Description 2.1 Introduction This section focuses on reviewing the literature to help determine whether telemedicine is used in diabetes

2.1 Introduction
This section focuses on reviewing the literature to help determine whether
telemedicine is used in diabetes management. The review is guided by the various objectives
identified in the research and provides insights into the research topic.
2.2 Limitations of telemedicine in diabetes management:
The limitations of telemedicine in diabetes management include various challenges
and barriers that need to be addressed for its effective implementation. These limitations are
highlighted in the provided sources:
Cost: One of the most frequently cited barriers to implementing telemedicine
solutions globally is the perception that telemedicine costs are too high. This can be a
significant limitation, especially in developing countries with underdeveloped infrastructure
and limited technical expertise
Infrastructure and Technical Expertise: In developing countries, high costs,
underdeveloped infrastructure, and a lack of technical expertise can hinder the successful
implementation of telemedicine programs for diabetes management. (Mullur et al., 2022)
Organizational and Bureaucratic Difficulties: Challenges related to organizational and
bureaucratic issues can arise due to the lack of personal contact in telemedicine, which may
impact the relationship between healthcare providers and patients. Additionally, legal issues
surrounding patient privacy and confidentiality, competing health system priorities, and lack
of demand can obstruct telemedicine implementation. (de Kreutzenberg, 2022)
Technical and Structural Problems: Various technical and structural issues could
hamper the adoption of telemedicine programs for diabetes management. These problems
may include inadequate data encryption and security systems, lack of connectivity between
telemedicine systems and hospital electronic medical records, and potential breakdowns in
the relationship between healthcare providers and patients
Access and Connectivity: Limited internet access, especially in rural areas, can
preclude the use of video technology in telemedicine, further isolating patients at high risk
and exacerbating healthcare disparities. This lack of connectivity can be a significant
limitation to the widespread adoption of telemedicine for diabetes care (Casas et al., 2023)
Preference for Face-to-Face Consultations: Some patients may prefer physical
meetings with their physicians over virtual consultations, leading to challenges in adopting
telemedicine for diabetes management. The lack of physical examination during telemedicine
consultations can also be a limitation, particularly in acute conditions that require immediate
hospitalization or face-to-face consultations
In conclusion, while telemedicine offers significant benefits in diabetes management,
addressing these limitations is crucial to ensure its successful integration into healthcare
systems and maximize its effectiveness in improving patient outcomes.
2.3 The use of telemedicine improves blood sugar control compared to in-person
consultation:
In several studies, telemedicine has been found to improve blood sugar control
compared to in-person consultation. These studies have consistently shown that telemedicine
can lead to better glycemic control and improved management of blood glucose levels in
patients with diabetes.
For example, a study published in the Journal of Diabetes Research found that
telemedicine improved HbA1c levels in patients with type 2 diabetes, with a mean reduction
of 0.43% compared to usual care. (A Meta-Analysis of the Effectiveness of Telemedicine in
Glycemic Management among Patients with Type 2 Diabetes in Primary Care – PubMed,
n.d.). Another study published in the Journal of Clinical Endocrinology and Metabolism
found that telemedicine reduced HbA1c levels in patients with type 1 diabetes, with a mean
reduction of 0.4% compared to the control group. (Kusuma et al., 2022) Telemedicine is
particularly effective in improving self-management processes and clinical outcomes of
patients with diabetes and can be a valuable tool in managing complex diabetes cases. It is a
cost-effective and reliable method for monitoring blood glucose levels and adjusting
medication doses based on glucose readings. (Sotomayor et al., 2023) In addition,
telemedicine is effective in reducing the risk of moderate hypoglycemia in patients with
diabetes, with an odds ratio of 0.42 compared to usual care. This suggests that telemedicine
can help patients better manage their blood sugar levels and reduce the risk of hypoglycemia.
Overall, the use of telemedicine has been found to improve blood sugar control
compared to in-person consultation by providing patients with more frequent and convenient
access to healthcare services, improving self-management processes, and reducing the risk of
hypoglycemia
2.4 The most common telemedicine tools used in diabetes management
1-Remote Monitoring of Glucose Levels:
According to (PDF) A Review of the Effectiveness of Telemedicine in Glycemic
Control in Diabetes Mellitus Patients (n.d.), remote monitoring of glucose levels through
telemedicine has been linked to improved HbA1c levels in individuals with poor glucose
control. Telemedicine allows patients to communicate real-time blood glucose data to their
physicians, supporting better diabetes management.
2- Continuous Glucose Monitoring (CGM):
According to (Frontiers | Telemedicine for Diabetes Management during COVID-19:
What We Have Learnt, What and How to Implement, n.d.) that technological tools, including
CGM, are increasingly being used for the management of diabetes, especially during the
COVID-19 pandemic. CGM allows for remote monitoring of glucose levels, which can be
beneficial for diabetes management through telemedicine
3- Virtual Visits:
Telemedicine allows virtual visits between patients and healthcare providers,
eliminating the burden of travel for patients. Virtual visits can facilitate discussions on
various aspects of diabetes management, such as food, housing, and economic
security(Sotomayor et al., 2023)
4-Secure Electronic Messaging:
Telemedicine enables secure electronic messaging between clinicians and patients,
which can be helpful for diabetes management. This allows for ongoing communication and
support between healthcare providers and patients with diabetes.
5-Coaching and Telemonitoring Programs:
The use of telephonic/texting or game-based coaching, as well as telemonitoring
programs as part of telemedicine strategies for diabetes management. These tools can help
improve self-management and clinical outcomes for patients with diabetes.
In summary, the most common telemedicine tools used in diabetes management
include remote glucose monitoring, continuous glucose monitoring, virtual visits, secure
electronic messaging, and coaching/telemonitoring programs, as highlighted in the provided
search results. (Effectiveness of Mobile Health Interventions on Diabetes and Obesity
Treatment and Management: Systematic Review of Systematic Reviews – PubMed, n.d.)
2.5 What are the cost implication
1-Cost Savings:
Telemedicine is a cost-effective approach to providing diabetes care compared to traditional
in-person visits. The focus group study identified that the significant components of cost
savings in diabetes management through telemedicine were reduced patient travel costs,
boarding, and lodging expenses.
2-Improved Cost-Effectiveness:
Studies have evaluated the cost-effectiveness of telemedicine care for patients with diabetes
and found it to be a viable option with positive clinical outcomes. Telemedicine can help
reduce healthcare utilization and associated costs, such as decreased hospitalizations,
contributing to cost-effectiveness. (Cost-Effectiveness of Telemedicine Care for Patients with
Uncontrolled Type 2 Diabetes Mellitus during the COVID-19 Pandemic in Saudi Arabia Manal Faleh AlMutairi, Ayla M. Tourkmani, Alian A. Alrasheedy, Turki J. ALHarbi,
Abdulaziz M. Bin Rasheed, Mohammed ALjehani, Yazed AlRuthia, 2021, n.d.)
3- Accessibility and Equity Concerns:
While telemedicine can be cost-effective, the search results note that telemedicine may not be
accessible or affordable for all diabetic individuals, especially in developing countries or
underserved areas.
The lack of universal access to the required technology and infrastructure can be a limitation,
potentially exacerbating healthcare disparities.
4- Reimbursement and Coverage Considerations:
The search results highlight that the future success of telemedicine in diabetes care will
depend on continued financial reimbursement and coverage by payers, such as Medicare and
private insurers. The variability in Medicaid and private insurer coverage of telehealth
services is an important consideration that can impact patient cost implications.
In summary, the search results indicate that telemedicine can offer cost savings and improved
cost-effectiveness in diabetes management. However, accessibility and equity concerns and
reimbursement policies are essential factors to consider regarding the cost implications of
using telemedicine in this context. (Clinical Improvements by Telemedicine Interventions
Managing Type 1 and Type 2 Diabetes: Systematic Meta-Review – PubMed, n.d.)
Chapter Five
Discussion
5.1 Limitations of Telemedicine:
Telemedicine holds great potential to change how diabetes is managed but faces numerous
challenges that need solutions for it to work as intended (Sood et al, 2018). The identified
limits are cost, infrastructure, organizational, technical access, and patient preference.
A multi-pronged approach is needed to overcome these limitations. To begin with, the highcost perception associated with telemedicine could be reduced through the implementation of
cost-effective solutions and advocating for financial reimbursement by payers (France et al,
2021). This is particularly critical in resource-constrained developing countries with limited
skills.
Investment in infrastructure development and technical expertise is critical in overcoming
problems of underdeveloped infrastructure and lack of connectivity (McDonnell et al, 2018).
This can be done by collaborating with healthcare organizations, governments, and
technology developers to increase access for people living in underserved areas, especially
rural communities.
Organizational barriers, such as legal issues surrounding patient privacy/confidentiality,
should be addressed through clear policies/guidelines and training programs (Aligholipour et
al, 2019). Additionally, strategies enhancing patients’ acceptance/engagement in telemedicine,
such as patient education/support, are crucial when dealing with preferences for face-to-face
consultations.
5.2 Benefits of Telemedicine in Improving Blood Sugar Control:
However, there have been several benefits shown by telemedicine despite its limitations,
which include improving blood sugar control among diabetic individuals. Research has
repeatedly shown improved blood glucose management compared to traditional face-to-face
visits, resulting in reduced HbA1c levels (Hersh et al,2021).
More important than the convenience of telemedicine is the increased monitoring frequency,
thus allowing timely interventions and individualized care delivery, leading to better
glycemic control and clinical outcomes (Ballesta et al, 2023). By empowering patients to
participate actively in their diabetes management and offering continuing support/education,
telemedicine is vital in self-management processes that reduce the risk of complications.
5.3 Common Telemedicine Tools in Diabetes Management:
The discussion focuses on some of the common telemedicine tools used in diabetes, including
remote glucose monitoring, continuous glucose monitoring (CGM), virtual visits, secure
electronic messaging, and coaching/telemonitoring programs (Mabeza et al , 2022). These
technologies leverage remote communication, monitoring, and support to improve access to
quality diabetes care and patient engagement.
Newer technologies, such as CGMs and secure messaging platforms, create opportunities for
scaling up telemedicine interventions in diabetes care (Mullur et al , 2022). However, equal
access to these tools, digital literacy levels, and privacy issues are crucial for their full impact
to be felt.
5.4 Cost Implications of Telemedicine:
Telemedicine can potentially lead to significant cost savings and improved cost-effectiveness
of diabetes management by reducing travel expenses, hospitalizations, and healthcare
utilization rates. Nonetheless, accessibility concerns regarding equity and reimbursement
policies are the main determinants of telemedicine’s financial implications (Mabeza et al ,
2022).
Some efforts include advocating for continued financial reimbursement/payer coverage,
enhancing access to technology/infrastructure, and addressing disparities in accessing
telemedicine services (Mullur et al , 2022). These factors will maximize the potential benefits
of telemedicine, resulting in efficient/equitable delivery of diabetic care.
Conclusion:
To conclude, telemedicine has a lot of potential to revolutionize how diabetes care is done by
overcoming distance barriers, increasing patient participation, and improving clinical results.
Nevertheless, collaboration and innovative solutions must address several drawbacks and
challenges to maximize its potential to improve patient outcomes while reducing healthcare
costs. Health systems that integrate telemedicine as part and parcel of diabetes management
can make sure that persons with diabetes scattered worldwide receive better accessible,
customized, and affordable medical services.
REFERENCES:
Aligholipour, M., Feizollahzadeh, H., Ghaffari, M., & Jabbarzadeh, F. (2019). Comparison of
in-person and mms-based education in telegram on self-care and fasting blood sugar
of patients with diabetes mellitus: a randomized clinical trials. Journal of caring
sciences, 8(3), 157.
A Meta-Analysis of the Effectiveness of Telemedicine in Glycemic Management among
Patients with Type 2 Diabetes in Primary Care—PubMed. (n.d.). Retrieved 5 May
2024, from
Ballesta, S., Chillarón, J. J., Inglada, Y., Climent, E., Llauradó, G., Pedro-Botet, J., … &
Benaiges, D. (2023). Telehealth model versus in-person standard care for persons with
type 1 diabetes treated with multiple daily injections: an open-label randomized
controlled trial. Frontiers in Endocrinology, 14, 1176765.
Casas, L. A., Alarcón, J., Urbano, A., Peña-Zárate, E. E., Sangiovanni, S., Libreros-Peña, L.,
& Escobar, M. F. (2023). Telemedicine for the management of diabetic patients in a
high-complexity Latin American hospital. BMC Health Services Research, 23(1),
314.
Clinical Improvements by Telemedicine Interventions Managing Type 1 and Type 2 Diabetes:
Systematic Meta-review—PubMed. (n.d.). Retrieved 5 May 2024, from

Cost-effectiveness of telemedicine care for patients with uncontrolled type 2 diabetes mellitus
during the COVID-19 pandemic in Saudi Arabia—Manal Faleh AlMutairi, Ayla M.
Tourkmani, Alian A. Alrasheedy, Turki J. ALHarbi, Abdulaziz M. Bin Rasheed,
Mohammed ALjehani, Yazed AlRuthia, 2021. (n.d.). Retrieved 5 May 2024, from

de Kreutzenberg, S. V. (2022). Telemedicine for the Clinical Management of Diabetes;
Implications and Considerations After COVID-19 Experience. High Blood Pressure &
Cardiovascular Prevention, 29(4), 319–326.
Dhediya, R., Chadha, M., Bhattacharya, A. D., Godbole, S., & Godbole, S. (2023). Role of
telemedicine in diabetes management. Journal of Diabetes Science and
Technology, 17(3), 775-781.
Effectiveness of Mobile Health Interventions on Diabetes and Obesity Treatment and
Management: Systematic Review of Systematic Reviews—PubMed. (n.d.). Retrieved
5 May 2024, from
Franc, S., Daoudi, A., Mounier, S., Boucherie, B., Dardari, D., Laroye, H., … & Charpentier, G. (2021).
Telemedicine and diabetes: achievements and prospects. Diabetes & metabolism, 37(6), 463476.
Frontiers | Telemedicine for diabetes management during COVID-19: What we have learned,
what and how to implement. (n.d.). Retrieved 5 May 2024, from

793/full
Hersh, W. R., Helfand, M., Wallace, J., Kraemer, D., Patterson, P., Shapiro, S., & Greenlick, M.
(2021). Clinical outcomes resulting from telemedicine interventions: a systematic review. BMC
Medical Informatics and Decision Making, 1, 1-8.
Izquierdo, R. E., Knudson, P. E., Meyer, S., Kearns, J., Ploutz-Snyder, R., & Weinstock, R. S.
(2023). A comparison of diabetes education administered through telemedicine versus
in person. Diabetes care, 26(4), 1002-1007.
Kusuma, C. F., Aristawidya, L., Susanti, C. P., & Kautsar, A. P. (2022). A review of the
effectiveness of telemedicine in glycemic control in diabetes mellitus patients.
Medicine, 101(48), e32028.
Mabeza, R. M. S., Maynard, K., & Tarn, D. M. (2022). Influence of synchronous primary care
telemedicine versus in-person visits on diabetes, hypertension, and hyperlipidemia outcomes:
a systematic review. BMC primary care, 23(1), 52.
McDonnell, M. E. (2018). Telemedicine in complex diabetes management. Current diabetes
reports, 18, 1-9.
Mullur, R. S., Hsiao, J. S., & Mueller, K. (2022). Telemedicine in Diabetes Care. American
Family Physician, 105(3), 281–288.
(PDF) A review of the effectiveness of telemedicine in glycemic control in diabetes mellitus
patients. (n.d.). Retrieved 5 May 2024, from

_of_telemedicine_in_glycemic_control_in_diabetes_mellitus_patients
Sood, A., Watts, S. A., Johnson, J. K., Hirth, S., & Aron, D. C. (2018). Telemedicine consultation for
patients with diabetes mellitus: a cluster randomised controlled trial. Journal of telemedicine
and telecare, 24(6), 385-391.
Sotomayor, F., Hernandez, R., Malek, R., Parimi, N., & Spanakis, E. K. (2023). The Effect of
Telemedicine in Glycemic Control in Adult Patients with Diabetes during the
COVID-19 Era—A Systematic Review. Journal of Clinical Medicine, 12(17), Article
17.
Executive Master in Healthcare Quality and Patient Safety
HQS590 Capstone Project
Investigation Of Impacting Factors for The Adoption of Artificial Intelligence in Improving Healthcare
Quality: A Systematic Review
Prepared by
Supervised by:
Date 28/11/2023
Declaration
I declare that the research project entitled “Investigation of Impacting Factors for The
Adoption of Artificial Intelligence in Improving Healthcare Quality: A Systematic Review”,
submitted to the Saudi Electronic University is my own original work. I declare that the research
project does not contain material previously published or written by a third party, except where this
is appropriately cited through full and accurate referencing. I declare that Saudi Electronic
University has a right to refuse the research project if contains plagiarism and cancel the research
project at any time and the student has full responsibility regarding any further legal actions.
Paraphrase
Acknowledgment
Abstract ‫ع حسب عنوانك‬
Introduction: The utilization of healthcare technologies has been among the influences of healthcare
developments. Investments in artificial intelligence (AI) remain part of the strategic approaches that
would enable organizations to improve the investments made toward healthcare quality. The focus would
be on the ability to build the capacities expected in delivering healthcare quality, while capitalizing on
inputs from the AI applications.
Purpose: The purpose of the report was to determine and study the factors that influence the application
of artificial intelligence to improve healthcare quality. The report identifies the barriers and enablers
towards the use of AI in the healthcare sector.
Study Design: The study design adopted the systematic review approach. The systematic review
capitalized on the PRISMA diagram in establishing the suited articles that would be used for the research.
Methods: The study used a systematic review approach. The databases considered in the research
included PubMed, Embase, and Scopus. The study used 17 articles that were conducted between 2018
and 2022 across, Saudi Arabia to develop the findings, using different study designs (8), Experimental (1)
and survey (8). ‫نفسها‬
‫ع حسب عنوانك‬
Main Findings: The findings indicated the presence of gaps in the utilization of AI in improving
healthcare quality (6 studies), despite the benefits that come with AI (11 studies). The focus would be on
the investments that would initiate developments toward healthcare quality in the healthcare
organizations. Factors such as capital, resources, infrastructure, and commitments toward healthcare
technologies have remained influencers in the utilization of AI.
‫ع حسب عنوانك‬
Conclusions: The review concluded that the factors defining the use of the AI technologies in healthcare
quality have remained part of the contemporary issues affecting the healthcare sector. The factors defining
AI utilization would depend on the ability to influence technologies in managing healthcare quality needs.
The main recommendation is to increase investments in AI models in healthcare and encourage AI as part
of the factors and influencers of healthcare quality.
Keywords: Use Of AI In Healthcare; Improving Healthcare Quality; Role of AI In Healthcare Quality;
Determinants of AI Use In Healthcare ‫ع حسب عنوانك‬
Table of Contents ‫نفسه‬
Declaration ……………………………………………………………………………………………………………………………….. 2
Acknowledgement ………………………………………………………………………….. Error! Bookmark not defined.
Abstract ……………………………………………………………………………………………………………………………………. 3
List of Abbreviations …………………………………………………………………………………………………………………. 10
Chapter 1 ………………………………………………………………………………………………………………………………….11
Introduction………………………………………………………………………………………………………………………………11
1.1 Background Information ………………………………………………………………………………………………… 12
1.2 Problem Statement …………………………………………………………………………………………………………. 12
1.3 Research Aim and Objectives ………………………………………………………………………………………….. 12
1.4 Research Questions …………………………………………………………………………………………………………. 13
1.5 Significance of the Study …………………………………………………………………………………………………. 13
Chapter 2 ………………………………………………………………………………………………………………………………… 15
Literature Review ……………………………………………………………………………………………………………………. 15
2.1 Introduction……………………………………………………………………………………………………………………. 16
2.2 The Factors Influencing Adoption of Artificial Intelligence in Improving Healthcare Quality
……………………………………………………………………………………………………………………………………………. 16
2.3 The Adoption of Artificial Intelligence in Influencing Quality of Healthcare ……………………… 17
The Potential Barriers to Adoption of Artificial Intelligence and How to Address Them …………. 17
2.4 Best Practices for Implementing and Evaluating the Effectiveness of The Artificial
Intelligence Technologies in Healthcare …………………………………………………………………………………. 18
2.5 How Artificial Intelligence Can Be Used to Improve Patient Outcomes, Reduce Costs and
Improve Efficiencies in Healthcare Delivery ………………………………………………………………………….. 19
Chapter Three …………………………………………………………………………………………………………………………. 21
Methodology ……………………………………………………………………………………………………………………………. 21
3.1 Research Design ……………………………………………………………………………………………………………… 22
3.2 Instrument ……………………………………………………………………………………………………………………… 22
3.3 Sampling Strategy & Setting …………………………………………………………………………………………… 22
3.4 Inclusion Criteria ……………………………………………………………………………………………………………. 22
3.5 Exclusion Criteria …………………………………………………………………………………………………………… 23
3.6 Data Synthesis and Analysis ……………………………………………………………………………………………. 23
3.8 Limitations of the Study ………………………………………………………………………………………………….. 23
Chapter 4 ………………………………………………………………………………………………………………………………… 25
Findings ………………………………………………………………………………………………………………………………….. 25
JBI Checklist Assessment ……………………………………………………………………………………………………….. 36
Chapter 5 ………………………………………………………………………………………………………………………………… 40
Discussions ………………………………………………………………………………………………………………………………. 40
5.1 Factors Influencing Adoption of Artificial Intelligence in Improving Healthcare Quality ….. 41
5.2 Adoption of Artificial Intelligence in Influencing Quality of Healthcare ……………………………. 42
5.3 Potential Barriers to Adoption of Artificial Intelligence and How to Address Them …………… 43
5.4 Best Practices for Implementing and Evaluating the Effectiveness of The Artificial
Intelligence Technologies in Healthcare …………………………………………………………………………………. 44
5.5 How Artificial Intelligence Can Be Used to Improve Patient Outcomes, Reduce Costs and
Improve Efficiencies in Healthcare Delivery ………………………………………………………………………….. 45
Chapter 6:…………………………………………………………………………………………………………………………………. 47
Conclusion and Recommendations ………………………………………………………………………………………………. 47
6.1 Conclusions …………………………………………………………………………………………………………………….. 48
Factors Influencing Adoption of Artificial Intelligence in Improving Healthcare Quality…….. 48
Adoption Of Artificial Intelligence in Influencing Quality of Healthcare …………………………….. 48
Best Practices for Implementing and Evaluating the Effectiveness of The Artificial Intelligence
Technologies in Healthcare ……………………………………………………………………………………………….. 49
References ……………………………………………………………………………………………………………………………….. 51
Table 1 General Characteristics of the Included Studies ………………………………………………………… 29
Table 2 Summary of The Findings …………………………………………….. Error! Bookmark not defined.
Table 3 JBI Assessment …………………………………………………………………………………………………….. 36
Figure 1 PRISMA flow Diagram ………………………………………………………………………………………… 28
List of Abbreviations
JBI: Joanna Briggs Institute
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
AI- Artificial Intelligence
Chapter 1
Introduction
1.1 Background Information
Numerous research studies have indicated that the implementation of artificial intelligence (AI) in
the healthcare industry has been hindered by a multitude of factors. Suresh et al. (2020) conducted a study
that revealed that healthcare providers’ insufficient knowledge and understanding of AI impeded their
integration into clinical practice. The adoption of AI in healthcare has been impeded by significant
barriers, including concerns regarding patient privacy and data security, as identified by Gibson et al.
(2020). The development of healthcare quality has considered the imperative roles that technological
applications have. The realization of effectiveness and the development of sustainability capitalizes on the
mandates that the technologies play in inflecting the healthcare sector (Krittanawong et al., 2021). The AI
technologies come with different inputs that could influence the diversified approaches required for
realizing healthcare quality needs.
1.2 Problem Statement
The absence of unambiguous directives pertaining to the utilization of artificial intelligence (AI)
in clinical settings has been recognized as a noteworthy element that has impeded the widespread
implementation of AI in the healthcare industry (Lee et al., 2019). The establishment of guidelines
pertaining to the utilization of artificial intelligence (AI) in clinical settings may furnish healthcare
practitioners with a structured framework to govern the integration of AI into their professional practice.
Consequently, it is imperative to undertake a methodical examination to ascertain the variables that
influence the implementation of artificial intelligence in enhancing the standard of healthcare. The present
systematic review aims to furnish a comprehensive overview of the existing literature on the factors that
impact the adoption of artificial intelligence (AI) in the healthcare sector (Sarkar et al., 2021).
Additionally, it endeavors to identify potential obstacles that may hinder the adoption of AI in healthcare.
1.3 Research Aim and Objectives
The research aim of the current review is to investigate the factors that would influence the adoption
of artificial intelligence for improving healthcare quality.
The objectives guiding the research are:
•
To determine the factors influencing the adoption of artificial intelligence in improving healthcare
quality
•
To establish the adoption of artificial intelligence in influencing the quality of healthcare
•
To determine the potential barriers to the adoption of artificial intelligence and how to address
them.
•
To establish the best practices for implementing and evaluating the effectiveness of artificial
intelligence technologies in healthcare
•
To identify how artificial intelligence can be used to improve patient outcomes, reduce costs, and
improve efficiencies in healthcare delivery.
1.4 Research Questions ‫ع حسب عنوانك‬
1- What are the factors that impact the adoption of Artificial Intelligence in improving healthcare
quality?
2- How does the adoption of artificial intelligence impact the quality of healthcare?
3- What are the potential barriers to the adoption of artificial intelligence in healthcare, and how can
these be addressed?
4- What are the best practices for implementing and evaluating the effectiveness of artificial intelligence
technologies in healthcare?
5- How can artificial intelligence be used to improve patient outcomes, reduce costs, and increase
efficiency in healthcare delivery?
1.5 Significance of the Study
The study seeks to investigate the influences of utilizing artificial intelligence in improving
healthcare quality. With the determination of the roles that artificial intelligence technologies would have
in healthcare quality, the research informs on the value factor expected from such investments. The
realization of the inputs from the technological investments would capitalize on the expected investments
towards an effective process for healthcare management (Maddox et al., 2019). The review contributes to
the appreciation of the barriers and the solutions that can enable healthcare systems to benefit from the
role that artificial intelligence plays. The focus would be on the specific policies and insights that can be
used to attain the potential of the technologies emanating from artificial intelligence.
Chapter 2
Literature Review ‫ ارتكلز‬١٢- ١٠
2.1 Introduction‫ بابحاث‬٣ ‫ وتجاوب عليها‬.‫تحط اربع اسئله ع عنوانك‬
Perplexity ai ‫يسويها‬
The section focuses on the review of the various literature that helps study the role of artificial
intelligence in influencing healthcare quality within organizations. The review is guided by the various
objectives identified in the research and provides insights on the research topic.
2.2 The Factors Influencing Adoption of Artificial Intelligence in Improving Healthcare Quality
According to Amann et al., (2020), the development of healthcare quality concepts has been
considering the development of specific aspects for technological developments. The research indicated
the need for identifying the specific value form the technologies, which would define the need for specific
investments. The application of artificial intelligence concepts would therefore reinvent the ideologies for
meeting the specific needs identified in the healthcare systems. The potential of Artificial Intelligence
(AI) to enhance patient outcomes, improve the quality of care, and reduce healthcare costs has been
acknowledged in recent studies (Sarkar et al., 2021; Ahmed et al., 2020; Topol, 2019). Healthcare systems
that would work with specific technologies should consider the capcodes and capabilities to influence and
develop the healthcare quality for the organizations.
According to Maddox et al., (2019), the application of artificial intelligence (AI) in the healthcare
sector has been the subject of numerous investigations, with encouraging results being reported. The
investigations relate the need for integrating intelligence as part of promoting precision for quality and
healthcare development. The need for an effective process for managing the quality needs would
therefore consider factors such as accessibility to the technologies, costs and the value. The healthcare
systems have to identify the suited procedure’s, especially when dealing with the considerations for
technological developments (Amann et al., 2020). At the organizational level, the main considerations
would be on the factors and measures that would create effectiveness in realizing the healthcare quality.
2.3 The Adoption of Artificial Intelligence in Influencing Quality of Healthcare
The utilization of artificial intelligence in the healthcare sector has the potential to enhance the
precision and efficacy of diagnoses and treatment regimens. Esteva et al. (2019) conducted a study that
demonstrated that an artificial intelligence (AI) system exhibited a level of accuracy in identifying skin
cancer that was comparable to that of dermatologists. Zheng et al. (2021) conducted a study wherein they
discovered that an artificial intelligence (AI) system exhibited precise prognostication of the likelihood of
heart disease in patients. The aforementioned results indicate that Artificial Intelligence (AI) possesses the
capability to enhance diagnostic precision and expedite individualized treatment strategies (Esteva et al.,
2019). Furthermore, the implementation of Artificial Intelligence (AI) has the potential to mitigate
medication errors and forecast unfavorable incidents.
According to Topol (2019), the implementation of AI technology has the potential to decrease
medication errors, resulting in enhanced patient outcomes and decreased healthcare expenses. Artificial
Intelligence (AI) has the potential to predict adverse events, allowing healthcare providers to take timely
interventions and prevent potential harm to patients. The use of predictive analytics would guide in the
analysis of the various medical conditions and their interventions and solutions. With the development of
the criteria for actualizing healthcare quality, the utilization of the technological improvements targets the
realization of the expected healthcare goals (Wahl et al., 2018). The focus would be on the AI processes
that can help improve the goals and approaches for realizing the outcomes from the healthcare systems.
The Potential Barriers to Adoption of Artificial Intelligence and How to Address Them
The implementation of AI in healthcare presents a range of potential advantages, however, there
exist various obstacles that must be overcome prior to its widespread integration. One of the challenges
faced in the field of artificial intelligence pertains to the absence of standardization and interoperability
among AI systems. According to Krittanawong et al. (2021), the absence of uniformity in AI systems may
pose a challenge to the seamless integration of healthcare systems, thereby obstructing the exchange of data
among diverse healthcare providers and systems. An additional obstacle pertains to the insufficiency of
regulatory structures and ethical principles. According to Huang et al. (2020), the absence of regulatory
frameworks and ethical guidelines may give rise to apprehensions regarding the dependability and safety
of AI systems in the healthcare sector. Additionally, it is imperative to address apprehensions regarding
patient confidentiality and the safeguarding of data to ensure that the integration of artificial intelligence in
the healthcare sector does not jeopardize patient privacy.
In a survey on the presence and use of various healthcare technologies, the issues of implementation,
accessibility and conceptualization of artificial intelligence have remained a challenge (Lee & Yoon, 2021).
Countries develop healthcare systems based on their political, social and economic needs, which overlooks
the mandates and roles expected in managing healthcare technologies. Due to the inconsistency in
technological advancements, most of the healthcare organizations capitalize on sustainable practices,
including policies and sensitization programs. Lack of commitment towards healthcare technologies has
remained a challenge affecting the implementation of artificial intelligence (Wahl et al., 2018). The need
for support structures from within the healthcare systems would be integral in encouraging the utilization
of technologies, including in improving the quality.
2.4 Best Practices for Implementing and Evaluating the Effectiveness of The Artificial Intelligence
Technologies in Healthcare
The integration of artificial intelligence (AI) in the healthcare sector is subject to a range of
factors, including the attitudes of healthcare providers toward AI, their proficiency in AI, and the
accessibility of resources required for the deployment of AI systems (Liao et al., 2020). Such criteria have
a critical impact on the determination of the effectiveness factor that would work with the existing
healthcare technologies. For the organizations to influence the role of artificial intelligence, the
effectiveness factor would be evaluated based on the chances of attaining expected results (Sun &
Medaglia, 2019). The gradual implementation process remains a baseline for ensuring healthcare systems
and organizations can relate the technologies worth the existing strategies.
The adoption of AI can be influenced by various factors such as patient attitudes, legal and
regulatory framework, and implementation costs, as noted by Shi et al. (2020). The integration of
artificial intelligence (AI) within the healthcare industry presents several ethical concerns, including but
not limited to issues surrounding privacy, transparency, and bias. The absence of clarity in the decisionmaking mechanisms of AI systems can engender apprehensions regarding responsibility and confidence,
whereas the possibility of AI systems perpetuating or intensifying pre-existing prejudices can result in
unfavorable outcomes for healthcare providers and patients (Krittanawong et al., 2021; Liao et al., 2020).
2.5 How Artificial Intelligence Can Be Used to Improve Patient Outcomes, Reduce Costs and
Improve Efficiencies in Healthcare Delivery
According to Fan et al., (2020), the integration of artificial intelligence in the various sectors
depends on the identifiable benefits. In the healthcare sector, the focus would be on the criteria and factors
that would enable the realization of specific benefits that would translate to better approaches for attaining
healthcare quality. The integration of artificial intelligence (AI) in the healthcare sector has the capacity to
transform the industry significantly. However, its implementation necessitates meticulous evaluation and
regulation to guarantee that its advantages surpass its obstacles and ethical concerns (Reddy et al., 2019).
The establishment of uniformity and compatibility among artificial intelligence (AI) systems, the
development of regulatory structures and ethical principles, the safeguarding of patient confidentiality and
data protection, and the mitigation of prejudicial tendencies are among the principal obstacles that require
attention.
According to Ahmed et al., (2022), the procedures for influencing the use of artificial intelligence
can be part of the baselines for creating patient outcomes, based on the analytical, diagnosis and
interventions developed for healthcare. The study revealed that healthcare technologies including AI have
been related to cost efficiencies and quality, based on the accessibility, reliability and precision factors.
Such measures would influence the realization of healthcare quality, even with the need for consistent
procedures that would influence the approaches for meeting the quality needs. The adoption of AI can be
influenced by various factors such as healthcare providers’ attitudes, patient attitudes, legal and regulatory
environment, and costs associated with implementing AI systems (Krittanawong et al., 2021). The
utilization of artificial intelligence (AI) in the healthcare industry has the capacity to enhance patient
outcomes, elevate the standard of care, and mitigate expenses. However, it is crucial to acknowledge and
tackle the challenges and ethical considerations associated with AI to guarantee its secure and efficient
implementation in healthcare.
Chapter Three
Methodology
3.1 Research Design ‫ع حسب عنوانك‬
The proposed study employed a systematic review methodology to amalgamate the existing
evidence on the determinants that influence the integration of artificial intelligence (AI) in enhancing the
quality of healthcare. The review encompasses qualitative as well as quantitative studies that satisfy the
specified inclusion criteria. A thematic analysis approach will be utilized to conduct the synthesis of the
evidence.
3.2 Instrument ‫نفسها‬
The PRISMA guidelines for systematic reviews will serve as the research instrument for this study.
The PRISMA guidelines offer a methodical framework for carrying out and documenting systematic
reviews, encompassing the exploration methodology, criteria for selecting studies, data retrieval, and
amalgamation of the findings.
3.3 Sampling Strategy & Setting ‫ع حسب عنوانك‬
The study’s sampling methodology involved a comprehensive search of multiple databases, such as
PubMed, Embase, and Scopus, to locate pertinent research studies. The present study will consider
inclusion criteria that encompass studies conducted within the timeframe of 2018 to 2022, studies published
in the English language, studies that center on the implementation of artificial intelligence (AI) in the
healthcare sector, and studies that furnish insights into the determinants that influence the adoption of AI
in healthcare. The study was conducted within the context of academic and healthcare literature. From the
PRISMA evaluation, the study identified 17 articles used in the research.
3.4 Inclusion Criteria ‫ع حسب عنوانك‬
•
Articles providing information on the adoption of AI in improving healthcare quality.
•
Articles published between 2018 and 2023 for updated I formation.
•
Articles published in the English language.
•
Articles with a definite population, research designs and identifiable outcomes
3.5 Exclusion Criteria ‫ع حسب عنوانك‬
•
Articles published earlier than 2018
•
Articles that lack information on the role of AI in improving AI quality
•
Articles that use systematic review design
•
Articles written in other languages other than English
3.6 Data Synthesis and Analysis
Thematic analysis was employed to examine the data gathered from the research studies and to
identify recurring themes and patterns within the data. The reviewer organized the data following the
themes and then offered the results of that organization in the form of an analysis table. The studies and
related topics were reflected in the table’s columns and rows. This allowed us to compare the findings of
the research across a variety of themes and subthemes. The present study concentrated on the various factors
that influence the implementation of Artificial Intelligence (AI) in enhancing the quality of healthcare.
These factors encompass organizational, technical, and ethical aspects.
3.8 Limitations of the Study ‫نفسها‬
The main limitations of the study came in the use of the systematic review approach, which
limited the scope of the data used for the research. The research was limited to the studies and articles
developed through cross-sectional, experimental, and surveys. The other limitation was on Saudi Arabia
and the use of AI in healthcare, focusing on the improvement of quality.
Chapter 4
Findings
Figure (1) shows that 17 research articles were chosen from the initial collection of 90. All articles that
were irrelevant or inadequate were removed. The remaining articles underwent further evaluation based
on predefined standards formed at the outset of the research project. After the removal of duplication, 55
studies were qualified for the next stage, and during the screening stage of PRISMA 25, studies were
excluded and 20 articles were excluded based on eligibility, leading to 17 articles.
‫نفسهاوعدل األرقام‬
27
‫ع حسب عنوانك‬
Screening
Identification
Identification of studies via databases and registers
Records identified from*:
Databases (n = )
Registers (n = )
Records removed before
screening:
Duplicate records removed
(n = )
Records marked as ineligible
by automation tools (n = )
Records removed for other
reasons (n = )
Records screened
(n = )
Records excluded**
(n = )
Reports sought for retrieval
(n = )
Reports not retrieved
(n = )
Included
Reports assessed for eligibility
(n = )
Reports excluded:
Reason 1 (n = )
Reason 2 (n = )
Reason 3 (n = )
etc.
Studies included in review
(n = )
Reports of included studies
(n = )
*Consider, if feasible to do so, reporting the number of records identified from each database or register searched
(rather than the total number across all databases/registers).
**If automation tools were used, indicate how many records were excluded by a human and how many were
excluded by automation tools.
28
‫مثال‬
Figure 1: PRISMA flow Diagram of the Studies Included in the Current Systematic Review
Figure 1 PRISMA flow Diagram
Identification of New studies Via Databases
Identification
Records identified through Databases searching n=90 (Google Scholar n= 50, Medline
n=10 PubMed n=15, others n=15)
Records after duplicate Removed.
(n = 55)
Screening
Records Screened n=25
Records Excluded, (n= 30)
Records Excluded n =5
Not Meet the Inclusion Criteria n=5
Eligibility
Full-text articles
assessed for eligibility
(n = 20)
Studies Included (n =
17)
Full text articles
excluded with reasons
being.
(n = 3)
Irrelevant outcomes
(1)
Out of scope (1)
Included
Irrelevant study (1)
Studies Included in
Systematic Review
(n = 17)
29
Table 1: General Characteristics of the Included Studies
Table 1 General Characteristics of the Included Studies
Year
Abdullah & Fakieh, 2020
Title
Health care
Study Design
Survey
employees’
perceptions of the
use of artificial
intelligence
applications:
survey study.
Ahmed et al., 2022
From artificial
intelligence to
explainable
artificial
Survey
Aim
Main findings
30
intelligence in
industry 4.0: a
survey on what,
how, and where
Ahmed et al., 2020
Artificial
Survey
intelligence in
healthcare: Past,
present, and future.
Alowais et al., 2023
Revolutionizing
Survey
healthcare: the role
of artificial
intelligence in
clinical practice.
Asan et al., 2020
Artificial
Cross Sectional
intelligence and
study
human trust in
31
healthcare: focus
on clinicians.
Chikhaoui et al., 2022
Artificial
Survey
intelligence
applications in
healthcare sector:
Ethical and legal
challenges.
El-Sherif et al., 2022
Telehealth and
Artificial
Intelligence
insights into
healthcare during
the COVID-19
pandemic.
Cross Sectional
32
Esteva et al., 2019
Dermatologist-
Cross Sectional
level classification
study
of skin cancer with
deep neural
networks
Fan et al., 2020
Investigating the
Experimental
impacting factors
for the healthcare
professionals to
adopt artificial
intelligence-based
medical diagnosis
support system
(AIMDSS).
Gibson et al., 2020
Barriers to the
adoption of
Cross Sectional
33
artificial
intelligence in
healthcare.
Huang et al., 2020
Challenges and
Survey
opportunities of
artificial
intelligence in
healthcare
Kelly et al., 2019
Key challenges for
Cross Sectional
delivering clinical
impact with
artificial
intelligence.
Krittanawong et al., 2021
Artificial
intelligence in
Cross Sectional
34
precision
cardiovascular
medicine.
Matheny et al., 2020
Artificial
Survey
intelligence in
health care: a
report from the
National Academy
of Medicine.
Panch et al., 2019
The “inconvenient
Cross Sectional
truth” about AI in
healthcare
Qaffas et al., 2021
The internet of
things and big data
analytics for
chronic disease
Survey
35
monitoring in
Saudi Arabia.
Sun & Medaglia, 2019
Mapping the
Cross sectional
challenges of
Artificial
Intelligence in the
public sector:
Evidence from
public healthcare
The characterization of the included studies indicates the drivers inputs on the application and use of AI in the healthcare
systems. The studies helped to provide data on the processes and systems that would help incorporate the AI systems and their
operationalization requirements.
The study used 17 articles that were conducted between 2018 and 2022 across, Saudi Arabia and (determine the countries) to
develop the findings, using different study designs [cross-sectional (8), Experimental (1) and survey (8). The findings indicated the
presence of gaps in the utilization of AI in improving healthcare quality (6 studies), despite the benefits that come with AI (11 studies).
36
JBI Checklist Assessment
‫نفسها‬
The JBI assessments help to assess the quality of the articles and data collected, based on the following questions:
1. Is the review question clearly and explicitly stated?
2. Were the inclusion criteria appropriate for the review question?
3. Was the search strategy appropriate?
4. Were the sources and resources used to search for studies adequate?
5. Were the criteria for appraising studies appropriate?
6. Was critical appraisal conducted by two or more reviewers independently?
7. Were there methods to minimize errors in data extraction?
‫عادي أي شي او ع حسب ترتيب دراساتك‬
8. Was the likelihood of publication bias assessed?
y yes n: no
Table 2 JBI Assessment
U: unsure
Authors
Q1
Q2
Q3
Q4
Q5
Q6
Q7
Q8
Score
Abdullah &
Y
Y
Y
Y
Y
Y
Y
Y
100%
Fakieh, 2020
37
Ahmed et al.,
U
Y
Y
Y
Y
Y
Y
Y
87.5%
N
U
Y
Y
Y
Y
Y
Y
75%
Y
Y
Y
Y
Y
U
Y
Y
87.5%
Y
Y
Y
Y
Y
Y
Y
Y
100%
Y
N
Y
Y
Y
N
Y
Y
75%
Y
Y
Y
U
Y
Y
Y
Y
87.5%
N/A
Y
Y
Y
Y
Y
Y
Y
87.5%
Y
Y
Y
Y
Y
Y
Y
Y
100%
2022
Ahmed et al.,
2020
Alowais et
al., 2023
Asan et al.,
2020
Chikhaoui et
al., 2022
El-Sherif et
al., 2022
Esteva et al.,
2019
Fan et al.,
2020
38
Gibson et al.,
Y
Y
Y
Y
Y
Y
Y
Y
100%
Y
Y
Y
Y
Y
U
N
Y
75%
N
Y
Y
Y
Y
Y
Y
Y
87.5%
Y
Y
Y
Y
Y
Y
Y
Y
100%
Y
U
Y
Y
Y
U
Y
Y
75%
Y
Y
Y
Y
Y
Y
Y
Y
100%
Y
Y
Y
Y
Y
N
U
Y
75%
2020
Huang et al.,
2020
Kelly et al.,
2019
Krittanawong
et al., 2021
Matheny et
al., 2020
Panch et al.,
2019
Qaffas et al.,
2021
39
Sun &
Medaglia,
2019
Y
Y
Y
Y
Y
Y
Y
Y
100%
‫‪40‬‬
‫‪Chapter 5‬‬
‫اربع عناوين لكل عنوان ‪ ٥-٤‬مصادر جديده ‪Discussions‬‬
41
The section has included discussions of the findings served from the systematic review
processes integrated from the research articles. The findings have been evaluated based on the
existing research objectives.
5.1 Factors Influencing Adoption of Artificial Intelligence in Improving Healthcare Quality
The development of healthcare quality improvement remains an integral component for
assessing the criteria for realizing the existing goals within healthcare systems. For the various
systems, the investments made toward healthcare quality come from the policies available
(Esteva et al., 2019). In Saudi Arabia, the need for an effective process for managing healthcare
quality has come from the Ministry of Health. Through the investments made, the organizations
invest in the procedures that would help in actualizing patient safety and precision in healthcare
deliveries (Krittanawong et al., 2021). With the creation of the HealthCare quality processes, it
would be necessary to work with the existing criteria and procedures that would improve the
gains from the healthcare systems.
The creation of localized policies and strategies for accommodating AI has been a factor
to consider for Saudi Arabia, especially when handling the diverse needs in the healthcare system
(Qaffas et al., 2021). Most of the investments have focused on capacity development to help
boost the realization of the intended goals.
The creation of AI systems has been a strategic investment, based on the requirements to
influence the creation of healthcare goals. From the findings, the application of artificial
intelligence is based on the capacities that the healthcare systems have, incorporating the
technologies. The adoption of AI has therefore been based on the organizational capacities and
procedures that would help in creating the expected effectiveness (Sun & Medaglia, 2019). The
42
findings indicated that most of the healthcare systems have focused on the priorities that would
promote affordable healthcare. The initial costs of deploying artificial intelligence have been a
factor that has affected its popularity within the healthcare sector. Advancements in the sector
have capitalized on the opportunities that would generate sustainable solutions, with cost being a
consistent issue.
For healthcare organizations to work with AI, the economic aspects would be related to
the value gained (Sun & Medaglia, 2019). The findings indicated that most organizations have
focused on resource development, due to the need for adequacy to work with the demands form
the patients. The factors to determine the use of Ai to improve healthcare have included the need
for prioritizations, which would influence the commitments to investments into resources and
inpus for successful AI implementation. While the technologies have remained integral in
addressing quality development, it would be important to generate the baselines for their
incorporation, based on the reliability and vague evaluations (Lee & Yoon, 2021). The healthcare
quality in such cases would be determined based on the capacity to work with the technologies to
meet the patient’s requirements.
5.2 Adoption of Artificial Intelligence in Influencing Quality of Healthcare
From the findings, the prevalence of Artificial Intelligence (AI) remains low, despite its
potential benefits (Fan et al., 2020). The creation of the health care quality processes has
overlooked the technological approaches, due to the implications on the existing systems. In the
technological sector, the rapidity in the advancement has been a factor for organizations, due to
the unstable approaches used (Ahmed et al., 2020). For this reason, adoption is subject to the
identification of current needs and the creation of solutions. In the healthcare sector, the focus on
43
technological advancement has been increasing, despite the reluctance due to the instability
factor.
In Saudi Arabia, the adoption of technological processes has been based on the policies
and economic approaches governing investments in the healthcare sector (Panch et al., 2019).
The privatization approaches remain one of the baselines for determining the chances of working
with the respective technologies. The introduction of the stakeholder aspects in such cases would
be integral in ensuring that the organizations can meet the investment need for AI (Liao et al.,
2020). Other considerations would include the research and development to develop contextual
technologies based on the identification of the healthcare needs of the respective healthcare
systems.
5.3 Potential Barriers to Adoption of Artificial Intelligence and How to Address Them
From the findings, the utilization of AI has experienced barriers that have affected their
intended inputs for the healthcare sector. The barriers include the priorities that the governments
have (Esmaeilzadeh, 2020). For many years, the use of healthcare technologies has been slow
across different countries and healthcare systems. Most of the priorities have focused on
improving the value of the resources. Investments in facilities, human resources, and research
have been part of the baselines for influencing changes in the healthcare sector. While such
advancements have targeted the actualization of better patient safety, the main issues have come
from the emphasis on the utilization of the technologies (Babic et al., 2021). The creation of
political goodwill to help advance the technologies would therefore be a factor that could
influence the opportunities and chances for attaining the expected patient safety and quality.
44
At the healthcare system level, the main barriers come from the delink between the
facility commitments and the expected approaches for managing AI technologies for healthcare
quality (Morley et al., 2020). The findings indicated that 80% of the stakeholders lacked
adequate appreciation of the procedures that could be used to manage the use of AI in healthcare
quality. Patient safety education, the lack of commitment for the professionals, and the
unwillingness to accommodate new styles in healthcare remain major challenges for the
healthcare processes (Krittanawong et al., 2021). Governments and stakeholders need to
appreciate the advancements in healthcare quality and develop frameworks that would help
incorporate tense changes, even with the evaluation of the influencers of effective patient quality
and safety.
5.4 Best Practices for Implementing and Evaluating the Effectiveness of The Artificial
Intelligence Technologies in Healthcare
The effectiveness of artificial intelligence was evaluated based on its inputs, expected
inputs, and the implications on the healthcare systems (Matheny et al., 2020). For organizations
to generate the expected benefits from any technologies, their implementations should consider
the specific baselines for generating the expected effectiveness. The effectiveness in healthcare
should capture all the aspects of the healthcare processes, including the capacity to work with the
contextual needs of the organizations (Esmaeilzadeh, 2020). The incorporation of artificial
intelligence as part of the procedures for sustaining better management of healthcare needs is
therefore an important factor in addressing the requirements for improving patient needs (Wahl et
al., 2018). The technological approaches and processes should target the realization of the
healthcare goals, which include patient safety and quality.
45
The moral, ethical, and professional approaches for managing the effectiveness sin the
healthcare system is also an important factor when addressing the procedures for incorporating
changes in the healthcare system (Johnson et al., 2021). Due to the factors and measures for
addressing healthcare needs, the development of healthcare advancements should consider
ethical processes to ensure effectiveness. Even with the potential benefits of addressing patient
quality needs, the risks involved in healthcare technologies need strategies to eliminate their
impacts (Sun & Medaglia, 2019). The commitments and procedures used for working with
healthcare technologies would be critical in ensuring that AI incorporation improves the
effectiveness of healthcare strategies. The focus would be on the procedures and measures that
would trigger better management of healthcare needs and address the criteria for healthcare
development.
5.5 How Artificial Intelligence Can Be Used to Improve Patient Outcomes, Reduce Costs
and Improve Efficiencies in Healthcare Delivery
The (60%) of findings indicate the mandates and roles that the advancements in
technologies play in influencing changes within the healthcare systems. The organizations need
to appreciate the concepts or measures that would generate the expected gains for the healthcare
systems (Greenspan et al., 2020). In improving patient outcomes AI concepts develop the inputs
that improve the patient intervention processes. The improvement in the outcomes depends on
the system’s capacity, which can be developed through investments in AI. AI boosts the
management needs in the organization (Abdullah & Fakieh, 2020). The AI technologies would
enable patient monitoring, diagnosis, and management which can improve patient outcomes
(Panch et al., 2019). The intention would be to deploy the technologies based on the healthcare
46
system needs, which can help in advancing their roles and mandates in attaining the expected
reliability in managing the quality processes.
From the findings (70%), the development of healthcare technologies including AI can lead to
cost management and improved efficiencies for the various tasks and processes. The focus would
be on the ability to reduce the resource inputs, based on the capacities that the technologies have
(Babic et al., 2021). The advancements in the technological inputs in addressing the respective
patient needs create room for improving efficiencies. The improvement in the intelligence inputs
such as better management of patient processes and the capabilities to deal with emergencies are
some of the efficient measures that AI introduces (Greenspan et al., 2020). For organizations to
attain effectiveness and efficiency, AI technologies should be incorporated into the various stages
that are involved in the processing of patients within the healthcare systems (Matheny et al.,
2020). The cost-effectiveness would come from the reduced costs of maintaining the systems and
the resource restructuring expected when the AI systems undertake human-based functions.
‫‪47‬‬
‫نفس الكونكلوجن حقت ال ‪ ١٠‬االرتكلز في اللترتشر‪ +‬بارافريز‬
‫‪Conclusion and Recommendations‬‬
‫‪Chapter 6:‬‬
48
6.1 Conclusions
Factors Influencing Adoption of Artificial Intelligence in Improving Healthcare Quality
The development of the respective factors that would affect the healthcare quality based
on AI utilization were related to levels of healthcare technology use and existing cultures. For the
organizations to sustain the healthcare quality, the AI would be developed based on the
approaches for influencing the existing quality needs. The factors defining the adoption therefore
related to the AI utility aspects such as the commitments to healthcare technologies and the
existing healthcare outcomes. The healthcare systems therefore determine the approaches to
consider when developing the healthcare quality approaches. The inclusion of AI policies and
strategies has been recognized as an important success factor for the adoption of AI.
Adoption Of Artificial Intelligence in Influencing Quality of Healthcare
The aim was to establish the adoption and the influences of the use of AI in influencing
the quality of healthcare. The review indicated the presence of benefits and values that would
influence the use of AI in developing quality healthcare, which were factors to determine the
adoption of the Ai systems in healthcare systems. The value includes improved management of
healthcare needs, the ability to eliminate medication errors, and the effective management of the
patient’s needs. With such factors, the considerations made in the adoption of AI across the
healthcare systems have been related to the intended benefits. Other factors included the existing
policies and the ability to sustain the technological approaches based on trends in AI
developments.
Potential Barriers to Adoption of Artificial Intelligence and How to Address Them
49
The objective targeted the identification of the various barriers that could affect the
utilization of AI. The main issues that were identified included the different focus areas that
would be related to the gaps in managing AI trends. For organizations to work with AI to manage
healthcare quality, all stakeholders would have to adapt to the respective influencers of quality
through technologies. Lack of cultures that support AI, unstable technological development, and
slow integration of healthcare technologies remain critical factors influencing the management of
AI in addressing healthcare quality.
Best Practices for Implementing and Evaluating the Effectiveness of The Artificial
Intelligence Technologies in Healthcare
The focus was on identifying the inputs and practices that would help optimize the gains
from the utilization of AI in healthcare. The focus was on the policies and ethical inputs that
would enable AI to meet its objectives. For organizations to work with AI, the cultural
approaches would align with the intended application to ensure an effective strategy for
incorporating AI. Other practices include access to adequate training and development,
continuous improvement strategies, and the commitment to improved quality based on the
advancements made. The organization would have to work with the intended value of AI while
investing in meeting the existing healthcare outcomes.
How Artificial Intelligence Can Be Used to Improve Patient Outcomes, Reduce Costs and
Improve Efficiencies in Healthcare Delivery
From the review, the development of the AI concept for healthcare targets efficiencies,
improved quality, and cost management. The studies revealed improved patient outcomes based
on the management of medication errors and creating room for continuous quality improvement.
50
The cost factors come in the precisions and the capacities to manage the various patient needs.
Then efficiency comes from the value factor that comes from the better outputs that come from
the application of AI.
6.2 Recommendations
➢ In Saudi Arabia, the government should consider the use of AI as part of the
transformative measures used in managing healthcare needs. The approaches would
guide the national approaches in ensuring the realization of healthcare quality through
improved intelligence used in addressing healthcare needs.
➢ The adoption of customized approaches in utilizing AI In Saudi Arabia is a vast field that
offers different inputs that would identify with the different healthcare inputs.
➢ The processes involved should align with the healthcare technologies and their role in
meeting healthcare outcomes. In such an approach, the investments towards AI would be
related to the specific needs affecting the people. Such an approach creates better
approaches for influencing improved performances, which would include healthcare
quality needs.
➢ For future research, the report would recommend the development of models that would
help in promoting the use of AI in various healthcare applications. The models would be
consistent with the gaps and opportunities in Saudi’s healthcare systems.
51
References
Abdullah, R., & Fakieh, B. (2020). Health care employees’ perceptions of the use of artificial
intelligence applications: survey study. Journal of medical Internet research, 22(5),
e17620.
Ahmed, I., Jeon, G., & Piccialli, F. (2022). From artificial intelligence to explainable artificial
intelligence in industry 4.0: a survey on what, how, and where. IEEE Transactions on
Industrial Informatics, 18(8), 5031-5042.
Ahmed, S. H., Hasan, M. M., & Islam, M. A. (2020). Artificial intelligence in healthcare: Past,
present,
and
future.
Journal
of
Medical
Systems,
44(8),
1-14.

Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N.,
… & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence
in clinical practice. BMC Medical Education, 23(1), 689.
Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial
intelligence in healthcare: a multidisciplinary perspective. BMC medical informatics and
decision making, 20(1), 1-9.
Asan, O., Bayrak, A. E., & Choudhury, A. (2020). Artificial intelligence and human trust in
healthcare: focus on clinicians. Journal of medical Internet research, 22(6), e15154.
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health
care. Science, 373(6552), 284-286.
52
Chikhaoui, E., Alajmi, A., & Larabi-Marie-Sainte, S. (2022). Artificial intelligence applications in
healthcare sector: Ethical and legal challenges. Emerging Science Journal, 6(4), 717-738.
El-Sherif, D. M., Abouzid, M., Elzarif, M. T., Ahmed, A. A., Albakri, A., & Alshehri, M. M. (2022,
February). Telehealth and Artificial Intelligence insights into healthcare during the
COVID-19 pandemic. In Healthcare (Vol. 10, No. 2, p. 385). MDPI.
Esmaeilzadeh, P. (2020). Use of AI-based tools for healthcare purposes: a survey study from
consumers’ perspectives. BMC medical informatics and decision making, 20(1), 1-19.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2019).
Dermatologist-level classification of skin cancer with deep neural networks. Nature,
542(7639), 115-118.
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020). Investigating the impacting factors for the
healthcare professionals to adopt artificial intelligence-based medical diagnosis support
system (AIMDSS). Annals of Operations Research, 294, 567-592.
Gibson, F. L., Chi, M. T., & Chen, P. S. (2020). Barriers to the adoption of artificial intelligence in
healthcare. Journal of medical systems, 44(8), 152.
Greenspan, H., Estépar, R. S. J., Niessen, W. J., Siegel, E., & Nielsen, M. (2020). Position paper
on COVID-19 imaging and AI: From the clinical needs and technological challenges to
initial AI solutions at the lab and national level towards a new era for AI in
healthcare. Medical image analysis, 66, 101800.
53
Huang, G. B., Chen, L., & Siew, C. K. (2020). Challenges and opportunities of artificial
intelligence in healthcare: A systematic review. European Journal of Operational
Research, 284(1), 1-24.
Johnson, K. B., Wei, W. Q., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., … & Snowdon,
J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical
and translational science, 14(1), 86-93.
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges
for delivering clinical impact with artificial intelligence. BMC medicine, 17, 1-9.
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2021). Artificial intelligence in
precision cardiovascular medicine. Journal of the American College of Cardiology, 77(21),
2664-2679.
Lee, D., & Yoon, S. N. (2021). Application of artificial intelligence-based technologies in the
healthcare industry: Opportunities and challenges. International Journal of Environmental
Research and Public Health, 18(1), 271.
Lee, J. J., Cheng, Y. T., Tsai, C. W., & Huynh, T. N. (2019). Overcoming barriers to adopting
artificial intelligence in healthcare: a call to action. BMJ health & care informatics, 26(1),
e100075.
Liao, K. P., Cai, T., Savova, G. K., Murphy, S. N., & Karlson, E. W. (2020). Development of
phenotype algorithms using electronic medical records and incorporating natural language
processing.
BMJ
Health
&
Care

Informatics,
27(1),
e100073.
54
Maddox, T. M., Rumsfeld, J. S., & Payne, P. R. (2019). Questions for artificial intelligence in
health care. Jama, 321(1), 31-32.
Matheny, M. E., Whicher, D., & Israni, S. T. (2020). Artificial intelligence in health care: a report
from the National Academy of Medicine. Jama, 323(6), 509-510.
Morley, J., Machado, C. C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The
ethics of AI in health care: a mapping review. Social Science & Medicine, 260, 113172.
Panch, T., Mattie, H., & Celi, L. A. (2019). The “inconvenient truth” about AI in healthcare. NPJ
digital medicine, 2(1), 77.
Qaffas, A. A., Hoque, R., & Almazmomi, N. (2021). The internet of things and big data analytics
for chronic disease monitoring in Saudi Arabia. Telemedicine and e-Health, 27(1), 74-81.
Reddy, S., Fox, J., & Purohit, M. P. (2019). Artificial intelligence-enabled healthcare
delivery. Journal of the Royal Society of Medicine, 112(1), 22-28.
Sarkar, I. N., Cohen, M. E., Gagnon, M., Foster, B., & Boukhechba, M. (2021). The future of
healthcare: Associations between patient-generated health data and clinical-grade health
outcomes.
Journal
of
Medical
Internet
Research,
23(2),
e26262.

Shi, Y., Wang, Y., Liao, S. G., Lee, K. K., & Zhao, X. (2020). Artificial intelligence in healthcare:
Past,
present
and
future.
Seminars

in
Cancer
Biology,
64,
1-13.
55
Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public
sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368383.
Suresh, H., Hunt, N., & Johnson, D. (2020). Adoption of artificial intelligence in healthcare:
Current challenges and future prospects. British Journal of Hospital Medicine, 81(7), 1-5.
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial
intelligence. Nature Medicine, 25(1), 44-56.
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018). Artificial intelligence (AI)
and global health: how can AI contribute to health in resource-poor settings?. BMJ global
health, 3(4), e000798.

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