Health informatics impact on patient safety and care in saudi arabia

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). 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