From fable to reality; Artificial Intelligence in
healthcare
Research on the controversial nature of Artificial Intelligence in healthcare
Name: XXX
Student number: XXX
Course: Quality and safety
Master Healthcare Management
Tutor: R. Medeiros
Date: 05/12/2019
Wordcount: 2172
1
, Introduction
In the near future, we are facing a moment in which the current healthcare system is no
longer sustainable. Populations are ageing rapidly and, as a consequence, the number of
chronic conditions increase at the same pace (Fernandez-Ballesteros, Robine, Walker, &
Kalache, 2013). Healthcare costs are rising due to new, and more expensive, medically
advanced treatments (Weisbrod, 1991). Patient centered care should be the core of
healthcare, empowering patients to be in control of their own care process (Barry &
Edgman-Levitan, 2012). And professionals need to spend a lot of time on administrative
tasks (Carlson, Gries, Yeung, Sullivan, & Garrison, 2014). These are just a few of the
challenges the current healthcare system faces.
One of the initiatives to address those challenges is the use of technology, and especially
Artificial Intelligence (AI), in healthcare (Øvretveit, Scott, Rundall, Shortell, & Brommels,
2007). AI is the use of computer systems to imitate human cognitive functions in
analyzing health data, including medical diagnostic work, advanced treatment planning,
and predicting outcomes of care (Jiang et al., 2017; Lysaght, Lim, Xafis, & Ngiam, 2019;
Macrae, 2019). AI is said to increase efficiency and safety of healthcare. However,
implementation of AI in the healthcare sector has some challenges that need to be
acknowledged and addressed (He et al., 2019). For example, ethical and privacy
considerations, and the tension between the intended goals of AI and the goals of
healthcare professionals (Char, Shah, & Magnus, 2018). Due to the risks and challenges
involved in the use of AI in healthcare, organizations are reluctant in implementing AI
(Char et al., 2018). The aim of this paper is to investigate the challenges organizations
face in the implementation process of AI. Therefore, this paper will critically elaborate the
following research question:
What barriers must be overcome to successfully implement Artificial Intelligence
in healthcare?
Because of the challenges healthcare systems face, investing in technologies that
positively affect efficiency and safety in healthcare is among the most important
strategies to keep the healthcare system sustainable. However, most studies are only
descriptive in nature. Therefore, this study will add to the existing literature by going
beyond those studies and investigate what challenges organizations face that prevent
effective implementation and acceptance of AI, and how those challenges can be
overcome. The results of this study can therefore be used to develop implementation
programs for AI in healthcare organizations, in order to successfully implement and
embed the technology in daily practice.
The remainder of this paper is organized into four sections. The first section, the
conceptual framework, focusses on organizational becoming, epistemic injustice, ex-post
2
healthcare
Research on the controversial nature of Artificial Intelligence in healthcare
Name: XXX
Student number: XXX
Course: Quality and safety
Master Healthcare Management
Tutor: R. Medeiros
Date: 05/12/2019
Wordcount: 2172
1
, Introduction
In the near future, we are facing a moment in which the current healthcare system is no
longer sustainable. Populations are ageing rapidly and, as a consequence, the number of
chronic conditions increase at the same pace (Fernandez-Ballesteros, Robine, Walker, &
Kalache, 2013). Healthcare costs are rising due to new, and more expensive, medically
advanced treatments (Weisbrod, 1991). Patient centered care should be the core of
healthcare, empowering patients to be in control of their own care process (Barry &
Edgman-Levitan, 2012). And professionals need to spend a lot of time on administrative
tasks (Carlson, Gries, Yeung, Sullivan, & Garrison, 2014). These are just a few of the
challenges the current healthcare system faces.
One of the initiatives to address those challenges is the use of technology, and especially
Artificial Intelligence (AI), in healthcare (Øvretveit, Scott, Rundall, Shortell, & Brommels,
2007). AI is the use of computer systems to imitate human cognitive functions in
analyzing health data, including medical diagnostic work, advanced treatment planning,
and predicting outcomes of care (Jiang et al., 2017; Lysaght, Lim, Xafis, & Ngiam, 2019;
Macrae, 2019). AI is said to increase efficiency and safety of healthcare. However,
implementation of AI in the healthcare sector has some challenges that need to be
acknowledged and addressed (He et al., 2019). For example, ethical and privacy
considerations, and the tension between the intended goals of AI and the goals of
healthcare professionals (Char, Shah, & Magnus, 2018). Due to the risks and challenges
involved in the use of AI in healthcare, organizations are reluctant in implementing AI
(Char et al., 2018). The aim of this paper is to investigate the challenges organizations
face in the implementation process of AI. Therefore, this paper will critically elaborate the
following research question:
What barriers must be overcome to successfully implement Artificial Intelligence
in healthcare?
Because of the challenges healthcare systems face, investing in technologies that
positively affect efficiency and safety in healthcare is among the most important
strategies to keep the healthcare system sustainable. However, most studies are only
descriptive in nature. Therefore, this study will add to the existing literature by going
beyond those studies and investigate what challenges organizations face that prevent
effective implementation and acceptance of AI, and how those challenges can be
overcome. The results of this study can therefore be used to develop implementation
programs for AI in healthcare organizations, in order to successfully implement and
embed the technology in daily practice.
The remainder of this paper is organized into four sections. The first section, the
conceptual framework, focusses on organizational becoming, epistemic injustice, ex-post
2
