in HRCMP 48
Negative Impact of Risk Factors in HRCMP 52
Risk Management and Problem-Solving Strategies 53
Triangulation of Data 55
Framework Design 56
Development 56
Framework Example 57
Summary 61
5. Conclusion 62
Research Questions 62
Research Question 1: What prospective benefits can be generated by using
AI revenue cycle applications for healthcare organizations? 62
Research Question 2: What are the risk factors associated with AI
implementation in healthcare? 62
Research Question 3: What outcomes are derived by using a Lean Six
Sigma (LSS) designed framework for healthcare executives deciding to
implement AI/RPA in the healthcare revenue cycle? 63
Limitations 63
Implications 64
Recommendations 65
vii
Summary 66
Appendices 70
A. Interview Questions 71
B. IRB Exempt Initial Memo 75
C. Email Invitation 77
D. Informed Consent 78
E. NVIVO Codes 82
F. Triangulation of Data 84
References 87
viii
List of Tables
Tables
1. Research Questions with Relevant Themes Hierarchy 46
2. Interview Overview with Duration of Interviews 47
3. Risk Viability Framework 58
4. Benefits Framework 59
ix
List of Figures
Figures
1. Word Frequency 48
2. Theme Hierarchy of Benefits of AI in HRCMP 50
3. Percentage Coverage of Benefits of AI in HRCMP 51
4. Theme hierarchy of Negative Impact of Risk Factors in HRCMP 52
5. Percentage Coverage of Negative Impact of Risk Factors in HRCMP 53
6. Theme hierarchy of Risk management and problem-solving strategies 54
7. Percentage coverage of risk management and problem-solving strategies 55
8. Framework Scatterplot 61
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Chapter 1
Introduction
Background
As healthcare institutions navigate the industry's varied challenges, they
increasingly rely on healthcare information technology (HIT) as a means of cultivating
solutions to recurrent problems (Bohr & Memarzadeh, 2020, p. 25-60; Stanfill & Marc,
2019). HIT includes diverse systems, hardware, and software that provide varied benefits
to selected users across organizational contexts. One specific HIT application includes
artificial intelligence (AI): A collection of systems and programs that rely on computer-
generated thinking to perform a range of prescriptive and predictive tasks. Current AI
applications in healthcare contexts include machine learning platforms that contribute to
decision-making in both clinical and administrative areas of operation (Lin et al., 2017).
Analysts predict that as technology advances and becomes increasingly manageable from
risk mitigation, and cost-effectiveness standpoints, planners across the healthcare sector
will likely adopt AI and Machine Language (ML) platforms to assist with multiple
functions (Hut, 2019). However, other reports indicate that planners will also need to
address a complex set of potential barriers to HIT implementation and use it to implement
technology-oriented solutions within their organizations (Christodoulakis et al., 2020).
Artificial intelligence (AI) and related technologies are increasingly common in business
and are beginning to be applied to healthcare. While these technologies have the ability to
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transform many aspects of patient care, as well as administrative processes within the
provider and payer organizations, researcher recommendations conflict regarding the
extent of benefits versus risks offered and posed by using AI in such settings
(Christodoulakis et al., 2020; Davenport & Kalakota, 2019). There are already several
research studies suggesting that AI can perform as accurately as or more accurately than
humans when it comes to essential healthcare tasks, such as diagnosing diseases
(Davenport & Kalakota, 2019).
Nonetheless, Christodokulakis et al. (2017) highlight the associated challenges
and risks introduced alongside such benefits. Therefore, a need exists for practitioners
and researchers in healthcare to understand the advantages as well as the barriers or
challenges associated and more comprehensively with AI implementation in clinical
settings, especially as such factors relate to organizational financial solvency so that AI
technologies can be implemented and used in the most appropriate and beneficial ways.
Another contextual factor complicating the consideration and implementation of AI
systems and their associated challenges and benefits is the current changing healthcare
regulation environment (Forcier et al., 2019; Gerke et al., 2020). Changing healthcare
regulations and evolving revenue cycle management lead to immense transformation in
the healthcare industry. Along with staying current on updates to the Affordable Care Act
(ACA), Medicaid, and other healthcare programs, healthcare providers need efficient
billing and tracking procedures in place. Staying up to date with changing regulations is
one area in which AI can assist organizations. Utilizing AI in the revenue cycle will assist
with some of the essential aspects, such as:
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1. Billing and Collections Mistakes - If healthcare establishments do not
have an effective billing process, they risk losing money. With
complicated insurance plans becoming more common, a billing and
collections department's need to continuously review payor receipts is
paramount.
2. Untrained Staff – Inaccurate data can cause billing issues in various ways,
such as improper medical coding, billing, and insurance claim filing.
These errors can add up to a significant amount of bad debt per year.
Unpaid bills can easily get lost in the shuffle, even after only 60 to 90 days
(DECO, 2019).
Based on the need to better understand associated risks, challenges associated
with, and benefits of AI systems in healthcare (Shaw et al., 2019), this research was able
to describe both the potential that AI offers to automate aspects of the revenue cycle and
some of the barriers to the rapid implementation of AI in healthcare. This research also
helped in filling a gap in understanding a conscience theoretical framework for healthcare
executives to use during implementation. Therefore, the results of this research
contributed to building a framework that administrators may use to leverage the benefits
of AI while minimizing the risks to improve organizational operability, productivity, and
financial solvency more successfully and appropriately.
Problem Statement
The problem addressed by this study was a lack of understanding regarding the
specific risks and benefits associated with AI implementation in healthcare settings.
Many administrative tasks are currently completed manually in healthcare, which takes
4
high labor costs and increases human computation error potential. However, it is
unknown to what extent AI may improve these administrative tasks and address
challenges (CAQH, 2018). To better understand this research was able to analyze the
issues affecting the healthcare industry revenue cycles. Despite some automation of claim
submission and other transactions, many administrative transactions are still primarily
driven by inefficient manual processes (CAQH, 2017). According to the 2017 CAQH
Index, an annual report of adopting electronic business transactions, the lack of
automation for these transactions costs the healthcare industry more than $11 billion per
year. In order to process a patient claim, the patient financial services department is
required to employ experts with advanced healthcare knowledge. Experienced
professionals are necessary for auditing the claims. The current manual claims auditing
methods involve extensive human efforts, time, and money and often result in claims
denial. One of the obvious solutions is to adopt automation, which, despite advantages, is
accompanied by many uncertainties and consideration of countless variables. Thus, this
dissertation analyzed the issues affecting the revenue cycle within the healthcare industry
to understand better the financial risks and benefits associated with AI implementation in
the healthcare setting and constructed a theoretical framework behind using Artificial
Intelligence (AI) and the financial benefits vs. the risks that will be gained by utilizing
this technology. The main goal of the study was to estimate the outcome of implementing
AI in the revenue cycle.
Furthermore, this study first examined theoretical trends in healthcare revenue
cycle processes by researching literature related to the topic. Existing literature was
analyzed to identify and address current gaps in understanding. By doing so, a broader set
5
of observations was generated that was applied to the report's follow-up section of
designing a lean process theoretical framework for the use of AI within the healthcare
revenue cycle process.
Problem Broader Context
Revenue cycle management in modern health systems can be viewed in three
ways (Becker & Ellison, 2019). First, the processes represent critical areas of fiscal
management and administrative oversight. In brief, a health systems approach to revenue
generation requires a systemized and efficient model. The literature defines an efficient
model as combining the separate billing, collections, reimbursement, and accounting
activities within the same framework (Becker & Ellison, 2019). Second, revenue cycle
management ensures a health system's effective ability to operate in immediate and future
terms. The revenue cycle must combine billing, collections, reimbursement, and
accounting in the immediate present and the future. Third, for the system to continue to
be efficient, it must anticipate how these domains will change in the future.
Administrative and medical employees focus almost half of their time addressing
revenue-oriented issues (Hillman, 2020). The same author additionally noted that
healthcare systems spend approximately $266 billion annually on revenue cycle
management operations. These same costs can also be compounded as systems seek to
reconcile problems generated through human error. Current regulations allow healthcare
entities to lower administrative costs and increase the rate of collections. However,
applying AI and ML should theoretically increase revenue by reducing the number of
timely filling errors and reducing the financial services team's administrative burden.
Considering these issues, this dissertation addressed the problem associated with
6
developing a framework for more successfully implementing AI and ML into healthcare
organizations' revenue cycle. The literature on this topic was evaluated by examining key
performance indicators (KPIs) that provide insight on reimbursements, denials, the price
per accession, price per unit, paid units, throughput, and write-offs (XIFIN, 2020).
Justification of the Study
The literature clearly demonstrates a role for AI and ML in the context of the
revenue cycle. For example, Blass and Porr (2019) argued that AI and ML could decrease
the risk of error within compliance and risk management, ultimately streamlining the
revenue cycle. However, this research was general and did not provide a specific
framework for integrating AI and ML into a system. Instead, the research stated that it
could be helpful. This trend has been present overall in all the research on this topic.
Accordingly, there is a significant gap in the literature concerning helping organizations
develop the appropriate frameworks and protocols to integrate AI and ML into their
revenue cycle systems successfully, thereby justifying this study's need and developing a
framework to follow. Current literature findings are not helpful for healthcare system
administrators who seek to integrate technology-based solutions within their existing
fiscal cycle management operations. According to Hamet & Tremblay (2017), to
incorporate AI into the revenue cycle, it is first necessary to identify the barriers to
implementation and then develop a framework to implement that addresses those barriers.
Hence, this dissertation aided in clarifying how AI and ML can provide tangible solutions
for healthcare systems by utilizing the theoretical framework. Understanding the
development of such a tangible solution requires research that presents solutions that can
universally apply to diverse healthcare operations.
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Dissertation Goals
The dissertation's primary research goals that were addressed and detailed in
future chapters are summarized as follows:
1) To expand on the current literature surrounding the use of AI in the health care
revenue cycle and provide a framework to allow health care executives to quickly
visualize the benefits or drawbacks of such a technology in their specific
healthcare revenue cycle departments.
2) To create a framework that may be applied to a healthcare organization in an
effort to migrate from their current revenue management technique to one that
includes the use of AI/ML/RPA as a means of future cost control and revenue
boost.
Research Questions
This dissertation explored an increasingly critical issue affecting healthcare
organizations related to the use of AI software systems as a means to improve financial
operability and solvency. This study used a mixed-methods approach involving a meta-
analysis of the literature and semi-structured interviews to inform the following research
questions:
R1. What prospective benefits can be generated by using AI revenue cycle
applications for healthcare organizations?
R2. What are the risk factors associated with AI implementation in healthcare?
R3. What outcomes are derived by using a Lean Six Sigma (LSS) designed
framework for healthcare executives deciding to implement AI/RPA in the
healthcare revenue cycle?
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Relevance and Significance
The research questions hypothesized in this study have high significance for the
field of healthcare. Discussions regarding the need for better fiscal management have
grown as the healthcare industry has matured. Before the 1950s, hospitals were mainly
non-profit, and financing was handled mainly through charitable campaigns (Cleverly &
Cleverley, 2018). When Medicare financing of many services delivered by hospitals
caused a significant growth in hospital revenues, this opened the door for a heightened
interest in healthcare accounting and finances. Hospitals started making the shift from
charities to big business. Both cost accounting and management control became essential
tools for managing finances in hospitals.
The most recent seismic shock to the system came in the 1980s when the federal
government started feeling pressure from hospital billings that seemed to be spiraling out
of control (Cleverly & Cleverley, 2018). At this point, the push began to have more
patients treated on an outpatient basis to control costs. With this, the federal government
created the Prospective Payment System, which created an opportunity for the creation of
other types of medical providers other than hospitals, such as ambulatory surgery centers
and other providers.
With more recent developments, such as the passage of the Patient Protection and
Affordable Care Act [ACA] (2010), healthcare providers have been put under increasing
pressure to find ways to achieve the "triple aim" of healthcare. The triple aim calls on
healthcare organizations to (1) improve patient care experiences, (2) improve the health
of populations, and (3) reduce the cost of healthcare per capita (J. Evans, 2017). The
latter component of the triple aim, the thrust to reduce healthcare costs, is at the heart of
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financial management. Healthcare organizations must be run professionally and
efficiently to be able to deliver high-quality healthcare for diminishing payments. This
has required those in the healthcare industry to seriously rethink their business structures
and find ways within those structures to maximize the payments that they already receive
so that they can benefit the organization to the most significant degree possible.
That is why the consideration of using AI to improve fiscal management is so
relevant and significant for the healthcare industry today. Successful financial
management of modern healthcare organizations, which are becoming increasingly
complex, requires timely, relevant information to make better business decisions
(Cleverly & Cleverley, 2018). Because the existing systems are still overly dependent on
humans to do the processing, they are inefficient. This leads to delays in the
reimbursement for services delivered and delays in delivering an up-to-date look at the
healthcare organization's financial situation. As a result, healthcare executives often find
themselves in a position to make critical business decisions based on information that is
out of date and often of questionable accuracy. If the use of AI can improve that situation,
then healthcare managers could move to a position where they have information that is
timely and accurate, enabling them to make better business decisions that will enable
them to improve the profitability and feasibility of the services provided to the public.
Barriers and Issues
Several barriers and issues were faced when doing this type of research. The field
of medicine has been primarily dominated by research that follows the scientific method.
As a result, literature reviews that are conducted regarding many healthcare topics
include discussions about levels of evidence used to support the study's assertions,
10
foundation, and findings. As Fineout-Overholt and Melnyk (2015) outline, levels of
evidence can be categorized in seven levels, with systematic reviews of randomized
controlled trials (RCTs) being the "best" evidence, or categorized as level I, and evidence
from opinions expressed by either authorities or expert committees as being the "lowest"
form of evidence, which is categorized as level VII. The distinction between levels of
evidence is important because the level of evidence that is used to support an argument or
assertion is often used as a basis for determining if the research applies to healthcare
decision-making.
The Agency for Healthcare Research and Quality, as cited in Fineout-Overholt &
Melnyk, 2015 defines levels of evidence by three criteria: quality, quantity, and
consistency. In this context, quality speaks to how the study was designed and if
approaches were used that ensured that the findings were accurately measured and that
measurement, selection, and confounding biases were avoided. This is in part why
systematic analyses of RCTs are generally considered the highest level of evidence.
Within the AHRQ definition, quantity refers to the number of studies, the participants
involved, the magnitude of the treatment, the strength from causality assessments on the
outcomes, such as odds ratios or relative risk. Consistency refers to whether or not
multiple researchers are reporting similar findings using the same basic study criteria.
High-level evidence has a lower risk of bias in addition to greater generalizability. The
latter refers to whether the findings can be generalized to a more significant population
(Fineout-Overholt & Melnyk, 2015).
As Frączek (2016) discusses, the financial field has started to put more emphasis
on using evidence-informed practices (EIP), which are analogous to evidence-based
11
practices (EBP), which are used heavily in healthcare delivery practices. Applying EIP to
financial questions permits the practitioner to analyze the information that they are
receiving against the levels of evidence to determine the strength of the recommendations
and the applicability of the information to a wide range of financial situations. It is
precisely in this context where performing studies regarding healthcare finance becomes
somewhat difficult. The majority of literature considered in this study in the literature
review is from level VII evidence or opinions expressed by either authorities or expert
committees. As such, it is difficult to assign a weight to such studies, given that the
opinion primarily informs them of experts in the field. However, these opinions are not
necessarily backed by any evidence that would be considered empirical, at least not from
a scientific standpoint.
The fact is, due to the newness of AI, ML, and RPA, the key concepts under
discussion in this study, there is a lack of actual research studies of any type applying
these topics to the field of healthcare finance. A cursory look at Google Scholar with the
search terms +" artificial intelligence" +finance AND +" randomized controlled trial"
revealed zero studies regarding the combined topics in 5 pages of searches (the top 50
results). This search range was delimited to the past ten years (only articles since 2012).
As expected, removing the time limitation did not reveal any new articles on the topics.
Assumptions, Limitations, and Delimitations
Assumptions
This study assumed that the findings presented in the literature are accurate and a
true reflection of the current state of affairs regarding using AI, ML, and RPA in
healthcare finance situations. This has to be an assumption because the "evidence"
12
presented and reviewed in the literature review is Level VII evidence. There are no
empirical means to identify the presence of biases or the accuracy of statements in the
articles. It is also assumed that the information collected during the semi-structured
interviews from select subject matter experts indicates and represents the current state of
affairs in the healthcare industry, similar to the assumptions made regarding articles in
the literature review.
Limitations
The design of this mixed methods research study presents certain limitations. For
example, the selection of participants for the semi-structured interviews is a non-
randomized convenience sample. It may be indicative of circumstances or feelings
specific to certain healthcare organizations or the attitudes and approaches used in a
specific region of the country. Due to this limitation, the findings may or may not be
generalizable to the population of healthcare finance professionals in the United States,
let alone the approaches used in other countries that use an entirely different approach to
healthcare financing and funding.
Delimitations
Certain delimitations have been selected that may also impact the generalizability
of this study. In order to keep the study manageable, an arbitrary number of 10
participants for the semi-structured interviews were selected. As Creswell and Creswell
(2018) noted, a phenomenology study generally involves a range of 3-10 participants, so
the number selected for this component of the study is not inappropriately small. Another
aspect that could impact the study is historical contamination. Unfortunately, this study
was conducted during the coronavirus pandemic. While participants' responses in this
13
study are expected to be as accurate as possible, there is a possibility that internal validity
could be compromised due to the impact of the coronavirus and related financial strains
that would not be present during other periods when a pandemic is not in process.
Definition of Terms
Analytical-oriented approaches – Analytical-oriented approaches utilize the
ability of a machine to perform sentiment analysis at the document and sentence levels as
well as based on the aspect. Through such approaches, insights that would ordinarily not
be extracted are identified and converted into decisions that can be acted upon (Gandomi
& Haider, 2015).
Artificial intelligence (AI) – Artificial intelligence is defined as a theory and
creation of computerized systems designed to perform actions that typically would be
done using human intelligence and senses such as hearing, vision, language translation,
and decision-making (McGrow, 2019).
Data mining - is a process that utilizes algorithms to comb through large data sets
(big data) to extract usable activity patterns or outcomes (Bautista et al., 2016).
Healthcare information technology (HIT) - is a blanket term used to delineate the
diverse systems, programs, and mechanisms of technology that collect, store, process,
and manipulate the information contained within them for various healthcare-related
purposes (Wager et al., 2017).
Lean Six Sigma (LSS) – This is a fact-based, data-driven philosophy of
improvement that values defect prevention over defect detection. It drives customer
satisfaction and bottom-line results by reducing variation, waste, and cycle time while
14
promoting the use of work standardization and flow, thereby creating a competitive
advantage. (ASQ, 2020).
Machine learning (ML) – This is the process of a computerized system advancing
"knowledge" of a selected phenomenon through testing and adaptation, using observed
patterns and trends to improve decision-making capabilities (McGrow, 2019).
Predictive modeling – This occurs when an analysis of past patterns of activity
can be used to accurately predict future events, such as analyzing past payments based on
a particular CPT code to predict when a current claim will be paid (Nilsson, 2019).
Revenue cycle management – This refers to the process of streamlining and
optimizing processes throughout the revenue cycle to achieve the best possible cash flow
outcome for the organization (LaPointe, 2020).
Robotic process automation (RPA) – This is a process whereby tasks previously
engaged in by humans are automated to be performed by computers. In the context of this
study, an analog would be a case where a human used to collect information from a
variety of inputs such as email, spreadsheets, and other sources, interpret and collate the
data, then transfer it to a business system like an enterprise resource planning (ERP) or
customer relations (CR) system (Lacity & Willcocks, 2016).
Summary
As this review has considered, healthcare organizations deal with tremendous
amounts of information that must be processed and handled. Current approaches to
financial management are primarily manual, and this requires a significant investment in
human resources at a considerable cost. Research suggests that many of the manual
processes that are currently being used in financial management could be replaced by a
15
combination of AI, ML, and RPA. With a switch to these technologies, the speed of
submitting claims, the accuracy of those claims, and predictions of when claims will be
paid can improve exponentially. This benefits healthcare organizations because the faster
claims are submitted and paid, the less strain this exerts on cash flow demands.
The following section will consider the current state of knowledge in the areas of
AI, ML, and RPA. These topics will be considered with a particular interest in how they
are currently utilized in connection with revenue cycle management. The following
literature review will also discuss gaps in the literature and areas where more information
is needed.
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Chapter 2
Review of the Literature
Literature Review
The themes explored by academic and healthcare industry journals surround
discussions of technology and applications, the benefits delivered through analytical-
oriented approaches to revenue cycle management, and the barriers to these same
innovations. A final set of discussions entailed assessments of likely risk variables and
viable risk management approaches to address these challenges. Analyses that explored
background themes related to the dissertation's topic focused on three areas of discussion:
the concept of artificial intelligence (AI) and machine learning (ML), process definitions
of revenue cycle management, and the broader assessment of ML's potential for
managing these same processes.
A large group of research in the areas of AI and ML that is specific to healthcare
finance revolves around the processing of claim requests and payments from third-party
payers. The research has indicated that a significant amount of money is lost due to the
complexity of claims and inaccurately completed claims. When a claim is inaccurately
completed, it must be returned to the institution filing the claim, and this must be
rectified. This creates additional time in reworking the claim and extends the time
between claim submission and payment, which negatively reflects on the organization's
17
financial health. Numerous studies have used novel approaches combining AI and ML to
automatically detect such errors and annotate them with reasons why they are being
flagged. Some of these systems boast a 25% improvement over any current claim
analysis software or methods.
This literature review identified several specific aspects of machine learning and
artificial intelligence related to the healthcare revenue cycle. Of importance, the revenue
cycle and the processes associated with it often have very repetitive tasks performed by
humans. However, many of these tasks would benefit from using machine learning or
artificial intelligence to automate them. In implementing these strategies, healthcare
organizations could likely reduce costs and improve accuracies related to payments and
other similar factors, thus increasing revenue from existing claims by reducing denials.
Justification for Inclusion and Exclusion
Inclusion
This literature review pursued studies that covered issues about AI, ML, and
RPA. Also, articles regarding process definitions for revenue cycle management were
sought. Finally, articles applying AI, ML, and RPA to financial processes in healthcare
were sought. Articles that focused on AI, ML, and RPA, especially how they applied to
the healthcare field, were selected. Articles published within the past ten years, available
in full text, and the English language were considered for inclusion. The full text was
required because, while abstracts provide a general overview, they do not provide details
that were needed for this report. Articles featuring expert opinion were included because
of a lack of research in this field, although research studies were preferred.
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Exclusion
Studies were excluded from this literature review if they were published more
than ten years ago. Studies that were not published in English were not considered. The
reason for these exclusions is that studies older than ten years would likely not reflect
current practice or thinking about the use of technology in financial issues.
Previous Work and Strengths and Weaknesses
As noted in the introduction to this study, most of the articles regarding AI, ML,
and RPA were based on an accumulation of research (secondary research) and expert
opinion (primarily interviews). For example, several studies were explorations of the
knowledge about AI and ML, with an attempt to explain how these could be applied to
various aspects of healthcare, but mainly focusing on the clinical side of things (Clancy,
2020; Davenport & Kalakota, 2019; McGrow, 2019; Shaw et al., 2019). Some articles
researched the application of AI and ML from entirely different applications and
industries, such as using them for automation in the supply chain (Dash et al., 2019), for
making general business decisions (J. R. Evans, 2015), generic applications of AI and
ML (Kühl et al., 2019), order processing in the telecommunications industry (Lacity &
Willcocks, 2016), the manufacturing and construction industry (Lee et al., 2019), and
financial management in the hospitality industry (Millauer & Vellekoop, 2019).
The literature review contained many articles from Healthcare Financial Review
(HFR), a respected peer-reviewed journal. Unfortunately, many of the articles were
interview pieces that relied upon experts in the field recounting different ways that they
were already or were planning shortly to utilize AI and ML in their financial operations
(Baxter et al., 2019; Hegwer, 2018; Hut, 2019). Other HFR articles were secondary
19
research articles, using other research to quantify the use and intents of AI and ML in the
healthcare finance industry (Hillman, 2020; Navigant Consulting, 2019; Nilsson, 2019;
Schouten, 2013).
The use of secondary research was not limited to HFM. Several other articles
from peer-reviewed journals mainly were, if not entirely, secondary research, compiling
information about AI and ML from other sources (Blass & Porr, 2019; Cheatham et al.,
2019; Christodoulakis et al., 2020). In a search to create a sufficient research foundation
to work from, some non-peer-reviewed sources, including interviews and quotes from
industry professionals, were included in the literature review (Becker & Ellison, 2019;
LaPointe, 2020).
There were a few research papers that looked to apply AI and ML to specific
healthcare financial issues. One paper resulted from the authors analyzing a healthcare
financial situation, using attributional tools to predict future discrepancies to reduce
billing rejections, then testing them on a group of claims to evaluate whether the method
would be successful (Wojtusiak et al., 2011). This study only tested a small group of
claims, which could cause questions about the generalizability of the research to other
real-world situations with far greater claim diversity. Several research studies addressed
using an AI/ML approach to identifying and rectifying medical claim errors as a
component of risk prevention (Chimmad et al., 2017; Kim et al., 2020; Wojtusiak et al.,
2011). A few research studies focused on ways to use AI and ML to promote deep
learning in several areas of medicine, including finance (Kumar et al., 2010; Rajkomar et
al., 2018; Wojtusiak, 2014). Other studies focused on using various techniques associated
20
with AI and ML to "scrub" medical claims or improve medical claims prediction
(Abdullah et al., 2009; Che & Janusz, 2013).
The lack of high-quality research studies in this area presents a challenge. It
makes it difficult to make a compelling case for or against a particular AI, ML, or RPA
practice, absent quantifiable evidence to support the practice. While several research
studies were found, they almost all were oriented at creating and testing means to
improve aspects of finance that have proven to be tricky, such as claim denial by third-
party payers. No studies were identified that identified specific performance
improvements as a result of applying AI principles. Therefore, there is no empirical
foundation to quantify the benefits of AI, ML, and RPA on the healthcare industry other
than "expert" reports and secondary research.
Gaps in the Literature
The especially glaring gap in the research that was identified in this literature
review is the lack of rigorous research studies in this area. While several authors created
algorithms and approaches to common problems experienced by healthcare finance
professionals, backing their effectiveness up through a scientific method of testing, the
broader picture appears not to be addressed in the literature. It would be most helpful if
one of the many organizations who have put AI/ML/RPA into practice in their
organizations would perform a retrospective review that could provide numbers of
differences between using this approach compared with the previous state of affairs.
Analysis of Research Methods Used
There were several research methods used in this study. Several of the articles
were "expert opinion" articles and focused on interviews and reports from several
21
healthcare finance professionals (Becker & Ellison, 2019; LaPointe, 2020). The large
majority of the other articles were secondary research articles, and the data for these
studies were accumulated mainly through reviews of the current literature, although not
systematic (Blass & Porr, 2019; Cheatham et al., 2019; Christodoulakis et al., 2020).
The proper "research studies" in this literature review used many approaches to
generate their findings. For example, in the article on deep learning for medical
predictions, Rajkomar et al. (2018) used predictive modeling. They reported the accuracy
of such predictions using an area under the receiver operator curve [AUROC] across
sites. In the study by Kim et al. (2020), the authors studied the accuracy of a new Deep
Claim system to identify potential payment rejections and found that using the new
system resulted in a 22.21% relative recall gain (95% precision). Wojtusiak et al. (2011)
was the only study that measured the performance of their model to use rule-based
prediction of medical claims payment in a before and after a fashion, providing actual
numbers on the increase in effectiveness in using the new approach over previous
performance.
Concept of Artificial Intelligence, Machine Learning, and Robotic Process
Automation
Kuhl et al.'s (2019) analysis provided an in-depth discussion of both AI and ML.
Their work specifically noted that while AI can be defined as an overarching conceptual
category that references a diverse set of computer intelligence-driven technologies,
machine learning represents a particular application. The authors noted that machine
learning could be understood as a program's ability to perform routine tasks, become
increasingly proficient in completing these same tasks, and utilize and apply known
22
information towards advanced problem-solving forms. Kuhl et al. (2013) also contended
that optimal approaches to machine learning involve base-level operations in which
programs perform repetitive tasks that gradually increase in their complexity (Kuhl et al.,
2013).
An anonymous report from the publication Healthcare Financial Management
noted that healthcare operations' revenue cycle management process often provides
unique machine learning applications opportunities. These tasks include these same traits
(Baxter et al., 2019). In the same publication, a follow-up report also noted that
healthcare systems increasingly rely on automated and analytics-driven revenue cycle
management approaches even as they outsource these processes to third-party specialist
firms (Navigant Consulting, 2019). Dash et al. (2019) demonstrated how increasing
complexity is helpful in the context of supply chain management. Much of their analysis
can be applied to the context of an automatic revenue cycle, specifically, to help provide
a framework for how artificial intelligence can adapt to increasingly complex tasks.
Robotic process automation (RPA) is an industrial response to the vast amount of manual
work that individuals perform daily, weekly, or monthly to support a broad array of high-
volume business processing (Lacity & Willcocks, 2016).
RPA is mainly associated with the task level. The application areas include
finance and accounting, IT infrastructure maintenance, and front-office processing. The
so-called robots are software programs that interact with enterprise resource planning and
customer relationship management systems. The robots can gather data from systems and
update them by imitating manual screen-based manipulations. RPA solutions are
23
appealing from a business perspective because they automate repetitive tasks while
minimally invasive into the overall processing they support.
Process Definitions of Revenue Cycle Management
Literature analyses describe the healthcare sector's current strategies and other
industries to implement automated revenue cycle management approaches. Millauer and
Vellekoop’s (2019) healthcare industry discussion noted that firms frequently utilize
these approaches for three main reasons. These models streamline the repetitive nature of
fiscal cycle operations by applying machine learning models and algorithms to these
tasks. This same approach additionally serves to mitigate the risks stemming from human
error within these processes. McGrow (2019) highlighted the importance of removing
human error from processes when it is possible to do so. However, currently, these
processes are still being completed by humans because there is not currently a
sufficiently sophisticated machine learning system to replace the human element with an
automated system completely. Analysis conducted by Becker and Ellison noted that the
same models' current healthcare industry applications include a multilayered set of
strategies. Among these entail using machine learning-based models to structure routine
billing operations efficiently, complete complex coding tasks, and generate predictive
data that can be used for risk assessment and management purposes (Becker & Ellison,
2019). Blass and Porr similarly noted that automated approaches to revenue management
typically include the ability of programs and their applied algorithms to gradually identify
patterns associated with payers and contracting groups (Blass & Porr). Over time, these
applications can detect risk variables that might indicate the client's inability to deliver
payment on time (Blass & Porr, 2019).
24
Evans cited automated forms of revenue cycle management as a valuable
instrument in helping firms in diverse industries achieve higher efficiency and
optimization levels in their internal areas of financial operations (J. R. Evans, 2015).
Similarly, Davenport and Kalakota identified revenue management as one specific
benefit derived from AI and ML learning applications across health systems (2019).
Based on all of these studies, if machine learning were implemented correctly in
the future, it would be possible to replace most, if not all, of the billing processes with
machines rather than humans.
Potential of Machine Learning for these Processes
Discussions of the benefits generated through ML-driven revenue cycle
management processes in healthcare include assessments of current and likely or
predictive benefits. Simultaneously, these collective assessments emphasize technology's
role as drivers in achieving current and future term benefits. Hut's discussions noted that
current generation AI and ML platforms could process and structure complex and
recurrent tasks within health systems (Hut, 2019). Revenue management represents one
specific example in this same context. Hegwer similarly noted that current ML
applications help firms achieve excellence in fiscal cycle management processes. The
author provided several discussions of cases of large systems that applied these
technologies and yielded notable improvements in their ability to process patient data,
predict reimbursement patterns, and predictively assess the likelihood of nonpayment
among specific groups or clients (Hegwer, 2018).
Nilsson (2019) also noted that the same applications can predict payer behaviors
and indicate the times in which they will likely remit payment and if they are at risk for
25
nonpayment or default. Schouten (2013) contributed to these same discussions by
assessing machine learning platforms' capabilities to examine recurrent payment loss
patterns by investigating multiple channels of revenue and reimbursement. While the
author's discussions referenced the current technologies currently utilized by health
systems, his analysis also identifies these technologies' ability to complete increasingly
complex and predictive-based assessments. In implementing these more complex
processes, errors would likely continue to be reduced, and organizations would have a
more efficient billing process. Schouten's commentary parallels Rosenfield's discussions
of future term machine learning applications and roles (Schouten, 2013). The latter author
noted that advanced machine learning benefits could include these platforms' ability to
conduct complex operations that subdivide payment systems according to billed
procedures and specialized coding (Schouten, 2013).
In both cases, the analyses cited ML-based algorithms' ability to conduct
increasingly complex operations as they engage in many of the same procedures over the
longer term. On a more global scale, Lee et al. (2019) found that artificial intelligence has
significant potential for automation in many industries and automation of non-robotic
tasks. This is important in understanding how organizations can implement artificial
intelligence to automate revenue cycle management. While Nilsson's analysis identified
the potential and prospective benefits generated through machine learning applications,
his report implicitly located one of these systems' critical risks (Nilsson, 2019). The
author specifically identified the necessity of cultivating a strategic plan to implement
and incorporate ML-based technologies in a healthcare firm's operations.
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This approach represents a vital aspect of technology management as it will better
ensure that an implemented strategy will achieve positive returns from a cost/benefit
perspective. The issue of cost represents another critical factor frequently identified by
related literature. Fundamentally, these applications represent a methodology for
achieving savings-based returns (Hillman, 2020). Accordingly, healthcare organizations
typically integrate and apply these innovations to avoid waste, identify redundant
expenses, and locate ways of optimizing fiscal cycle operations. However, achieving
these outcomes often requires an organization's ability to mitigate short-term risks that
accompany implementation strategies. Accordingly, the costs associated with purchasing
technology and integrating it into existing networks can present firms with a combined
set of fiscal and technical challenges that they will have to address as they develop
change management plans. For example, Clancy (2020) made a point of describing the
importance of organizations using artificial intelligence to automate certain aspects of the
revenue cycle, particularly those aspects that are repetitive and are an inappropriate use of
human resources.
The risks encountered during these initial stages can additionally affect
organizations in the longer term in cases where firms do not explicitly identify the
specific functions that integrated ML platforms will achieve in the context of a firm's
fiscal cycle management processes. For example, issues related to a platform's immediate
use, its prospective future term value, and the role that human agents will have in
monitoring the applications represent core issues that decision-makers need to address
during planning sessions (LaPointe, 2020). Similarly, analysts identify the need for
carefully value mapping a proposed model before its implementation: a methodology that
27
can evaluate the specific departments and stakeholders that will benefit from the
applications. In cases where departments or individual employees exhibit a reluctance to
accept the proposal, the same strategies can be used to identify the role these stakeholders
will play in managing the applications (Christodokulakis et al., 2017).
A final set of recommendations includes the need for cultivating a set of
measurable objectives that clearly define the role, purpose, and strategy of the integrated
systems across the technology's prospective lifecycle. Cheatham et al. conducted an in-
depth analysis explaining some of the risks associated with artificial intelligence
(Cheatham et al., 2019). Organizations, including hospitals that implement artificial
intelligence, must also use specific protocols to mitigate its risks. Specifically, they must
have a clear structure that pinpoints the specific risks associated with AI. The structures
must also have institution-wide controls rather than limited controls.
Lastly, there must be a nuance in analyzing the risk in light of the risk's nature.
This is important because organizations must understand the risks and how to mitigate
them before implementing new protocols when they plan to implement artificial
intelligence.
Application of Machine Learning/Artificial Intelligence to Healthcare Issues
The fact that vast amounts of money are lost due to inaccurate claim processing is
well established in the literature (Kim et al., 2020; Wojtusiak et al., 2011). One of the
problems that frequently occur that causes claims to be rejected is the inclusion of an
incorrect ICD code for diagnosis or CPT code for diagnostic tests (Abdullah et al., 2009;
Chimmad et al., 2017). This is the reason that multiple researchers have looked for ways
to use AI and ML to automatically analyze massive bodies of medical claims to detect
28
and, in some cases, repair information that was incorrectly entered (Abdullah et al., 2009;
Chimmad et al., 2017; Kim et al., 2020; Zhong et al., 2019). Improving the information
on medical claims can reduce lag time between claim submission and payment, which is
a critical financial measurement indicating the financial health of a healthcare
organization (Cleverly & Cleverley, 2018).
Kim et al. (2020) proposed a novel implementation of AI/ML that they call Deep
Claim. The Deep Claim approach uses a three-step process to improve the accuracy of
predicting the exact amount that third-party payers will present. The first step is the
development of clinical contextual interrelations at the high level of claims, which uses
ML against raw claims data, avoiding the need for expert knowledge or extensive
preparation of the data before processing. The next stage is deploying Deep Claim in real
deployment scenarios. The third step is where Deep Claim flags questionable fields in the
claim based on what it learned in the ML process. This gives it high prediction
interpretability, along with data presented that explains why the fields were flagged so
they can be double-checked and rectified. This novel approach asserts that it can identify
22.21% more denials than the best system that is currently in place.
In research by Wojtusiak et al. (2011), the researchers developed an ML
application that would permit the AI system to combine rules that were already known
for claim rejection and combine these with new rules that were detected by the AI
algorithms. The ability of the AI to generate new rules was particularly important because
healthcare is continually changing, and this architecture permits the system to adapt as
new changes in the healthcare system occur. The system effectively identified new errors
that had slipped through the system, with 60% of Medicaid, 50% of DRG 371, 55% of
29
DRG 372, and 44% of DRG 373 abnormalities detected. The false-positive rates were
relatively low, ranging from 5% to 30% for the same groups.
In the study by Kumar et al. (2010), the researchers used data mining, which
could then be used for an ML process to improve the prediction of claims that need
reworking. They noted that 30% of the administrative staff in health insurers are
dedicated to reworking incorrect claims, which be rectified using AI. The researchers
developed a method of detection based on ML, then deployed that model at one of the
nation's largest health insurers. Because the new system was much more precise, it
generated a substantial increase in hit rates, identifying faulty claims. The improved
accuracy provided by this novel application of AI and ML could potentially generate cost
savings of between $15-25 million for each standard insurer using the system.
Other researchers explored ways to label data or develop concept representations
from existing data sets using combinations of AI and ML (Bai et al., 2019; Che & Janusz,
2013; Lu et al., 2020; Zhong et al., 2019). Being able to generate rules and label data or
categorize it in a way that ML systems can easily interpret is a crucial stepping stone to
practically using such data to apply to numerous healthcare applications, such as financial
management (Che & Janusz, 2013; Wojtusiak, 2014).
Summary and Thematic Analysis
This literature review has considered numerous aspects of how the healthcare
finance industry has considered and implemented ML, AI, and RPA into their business
frameworks. The financial process, especially that of submitting claims, is complex and
regularly involves touching tens of thousands of documents. The potential for errors is
high, and as the research has indicated, some organizations have up to 30% of their staff
30
dedicated to "reworking" claims that were incorrectly submitted. With the constant
pressure on healthcare organizations to decrease costs, finding ways to use AI, ML, and
RPA to streamline finance department processes and make them more efficient is highly
attractive. Even more so, the potential cost savings, which are projected into the tens of
millions, are sufficient to get the attention of healthcare finance professionals. This
review has explained how AI, ML, and RPA can be applied to the healthcare finance
field. It has also demonstrated that systems currently in use generate considerable savings
for many healthcare organizations.
These findings are especially significant to healthcare finance professionals who
are under considerable pressure to find ways to reduce costs in their departments and
improve cash flow through efficient claims processing and payment turnaround. The
findings are also relevant to the healthcare finance field because the application of certain
aspects of the research has been demonstrated to generate considerable cost savings.
Healthcare finance departments are also responsible for maintaining the organization's
financial health. The potential of AI and ML technologies to improve payment
turnarounds is highly relevant, as this is a key financial performance metric for all
hospitals.
The literature review presents valuable information. Specifically, these analyses
identify analytics-based approaches to revenue cycle management as an increasingly
utilized strategy among diverse healthcare systems. The findings indicate that sector
decision-makers identify vital benefits that can be derived from these applications as
fundamental savings and risk-management tools. While discussions of the model's
current and prospective capabilities differ in terms of their understanding of the benefits
31
derived from their application, they are interconnected by their mutual contention that
these outcomes are directly correlated with the platform's existing and emerging
technological features and capabilities. In brief, these views suggest that as AI and ML
systems advance, healthcare organizations will be able to apply them in increasingly
sophisticated ways. Assessments of risk identify the challenges related to initial and start-
up processes as being the most significant. Recommended risk management approaches
include applying detailed and precise technology strategies that identify an implemented
model's specific role within the organization and outline the specific objectives that the
platform will help the company achieve.
The implications derived from the preliminary literature review relate to the
following themes: 1) the current state of the use of AI/ML/RPA and 2) the continuing
gaps in the healthcare revenue cycle areas that could benefit from this technology. As the
review indicated, the current field provides prospective decision-makers with an in-depth
set of data that explains the concept of analytics-driven approaches to revenue
management. It generally outlines the types of benefits derived from its application.
Analyses that reference individual health systems as case studies provide contextual
information that identifies how single organizations apply these innovations. While
informative and descriptive, this information lacks a level of specificity that could
otherwise help planners make targeted decisions. Accordingly, these preset gaps require
follow-up studies that assess common thematic issues from an organizational perspective.
Evaluating the variables of benefits, drawbacks, risks, and appropriate risk management
strategies from a healthcare organization's strategic perspective can aid in balancing the
32
current tendency for associative literature to focus on macro-level themes related to these
same issues.
33
Chapter 3
Methodology
Approach
The purpose of this study was to identify the potential risks and benefits of using
AI-based applications in the revenue cycles of large healthcare organizations.
Specifically, the study closed the research gaps by identifying and analyzing the
perspectives of key stakeholders responsible for managing revenue cycles. As mentioned
in Chapter 1, the study followed a qualitative methodological approach when the
researcher conducts semi-structured interviews with recruited participants. Semi-
structured interviews provide researchers with opportunities to identify significant themes
across participants and establish an appropriate context for developing theoretical
explanations of emerging themes found in coded data. In many ways, the selected
approach provided a solid basis for identifying broader issues noted in earlier published
studies. Conducting semi-structured interviews with participants also provided the
researcher's opportunities to discuss how systems thinking concepts and risk management
strategies apply in different healthcare settings (Alam, 2016; Anderson, 2016). By
including interview data in this study, its overarching goal was to determine how
researchers may perform similar investigations using qualitative, quantitative, or mixed
methods approaches.
34
Justification for the Methodology
Following Hissong et al. (2015), a qualitative methodological framework applies
to this study proposed when it guides how researchers understand the meaning derived
from lived experience. Accordingly, qualitative research involves studying individuals as
they behave in different social, organizational, or institutional contexts. Given that few
currently published studies address how key stakeholders manage revenue cycles in
healthcare, the decision to apply a qualitative framework involved accounting for
differences between individual experiences, meanings placed on individual experiences,
how individuals respond to different environments, and developing models whereby
researchers performing future investigations may design empirically verifiable
instruments.
For example, qualitative researchers may apply a phenomenological design when
investigating the relationship between professional development and barriers to accessing
consistent healthcare (Creswell & Creswell, 2018; Hissong et al., 2015).
Phenomenological study designs typically involve researchers performing semi-
structured interviews with a sample size of no more than ten (n = 10) participants. While
quantitative researchers may perceive that such a small number of participants cannot
produce generalizable results, they may provide an in-depth analysis of how significant
themes coded in the interview data can inform future investigations. The lack of
generalizability will constitute a significant limitation that influences how researchers
performing future investigations may attempt to replicate the study design across other
settings (Creswell & Creswell, 2018). Instead, the selected methodology refers to an
inductive process from which data inform theory development.
35
Theoretical Framework and Development
The theoretical framework developed for this study included three key areas of
analysis. First, the proposed framework will draw from key concepts impacting
healthcare administration. Examples of concepts included in this framework are systems
thinking and risk management strategies. Whereas systems thinking spans multiple
disciplines and apply to various organizational contexts, its applications to the healthcare
industry are such that researchers often explain why relationships between different
components are more complex than others (Anderson, 2016). Given that the healthcare
industry is complex, its relationship to systems thinking indicates further where
researchers can detect patterns and failure probability. In relation, risk management
strategies involve healthcare professionals emphasizing financial and business viability
from an organizational perspective.
Healthcare professionals must follow specific steps when addressing risks, which
entail identifying the context, explaining known risks, analyzing these risks, evaluating
the risks, and managing the risks properly (Alam, 2016). By combining elements of
systems thinking and risk management strategies, the researcher applied specific concepts
to technology management practice in artificial intelligence (AI) applications. The
emerging theoretical framework then aligned with significant themes identified in the
current planning and risk management literature. More specifically, the theoretical
framework developed from an analysis of themes coded from the interview data aligned
with how researchers previously utilized AI models for revenue management practices in
healthcare. As the interview portion of the study will receive attention, the researcher will
36
apply systems thinking concepts and risk management strategies to indicate the presence
of significant themes.
Next, concepts found in the research on risk management strategies were applied
to identify themes like AI risk and risk mitigation. An informed view of principles
guiding risk management strategies will likely improve how the researcher interprets the
interview data and accordingly builds a theoretical framework. From there, the ADDIE
model used to evaluate practice among e-learning designers and developers will guide
theory development. Researchers note further how the ADDIE model supports a process
of analyzing, designing, developing, implementing, and evaluating AI designs in complex
healthcare environments when their implications for revenue generation along cyclical
lines are vast (Anderson, 2016; Gawlik-Kobylinska, 2018). While the ADDIE model also
supports improvements to healthcare decision-making, it corresponds more closely to risk
identification. Subsequently, the theoretical framework developed from the findings will
inform problem-solving approaches significant stakeholders in healthcare may use when
measuring the risks and benefits associated with integrating AI technologies into revenue
cycle management.
Interview Structure and Design
The researcher used a set of 12 interview questions (see Appendix A) that each
participant received. All participants received both a standard email invitation and
NSU’s standard informed consent (see Appendix C and D). All responses provided by the
participants will provide keywords that the researcher may use to ask further questions.
Following this design provided a rich context for analyzing the interview data as they
coincide with this study's three central research questions. As detailed below, a set of four
37
interview questions addressed themes related to how AI-based technologies will benefit
the healthcare revenue cycle management processes. Three interview questions involved
asking the participants to address risk factors that negatively impact the healthcare
revenue cycle management processes. A final set of five interview questions invited the
participants to discuss risk management and problem-solving strategies that guide
decision-making processes in the organizational context.
First, the interview questions addressing themes related to how AI-based
technologies will benefit healthcare revenue cycle management processes are as follows:
1. What types of patterns or processes have you seen as a healthcare administrator,
accounting/financial management officer, or information technology (IT) staff
member who influenced your healthcare revenue cycle management perceptions?
2. Which of these patterns or processes left the most significant impact on
organizational performance? Why do you believe these patterns or processes play
such an essential role?
3. How do you believe that AI-based technologies can inform the triple aim of
healthcare when financial and related schemes appear challenging to manage?
4. How do you believe AI-based technologies may contribute to improvements in
administrative, financial, or other forms of professional decision-making?
Responses to these questions will guide how the researcher steers each interview
and links major themes to those found in the extant research literature. The exact process
will apply to the two remaining sets of interview questions on risk factors that negatively
impact healthcare revenue cycle management processes and risk management strategies
that also guide decision-making processes in the organizational context.
38
The second set of questions will involve the researcher asking participants the
following:
1. Which risks specific to your organization left the most significant impacts on
decision-making after integrating AI-based or other types of technologies into
healthcare revenue cycle management?
2. How have these risks driven past performance and shaped decision-making?
Which risks still demand critical attention in the present?
3. What do you believe will mitigate future risks when some issues impacting
healthcare revenue cycle management remain?
While this set of questions will initially lead the participants to provide closed-ended
responses, its relation to the specific findings discussed in Chapter 2 will provide an
appropriate context for developing theory from the interview data and answering the
three central research questions.
Lastly, the third set of questions will involve the researcher asking participants the
following:
1. Which risk management strategies, if any, did you apply in the past to ensure that
your organization could integrate AI-based technologies effectively?
2. How did you apply these strategies to mitigate current and future risks?
3. Which strategies do you believe are still effective and why?
4. Which strategies do you wish the organization would eliminate? What examples
can you provide to support any changes in administrative or other types of
decision-making in the organizational environment?
39
5. How have the past and current strategies impacted your ability to develop yourself
professionally while gaining knowledge of how revenue cycle management
functions?
As with the first two sets of semi-structured interview questions, this third set will
ensure that each participant will provide rich insights into how AI-based technologies
inform a lived experience among staff members at different levels within the same
organization.
Data Collection and Research Questions
The study followed a qualitative design to generate three distinct datasets. Next,
the datasets produced in this study were aligned with the responses to interview
questions. From there, the artifacts produced contributed to the discussion and answered
all three research questions (Hissong et al., 2015; Regnault et al., 2018). After the
researcher combined all three datasets, a triangulation process followed to ensure that all
responses provided by each participant establish an appropriate context allowing for
comparisons against previous research findings (Creswell & Creswell, 2018). To reiterate
from Chapter 1, three central research questions guide this study as follows:
R1: What prospective benefits are possible from using AI revenue cycle
applications in the healthcare industry?
R2: What are the risk factors associated with implementing AI-based technologies
in the healthcare industry?
R3: What outcomes are derived by using a Lean Six Sigma (LSS) designed
framework for healthcare executives deciding to implement AI/RPA in the
healthcare revenue cycle?
40
All three research questions contributed to theoretical development in the following
ways. First, the research questions encouraged the researcher to focus on specific sub-
topics related to the broader issue. Second, the research questions provided a set of
discussion points applicable to all three datasets emerging from the study design. Third,
the research questions provided the basis for exploring AI-based technology in health
systems.
Sampling
The study included semi-structured interview data provided by at least five (n = 5)
participants with experience managing AI-based technologies while managing healthcare
revenues. For this study, a quota sampling strategy works best to ensure that all recruited
participants meet specific characteristics (Creswell & Creswell, 2018; Hissong et al.,
2015). The selected strategy allows qualitative researchers to focus their attention on
which recruited individuals will demonstrate the highest possible degree of knowledge or
expertise in their field. From there, the researcher will use recruitment strategies
appropriate to location, culture, and population until achieving a specific quota.
Quota sampling is a nonprobability strategy that resembles purposive sampling in
selecting participants according to criteria relevant to answering one or more research
questions. While the sample size depends mainly on the time, resources, and study
objectives, they may differ when an investigator approximates how many participants
will provide interview data (Creswell & Creswell, 2018). However, the quota sampling
strategy applies when researchers evaluate populations with characteristics that
correspond to set properties.
41
The researcher selected participants from the quota sampling strategy, including
healthcare administrators, accounting/financial management officers, and information
technology (IT) staff members employed within the same organization. The selected
group of participants represents a collective of internal stakeholders capable of providing
in-depth feedback to semi-structured interview questions regarding their experience using
AI-based technologies in revenue cycle management. Specific inclusion criteria that
apply here include 1) employment in one area of healthcare administration that reflects
professional responsibilities and experience in revenue cycle management; 2)
employment within the same organization; and 3) two to three years of experience with
the same organization. Each of the recruited participants will receive an informed consent
letter explaining this study. All responses to semi-structured interview questions will
remain confidential to maintain the anonymity of each participant.
Data Analysis Procedures
As previously mentioned, the semi-structured interview data obtained from each
participant underwent a triangulation process to determine the validity of responses in
alignment with the extant literature. Following Creswell and Creswell (2018),
triangulation entails a process of comparing outcomes and evaluating whether lived
experiences described by participants match those observed in previous studies. Since the
study involved performing semi-structured interviews with recruited participants, data
triangulation is an optimal strategy for comparing how individuals responded to each
question. Data triangulation ensured further that significant themes found in the interview
data answered the central research questions. While investigator triangulation that
involves the use of different evaluators ensured the interview data was feasible in
42
answering the central research questions, time and resource constraints limit
opportunities to make closer comparisons between closer observations. Despite how
participants may view their experiences of AI-based technologies and healthcare revenue
cycle management differently, their responses to each interview question produced
outcomes that researchers should consider when performing future investigations.
Further, the data analysis procedure invited the researcher to address how AI-
based technologies impacted healthcare revenue cycle management by addressing
potential outcomes like associated benefits, associated weaknesses, risk mitigation, and
effective strategies. Each outcome reflected how the recruited participants described their
lived experience of using technology to improve revenue cycle management. The use of
an NVivo-based application was applied here when interview data initially appeared
unstructured in a raw format. By using NVivo, the researcher coded and segmented data
according to patterns recorded in the interview data. The coded data then informed the
theoretical framework development to indicate where participants offered similar and
different perspectives on their experience using AI-based technologies.
Trustworthiness and Reliability
Ensuring trustworthiness in this qualitative study required attention to factors like
credibility, transferability, dependability, and confirmability. As Nowell et al. (2017)
explained, credibility occurs whenever qualitative researchers account for participants'
lived experiences and align them with the extant literature. Researchers may use
procedures like data triangulation as an external check to increase credibility (Creswell &
Creswell, 2018; Nowell et al., 2017). However, most cases involve researchers checking
preliminary findings and comparing them to raw interview data obtained from
43
participants. Member checking and the use of external auditors may inform this process.
However, time and resource constraints limit how many individuals will participate in the
data analysis process.
Second, transferability establishes that findings analyzed from the interview data
should generalize across populations in some way. While smaller samples rarely make
the findings of qualitative studies generalizable, researchers must still provide thick
descriptions to account for where gaps in theory development remain (Creswell &
Creswell, 2018; Hissong et al., 2015; Nowell et al., 2017). Along these lines,
dependability allows qualitative researchers to trace and document the sources of
interview data logically. Researchers may better judge the dependability of investigations
by examining data collection and analysis procedures as informed how accurately they
interpret the findings (Nowell et al., 2017). Here, researchers may achieve confirmability
by explaining how the findings answer specific questions and inform theoretical
development. Confirmability entails further that researchers performing future
investigations may replicate study designs and understand how some decisions were
made.
Considering how this study aimed to include three datasets, ensuring the validity
of each will require an application of reliability-oriented approaches reflecting potential
outcomes in future investigations. Accordingly, the researcher double-checked that each
dataset corresponded to significant themes found in the interview data by performing an
audit trail (Nowell et al., 2017). Documenting an audit trail will also inform the
confirmability of study findings when the lived experiences described by each participant
match a defined research context. However, increasing familiarity with the data will
44
remain necessary to explain similarities and differences in perceptions regarding how AI-
based technologies produce benefits or risks within a specific organizational context.
Especially as qualitative studies increase in popularity, researchers will need to
familiarize themselves with various tools for ensuring the data collected from participants
have more extensive applications. Aligned with the purpose of this study, a qualitative
methodology will support theory development when each data source provides evidence
of which strategies work and where decision-making can improve.
Summary
The goal of this chapter was to outline the research method used to answer the
research questions. A discussion of the procedure, study participants, data collection, and
interview questions outlined the specifics of how the study was conducted and who
participated in the study. The methodology overview detailed the steps for creating the
interview structure and how that data would flow into a triangulation to ensure the
validity of the responses. This chapter outlined a listing of required resources that were
needed to support data collections, analysis, and suggested sampling. The instrument
development and validation process provided insight into combining interview data and
performing the triangulation to form the questions for the theoretical framework. The
goal of Chapter 4 is to provide the study results and demonstrate that the methodology
described in Chapter 3 was followed and supported.
45
Chapter 4
Results
Data Analysis
There was a total of twelve interview questions conducted from a sample size of
ten participants that included healthcare administrators, accounting/financial management
officers, and information technology (IT) staff members employed within the same
organization.
Thematic Analysis Approach
The researcher recorded the interviews via a Microsoft Forms engine and
transcribed into text, then arranged and sorted them in NVivo 12 computer-assisted
qualitative data analysis software (CAQDAS) (see Appendix B).
The sixth stage process of thematic analysis by Braun and Clarke (2006) (i.e.,
familiarizing yourself with your data, generating initial codes, searching for themes,
reviewing themes, defining and naming themes, and producing the report) was followed
to analyze the transcribed information. Appendix E details the coding process that was
used.
46
Table 1
Research Questions with Relevant Themes Hierarchy
Research Question Themes
R1: What prospective benefits are possible
from using AI revenue cycle applications in
the healthcare industry?
1. Benefits of AI in HRCMP
1.1 Cost reduction and revenue growth
1.2 Improved data quality
1.3 Organizational benefits
1.3.1 Decrease or reduce workforce
1.3.2 Enhances teamwork
1.3.3 Make better and quick decisions
1.4 Patients benefits
1.4.1 Help in early diagnosis
1.4.2 Improved patients' experience
1.4.3 Reduces patients’ denial rate
R2: What are the risk factors associated
with implementing AI-based technologies
in the healthcare industry?
2. Negative impact of risk factors on
HRCMP
2.1 Impact of the human component
2.2 Increase in cost
2.3 Need to retrain employees
2.4 Security and privacy concerns
2.5. Technological complexity
R3: What outcomes are derived by using a
Lean Six Sigma (LSS) designed framework
for healthcare executives deciding to
implement AI/RPA in the healthcare
revenue cycle?
3. Risk management and problem-solving
strategies
3.1 Data security
3.2 Identification of risks
3.3 Implementing NLP
3.4 Properly trained staff
3.5 Review and audit of Processes
3.6 Transparency of processes
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Details of Interviews
A total of ten interviews were conducted with recruited participants. Table 2
exhibits an overview of each participant (interviewee) with the interview duration.
Table 2
Interview Overview with the Duration of Interviews
Interviewee Number Duration (In minutes)
Participant -1 2 hrs 23 min
Participant -2 14 min
Participant -3 3 hrs 22min
Participant -4 43 min
Participant -5 45 min
Participant -6 1 hr 48 min
Participant -7 1 hr 12 min
Participant -8 10 min
Participant -9 59 min
Participant -10 1 hr
Word Frequency
Word frequency query of fifty most repeating words having a length of 4 (four
alphabets) was run to get the initial familiarity of the data/document. The following
figure or word cloud exhibits a list of the most frequently occurring words or concepts in
responses of the ten participants.
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Figure 1
Word Frequency
Analysis Process in NVivo
A total of fifteen axial codes were grouped into three broad categories labelled
based on the research objective and research questions: the categories were defined based
on already developed questions in the semi-structured interviews (see Appendix E). The
data in each category was further mined, and various concepts (themes) and sub-concepts
(sub-themes) were identified and interpreted.
Findings
Benefits of AI in HRCMP (Healthcare Revenue Cycle Management Process)
To define HRCMP stands for Healthcare Revenue Cycle Management Process.
The first research question, “What prospective benefits are possible from using AI
49
revenue cycle applications in the healthcare industry?” was answered by formulating a
level-3 theme of “Benefit of AI in HRCMP.” This theme was made up of four sub-
themes 1). Patients benefits, 2) Organizational benefits, 3) Improved data quality, and 4)
Cost reduction and revenue growth. The themes of patient benefits were further
categorized as 1) Reduces patients’ denial rates, 2) Improved patients experience, and 4)
Help in early diagnosis. The theme organizational benefits were further categorized as
enhancing teamwork, decreasing or reducing the workforce, and making better decisions.
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Figure 2
Theme Hierarchy of Benefits of AI in HRCMP
Figure 3 below represents the top beneficial themes from the collected interview
data. The data is presented based on the percentage of text coded at each theme (node),
and it is calculated by NVIVO based on how many times and the amount of text
referenced at each node for the source document. The top benefit identified by the
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participants is the organizational benefits of using automation within the healthcare
revenue cycle, as cited below:
“AI would allow for more efficient processes in every department throughout the
revenue cycle, which would produce better data creation, which would create
better data reporting, which will allow the management team to better pinpoint
and address issues affecting both patient health and organizational health.”
“As AI is based on data, visa Data warehouse, Data lakes, and data marts,
essentially data stored from all facets of technology systems and integrations, the
possibilities of data aggregations combined with logic, yields to timely and
accurate reports or dashboards.”
Figure 1
Percentage Coverage of Benefits of AI in HRCMP
13.18%
8.87%
5.42%
2.86%
Organizational benefits
Patients benefits Improved data quality Cost reduction and revenue growth
Percentage coverage of Benefits of AI in HRCMP
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Negative Impact of Risk Factors on HRCMP
The second research question, “What are the risk factors associated with
implementing AI-based technologies in the healthcare industry?” was answered by
formulating a level-3 theme of “Negative impact of risk factors on HRCMP.” This theme
was made up of five sub-themes of Need to retrain employees, Security, and privacy
concerns, increase in cost, technological complexity, and Impact of the human
component
Figure 2
Theme Hierarchy of Negative Impact of Risk Factors on HRCMP
The chart below displays how much of the respondents in percentages are talking
about the theme related to the negative impact of risk factors on HRCMP. The highest
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one is the “impact of the human component,” which means the risk of human error will
still exist even after software implementation.
“If the data that we are generating through staff creation is poor, even the best
reporting will still be inaccurate, leading to inaccurate, and possibly incorrect
decisions by management.”
Figure 3
Percentage Coverage of the Negative Impact of Risk Factors on HRCMP
Risk Management and Problem-Solving Strategies
The third research question, “What outcomes are derived by using a Lean Six
Sigma (LSS) designed framework for healthcare executives deciding to implement
AI/RPA in the healthcare revenue cycle?” was answered by formulating a level-3 theme
of “Risk management and problem-solving strategies.” This theme comprised six sub-
themes of implementing NLP, Transparency of processes, Data security, Identification of
risks, adequately trained staff, and review and audit of processes.
3.15%
2.12% 2.06% 1.78%
1.14%
Impact of human
component
Need to retrain employees
Technological complexity
Security and privacy
concerns
Increase in cost
Percentage coverage of Negative impact of risk factors on HRCMP
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Figure 6
Theme Hierarchy of Risk Management and Problem-Solving Strategies
The review and audit of the processes is the highest coded theme among the codes
in risk management and problem-solving strategies.
“Having proper dedicated reviewers of all information and processes is
incredibly important.”
“Six Sigma strategies, standardization, and process improvement will pave the
way for AI.”
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Figure 7
Percentage Coverage of Risk Management and Problem-Solving Strategies
Triangulation of Data
The themes obtained from semi-structured interviews went through a triangulation
process to determine the validity of responses aligned with the extant literature. A total of
four research articles were selected on the keywords of AI optimizing hospital revenue
cycle management. This triangulation tested the validity by converging information from
the interview questions and the research articles to ensure that the data gathered was
consistent. The results of the triangulation process are represented via table format in
Appendix F.
6.74%
4.15%
2.62% 2.38% 1.96%
0.83%
Review and audit of
Processes
Transparency of processes
Data security Identification of risks
Properly trained staff
Implementing NLP
Percentage coverage of Risk management and problem solving strategies
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Framework Design
Development
The development of the framework presented below was in conjunction with one
of the goals of this research:
To create a framework that may be applied to a healthcare organization in an
effort to migrate from their current revenue management technique to one that
includes the use of AI/ML/RPA as a means of future cost control and revenue
boost.
The researcher was able to construct a framework to provide guidance to
healthcare executives on selecting appropriate tasks for artificial intelligence (AI)/robotic
process automation (RPA) by using the data gathered from the literature review as well as
the responses from the interview data. The framework represents the main areas of
opportunities and concerns that were expressed in the various interviews. The themes and
various subthemes of the benefits of AI in HRCMP and the negative impact of risk
factors on HRCMP were used to create the questions and rankings within the framework.
These themes were aligned with the previous research, such as Deloitte’s study of
the Smart use of artificial intelligence in health care, Seizing opportunities in patient care,
and business activities (Chebrolu et al., 2020). In this article, the main areas of benefit
were increasing efficiencies and minimizing risks, and the largest area of concern was
ensuring that the technology complied with regulations. By using articles such as these,
the researcher was able to complete a data triangulation to validate the interview
responses. The validation was done by utilizing NVivo to mine the interview data and
57
align the responses to the sub-themes and existing literature. The researcher was able to
construct the below framework using the results.
Framework Example
Step 1:
Answer the question below for each task the company is considering automating. If the
answer is “no,” then the task is not appropriate for automation, and you do not need to
continue with steps 2 and 3.
1. Can automation be used for the task under consideration?
a. Bots may not be allowed because of government regulation or company
policies.
Step 2:
Fill out the below tables for each revenue cycle task the company is considering
automating. Once finished answering the questions, create an overall final evaluation
score by considering the responses to the individual questions. Additional information for
each category or question is included after each table. If unable to answer a specific
question, leave it blank.
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Table 3
Risk Viability Framework
Risk Viability Lower Viability Higher Viability
Activity Type 1
Judgment
Based
2 3 4 5
Process Structure
and Risk
1
Low
2 3 4 5
High
Data Risk 1
Unstructured
2 3 4 5
Structured
Custom
Development
Required
1
High
2 3 4 5
Low
Automation as
Preferred
Solution
1
No
2 3 4 5
Definitely
Final Risk
Viability
Evaluation
1
Low
Viability
2 3 4 5
High
Viability
Additional information about “Risk Viability” categories:
• Activity Type refers to the extent to which the audit activity requires human
judgment or learning.
• Process Structure and Risk refer to the frequency with which the underlying
process changes. Some processes remain the same over time, whereas other
processes are constantly fluctuating. Frequent changes to the underlying process
will require constant updates to the bot or advanced programming.
• Data Risk refers to the extent to which bot technology will be processing data
that has a high-risk category.
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• Custom Development Required refers to the amount of time, money, and
expertise needed to create the bot. Development requirements tend to increase
with the complexity of the process.
• Automation as Preferred Solution refers to the that not every process should be
automated.
Table 4
Benefits Framework
Benefits of Automation Less Beneficial More Beneficial
Effort Required for
Manual function
1
Low
2 3 4 5
High
Frequency of the
Function
1
Low
2 3 4 5
High
Staffing Concerns
(Turnover/Overtime)
1
High
2 3 4 5
Low
Data Accuracy Concerns 1
High
2 3 4 5
Low
Compliance Concerns 1
High
2 3 4 5
Low
Final
Evaluation
of Benefits
1
Low Benefit
2 3 4 5
High Benefit
Additional information about Benefits of Bot categories:
• Effort Required for Manual function refers to the amount of time and mental
energy needed to perform the Revenue Cycle activity (and not the creation of the
automation).
• Frequency of the Function refers to the number of times the activity occurs
within a given time period. Activities that occur more often are more beneficial to
automate.
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• Staffing Concerns (Turnover/Overtime) refers to the amount of staffing issues
your department faces. By creating automation, companies should be able to
redeploy staff to perform more complex tasks, increasing employee engagement
thus creating a 24x7 workforce.
• Data Accuracy Concerns refers to the amount of human error and variations
from the standard.
• Compliance Concerns refer to the amount of concern an organization has with
possible data errors resulting in a reportable offense.
Step 3:
Plot the scores for each potential bot activity on the matrix below. Automation activities
that are in Quadrant 2 should be prioritized for immediate development. Once all
Quadrant 2 activities are developed, Quadrant 1 activities should be developed, and
activities in Quadrant 3 and 4 should not be developed.
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Figure 8
Framework Scatterplot
Summary
This chapter revealed the study findings. First, the subject matter expert’s results
were outlined. Second, the custom coding via NVIVO was described along with its
results and ended with ten qualified employees. Third, data triangulation was done to
validate the interview responses against current literature. Utilizing this data, the
researcher was able to answer the three research questions and construct a theoretical
framework for healthcare executives to use when deciding to implement AI/RPA within
their organizations.
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Chapter 5
Conclusion
The conclusion begins by exploring the results of the three research questions.
Limitations of the study are then described, noting how they may have had an impact on
the results. Next, implications and recommendations are theorized to offer a context for
further evolution of the concept of the use of AI/RPA in the healthcare revenue
management cycle. A summary of the research study concludes the chapter.
Research Questions
Research Question 1: What prospective benefits can be generated by using AI revenue
cycle applications for healthcare organizations?
As noted in the literature review, many studies have been done in relation to the
use of AI/RPA in other industries. This study attempted to incorporate these studies as
well as the interview data to answer the first research question. Similar to the other
studies, the researchers noted a core group of proposed benefits: 1) Patient benefits, 2)
Organizational benefits, 3) Improved data quality, and 4) Cost reduction and revenue
growth.
Research Question 2: What are the risk factors associated with AI implementation in
healthcare?
Most healthcare organizations are forced to assess risk levels prior to
implementing any innovative technologies. Similarly noted in other research studies that
were targeted to healthcare implementations, this study noted the following strategies that
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need to addressed potential high risks in the use of AI/RPA in the revenue cycle,
including external, physical, and digital, as well as maintaining a governance framework
to assure patient privacy and other HIPAA requirements. Utilizing the proposed
framework developed in research question 3, healthcare companies should assess whether
the potential benefits sufficiently outweigh the associated risks.
Research Question 3: What outcomes are derived by using a Lean Six Sigma (LSS)
designed framework for healthcare executives deciding to implement AI/RPA in the
healthcare revenue cycle?
This research study provides a theoretical framework for using AI/RPA in the
healthcare revenue cycle, which will allow for waste reduction and elimination of non-
value-added activities along with variability reduction. Lean tools reduce waste and non-
value-adding activity and enhance the effectiveness of equipment, tools, and machines.
For this research question, a theoretical framework was constructed, the Lean Six Sigma
framework should be implemented to reduce the defects which occurred during the
revenue cycle. The theoretical framework combines the interview data and literature as
well as Lean Six Sigma methods to mitigate the errors and defects and increase patient
satisfaction while reducing overhead costs.
Limitations
Upon retrospective review, multiple study limitations were identified. Firstly, a
relatively small sample size was utilized to accomplish this study. Due to this limitation,
the findings may or may not be generalizable to the population of healthcare executives
in the United States or healthcare revenue cycle strategies used in other countries.
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Secondly, a limitation was due to conducting this research during the COVID-19
pandemic. This limitation caused the researcher to adapt from a typical face-to-face
interview. Face-to-face interviews have been considered the gold standard in qualitative
interviewing, especially for their potential to elicit honest views on sensitive topics by
building trust with research participants. At present, this is not feasible, and remote
methods were required to conduct this study. This caused the researcher to possibly miss
valuable data and insights due to a lack of an unstructured conversation. These
conversations may have led to other topic areas that might have influenced the outcomes
of the study.
Implications
The theoretical framework constructed in this research is subject to several
limitations that suggest several opportunities for additional research. First, the framework
focuses on the prioritization of the development of new automation tasks. Healthcare
organizations would benefit from research regarding the maintenance of these
technologies, including governing the tasks with respect to ever-changing government
and insurance regulations.
Secondly, the theoretical framework developed in this research study was not
tested or validated. We believe, but have not empirically validated, that this framework
will help healthcare executives in their decision-making process with regards to AI/RPA
automation. Thus, future research or case studies should be done to validate the
framework for its effectiveness and efficiency.
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Recommendations
AI/RPA technologies are at the forefront of disruptive technologies and have
tremendous potential to transform the healthcare revenue cycle. However, there is much
to be explored about the implications of this emerging technology on the healthcare
revenue cycle before it can be fully implemented. Additional testing of RPA and actual
implementation on sections of the revenue cycle is necessary to obtain a better
understanding of its benefits and challenges. This study focused on developing a
theoretical framework to assist healthcare executives in determining if AI/RPA
implementations aligned with their organizational needs.
In the meantime, it seems that AI/RPA can be used to automate segments of the
RCM process. However, caution and due diligence are needed in its development,
implementation, and monitoring due to the unknowns with the payor and federal
regulatory issues. The higher level of monitoring may cancel out the organizational
benefits until more knowledge is gained around the risks of these technologies.
Although initial assessments of the value-add of AI/RPA indicate that it can lead
to improved patient satisfaction and better financial and organizational benefits, it would
be interesting to measure its usefulness in real-time with a large RCM department.
However, this type of implementation may not be easy to do until prototypes are ready
for deployment. As more about the cost and benefits of AI/RPA is revealed over time, it
will be necessary for healthcare executives to become familiar with its potential
application in their organization.
This study recommends that future studies continue examining other factors that
may influence the cost/benefit analysis of AI/RPA implementations. For example, this
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study suggests adding other risk constructs to the model, such as new government
regulations. These areas are changing so fast that these items would be required to be
built into the model. Additionally, utilizing major payor rules and regulations is highly
recommended to examine other factors that influence the expected outcomes of AI/RPA
within the healthcare RCM processes.
Summary
The problem addressed by this study is a lack of understanding regarding the
specific risks and benefits associated with AI implementation in healthcare settings.
Many administrative tasks are currently completed manually in healthcare, which takes
high labor costs and increases human computation error potential. However, it is
unknown to what extent AI may improve these administrative tasks and address
challenges (CAQH, 2018).
There is a lack of understanding regarding the risks and benefits associated with
AI/RPA implementations in healthcare revenue cycle settings. Healthcare companies are
confronted with stricter regulations and billing requirements, underpayments, and more
significant delays in receiving payments. Despite the continued interest of practitioners,
revenue cycle management has not received much attention in research.
In order to expand the knowledge of the use of AI/RPA in the healthcare revenue
cycle, the researcher conducted a thorough analysis of the existing literature and
combined that with conducting interviews of key individuals. Using this data, the
researcher conducted a triangulation of the responses and current literature to help
develop a theoretical framework that may be applied to a healthcare organization in an
67
effort to migrate from their current revenue management technique to one that includes
the use of AI/ML/RPA as a means of future cost control and revenue boost.
The goals of this research study were:
1. To expand on the current literature surrounding the use of AI in the health care
revenue cycle and provide a framework to allow health care executives to quickly
visualize the benefits or drawbacks of such a technology in their specific
healthcare revenue cycle departments.
2. To create a framework that may be applied to a healthcare organization in an
effort to migrate from their current revenue management technique to one that
includes the use of AI/ML/RPA as a means of future cost control and revenue
boost.
To achieve the stated goals of the research, the main research questions were:
R1. What prospective benefits can be generated by using AI revenue cycle
applications for healthcare organizations?
R2. What are the risk factors associated with AI implementation in healthcare?
R3. What outcomes are derived by using a Lean Six Sigma (LSS) designed
framework for healthcare executives deciding to implement AI/RPA in the
healthcare revenue cycle?
In order to answer these research questions, a qualitative semi-structured
interview was conducted with ten key stakeholders responsible for managing or
developing revenue cycles, including healthcare administrators, accounting/financial
management officers, and information technology (IT) staff members.
The semi-structured interview consisted of 12 questions in three thematic areas:
68
1. How AI-based technologies will benefit the healthcare revenue cycle management
processes
2. How to address risk factors that negatively impact the healthcare revenue cycle
management processes
3. Inviting the participants to discuss risk management and problem-solving
strategies that guide decision-making processes in the organizational context
Finally, the interview responses underwent a triangulation process against
multiple existing studies to determine the validity of responses aligned with the extant
literature. Following Creswell and Creswell (2018), the triangulation process ensured
that the outcomes of the research participant's responses were aligned with those in
previous studies done in the areas of AI/RPA studies. An audit trail was developed by
transcribing the semi-structured interview responses that were recorded via a Microsoft
Forms engine. These responses were stored without any personally identifiable
information to ensure the confidentiality of the interview participants.
The research findings suggest that AI/RPA implementations can improve the
healthcare revenue cycle's effectiveness and efficiency. Healthcare organizations should
be cautious about which workflows that they implement AI/RPA into due to
governmental regulations and payor complexities. These findings are consistent with
recent literature and the interview data collected, which suggests that some tasks do not
benefit from risk payoff.
The limitations of this research study included factors, such as sample size and
sample technique. The sample size was small, which affected the accuracy of the results,
and the sampling technique was convenient, which is not generalizable. Additionally, this
69
study collected information about AI/RPA thoughts from key individuals. This research
was based on semi-structured interviews, which could affect participants’ truth in
answering the questions and, consequently, the study results' accuracy.
This research study contributed to prior healthcare literature in three main ways.
First, it expanded on the current literature surrounding the use of AI in the health care
revenue cycle. Second, the research quantified the past research and was able to draw
similarities and likeness by interviewing prominent information technology professionals
as well as healthcare executives. Finally, this research was able to construct a theoretical
framework, thereby allowing health care executives to quickly visualize the benefits or
drawbacks of such a technology in their specific healthcare revenue cycle departments.
This study recommended opportunities for future research to examine other
AI/RPA implementations in different organizations while modifying the developed
theoretical model to fit the organization's terminology. Future research is needed to test
the theoretical model to ensure that it has the intended outcomes and displays the benefits
as expected. Moreover, major payor and government regulations could be added to the
theoretical model for further investigation. Another recommendation is to recruit a large
and diverse sample using experimental research design to ensure the generalizability of
results.
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Appendices
71
Appendix A: Interview Questions
72
73
74
75
Appendix B: IRB Exempt Initial Approval Memo
76
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Appendix C: Email Invitation
Dear (Participant),
I am conducting interviews as part of a research study at Nova Southeastern University.
This study is in fulfillment of my dissertation requirements. The study aims to increase
the understanding of the specific risks and benefits associated with Artificial Intelligence
and/or Robotic Process Automation (AI/RPA) implementations in healthcare revenue
cycle settings.
As an experienced healthcare administrator, accounting/financial management officer,
and/or information technology (IT) staff member, you are in an ideal position to give us
valuable first-hand information from your perspective.
The interview takes around 30 minutes and is very informal. We are simply trying to
capture your thoughts and perspectives on the use of AI/RPA within the revenue cycle.
Your responses to the questions will be kept confidential. Each interview will be assigned
a number code to help ensure that personal identifiers are not revealed during the analysis
and write-up of findings.
There is no compensation for participating in this study. However, your participation will
be a valuable addition to my research, and findings could lead to a greater understanding
of the use of AI/RPA in the healthcare setting.
If you are willing to participate, please suggest a day and time that suits you, and I will do
my best to be available. If you have any questions, please do not hesitate to ask.
Thank you for your time and consideration.
Leonard Pounds
lpounds@mynsu.nova.edu
954-661-2794
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Appendix D: Informed Consent Form
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80
81
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Appendix E: NVIVIO Codes
Name of codes Description
Benefits of AI in HRCMP How AI-based technologies will benefit the healthcare
revenue cycle management processes
Cost reduction and revenue
growth
AI is a massive enabler in improving funds flow and
reducing billing mistakes resulting in reduced capital cost.
Improved data quality AI will eliminate numerous manual mistakes, timing issues
with manual inputting of data by providers and front desk
staff, and delays in submission of claims
Organizational benefits
Decrease or reduce
workforce
Decrease of the workforce means the quantity of work
needed by staff become less and reduce means to bring down
the size (less no of people are required to perform a task)
Enhances teamwork A thorough examination of all systems and processes of AI-
based projects has brought organizations together.
Make better quick decisions Using AI technologies, data will be better and easily
accessible, resulting in more time for analysis that leads to
better quicker, better decisions.
Patients benefits
Help in early diagnosis The patient population's needs are anticipated by AI systems
and by its early intervention, results in preventing more
severe conditions AI systems can alert patience of visits,
medication refills, and can also monitor the progress of
improved health outcomes
Improved patients'
experience
AI would be able to enhance the patient experience by
streamlining their admission and care process and giving
doctors and medical staff more time to focus on the patients
and not the process.
Reduces patients’ denial rate Claim denials are one of the most common barriers to
effective revenue cycle management. Using AI systems can
anticipate denials, edits can be put in place, and new claims
will be paid on initial submissions.
The negative impact of risk
factors on HRCMP
risk factors that negatively impact the healthcare revenue
cycle management processes
Impact of the human component The risks of human errors will still exist and can adversely
affect even the most finely implemented software.
Increase in cost The cost of implementing and maintaining the entire web of
processes results in extra costs to healthcare
Need to retrain employees The staff does not perform the processes manually soon.
They will lose the knowledge which is vital for the system's
upkeep and adjustment
Security and privacy concerns Data integrity and security are significant concerns with
implementing any new technology as data going out to a
third party.
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Technological complexity Understanding how new technologies work and how can they
be practically implemented in cycle management is quite
complex and is an ever-changing paradigm,
Risk management and problem-
solving strategies
Data security Data security should be paramount. The evaluation must be
carried out to keep the patient's data and the company's
financial information safe.
Identification of risks Any potential risk should be identified and monitored to
minimize its impact
Implementing NLP Implementing NLP (Natural language processing) to
translate the clinical notes automatically
Properly trained staff Training of the staff to use the AI processes
Review and audit of Processes The data should be analyzed and assessed at regular
intervals.
Transparency of processes It means regular reports should be generated to identify
issues in the system and the documentation of all the
processes and workflow designs to understand easily
84
Appendix F: Triangulation of Data
85
86
87
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