The University of Bedfordshire 

BSS056-6 Theory Into Practice Project 

Due date: 

January 15, 2024 

 

Unit Coordinator:  

Pauline Loewenberger 

 

Pathway:  

MBA in Hospital and Health Services Management 

 

Thesis Topic: 

A critical analysis of the challenges around the practical implementation of AI-based medical 

technology in the public healthcare industry in the United Kingdom 

 

 

  

 

 

Prepared by: 

Gail Narinesingh 

SID 2018091 

 



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Executive Summary 

 

The advent of Artificial Intelligence in healthcare has transformed the way the industry 

currently functions. While some technology experts believe the implementation process has 

many daunting challenges, others look beyond to its benefits. The topic selected on Artificial 

Intelligence is both current and relevant in today’s world and focuses on a critical analysis of 

the challenges around the practical implementation of AI-based medical technology, in the 

public healthcare industry, in the United Kingdom. Applying the principles of a systematic 

review of the literature, several articles were selected to analyse the challenges around the 

practical implementation of AI-based technology. The findings revealed a thematic 

presentation identifying challenges, benefits and influential factors toward AI-based 

technology. The discussion of the research findings provides an analytical perspective and 

analyses its alignment with the various influential theories previously identified in the literature 

review. The research highlights the lack of legislation, governance issues and resistance to 

change as major global challenges to implementing AI-based technology. These challenges are 

further linked to issues of trust, poor communication and collaboration, a lack of transparency, 

poor accountability and the organization’s culture. Therefore, a need for ongoing training and 

education to build knowledge and establish trust, improve stakeholder collaboration and the 

use of expert knowledge on how to achieve these are required to improve the chances of 

implementing AI-based medical technology within the UK healthcare industry.  

 

 

 

 

 

 

 

 

 



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Table of Contents 

1. Chapter 1: Introduction          1 

1.1 Topic           1 

 1.2 Background          1 

 1.3 Rationale           1 

 1.4 Scope           2 

 1.5 Aim           3 

 1.6 Objectives          3 

 1.7 Influential Theories and Models                        3-5 

  1.7.1 Stakeholder Theory        3 

  1.7.2 Transformational Leadership Theory      4 

  1.7.3 Unified Theory of Acceptance and Use of Technology   4 

1.7.4 Lewin’s Change Model                         4-5 

1.8 Method of Analysis                  5-6 

1.9 Overview of Chapters         6 

 

2.  Chapter 2: Literature Review 

 2.1 Introduction          8 

 2.2 Definition           9 

 2.3 The TRIAD Process                  9-17 

  2.3.1 The People’s Perspective: Stakeholders and AI                                  9-13 

  2.3.2 The Process: Toward AI Governance                                                 13-15 

  2.3.3 Technology: The tools that support human functioning                     15-17 

 2.4 Conclusion           17 

 

3.  Chapter 3: Systematic Analysis of Literature 

 3.1 Introduction          19 

 3.2 Scope of the Study         19 

  3.3.1 Keyword and Search Terms       19 

  3.3.2 Inclusion criteria        20 



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  3.3.3 Exclusion criteria        20 

3.3 Results based on Keyword search       21 

3.4 Search Result Strategy         21 

3.5 PRISMA Statement         22 

3.6 Descriptive Summary of Journal Articles              23-32 

3.7 Thematic Analysis of Findings        33 

3.8 Data Extraction and Thematic Presentation      34 

3.9 Analysis of findings                 37-38 

 3.9.1 Theme 1: Challenges to implementing AI     37 

 3.9.2 Benefits to AI implementation       37 

 3.9.3 Influential factors that improve AI implementation    38 

 

4.  Chapter 4: Discussion, Conclusion & Recommendations 

 4.1 Introduction          39 

4.2 Discussion          39 

  4.2.1 Theme 1: Challenges to implementing AI             39-40 

  4.2.2 Theme 2: Benefits to AI implementation     41 

  4.2.3 Theme 3: Influential factors that improve AI implementation           41-42 

 4.3 Limitations          42 

 4.4 Conclusion                 43-44 

 4.5 Recommendations                            44-45 

  4.5.1 Training and education        44 

  4.5.2 Stakeholder collaboration       45 

  4.5.3 Use of expert knowledge       45 

  4.5.4 Creation of new organizational position     45 

 

5. List of references                   46-58 

 

6. Appendix I            59 

  



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List of Tables 

 

3.1 Inclusion Criteria           20 

3.2 Exclusion Criteria          20 

3.3 Results based on keyword search        21 

3.4.1 Descriptive summary of Journal Article 1       23 

3.4.2 Descriptive summary of Journal Article 2       24 

3.4.3 Descriptive summary of Journal Article 3       25 

3.4.4 Descriptive summary of Journal Article 4       26 

3.4.5 Descriptive summary of Journal Article 5       27 

3.4.6 Descriptive summary of Journal Article 6       28 

3.4.7 Descriptive summary of Journal Article 7       29 

3.4.8 Descriptive summary of Journal Article 8       30 

3.4.9 Descriptive summary of Journal Article 9       31 

3.4.10 Descriptive summary of Journal Article 10       32 

3.5 Data Extraction and Thematic Presentation: Theme 1      34 

3.6 Data Extraction and Thematic Presentation: Theme 2      35 

3.7 Data Extraction and Thematic Presentation: Theme 3      36 

 

 

 

 

 

 

 

 

 



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List of Figures 

 

1.1 Influential Theories and Models         5 

1.2 Steps in a Systematic Literature Review        6 

1.3 Overview of Chapters          7 

2.1 The TRIAD Framework: People, Process and Technology     8 

2.2 Performance improvement with human-machine collaboration     10 

2.3 Benefits of AI           12 

2.4 Number of patients waiting for consultant-led elective care in the NHS   16 

3.1PRISMA 2009 Flow Chart         22 

3.2 Thematic Analysis of AI implementation       33 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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List of Abbreviations 

 

Artificial Intelligence (AI) 

Chief Information Officer (CIO) 

Key Performance Indicators (KPI) 

Medicines and Healthcare products Regulatory Agency (MHRA) 

National Health Service (NHS) 

People, Process and Technology (PPT) 

Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 

Stakeholder Theory (ST) 

Transformational Leadership (TL) 

United Kingdom (UK) 

Unified Theory of Use and Acceptance of Technology (UTAUT) 

World Health Organization (WHO)



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CHAPTER 1: Introduction  

 

1.1 Topic  

A critical analysis of the challenges around the practical implementation of AI-based medical 

technology in the public healthcare industry in the United Kingdom. 

 

1.2 Background  

Artificial Intelligence (AI) has quickly proven its capabilities to transform and enhance human 

life within various industries such as entertainment, agriculture, and manufacturing (Espina-

Romero et al, 2023; Marr, 2023). The healthcare industry is no different, as AI-based 

technology can revolutionize population health management in several ways (Bohr and 

Memarzadeh, 2020). Over the past decades in the United Kingdom, a greater public demand 

for an efficient and effective healthcare delivery system (Montgomery et al., 2017; Papanicolas 

et.al., 2019) created greater constraints on the government and the National Health Service 

(NHS) to provide high quality healthcare. This has been due to issues such as staff shortages 

(NHS, 2019), an aging population and other human health complexities (Raymond et al., 2021) 

that requires some type of intervention to improve health outcomes.  

To improve how care is delivered, the NHS has evolved to its present-day ‘Long Term Plan’ 

launched in 2019 (NHS, 2019). This is its latest health sector reform strategy with technology 

as its main driver intended to bring about efficient and effective high quality healthcare 

(Aldwick and Dixon, 2019). As such, the NHS Digital has already been leading the way by 

embracing AI-based medical technology, in areas such as radiology analysis for x-ray and 

mammogram assessments (NHS Transformational Directorate, 2023) that focus on improving 

efficiency and effectiveness (Tariq et al., 2021). However, while AI-based medical technology 

is proving to be beneficial in these areas (Chada and Summers, 2022), there are some 

challenges to its further practical implementation. 

 

1.3 Rationale  

Globally, the healthcare industry is faced with a greater burden to deliver quality healthcare 

due to key issues such as staff shortages, an aging population, and a growing chronic disease 



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burden (WHO, 2022). During the past decade, AI-based technological advancement in various 

industries such as manufacturing, have been improving the efficiency and effectiveness in these 

industries (Barbosa et al., 2023). Likewise, the healthcare industry has also been able to 

advance itself by applying AI-based medical technology in some aspects of healthcare. This 

has brought about efficiency and effectiveness in the delivery of some aspects of care and thus 

better patient outcomes (Alowais et al., 2023; Chebrolu, 2020). 

In the United Kingdom, the NHS has been engaging in the use of AI-based medical 

technology such as Predictive Care Pathways (Phiri and Delanerolle, 2023) and specialized 

diagnostic imaging such as mammograms and X-rays enhanced by artificial intelligence. This 

AI-based medical technology supports the work of healthcare providers by improving 

diagnostics and early detection toward a more efficient and effective patient outcome 

(Holdsworth and Zaghloul, 2022).  

The United Kingdom is considered one of Europe’s leading developers of artificial 

intelligence (Bughin et al., 2019). As such, using AI-based medical technology to improve the 

efficiency and effectiveness in the delivery of quality healthcare has already begun. However, 

there are some concerns such as data security and patient privacy, the absence of legislation 

for AI adoption and addressing resistance to change (Felzmann et al., 2019) in some instances 

and are potential reasons why adopting this innovative technology may not have been fully 

implemented. Therefore, this research topic aims to analyse the challenges surrounding the 

practical implementation of AI-based medical technology within the NHS in the United 

Kingdom. 

 

1.4 Scope 

The methodology of this research topic follows a Systematic Analysis Review of the 

literature. It was used to critically analyse the challenges around the practical implementation 

of AI-based medical technology in the United Kingdom’s public healthcare industry. The 

focus surrounded the healthcare industry and AI-based medical technology. Three primary 

areas around the challenges to implementing AI-based medical technology included (1) A 

lack of updated legislation and current regulatory frameworks (2) AI Governance and ethical 

issues such as risk related to data security and patient privacy and (3) organizational issues 

pertaining to resistance to change. The research material focused on peer-reviewed journal 



3 
 

articles from 2013 to 2023. This helped to identify the challenges surrounding the 

implementation of AI-based medical technology.  

1.5 Aim 

To analyse the challenges of implementing AI-based medical technology within the public 

healthcare industry in the United Kingdom. 

 

1.6 Objectives 

1. To identify the challenges affecting the implementation of AI-based technology within 

the healthcare industry. 

2. To analyse the effects of those challenges on the healthcare industry.  

3. To assess the factors that can improve the chances of AI-based medical technology 

within the healthcare industry. 

4. To recommend evidence-based strategies for improving the implementation of AI-

based medical technology within the UK healthcare industry. 

 

1.7 Influential Theories and Models 

 

1.7.1 Stakeholder Theory (ST) 

Stakeholder Theory (ST) is grounded in the field of business ethics and organizational 

management (Mahajan et al., 2023) that seek the interest of key players involved 

including employees, patients, and other individuals in the community, government, 

regulatory bodies, and public partners (Bhavaraju et al., 2023; Hogg et al., 2023) and who 

themselves have an interest in the competitive advantage of the organization. 

Additionally, management decisions are guided by ethical practices through a 

collaborative process (Harrison et al., 2015) that helps to evaluate how policies and 

regulation are developed, adapted, or adopted; what are its social and ethical implications; 

how it guides decision-making and corporate governance and provides feedback through 

clear communication (Ali and Haapasalo, 2023). As such, Stakeholder theory is the lens 

through which issues such as corporate governance, leadership and decision-making have 

been analysed for the implementation of AI-based medical technology. 



4 
 

1.7.2 Transformational Leadership theory  

Transformational leadership theory was originally developed by Burns (1978) and have 

since evolved into a modern day approach to management (Bagga et al., 2022). Its role 

has shifted from traditional and autocratic in nature, to a motivational and inspirational 

one to bring about change. These leaders use charisma, compassion, and personality to 

engage people toward a shared vision and goal achievement (Lai et al., 2020). This 

futuristic approach inspires empathy, motivation, productivity morale, communication 

and collaboration for an ongoing approach to sustainability (Khan et al., 2020). Therefore, 

transformational leadership is the most suitable approach since these leaders can lead 

their organization through the innovative changes needed for Ai implementation to occur.  

 

1.7.3 Unified Theory of Acceptance and Use of Technology (UTAUT) 

Unified Theory of Acceptance and Use of Technology (UTAUT) is a combined modern 

version of several technology acceptance models combined by Viswanath Venkatesh and 

others in 2003. It is used to analyse four factors in technology use, namely (1) 

performance expectancy, (2) effort expectancy, (3) social influence and (4) facilitating 

conditions. Additionally, there are four intermediate individual variables, namely (1) age, 

(2) gender, (3) experience and (4) volunteerism of use (Ayaz and Yanartaş, 2020). 

Therefore, when there is a concern about the acceptance of technology or a persons’ 

intention to use technology in public institutions such as the NHS hospitals (AlQudah et 

al., 2021) this becomes a relevant tool.  

 

1.7.4 Lewin’s Change Model  

Lewin’s Change Model was developed by Kurt Lewin in 1951 as a 3-step change model 

to organizational change. These three steps include Unfreezing, Change and Refreezing, 

taking the organization from a state of known to unknown or a period of instability to 

stability (Hussain et al., 2018). Each phase is considered timely and requires clear 

communication to support the change that is required. During each step, those involved in 

the change process need to first recognize the need for change, then see how it will 

benefit them and finally, internalize that change to bring about a shift in organizational 

culture and the stability required by the organization (Deborah, 2018). Therefore, Lewin’s 



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change model is an important tool in implementing AI-based medical technological 

changes in healthcare, since exploring those steps were necessary for accepting the new 

technology (Hospodková, 2021) not only at the hospital level where culture is ingrained 

into practice but also in terms of policy makers being able to make decisions at a more 

reasonable pace in line with the evolving technology.  

 

Figure 1.1 Influential Theories and Models 

 

Source: Author generated (2023). 

 

1.8 Method of Analysis 

A Systematic Analysis is a 7-stage approach illustrated in Figure 1.2, which was used to 

synthesise the results on the analysis of ten articles as illustrated in Chapter 3 which was related 

to achieving the aim and objectives (Martinic et al., 2019) for this topic. Therefore, it provided 

clarity and valuable insight into the research topic founded on the work of other researchers’ 

primary data collection (Phillips and Barker, 2021; Sataloff et al., 2021). The analysis was 

guided by an inclusion and exclusion criteria using a keyword search approach which helped 

to zoom into the proposed research topic to be analysed. As a result, this systematic method of 

analysis was used for the identification and discussion of the findings and helped to identify 

Stakeholder theory
Transformational Leadership 

theory

Unified Theory of Acceptance 
and Use of Technology

Lewins 3-stages of Change 
Management

Influential Theories 
and Models



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any variances in the literature (Gupta et al., 2018). The PRISMA statement guided how to do 

the systematic literature review and reflected what was included and excluded. This is 

demonstrated on a flow chart in Chapter 3. 

 

Figure 1.2 Steps in a Systematic Literature Review 

Source: University Library (2023). 

 

1.9 Overview of Chapters 

This thesis paper consists of four chapters and commences with the introduction as chapter one 

(1). This chapter gives a general overview of the research paper and why doing this topic is 

relevant. Then, Chapter two (2) consists of the Literature Review. This chapter reviews the 

literature and theories relevant to the topic, aim and objectives to develop the context and set 

the scene for the topic in a comprehensive way while answering some of the objectives set out 

in this report. In Chapter three (3), the systematic analysis of the available literature describes 

the method of data collection and are reflected using a PRISMA statement. Chapter four 

consists of three main sections, namely the discussion, conclusion, and recommendation. The 

discussion analyses whether the findings from Chapter three are aligned with the influential 

theories and are analysed based on the themes that emerged from the systematic review of the 

literature for simplicity. The conclusion discusses whether the aim and objectives were 

achieved, and the recommendations are based on the conclusion as well as provides an answer 

for the forth objective. Finally, the paper closes with a reference list following the Harvard UK 

style in both the in-text citation and reference list reflecting the sources of data used throughout 

the body of the report. 

 



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Figure 1.3 Overview of Chapters 

 

 

Source: Author generated (2023). 

 

 

 

 

 

 

 

 

 

 

Introduction: General overview of this thesis

Literature Review: Theoretical perspectives 
relevant to the topic, aim and objectives

Systematic Literature Review: Uses a PRISMA 
statement to reflect the most relevant and 

highest quality publications 

Discussion, Conclusion and Recommendation: 
findings from systematic literature review 

discussed 

C
H

A
P

TE
R

S 



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CHAPTER 2: Literature Review 

2.1 Introduction  

The literature review provides a body of knowledge through the lens of various authors to 

support and justify this research paper (Leite et al., 2019). An overview of the existing literature 

provides an insightful view of Artificial Intelligence (AI). This chapter reviews some of the 

most relevant factors that act either as barriers or facilitators that affects the implementation of 

AI technology. The following TRIAD approach simplifies the way in which these factors can 

be identified and analysed. It includes the broad headings of the People, Processes and 

Technology. Leveraging this framework, as illustrated in Figure 2.1, also helps to assess some 

of the ways in which an organization can improve its chances of AI-based technology adoption. 

 

Figure 2.1 The TRIAD Framework: People, Processes and Technology 

 

 

Source: Author generated (2023). 

 

 



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2.2 Definition of Artificial intelligence  

Artificial Intelligence (AI) can be defined as a multipurpose technologically based tool that can 

be used to support the work of humans through machine learning (ML) and deep learning (DL) 

(Pattam, 2021). It is powered by algorithms used to develop new content using audio and visual 

material that make its contents more impactful across various industries. Its capabilities such 

as automation and analysis can help an organization improve its overall performance and 

position it at a competitive advantage (Barbosa et al., 2023). 

 

2.3 The TRIAD Framework 

2.3.1 The People’s Perspective: Stakeholders and AI 

In the business world, AI is considered as a supportive tool for the purposes of automation, 

analytics, and engagement as seen in the use of chatbots to answer customers’ frequently asked 

questions (Davenport and Ronanki, 2018). However, humans interact in various capacities as 

stakeholders with AI. These include its individual designers, internal and external 

organizational users and national and/or international participants that impact on the regulatory 

and legislative guidelines involved in its implementation and use (Deshpande and Sharpe, 

2022). Therefore, each stakeholder group may have differing interests, expectations and 

varying concerns pertaining to AI. 

The adoption of AI by some stakeholders in the workplace have been low (Dai et al, 2022). 

This has been attributed to high levels of distrust about AI’s ability to make decisions and the 

fear of job loss (Ganapathi Muppidath, 2020; Sanan, 2017; Zirar et al., 2023). At the individual 

and organizational levels, lack of understanding due to poor communication and transparency 

(Borges de Nascimento et al., 2023) about how AI can transform decision making has affected 

human perception about the capabilities of AI and is therefore perceived as a job threat (Booyse 

and Scheepers, 2023).  

According to the tenets of Stakeholder theory (Harrison et al. 2015), open communication 

within and among the various stakeholder levels such as Leaders, Managers, and lower level 

employees, help to minimize distrust by creating a culture of inclusivity in the decision-making 

process. This will help them understand how AI will affect their organizational role and 

function and reduce the perception of job loss. Also, this may facilitate the process of 

generating a third culture between AI technology and humans that can improve the efficiency 



10 
 

and effectiveness of the organization (Kolbjornsrud et al., 2017). For example, as illustrated in 

Figure 2.2, Wilson, and Daugherty (2018, para. 3) reflected how technology supplemented and 

amplified organizational performance levels when there is ‘Human-Machine collaboration’ 

and therefore, creates the synergy required for the development of a third culture. 

 

Figure 2.2 Performance improvement with Human-Machine Collaboration 

 

Source: Wilson and Daugherty (2018). 

 

Additionally, this paradigm shift can set the foundation for organizational sustainability 

through an AI and human partnership which will seek to create value through a multi-

stakeholder collaborative, decision-making process. However, for this partnership to prosper, 

the organization must have a Leader who is open to change and is able to make change happen. 

Various leadership styles exist but few can help organizations affect change management when 

technology is involved. Therefore, a strong leadership is critical that not only focus on the 

people and the processes but also on the technological aspect (Bennett, 2023) to bring about 

innovative change in an organization. As such, Transformational leadership (TL) has been 

widely discussed as an effective leadership style that displays charisma, provides inspiration, 

is visionary and can transform an entire organization when change related to innovation is 

required.  



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Among an organization’s tools for a competitive advantage, is its knowledge base embedded 

in its people or human resources. From a resource-based view perspective, for an organization 

to be sustainable, its people resources must have the necessary tools, knowledge and skillset 

required for growth and innovative development in today’s world (The World Bank, 2023). 

Therefore, through the transformational leadership’s leader-follower or role model approach, 

it is ideal for developing the human resource aspect of any organization (Caputo et al., 2023). 

That is, because of the motivation and intellectual inspiration these leaders provide toward 

future workforce planning, it stimulates employees at all levels to recognize the need for 

change, as seen in Lewin’s unfreezing stage through knowledge acquisition and upskilling in 

AI as it continues to evolve (Indeed Editorial Team, 2023). This is important since leaders can 

help employees adapt to the vision and mission of the organization by providing the training 

required for leveraging AI. Therefore, the learning process for employees according to Koziol-

Nadolna (2020), is organized around the organization’s need to create value through its 

employees or human resource assets to maintain a competitive advantage and sustainability 

using an innovative approach.  

This charismatic approach can further facilitate knowledge creation by developing the personal 

interaction and varying levels of professional relationships and trust building (Ghasabeh, 2020; 

Lis et al., 2015) that will help to mitigate resistance to change. This is facilitated through clear 

and constant communication to all stakeholders that create an environment for a more effective 

technology adoption rate (Dennison, 2023). Therefore, employees begin to see the possibilities 

toward improving the efficiency and effectiveness of their human tasks and are less reluctant 

to changes in the organization’s culture (Wilson and Daugherty, 2018).  

In a business sense, AI can make a valuable contribution to the efficiency and effectiveness of 

a company’s operations as seen in Davenport and Ronanki’ s (2018) adaptation of a Deloitte 

2017 Executive’s poll, illustrated in Figure 2.3. Company leaders expressed their views on the 

benefits of AI to improve the operations of their organization. As such, since transformational 

leaders help to reshape their organization’s business model through effective motivation, 

knowledge sharing and talent development, their leader-follower role can also potentially 

influence their employees in knowledge acquisition and leveraging AI adoption.  

 

 

 



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Figure 2.3 Benefits of AI 

 

Source: Davenport and Ronanki (2018). 

Employees are supported by their leaders through training and development and become 

familiar with latest changes over time (Sreekumar, 2023). This marks the entry of the 

transitioning stage where employees see AI as a beneficial support tool in their functioning and 

where the organization exploits its operational management processes and overall competitive 

advantage opportunities (Townson, 2021). Therefore, both the employer and the employee 

benefit as humans become more skilled and the organization becomes efficient and effective in 

its processes.  

Organizational leaders provide one of its main stakeholders – its employees, with continuous 

support, on-demand training, open communication for transparency, and an opportunity for 

feedback to maintain a trusting and empathetic relationship. This drives the change process to 

its refreezing stage where acceptance occurs. At this stage, transformational leaders use the 

new organizational culture to solidify the desired change toward AI adoption (Raza, 2019). 

This behaviour change is reinforced with a reward mechanism that some organization provide 

to early adopters of change (Addon Skills, 2023).  

Through the lens of a transformational leader, the insightful view of the people’s perspective 

as stakeholders, and as a valuable human resource asset, can be skilfully utilized as a catalyst 



13 
 

for AI implementation despite its challenges such as resistance to change at the organizational 

level. However, while leaders maneuver the internal change process, issues such as regulatory 

and legislative frameworks must also be explored as part of the processes that guide the 

implementation of AI-based technology. 

 

2.3.2 The Processes: Toward AI Governance 

As part of the three elements comprising the People, Process and Technology (PPT) 

framework, the process defines ‘how’ the efficiency and effectiveness within the PPT 

framework can be achieved. Additionally, it reviews how to use the people and technology to 

leverage coordination of support to the needs of customers. Regarding AI implementation, the 

regulatory, legislative and governance agendas that guide the development and implementation 

of AI must be considered as part of that coordination process. That is, particularly in healthcare, 

there are major concerns for AI governance such as ethics in patient privacy, confidentiality, 

and risk management (Pusca, 2018) as it relates to AI’s use of real health data. This is the data 

used to develop and train AI algorithmic models for accuracy. However, AI has also provided 

much beneficial support for improved population health management through better patient 

outcomes, reducing healthcare cost, improving diagnostic and treatment interventions (Kwint, 

2023).  

The World Health Organization (2021) indicated that all stakeholders in both the public and 

private sector must be responsible for the ethical impact of AI on all levels of society to ensure 

there is benefit in healthcare. In October 2023, the WHO further recognized the contribution 

of AI to improved health outcomes and have outlined six areas for AI regulations in healthcare. 

Since we seek to discuss the ‘how’ aspect representing the processes involved in implementing 

AI, it is important to review how these regulatory frameworks guide ethical conduct. 

Globally, there is no implemented legislation on AI. However, there have been recent 

legislative developments transitioning toward implementing AI (Whyman, 2023). Several 

countries including the United States, European Union countries and the United Kingdom have 

active regulatory frameworks that guide data privacy and patient confidentiality. However, this 

has been a grey area for AI creators in healthcare as these stakeholders (Lebcir et al., 2021) 

require access to real-time, patient data for developing algorithms and models to train AI. 

Creators and users of AI in healthcare are also held accountable for the use of AI tools as this 

has implications (Habli et al., 2020), including breaches in confidentiality, potential injury to 



14 
 

patients, and litigation for workers. This is due to poorly established ethical standards and 

regulatory frameworks that are either absent or minimal (Ananny and Crawford, 2018). 

Therefore, AI risk related to its reliance on real-time data can pose many challenges for the 

healthcare industry without the right governance features. 

While AI risk can be characterized in several ways including being high or low (Bollan and 

Howie, 2023), it requires a mitigation strategy that prevents or minimizes harm such as 

breaches in human rights and confidentiality at every level of society (NIST, 2023). Hence the 

reason many countries including the United Kingdom have taken on a macro level, Control 

Objectives for Information and Related Technologies (COBIT) approach toward AI 

management (IT Management, 2023). That is, COBIT 19, the latest version, will help to create 

better strategies for IT Governance (White, 2023) and holistically work toward strengthening 

stakeholder relations which would include AI creators, patients, medical staff, government, and 

national regulatory bodies such as the Medicines and Healthcare products Regulatory Agency 

(MHRA) and NHS England. This partnership will develop trust and promote open 

communication (Mohammed et al., 2023) creating stakeholder values and community 

dynamism for continuous feedback and adaptation that will help to mitigate AI risk as part of 

IT governance principles. However, for some stakeholders, the risk appetite may not be as 

significant as others and hence the reason regulatory and legal compliance is as important as 

advocacy for new laws and guidelines that can sustain AI-based medical technology in the 

healthcare industry (ITA, 2023). 

Nuthi (2023) provides a different view for various regulatory partners and stakeholders in the 

UK. Presently, there is a plethora of cross over regulations and statutory acts that already exists 

across various UK industries. Consequently, the UK government’s approach to AI 

implementation will focus on establishing partnerships between regulators and AI creators. 

This way, there is transparency on how regulations affect AI technology development. To this 

end, statutory action will only be considered as a necessity for creating guidelines to empower 

regulators unlike other nations (Connell and Lim, 2023) where legislative action such as where 

the EU’s European AI Act is being proposed. This way, in the UK, the process becomes flexible, 

and AI can continue to evolve and become more sophisticated. Therefore, the UK, recognized 

as a world leader in AI technology, can leverage AI to optimize healthcare outcomes, improve 

decision-making, reduce cost (McKendrick, 2023), ensure sustainability, and maintain 

competitive advantage in the AI world.  



15 
 

The process plays an equally significant role in the TRIAD as it brings together the ‘how’ 

toward achieving AI implementation. The importance of legislation, regulations and IT 

Governance including risk management, improved decision-making, transparency, 

accountability and a feedback mechanism, can create a smoother transition toward AI 

implementation in the UK healthcare industry. Therefore, operational efficiency and 

effectiveness at the industry and governmental levels can achieved for all stakeholders.  

  

2.3.3 Technology: the tools that support human functioning. 

The technology aspect of the TRIAD addresses the tools that are required by the people and is 

supported by the processes involved to create efficiency and effectiveness in an organization. 

It must be well aligned with the business model and overall goals of the organizations to be 

properly utilized for creating a strategic advantage (Scherr, 2023). However, the organization 

must ensure that they understand the problem they are attempting to resolve which for example, 

in the UK healthcare industry, is to improve how care is delivered. The UTAUT Model which 

incorporates individual benefits determined by core determinants and moderators help to 

diffuse the technology acceptance rate through a different lens and provides some clarity in 

understanding how AI tools can be leveraged to bring about efficiency and effectiveness in 

healthcare. Several authors across various disciplines agree that the UTAUT model has its 

usefulness in implementing technology including AI (Andrews et al., 2021; Jain et al., 2022; 

Raffaghelli et al., 2022) which can potentially be utilized in the healthcare industry. 

In the healthcare industry, the design thinking strategy is shifting into the 4.0 industry, defined 

as the latest healthcare technology which includes new digital tools such as AI and automation 

that can make the healthcare industry efficient and effective and promote better decision-

making in the process (Huda, 2023). Hence, new digital business models are leveraging AI as 

an evolving tool to support the work of humans (Han, 2022). For example, AI has already 

begun to transform healthcare through faster diagnostic and treatment capabilities, thus 

reducing the amount of time clinicians spend trying to diagnose and find the appropriate 

treatment options for patients (Alkhaldi, 2023). Therefore, AI is primed to take healthcare 

stakeholders including patients, their families, healthcare providers, organizational leaders and 

Managers, and their hospitals to a whole new level of care (Council Post, 2023). Additionally, 

the application of AI technology in healthcare will also redefine communication, collaboration, 

and the team approach which will help to define the performance expectation for the employee 



16 
 

(Seeber et al., 2020). That is, users of AI will be able to have access to a more efficient and 

effective healthcare system that help them improve how care is delivered. 

Digital transformation using AI, is considered a disruptive communication pathway, and is 

ushering in a reduction in time and cost (Maruping and Magni, 2015) for the people who use it 

(Verhoef et al., 2021). This includes both the employees as internal customers and citizens as 

external customers. Hence, effort expectancy is improved and requires less time from the 

employee to perform a task such as interpreting a radiography scan, thereby freeing the clinical 

staff to perform other human-centric tasks. This is vitally important since many public 

healthcare organizations experience a bottleneck in the time spent waiting for delivery of care. 

This includes the NHS in the UK as seen in Figure 2.4 where waiting time is a key factor to 

the delivery of care. However, with the support of AI tools, waiting time backlog can potentially 

be reduced. Therefore, user behaviour in the application of AI technology rationalization and 

acceptance remains an equally important part of the triad for firm performance (Chakravarty 

et al., 2013). Additionally, patient feedback is used to measure how well care is being delivered 

against the organization’s preset objectives also known as Key Performance Indicators (KPIs) 

(Maxwell and Lamont, 2020). However, patient feedback may not only be influenced by their 

experience but also by social influence that plays a significant role in technology adoption 

(Jacob et al., 2022). 

 

 

 

 

 

 

 

 

 

 

 



17 
 

Figure 2.4 Number of patients waiting for consultant-led elective care in the NHS.  

Source: BMA (2023). 

As AI continues to influence a change in thinking at the societal, organizational, national, and 

international levels, each influential stakeholder plays a fundamental role on the use and impact 

of AI-based technology (Kelly et al., 2023). When these stakeholders such as employees 

recognize how beneficial technology has been to others, this influences the adoption rate and 

helps to mitigate the resistance to change (Scholkmann, 2020). However, some authors argue 

that employees may not be as easily convinced, and this may require organizations to find other 

facilitating conditions such as incentives, communication, training, and input in the decision-

making process (Richter and Sinha, 2020; Damawan and Azizah, 2020; Sharp, 2023) to 

influence behaviour change. While this may provide some influence over the technology 

adoption among employees, the UTAUT model identified moderators that also support the 

adoption process and are linked to each of the core determinants. These include age, gender, 

experience, and voluntariness of use (Marikyan and Papagiannidis, 2023). While these 

determinants may have been applied to employees, some authors argue that these mediators 

also apply to citizens as they also play a significant role in technology adoption and use (Guo, 

2015; Natarajan et al., 2017; Tsourela and Roumeliotis, 2015). Therefore, their relevance must 

not be overlooked as they too can be considered the end-users of the technology and affect 

adoption rates. 



18 
 

2.4 Conclusion 

Investment in the right technology, which provides the right tools to the people, also help to 

define the right processes that support those tools grounded in AI technology. Throughout this 

people, process, and technology framework, is the integration and alignment of various theories 

and models including Stakeholder and Transformational Leadership theories, Resource-Based 

View, Lewin’s Change Management, IT Governance and COBIT 2019 and the UTAUT model 

that sets the foundation for operational efficiency and effectiveness to the implementation of 

AI-based technology.  

Various barriers, and facilitators to implementing AI-based technology were identified, and 

mitigating factors explored through the lens of various authors in this literature review utilizing 

the perspective of the People, Process and Technology triad. However, while the application of 

this triad framework has been useful in analysing the existing literature on AI and its 

implementation challenges, there is still a need for further research on the topic. That is, 

although the literature explains what challenges currently exists, our understanding is still 

limited since AI represents a new and constantly evolving form of medical technology for the 

healthcare industry. Therefore, further research is required to analyse the challenges of 

implementing AI-based medical technology within the public healthcare industry in the United 

Kingdom. 

 

 

 

 

 

 

 

 

 

 



19 
 

Chapter 3: Systematic Analysis of Literature 

3.1 Introduction 

The aim of this chapter is to develop a detailed learning on the challenges of implementing AI-

based medical technology within the healthcare industry. To accomplish this, a systematic 

analysis method was chosen as it offers a comprehensive scope of literature that captures the 

relevant information required for a holistic view (Chigbu, Atiku and Du Plessis, 2023) of the 

challenges to implementing AI in the healthcare industry. The Preferred Reporting Items for 

Systematic Reviews and Meta-Analysis (PRISMA) framework was used to capture the relevant 

information for this research topic as its methodology follows a rigorous strategy for data 

extraction that minimizes bias in data collection from its pre-set inclusion and exclusion criteria 

and maximizes its objectivity (Sinha, Menon and Denny, 2022) as illustrated in Tables 3.1 and 

3.2 respectively. Thus, reliability and validity of the research are preserved. Through this 

robust, structured approach, various themes emerged as seen in Figure 3.2, that helped to 

identify the various challenges, benefits, and influential factors to AI implementation in the 

healthcare industry. Additionally, this method was chosen for its reproducibility and 

transparency (Shokraneh, 2019) that can easily allow other researchers to reproduce the 

research thereby making it credible and a significant contributor to a wider body of knowledge 

on the challenges to implementing AI-based medical technology.  

 

3.2 Scope of the Study 

3.2.1 Keyword and Search Terms 

The keywords and Boolean operators utilized in this research paper were determined based 

on the aim and objectives of the topic. The key words selected included the following: 

artificial intelligence, healthcare industry, medical technology, implementation challenges, 

improving implementation, AI-based medical technology, UK healthcare industry and NHS 

healthcare system. The Boolean operators used were “AND” and “OR” which were utilized 

to broaden the search as seen in Table 3.3. Three online databases were used, and the same 

key words were used across each one to maintain a search standard. The search engines 

included were Scopus, PubMed and ResearchGate. 

 

 

 



20 
 

3.2.2 Inclusion Criteria 

Table 3.1 

 

Source: Author generated (2023). 

 

3.2.3 Exclusion Criteria 

Table 3.2 

Source: Author generated (2023). 



21 
 

3.3 Result Based on Keywords Search 

Table 3.3

 

Source: Author generated (2023). 

 

3.4 Search Result Strategy 

As reflected in the PRISMA Statement below, 3,753 articles were identified from the data 

bases identified in Table 3.3 and none from other sources. There were 3,580 articles after 

duplicates were removed. However, 2,920 articles were further excluded based on the criteria 

identified in Table 3.2. However, from this figure, sixty-six articles were further selected 

based on the availability of full text articles and research methodology. The final ten (10) 

articles were selected based on a more in-depth reading and further exclusion based on issues 

such as non-availability of information to complete the descriptive summary of the journal 

articles. For example, no participant numbers or the article was not related to healthcare. 

Overall, most articles were excluded due to them being systematic reviews. 

 

 

 

 

 



22 
 

3.5 PRISMA Statement 

Figure 3.1 PRISMA 2009 Flow chart 

 

 

 

Source: Adapted from Moher et al. (2009). 

 



23 
 

3.6 Descriptive Summary of Journal Articles 

The following tables reflect a synopsis of each full-text article selected for this systematic 

review as highlighted in the PRISMA Statement. It provides an insightful view on AI as it 

relates to the healthcare industry. Each article provides a unique perspective on the 

implementation of AI in the healthcare industry as it relates to challenges, benefits and 

influential factors which is later reflected in Figure 3.2 as a thematic analysis uncovered in 

the selected articles. 

Table 3.4.1 

Journal Article # 1 

 

Author(s) and year of 

publication 

 

 

Petersson et al., 2022 

Journal BMC Health Services Research 

Article Title 

 

Challenges to implementing artificial intelligence in 

healthcare: a qualitative interview study with healthcare 

leaders in Sweden 

Sample Size & Participants 26 

Healthcare leaders 

Country of research Sweden 

 

Research Purpose: To explore the challenges perceived by leaders in regional 

Swedish healthcare setting in relation to the implementation 

of AI 

 

Research Method 

 

Semi-structured interviews using phone or video 

communication 

 

Key Findings The authors identified four main challenges to implementing 

AI which included (1) conditions external to the healthcare 

system such as laws and policies (2) internal capacity for 

strategic change management such as culture and 

performance (3) transformation of healthcare professions 

through leadership and (4) healthcare practices that could 

potentially reduce barriers such as communication and a 

collaborative approach.  

 

Theoretical Perspective Diffusion of Innovation Theory 

Theory of Organizational readiness for change 

Stakeholder Theory 

Transformational leadership 

Source: Author generated (2023). 



24 
 

Table 3.4.2 

Journal Article # 2 

 

Author(s) and year 

publication  

Sun, 2021 

 

Journal International Journal of Environmental Research and Public 

Health 

Article Title  

Adopting Artificial Intelligence in Public Healthcare: The 

Effect of Social Power and Learning Algorithms 

 

Sample Size & Participants 24 

Healthcare providers and patients in three (3) hospitals 

Country of research China 

 

Research Purpose To determine how social power among various stakeholders 

affect IT adoption in healthcare 

 

Research Method Semi-structured interviews and participative observation 

Key Findings The research was conducted in three Chinese hospitals all of 

whom used AI systems provided by the same supplier. The 

research highlighted the applicability of social power in 

relation to resistance to change and adoption of AI into 

healthcare. Influence from various stakeholders on adoption 

rate was referred to as either being high or low based on the 

influencers’ knowledge and skills. Low level learning was 

found among medical staff users and patient users who were 

influenced by other users while high level were medical staff 

influenced by reward and legitimate power systems 

 

 

Theoretical Perspective Stakeholder theory 

Social power theory 

Source: Author generated (2023). 

 

 

 



25 
 

Table 3.4.3 

Journal Article # 3 

 

Author(s) and year of 

publication 

Cornelissen et al., 2022 

 

 

Journal JMIR Formative Research 

Article Title The Drivers for Acceptance of Artificial Intelligence-

Powered Care Pathways Among Medical Professionals: 

Web-Based Survey Study 

 

 

 

Sample Size & Participants 67  

Medical professionals or other hospital staff employed at a 

Dutch hospital and worked on improvement of quality 

healthcare projects 

 

Country of research Netherlands 

Research Purpose To identify the factors that influence the acceptability of AI-

based technology among medical professionals in the 

Netherlands 

 

Research Method Web-based survey 

Key Findings The researcher utilized the UTAUT theory as the lens 

through which to examine technology adoption in medical 

professionals using patient care pathways. This pathway is 

considered a useful technology tool in creating efficiency in 

the healthcare given the existence of an aging population and 

increasing life expectancy in the Netherlands. The findings 

concluded that predictor variable such as medical 

performance expectancy, nonmedical performance 

expectancy, performance expectancy, facilitating conditions, 

perceived trust, and professional identity to technology 

adoption were much better when it comes to behaviour 

changes to adoption. 

 

Theoretical Perspective 

 

UTAUT Model 

Stakeholder theory 

Source: Author generated (2023). 



26 
 

Table 3.4.4 

Journal Article # 4 

 

Author(s) and year of 

publication 

 

Fritsch et al., 2022 

 

Journal Digital Health 

 

Article Title Attitudes and perceptions of artificial intelligence in 

healthcare: A cross-sectional survey among patients 

Sample Size & Participants 452  

 

Patients and their companions at a German tertiary level 

hospital 

 

Country of research Germany 

Research Purpose To evaluate where patients in general locate themselves on 

the spectrum of using AI in healthcare  

 

Research Method Cross-sectional study using a paper-based questionnaire 

Key Findings The researchers examined the view of patients and their 

partners’ perception of AI in healthcare. The majority 

respondents indicated they recognized there is benefit in AI 

use in terms of diagnosis and therapy. However, they also 

indicated a preference for a final decision to come from a 

physician. Significant to the research, there was more 

scepticism among elderly females, less educated persons, 

and those with a low use of technology.  

 

Theoretical Perspective Stakeholder theory 

Source: Author generated (2023). 

 

 

 

 

 

 



27 
 

Table 3.4.5 

Journal Article # 5 

 

Author(s) and year of 

publication 

 

Yu and Li, 2022 

Journal Behavioural Sciences 

 

Article Title Artificial Intelligence Decision-making Transparency and 

Employees’ Trust: The Parallel Multiple Mediating Effects 

of Effectiveness and Discomfort 

 

Sample Size & Participants 235 

 

Both male and female persons with previous work 

experience 

 

 

Country of research China 

Research Purpose The research focused on the human and AI collaboration 

where AI was the primary decision-maker and how 

transparency in AI decision-making affects employees’ trust 

in AI 

 

Research Method Experimental vignette methodology with online recruiting 

Key Findings This research suggests that more transparency in the AI 

decision-making process increases the trust of employees in 

AI decision-making and perceived effectiveness. The 

authors explored the topic from two perspectives, the 

cognitive and emotional aspects which contributes to the 

trust employees develop in AI decision-making. 

Theoretical Perspective Social Identity Theory; Stimulus Organism Response (SOR) 

theory; Algorithmic reductionism 

Source: Author generated (2023). 

 

 

 

 



28 
 

Table 3.4.6 

Journal Article # 6 

 

Author(s) and year of 

publication 

 

Petersson et al., 2023 

Journal Digital Health 

Article Title Ethical considerations in implementing AI for mortality 

prediction in the emergency department: Linking theory and 

practice 

 

 

Sample Size & Participants 18 

 

Healthcare managers and healthcare professionals connected 

to emergency room settings in southern Sweden 

Country of research Sweden 

Research Purpose To use the implementation of AI to predict patient outcomes 

at the Accident and Emergency department and to develop a 

model to guide ethical considerations  

 

Research Method Semi-structured interviews using phone and video calls 

Key Findings The research determined that while ethics plays a crucial 

role in patient care and decision-making, an additional 

principle is required. Professional governance was proposed 

as such and is expected to address the ethical conflict that 

can potentially occur between human healthcare providers’ 

expert knowledge and the information that AI applications 

provide as a decision about a patients’ survival rate and/or 

plan of care. They also found that AI can assist in preventing 

and managing challenges such as lack of communication, 

collaboration across departments and legal regulations that 

could affect how decision-making occurs. 

 

Theoretical Perspective Ethical theory 

Source: Author generated (2023). 

 

 



29 
 

Table 3.4.7 

Journal Article # 7 

 

Author(s) and year of 

publication 

Rainey et al., 2021 

 

Journal Frontiers in Digital Health 

Article Title Beauty is in the AI of the Beholder; Are we ready for the 

clinical integration of Artificial Intelligence in radiography? 

An Exploratory Analysis of Perceived AI Knowledge, Skills, 

Confidence, and Education Perspectives of UK 

Radiographers 

 

Sample Size & Participants 411 

All UK based Radiographers, including students and retirees  

 

Country of research United Kingdom 

Research Purpose To determine the perceived knowledge, skills, and 

confidence in AI amongst UK Radiographers and highlight 

priorities for educational provisions to support a digital 

healthcare ecosystem 

 

Research Method A Survey using a snowball sampling  

Key Findings The research was centred around Radiology and 

Radiography staff and their perception on using AI in 

clinical practice. Threat to job security and advanced role 

development were the two major perceptions on AI 

adoption. However, both were open to training and 

education on AI use and felt it should be done at the 

undergraduate, pre-registration period. The research also 

found a relationship between males having a higher 

confidence in AI use as opposed to females. Age also was 

significant where younger persons and those with fewer 

years’ experience felt less confident in AI use. Overall, both 

groups verbalized that there is a need for training and 

education to increase confidence and use and they want to be 

included in the designing, developing and decision-making 

process of AI in their field. 

  

Theoretical Perspective UTAUT theory, stakeholder theory 

 

Source: Author generated (2023). 



30 
 

Table 3.4.8 

Journal Article # 8 

 

Author(s) and year of 

publication 

Rivera et al., 2021 

 

Journal Therapeutic Innovation and Regulatory Science 

Article Title Advancing UK Regulatory Science Strategy in the Context 

of Global Regulation: A Stakeholder Survey 

Sample Size & Participants 145 

Persons with expertise in regulatory science and in 

developing or applying regulations in healthcare and 

included UK healthcare professionals, academics, patients, 

health technology assessment agencies, ethicists, trade 

enterprises. It also included international regulators, 

pharmaceutical companies and small or medium enterprises 

with regulatory expertise. 

 

Country of research United Kingdom 

Research Purpose To identify the most accurate definition of regulatory science 

as it relates to AI and innovation that will allow for the 

exploration of training and infrastructure needs to advance 

the UK in regulatory science. 

 

Research Method Survey  

Key Findings The researchers found that regulatory science must be 

clearly defined to create a framework that is most applicable 

for the UK to maintain competitive advantage in innovation. 

They identified several areas including: 1. the need for 

flexibility within the framework to be able to adapt to novel 

innovative product. 2. Collaboration across sectors including 

patients in developing an appropriate framework and the 

required training needed to do so. 3. The framework must be 

able to weigh the risks and benefits of innovative products 

and data sharing. When this is achieved, patients would be 

able to benefit from timely access to innovative products and 

healthcare providers would have the necessary information 

to make informed decisions on care. 

 

Theoretical Perspective Stakeholder Theory 

Source: Author generated (2023). 



31 
 

Table 3.4.9 

Journal Article # 9 

 

Author(s) and year of 

publication 

Kulkov et al., 2023 

 

Journal International Journal of Entrepreneurial Behaviour and 

Research 

Article Title Stand-alone or run together: artificial intelligence as an 

enabler for other technologies 

 

Sample Size & Participants 22 

Persons who had the experience of integrating or applying a 

solution based on innovative technology such as blockchain, 

virtual reality or Internet of Medical Things (IoMT), with AI 

in the healthcare industry 

 

Country of research USA, EU, Asia, and South America 

Research Purpose To examine the role of artificial in transforming the 

healthcare sector 

 

Research Method Exploratory qualitative methodology using a specialized 

podcast platform 

 

Key Findings The researchers identified that AI coupled with evolving 

technologies such as blockchain, created value for the 

organization by enhancing data security and patient privacy. 

AI powered devices created better risk reduction strategies 

for organizations and optimized patient outcomes. However, 

creating an organizational culture receptive to this evolving 

technology required leadership that encourages stakeholder 

engagement, communication, collaboration and training that 

would build trust and mitigate the implementation 

challenges for AI and other technology integration in 

healthcare. 

 

Theoretical Perspective Influence model in Change management 

Resource Based View (RBV) Theory 

Service-Dominant logic 

Source: Author generated (2023). 



32 
 

Table 3.4.10 

Journal Article # 10 

 

Author(s) and year of 

publication 

 

Litchfield et al., 2023 

 

Journal Health and Social Care in the Community 

Article Title  

“Trying to find people to fit tech…”: A Qualitative 

Exploration of the lessons Learnt Introducing Artificial 

Intelligence-Based Technology into English Social Care 

 

 

Sample Size 18 

Operational leads, decision makers and care staff were 

identified as key stakeholders who used sensor-based 

technology (SAT) powered by AI to collect patient data in 

their home 

 

Country of research United Kingdom 

Research Purpose The research focused on the potential benefit of new and 

emerging AI technology that can collect and provide home-

based evidence to support the potential needs of patients at 

home over time.  

 

Research Method Interviews  

Key Findings  The use of technology in healthcare can help to improve the 

lives of patients and create value for an organization by 

saving time and reducing cost. However, challenges such as 

a lack of expert knowledge, an inconsistent evidence base 

for best-practice standards and the rapid emergence of 

innovative technology contribute to a slow uptake by 

healthcare organization and resistance in the workforce. The 

public must also feel there is privacy, and their data is secure 

with minimal risk.  

The researchers found that a shift in organizational culture, 

more collaboration and communication among healthcare 

partners, training and upgraded infrastructure regarding 

legislation and policies, and early workforce engagement 

may reduce the perceived risk in AI implementation in 

healthcare 

 

Theoretical Perspective Stakeholder theory 

Source: Author generated (2023). 



33 
 

3.7 Thematic Analysis of Findings 

 

AI-based 
medical 

technology in 
healthcare

Challenges to 
Implementing 

AI

Legislation Risk and liability 

Resistance to 

change

Lack of trust, self-
confidence ,  

organizational
culture, poor 

communication

AI Governance & 
Ethics

Accountability, 
Transparency, Patient 

privacy and data 
protection, Risk of AI 

in decision-making

Lack of team 
diversity 

Specialists  with various 
backgrouds such as CIO, 

Policymakers, 

Leaders/CEO  and
patients

Benifits to 
implementing 

AI

Quality healthcare

Patient-centred 
care

Value creation

Enhanced analytic helps 

to create efficiency
and effectiveness in care

Patients
participate in their 

own careBroadens skillset of 
staff

Builds trust and 
confidence in 

use

Influential 
factors for 

successfully 
implementing 

AI

Collaboration & 
Communication

Improves chances 
of adotption, 

should be done 
early in the process

Leadership  that 

develops right 
organizational 

culture

Change should be 
planned, and introduced 

gradually

Ongoing training
and education on AI

Will help 

stakeholders to 
keep up with changes

Having a feedback 
system

Figure 3.2 Thematic Analysis of AI 

Implementation  

Source: Author generated (2023). 

 



34 
 

3.8 Data Extraction and Thematic Presentation 

Table 3.5 Theme 1 

Themes Details of Themes Data Sources by Author(s) and year 

 

 

Theme 1: 

Challenges of 

implementing AI 

Legislation that currently exists 

requires updating and are too 

strict for AI adoption to occur  

Fritsch et al. (2022) 

Petersson et al. (2022) 

Petersson et al. (2023) 

Lack of flexibility in the 

existing legislation and lack of 

further development in the 

existing regulatory framework 

increases the risk involved in 

maintaining patient privacy  

 

Rainey et al. (2021) 

Rivera et al. (2021) 

 

Staff resistance to change 

related to trust 

Fritsch et al. (2022) 

Kulkov et al. (2023) 

Litchfield et al. (2023) 

Petersson et al. (2023) 

Sun (2021) 

Yu and Li, (2022) 

 

Poor self confidence in 

technology use  

 

Rainey et al. (2021) 

 

AI Governance and ethical 

issues related to lack of 

transparency, confidentiality, 

collaboration, communication 

and accountability 

Cornelissen et al. (2022) 

Petersson et al. (2022) 

Petersson et al. (2023) 

Rivera et al. (2021) 

Yu and Li, (2022) 

 

Lack of team diversity Petersson et al. (2022). 

Petersson et al. (2023) 

 

Risk related to AI decision-

making, data protection and 

patient confidentiality issues 

 

Cornelissen et al. (2022).  

Litchfield et al. (2023) 

 

Source: Author generated (2023). 

 

 

 



35 
 

Table 3.6 Theme 2 

Themes Details of Themes 

 

 

Data Sources by Author(s) and year 

 

Theme 2:  

Benefits of AI 

implementation 

Value creation for the 

organization occurs when new 

job positions are created 

 

Litchfield et al. (2023) 

Petersson et al. (2022) 

 

Value is created through 

enhanced data collection, 

security and integration of 

other innovation for better 

planning of patient outcomes. It 

improves efficiency and 

effectiveness of the 

organization.  

 

Cornelissen et al. (2022) 

Kulkov et al. (2023) 

Litchfield et al. (2023) 

Yu and Li, (2022) 

 

Allows patients to participate 

in their own care 

 

Petersson et al. (2022) 

 

Opportunities for quality 

healthcare that is patient 

cantered is created 

Cornelissen et al. (2022) 

Petersson et al. (2022). 

Petersson et al. (2023) 

 

Reduced clinical workload with 

AI analytics that guides clinical 

pathways and decision-making 

 

Rainey et al. (2021) 

Sun (2021) 

 

Allows clinicians more time to 

expand clinical knowledge and 

expertise  

 

Sun (2021) 

 

Broaden the skillset of staff and 

builds trust and confidence in 

use 

 

Petersson et al. (2022) 

 

Source: Author generated (2023). 

 

 

 

 

 



36 
 

Table 3.7 Theme 3 

Themes Details of Themes Data Sources by Author(s) and year 

 

 

Theme 3:  

Influential factors 

that improve AI 

implementation 

A collaborative approach to 

healthcare that involves all 

stakeholders 

Kulkov et al. (2023) 

Litchfield et al. (2023) 

Rivera et al. (2021) 

Sun (2021) 

 

Early communication with all 

stakeholders is required  

Petersson et al. (2022) 

Sun (2021) 

 

 

Creating a feedback system  Petersson et al. (2022) 

 

Leadership must drive the 

development of an appropriate 

organizational culture that is 

receptive to change and AI 

adoption 

 

Kulkov et al. (2023) 

Litchfield et al. (2023) 

Petersson et al. (2022) 

 

Involve staff and patients in the 

decision-making process on AI 

tools and begin staff 

engagement earlier to get buy-

in for AI adoption 

 

Rainey et al. (2021) 

 

Social influence also plays a 

role in AI-based technology 

adoption rates 

 

Sun (2021) 

 

Educate both staff and patients 

on AI use to promote trust, 

transparency and familiarity 

 

Kulkov et al. (2023) 

Petersson et al. (2022). 

 

 Rainey et al. (2021) 

Sun (2021) 

 

Source: Author generated (2023). 

 

 

 

 



37 
 

3.9 Analysis of Findings  

3.9.1 Theme 1: Challenges to implementing AI  

Analysis of the eligible articles in this systematic review found that there were five (5) main 

categories identified in the literature as challenges to implementing AI. Multiple authors have 

identified legislation that requires updating as the main challenge and one that can lead to 

liability (Fritsch et al., 2022; Petersson et al., 2022; Petersson et al., 2023; Rainey et al., 2021; 

Rivera 2021 et al., 2021; Sun 2021). Staff resistance to change was identified in the analysis 

by several authors (Fritsch et al., 2022; Kulkov et al., 2023; Litchfield et al., 2023; Petersson 

et al., 2022; Sun, 2021) who have also linked this to sub-categories such as lack of trust and 

self-confidence, strong organizational culture, poor communication, and late staff 

engagement in the change process (Fritsch et al., 2022; Kulkov et al., 2023; Petersson et al., 

2023; Rainey et al., 2021; Yu and Li, 2022). AI governance and ethics in healthcare were 

other areas identified in the literature in terms of accountability, transparency, patient privacy, 

data protection and risk in AI decision-making that challenges the sustainability of an 

organization pertaining to AI implementation (Cornelissen et al., 2022; Kulkov et al., 2023; 

Litchfield et al., 2023; Petersson et al., 2022; Petersson et al., 2023; Yu and Li, 2022).  

Additionally, a lack of team diversity emerged as a challenge since new skills and knowledge 

about AI technology were required that clinicians did not possess (Petersson et al., 2022; 

Petersson et al., 2023).  

 

3.9.2 Theme 2: Benefit to AI implementation 

On identifying the benefits of implementing AI, several categories and sub-categories 

emerged. However, these benefits also illuminate how to improve the chances of AI 

implementation. The main categories emerging from the literature include quality healthcare 

for patients where clinicians could focus on patient-centred care with more time to be spent 

with the patient. This is done through the capabilities of AI analytics that help reduce the 

clinicians’ workload by developing algorithms that create applications such as a care pathway 

that guides clinician decision-making or aids in reading imaging results (Cornelissen et al., 

2022; Petersson et al., 2022; Petersson et al., 2023; Rainey et al., 2021; Sun, 2021). Value 

creation for the organization occurs. This is through enhanced AI data collection, security, 

and integration of other innovation for better planning of patient care that improves patient 

outcome and allows them to participate in their own care (Cornelissen et al., 2022; Kulkov et 

al., 2023; Litchfield et al., 2023; Petersson et al., 2022; Yu and Li, 2022). Also, the 



38 
 

requirement of new knowledge, broadens the job descriptions and skillset and therefore new 

jobs are created due to the role diversity created by AI tools (Litchfield et al. 2023; Petersson 

et al., 2022). 

 

3.9.3 Theme 3: Influential factors that improve AI implementation 

This theme emerged as several factors were identified within the systematic review that 

supports a smoother AI implementation strategy in the healthcare industry. A collaborative 

approach and open communication among all stakeholders including patients, clinicians, 

designers of AI technology, policy makers, and hospital leaders that come together to make 

decisions on how AI should be developed and deployed as part of the patient care decision-

making process (Kulkov et al., 2023; Litchfield et al., 2023; Petersson et al., 2022; Rainey et 

al., 2021; Rivera et al., 2021; Sun, 2021). Having a leadership that can develop the right 

organizational culture was also determined by the literature as a key aspect to facilitate 

technology adoption within an organization (Kulkov et al., 2023; Litchfield et al., 2023; 

Rivera et al., 2021). However, this change should be planned and introduced early to the 

workforce as a strategy for buy-in and implemented on a gradual basis so that learning can 

take place (Kulkov et al., 2023; Rainey et al., 2021; Rivera et al., 2021). Additionally, since 

AI technology is a rapidly growing, yet newly developed field, ongoing education and 

training was identified as an important aspect to implementation since it would allow for trust 

development and transparency that would help to improve adoption of AI-based medical 

technology (Kulkov et al., 2023; Petersson et al., 2022; Rainey et al., 2021; Rivera et al., 

2021; Yu and Li, 2021).  

 

 

 

 

 

 

 

 

 



39 
 

CHAPTER 4:  

Discussion, Conclusion & Recommendation 

 

4.1 Introduction 

This chapter consists of three main sections consisting of the discussion, conclusion and 

recommendations. The discussion section focuses on the findings of chapter three and defines 

whether it aligns with the theories or literature from chapter two. The conclusion section 

outlines whether the pre-set aim and objectives were met, unmet or partially met based on the 

findings of chapter three. The recommendations are based on the conclusion section as it 

relates to the findings of chapter three and contributes to answering objective four as stated in 

in chapter one. 

 

4.2 Discussion 

4.2.1 Theme 1: Challenges to implementing AI 

Legislation, governance issues and resistance to change, were the main categories emerging 

from the research findings in the preceding chapter. Risk related to AI decision-making, data 

protection and patient confidentiality issues (Cornelissen et al., 2022; Litchfield et al., 2023) 

amidst the rapid emergence of AI, along with regulations being too strict for AI adoption to 

occur (Fritsch et al., 2023; Petersson et al., 2022; Petersson et al., 2023) were among the major 

concerns surrounding regulations and lack of legislative structures to guide the AI 

implementation process. The findings from the systematic review suggests that a lack of 

flexibility in the existing legislative framework and further development in the existing 

regulatory framework (Rainey et al., 2021; Rivera et al., 2021) increases the risk involved in 

maintaining patient privacy. Therefore, the findings suggest there is need for improvement in 

AI standardization and accountability that can help to improve AI-based technology 

implementation in the UK.  

Additionally, healthcare leaders are required to drive the implementation process through a 

transformational leadership approach and are expected to motivate employees toward 

developing an appropriate organizational culture (Kulkov et al., 2023). The systematic review 

also identified a lack of collaboration (Petersson et al., 2022; Petersson et al., 2023; Rivera et 

al., 2021) and poor communication with other stakeholders including patients (Rivera et al., 



40 
 

2021) as barriers to having timely access to innovative AI-based medical technology. This 

finding was also supported in the literature review (chapter 2) and described as a third culture 

between AI and humans (Kolbjornsrud et al., 2017). Therefore, through a transformational 

leadership approach, AI-based technology can be influenced by these leaders who have the 

capabilities to motivate their staff and foster a collaborative environment with better 

communication. This finding is also supported theoretically in the literature review by Khan et 

al. (2020) in the literature review. 

Resistance to change due to a lack of trust, self-confidence in technology use, and poor 

communication (Fritsch et al., 2022; Petersson et al., 2023; Rainey et al., 2021; Sun, 2021; Yu 

and Li, 2022) were some of the challenges identified in the systematic review. That is, staff 

concerns such as too much confidence being placed in AI decision-making and how to go about 

sharing AI decisions with patients (Petersson et al., 2023), a lack of transparency (Yu and Li, 

2022) and poor communication (Kulkov et al., 2023; Sun, 2021) contributed to poor AI 

adoption rates among staff.  

Additionally, poor knowledge and self-confidence in AI use contributed toward the fear of job 

loss (Rainey et al., 2021) and added to an employee’s distrust in AI. Furthermore, the findings 

from chapter three concluded that the four intermediate variables of age, gender, experience 

and volunteerism utilized in the UTAUT theory (Ayaz and Yanartaş, 2020) as identified in 

chapter two, does have some level of influence over an employee’s acceptance and use of AI 

technology. Similarly, patients who have distrust in AI decision-making as seen in the German 

study (Table 3.4.4), also displayed features of the UTAUT theory’s intermediate variables, 

namely age and gender (Fritsch et al., 2023). Therefore, the studies in both the systematic 

review by Rainey et al. (2021) and those found in the literature review (Andrew et al., 2021; 

Jain et al., 2022; Raffaghelli et al., 2022)   conclude that intermediate variables such as age, 

gender, experience and volunteerism as seen in the UTAUT theory (Ayaz and Yanartaş, 2020) 

does have a significant impact on the adoption rate of technology. 

A lack of team diversity was identified in the systematic review (Petersson et al., 2022; 

Petersson et al., 2023) as having an impact on AI implementation. That is, given that AI-based 

medical technology has been rapidly expanding, new skillsets and knowledge are required that 

go beyond the scope of clinicians (Sun, 2021). Therefore, a skill gap is created that can further 

exacerbates the staff shortages if new AI specialist roles are not created (Rainey et al., 2021). 

This finding on lack of team diversity was not identified in the literature review and therefore 



41 
 

represents new evidence about the challenges to AI implementation. Therefore, this unique 

finding from the systematic review can help healthcare organizations to minimize some of the 

challenges faced when implementing AI-based technology in healthcare. 

4.2.2 Theme 2: Benefit to AI implementation 

The findings from the systematic review indicated that value is created for the organization 

when new job positions (Litchfield et al., 2023) are created. That is, clinically unrelated 

skillsets through knowledge acquisition from training (Kulkov et al., 2023) can broaden the 

human resource value of the organization (Yu and Li, 2022) and its sustainability (Litchfield et 

al., 2023) which aligns with Lewin’s unfreezing stage in the literature review where knowledge 

acquisition takes place in the change management process (Koziol-Nadolna, 2020).  

Additionally, AI’s analytical role is two-fold and perceived as beneficial for both patients and 

staff. Patients can participate in their own care through digital literacy (Petersson et al., 2022) 

and AI tools that guide their home-based, tailored care plan which only prompts them, when 

necessary, to seek hospital treatment. This analytical feature reduces hospital bottlenecks and 

waiting time while improving efficiency and effectiveness. Staff performance optimization is 

also improved as quality time is spent directly with patients (Kulkov et al., 2023) while AI 

analytics help to optimize diagnostic and patient care pathways. Thus, allowing healthcare 

providers time to further expand their clinical knowledge and expertise toward improving 

patient outcomes (Sun, 2021).  

 

4.2.3 Theme 3: Influential factors that improve AI implementation 

Several authors from the systematic review identified early communication, collaboration, a 

planned, gradual introduction to AI use over time, coupled with ongoing training and 

education and a robust feedback system as important leadership strategies (Kulkov et al., 

2023; Litchfield et al., 2023; Petersson et al., 2022) that influence the AI adoption rate. These 

strategies not only utilize the tenets of Lewin’s change management strategy of unfreezing, 

transitioning and refreezing, but introduces opportunities for building trust, transparency that 

helps to maintain that broader collaboration open communication. Likewise, Sun, (2021) 

argues that social influence also improves the adoption rate of AI among clinicians and 

patients, although propelled by different tactics such as other users and a reward system. 

Moreover, while several influential theories were previously identified, Kulkov et al. (2023), 



42 
 

utilized the resource-based view to discuss the role of the people as it pertained to creating 

value for the organization. They found that while human resource is vital, it is equally 

important that AI coupled with evolving technology such as blockchain, will also optimizing 

the patient’s outcome and should be incorporated into the ongoing training of staff for a more 

receptive implementation of AI into the organizational culture. The evidence presented by 

Kulkov et al. (2023), where innovative technology such as blockchain is incorporated into the 

ongoing training and development of staff represents new information that can influence the 

AI implementation process. This type of visionary training which incorporates the latest 

technology, can transform an entire organization’s culture. Therefore, transformational 

leadership as identified in the literature review by Bennett (2023), also aligns with the 

findings of the systematic review since innovative change and visionary characteristics define 

this type of leadership. 

A critical analysis of the findings revealed that there are several influential factors to 

mitigating the challenges of implementing AI-based medical technology. These include a 

transformational leader who has vision and understands the operational environment and can 

deploy a well-executed strategy grounded in theory. Therefore, Stakeholder, Transformational 

leadership and Unified Theory of Accept and Use of Technology theories along with Lewin’s 

change management have proven its usefulness in innovation and its tenets are aligned to 

some of the findings in the systematic review. These findings from the systematic review are 

illustrated in Figure 3.2. and in the literature review and illustrated in Figure 1.1 for clarity. 

 

4.3 Limitations 

The articles reviewed for this research paper were among hundreds of studies examining AI 

implementation and its challenges. However, many of those papers were completed using a 

systematic review as its methodology and could not be utilized for this study. Therefore, this 

proved to be a limitation for this research since studies using systematic reviews were 

excluded from the search criteria. There were even fewer articles available from the literature 

search when this exclusion criteria were applied. This limitation further provides an 

opportunity for other researcher to conduct primary research in the field of AI 

implementation as few studies collected primary data to analyse the challenges of 

implementing AI. 

4.4 Conclusion 



43 
 

This systematic review, with an aim of analysing the challenges of implementing AI-based 

medical technology within the public healthcare industry in the United Kingdom, concluded 

that the aim and objectives of this paper were fully met.  

Objective 1: To identify the challenges affecting the implementation of AI-based 

technology within the healthcare industry. 

The challenges that affect the implementation of AI-based technology within the healthcare 

industry were identified. One such challenge was legislation that requires updating. This was 

identified in the systematic review findings as the main challenge (Fritsch et al., 2022; 

Petersson et al., 2022; Petersson et al., 2023; Rainey et al., 2021; Rivera 2021 et al., 2021; Sun 

2021). Another major challenge was staff resistance to change (Fritsch et al., 2022; Kulkov et 

al., 2023; Litchfield et al., 2023; Petersson et al., 2022; Sun, 2021). Staff resistance to change 

was linked to a lack of trust and self-confidence, strong organizational culture, poor 

communication, and late staff engagement in the change management process (Fritsch et al., 

2022; Kulkov et al., 2023; Petersson et al., 2023; Rainey et al., 2021; Yu and Li, 2022).  

  

Objective 2: To analyse the effects of those challenges on the healthcare industry.  

The effects of those challenges affecting the implementation of AI-based technology within the 

healthcare industry were analysed. The effects of those challenges were poor AI governance 

and inappropriate ethical behaviour in healthcare. These were linked to the poor practice of 

accountability, transparency, patient privacy, data protection and increased risk in AI decision-

making. These were also further linked to the sustainability of the organization in which care 

is being transmitted (Cornelissen et al., 2022; Kulkov et al., 2023; Litchfield et al., 2023; 

Petersson et al., 2022; Petersson et al., 2023; Yu and Li, 2022). Additionally, a lack of team 

diversity (Petersson et al., 2022; Petersson et al., 2023) also influenced the challenges to AI-

based technology implementation. That is, since clinicians would require non-clinical 

specialized training beyond their scope, an AI-based skill gap is created that requires an AI 

specialist role to be created (Rainey et al., 2021). 

 

 



44 
 

Objective 3: To assess the factors that can improve the chances of AI-based medical 

technology within the healthcare industry. 

The factors that can improve the chances of AI-based technology within the healthcare industry 

were assessed. Collaboration, and open communication among all stakeholders including 

patients, clinicians, designers of AI technology, policy makers, and hospital leaders in the 

decision-making process (Kulkov et al., 2023; Litchfield et al., 2023; Petersson et al., 2022; 

Rainey et al., 2021; Rivera et al., 2021; Sun, 2021) were found to be influential implementation 

factors. Also, a leader that can develop the right organizational culture (Kulkov et al., 2023; 

Litchfield et al., 2023; Rivera et al., 2021) using a planned, gradual approach (Kulkov et al., 

2023; Rainey et al., 2021; Rivera et al., 2021) were found to be useful factors to AI-based 

implementation. 

Additionally, ongoing education and training was identified as another important aspect to AI-

based implementation since it would allow employees to develop trust and create better 

transparency that would improve the chances of AI-based medical technology (Kulkov et al., 

2023; Petersson et al., 2022; Rainey et al., 2021; Rivera et al., 2021; Yu and Li, 2021).  

In conclusion, the aim of this research paper was achieved. It was established from the 

systematic literature review that issues surrounding legislation and staff resistance to change 

were the major challenges experienced in several countries including Sweden, China, 

Germany, the USA and even in the UK. However, this research has also provided an 

opportunity to contribute to the existing body of knowledge on the challenges to the practical 

implementation of AI-based medical technology. A lack of team diversity was identified as a 

unique finding in this research paper and calls for future studies to verify this finding.  

 

Objective 4: To recommend evidence-based strategies for improving the implementation 

of AI-based medical technology within the UK healthcare industry.  

4.5 Recommendation 

The following recommendations are based on the aim of this paper and on answering 

objective four which is stated above. The related findings highlighted in chapter 3 and related 

conclusion section above, are used below to provide strategies that can improve the chances 

of AI-based medical technology implementation in the UK’s NHS system. 



45 
 

4.5.1 Training and Education: 

Since the NHS is state-funded, the British government should provide ongoing 

training and education for clinical and non-clinical staff on using AI-based medical 

tools that already exist to boost user confidence. Also, gradually providing them with 

latest information on the latest technology would introduce new ideas and concepts on 

how these AI-based tools can benefit them and expand their knowledge base. Training 

and education should also commence as early as the curriculum level in medical-

related training institutes, which can provide the familiarity and grounding required 

for entering the world of work with AI-based medical technology.  

 

4.5.2 Stakeholder Collaboration: 

Collaboration among various AI users including clinicians, and non-clinical staff and 

patients should be encouraged since this will boost confidence and encourage use. It 

will also encourage more excellent dialogue among various stakeholders including AI 

developers, policymakers, users and hospital leaders who provide varying views on 

how implementation should occur and how it will affect them. This way, AI-based 

medical technology can be standardized across all NHS institutions for ease of use. 

4.5.3 Use of expert knowledge: 

Since resistance to change can be challenging to manage without the right expertise, a 

Change Management expert should be utilized for at least a year. This way, the 

change process can be expertly and collaboratively planned and executed on a 

staggered basis so that employees are not overwhelmed but rather eased into 

accepting and using AI-based medical technology. 

 

4.5.4   Creation of new organizational positions:  

Due to the fast-paced development of AI-based medical technology, it is essential to 

have expert guidance on how, what and when AI-based medical technology should be 

integrated into the healthcare system. Therefore, new positions should be created, 

such as AI Implementation Managers who support the Chief Information Officer’s 

(CIO) role. This new position can manage the AI integration process and other 

unforeseen infrastructural challenges that may arise while allowing the CIO to 

continue focusing on other pertinent technology matters. 

 



46 
 

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https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces
https://core.ac.uk/download/pdf/301368795.pdf
https://www.researchgate.net/publication/360918128_Artificial_Intelligence_Decision-Making_Transparency_and_Employees'_Trust_The_Parallel_Multiple_Mediating_Effect_of_Effectiveness_and_Discomfort
https://www.researchgate.net/publication/360918128_Artificial_Intelligence_Decision-Making_Transparency_and_Employees'_Trust_The_Parallel_Multiple_Mediating_Effect_of_Effectiveness_and_Discomfort
https://www.researchgate.net/publication/360918128_Artificial_Intelligence_Decision-Making_Transparency_and_Employees'_Trust_The_Parallel_Multiple_Mediating_Effect_of_Effectiveness_and_Discomfort
https://doi.org/10.1016/j.technovation.2023.102747


59 
 

 

Appendix I 

 

 

Business School Research Ethics Committee 

No Specific Ethics Risk Declaration 

Student Name/SID Gail Narinesingh, 2018091 

Course MBA in Hospital and Health Services Management 

Unit BSS056-6 Theory into Practice Project 

Research Project Title A critical analysis of the challenges around the practical implementation of 

AI-based medical technology in the public healthcare industry in the 

United Kingdom. 

 

In signing this declaration I am confirming that my proposed project does not involve:  

• Primary research 

• Access to identifiable personal data for living individuals not already in the public 

domain 

• Ownership of the original data not acknowledged 

• Research into potentially sensitive areas (e.g., sexual activity, substance abuse) 

My proposed project does not therefore require an ethics review and I have not submitted a 

Research Ethics Application Form. 

If any changes to the project involve any of the criteria above, I undertake to resubmit the 

project for approval. 

 

Signature of Student:       Date: 25/10/2023 

 

In signing this Declaration I confirm that I have reviewed the proposed project and am 

satisfied that that it does not involve any specific ethics risk as defined by the University of 

Bedfordshire (https://www.beds.ac.uk/research-ref/rgs/support). 

 

Signature of Unit coordinator/Supervisor:    Date: 02.11.23 

https://www.beds.ac.uk/research-ref/rgs/support