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Original Research Article

Digital Transformation Management in Health Services: Health 
Professionals Perceptions as an Implementation Factor

Theodoros S. Tanis*, Chryssoula Chatzigeorgiou, Ioanna Simeli and Evangelia Stalika

 International Hellenic University, Thessaloniki, Greece.

* Corresponding Author Email: thtanis1@gmail.com 

ABSTRACT

Background and Objectives: The explosion of new digital technologies is fundamentally disrupting the world as it has been 
perceived until now, transforming it multilevel and at an unprecedented speed. At the same time, with traditional ways of pro-
viding health services, their quality and scale cannot meet user’s needs and expectations. Within this context of constant search 
for improved quality, the path of health services towards a digital and value-based transformation is now a one-way street, with 
drastic and immediate effects that are capable of disrupting the sector and making it sustainable. The most defining issue is how 
an organization adapts its organizational culture, strategy, and leadership and mostly prepares the staff to operate effectively 
in a digital world, adding value to users and sustaining prosperity. The main goal of this study is to investigate the perceptions 
of health professionals regarding the usability and ease of use of digital transformation applications.

Material and Methods: To investigate the aim of the study, the USE Questionnaire was used. It was distributed completely 
paperless, exclusively through Google forms. For better common understanding, we edited an auxiliary video and embedded it 
in the Google form, to be watched before starting answering it. Our sample was healthcare professionals who worked in various 
Hospitals and health providers in Northern Greece.

Results: Age appears to have a greater influence on health professional self-efficacy. Regardless of specialty, they show posi-
tive perceptions of both the usefulness and ease of use and learning of digital applications. Those with a lower level of education 
showed a higher perceived ease of use and learning, as well as their usefulness, than expected.

Conclusion: The acceptance of digital transformation in healthcare professionals is based on understanding the concerns 
and feelings of insecurity that overwhelm healthcare professionals. Our findings can help us better understand the factors that 
influence their adoption of new digital technologies. Likely, this will help us to reduce the time required to make all the structural 
changes that are necessary, but also to guide us properly for the best use of our already limited available resources. As people 
accept change at different rates, there is no time for delay and their preparation should begin immediately.

Keywords—Digital transformation, Health service management, Healthcare services, Healthcare professional’s percep-
tions, Implementation factors.

Copyright © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - 
Attribution 4.0 International - CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright 
owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduc-
tion is permitted which does not comply with these terms.

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13 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

INTRODUCTION

People adopt new technologies quickly and completely, 
regardless of whether they are intermediate or end users. 
They are more experienced in the use of technology, and 
how organizations take advantage of it, and are becom-
ing increasingly selective and demanding about what 
they are going to use.1 Many, mistakenly believe it will 
be a seamless experience, powerful and adaptable, al-
lowing healthcare professionals to function as they have 
already embraced the digital world in their lives.2 As this 
is complex and the existing structures and cultures of 
healthcare organizations are not sufficient to promote 
and harmoniously integrate innovative functions, the 
simple appearance of new digital technologies does not 
yield the expected service improvement.3 Health services 
are inherently high-risk and have complex structures that 
strongly resist any change.3,4 It therefore seems to make 
no sense to invest in cutting-edge technology if there is 
not the right workforce with the right roles and skills to 
fully exploit its potential for the benefit of patients.4 We 
fully understand that people are the real key to digital 
transformation.5,6

This transition is essentially slowed down by strict 
regulations, the reluctance and resistance to change 
shown by all healthcare stakeholders, thus ignoring the 
importance of changes in the organization’s culture and 
the human factor in an increasingly broad technological 
ecosystem taking shape.7–9 The coronavirus disease has 
forced many healthcare-related processes to move online, 
almost overnight. However, it will take some time to fully 
understand the multiple impacts of the recent digital changes 
that have occurred in response to the current pandemic.10 
Professionals have different interests, perceptions, and 
beliefs. Change management programs focus on trying to 
convince people why they need to change. These reasons 
are usually not in line with their individual interests and 
beliefs. People don’t change unless they want to. They 
have very little confidence in the new environment being 
formed mainly because of all these changing elements 
such as skills, processes, organizational structure, and 
hierarchy.11

However, changing working methods in health ser-
vices is not an easy task for whoever undertakes it. The 

complex organization and high degree of complexity cre-
ated by the variety of professional groups and regulatory 
systems complicates and often precludes the application 
of successful management techniques that perform excep-
tionally well in other forms of organizations. Deep-rooted 
perceptions, organizational norms, and established culture 
complicate and hinder efforts to introduce new systems 
in healthcare.12,13 A primary task of management when 
starting a change process remains to increase the degree 
of emotional attachment of employees, because this not 
only affects their satisfaction, but also the performance 
of each one individually. The effect of an emotional de-
nial from disengaged employees is manifold. Without an 
emotional bond, they are much more likely to simply be 
absent from this endeavor.14

The purpose of this paper deals with the overall context 
of the management practice, during the process of digital 
transformation in health services. The main goal was the 
systematic investigation of the factors that influence health 
professionals in order to be committed and get involved 
in its implementation. For this purpose, the perceptions 
of health professionals regarding the usability of digital 
transformation applications were investigated. Our find-
ings can likely contribute to a broader understanding 
of the factors influencing the adoption of new digital 
technologies by healthcare professionals. In this way, it 
will be possible to reduce the time of carrying out all the 
structural changes that are imposed and also to make the 
most of our already limited available resources.

METHODS

Research Design

To investigate the aim of the study, the USE Question-
naire15 was used. The USE questionnaire (Usefulness, 
Satisfaction, Ease of Use) has been proposed by Lund 
2001 as a tool to categorize user responses into the 4 
dimensions of usefulness (8 questions), ease of use (11 
questions), ease of learning (4 questions) and satisfac-
tion (7 questions). It includes a total of 30 questions, to 
be answered on a 7-level Likert scale.

The questionnaire was distributed completely paperless, 
exclusively through Google forms. The Questionnaire also 

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 14

recorded Demographic Data regarding gender, age with a 
range of ten years, level of education, the Directorate they 
belong to, the hospital they work for, the Health Region 
to which the hospital belongs, and whether they hold a 
position of responsibility. It was possible to answer from 
any PC, or smart device regardless of operating system. 
Each participant had the possibility of a single answer.

For a better understanding of how everyday work is 
changing through digital transformation applications 
and to explore the perceptions of different categories 
of professionals, we had to create an auxiliary video of 
3 min 16 sec duration. The video was embedded in the 
Google form, before the start of the questionnaire and 
immediately after the introductory informational notes. 
Each participant needed to watch it in order to continue 
with the questionnaire answers to participate in the re-
search. At the end of the survey, there was the possibility 
to consent and to state his email in order to be informed 
early of the results of the survey.

Sample–Data Collection

The research lasted 2.5 months and ended a little pre-
maturely due to the special conditions created for health 
professionals due to the COVID-19 pandemic. It was divided 
into two parts. The first part, which lasted two weeks, 
concerns a weighted sample in terms of the composition 
of professionals according to the departments to which 
they belong. The composition of the Directorates of the 
Papageorgiou Hospital was used as a standard sample. So 
initially the questionnaires were sent in digital form to 323 
health professionals who had the following composition: 47 
employees of the Administrative Department, 87 doctors 
of the Medical Department, 169 employees of the Nursing 
Department, 7 employees of the Financial Department, 
4 employees of the IT Department and 9 employees of 
other Directorates. These professionals worked in vari-
ous Hospitals and health providers in Northern Greece. 
Seven days after sending the questionnaire a reminder 
message was sent to complete it.

Data Analysis

For statistical analysis, Chronbach’s alpha test was used 
to check the reliability of the questions of each dimension 
of the questionnaire.16 Independent samples t-tests were 

also used to investigate the variables of gender, Hospital of 
service, and position of responsibility17, while to investigate 
the variables of age, level of education, Department, and 
the HR owned by health professionals, one-way ANOVA 
was used.18,19 To further investigate differences between 
samples Hochberg’s GT2 test was used as the sample sizes 
were dissimilar.20

RESULTS

Descriptive Statistics

Our sample (Figure 1) consisted of 224 health profes-
sionals, 63 men (28.1%) and 161 women (71.9%). Of 
these, 40 (17.9%) were aged 25–35, 85 (37.9%) 36–45, 88 
(39.3%) 46–55 and 11 (4.9%) from 55+ years. 21 (9.4%) 
health professionals belonged to the basic education 
level, 103 (46%) to the Technological (TE) level, while 
30 (13.4%) to the University (UE) level, 58 (25.9%) were 
holders of an MSc degree and 12 (5.4%) PhD holders. 127 
(56.6%) of them worked at Papageorgiou Hospital, while 
the remaining 97 (43.4%) worked at other hospitals in 
Northern Greece. At the same time, 151 (67.4%) belonged 
to the potential of the 3rd Health Region, 65 (29%) to 
the 4th Health Region and 8 (3.6%) to the 6th Health 
Region. 51 (22.8%) held positions of responsibility while 
the remaining 173 (77.2%) did not hold any position of 
responsibility.

Participants’ overall responses to the USE questionnaire 
showed a mean value (M = 5.56, SD = 0.89) (Figure 2). 107 
(47.8%) seemed to strongly agree (M > 5.5), while 200 
(89.2%) agreed (M > 4.5). In the usefulness dimension, 

FIGURE 1. The profile of the average participant.

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15 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

they showed a mean value (M = 5.76, SD = 0.95), and 158 
(70.5%) seemed to strongly agree (M > 5.5), while 204 
(91%) agreed (M > 4.5). In the dimension of ease of use, 
they showed a mean value (M = 5.42, SD = 0.96), and 
115 (51.3%) seemed to strongly agree (M > 5.5), while 
191 (85.2%) agreed (M > 4.5). In the dimension of ease 
of learning, they showed a mean value (M = 5.61, SD = 
1.00), and 139 (62%) seemed to strongly agree (M > 5.5), 
while 192 (85.7%) agreed (M > 4.5). In the dimension 
of satisfaction, they showed an average value (M = 5.45, 
SD = 1.06), and 112 (50%) seemed to strongly agree (M 
> 5.5), while 188 (89.2%) agreed (M > 4.5) (Figure 3).

Reliability 

All dimensions were tested for and found to have ac-
ceptable limits for reliability using Chronbach’s alpha 
test. For the dimension of usefulness, it was found that a 
= 0.94, for the dimension of ease of use it was found that a 
= 0.95, for the dimension of ease of learning it was found 
that a = 0.95, while for the dimension of satisfaction it 
was found that a = 0.96 (Figure4).

Inductive Statistics

Use

Independent samples t-tests (Table 1) were conducted 
to compare gender, Hospital of Service, and position of 
responsibility with usability and usability of digital ap-
plications. There appeared to be no significant difference 
in the overall evaluation of usability and ease of use of 
digital applications between men (M = 5.58, SD = 0.87) 
and women (M = 5.55, SD = 0.90), t(222)= 0.20, p > 0.05, 
between health professionals working at the Papageorgiou 
Hospital (M = 5.52, SD = 0.90) and at the other hospitals (M 
= 5.61, SD = 0.88), t(222) = 0.73, p > 0.05, as and between 
health professionals who hold a position of responsibility 
(M = 5.65, SD = 0.76) and those who do not (M = 5.53, SD 
= 0.92), t(222) = 0.81, p > 0.05.

A one-way ANOVA of the populations was performed 
in order to investigate the effect of age on the usability 
of digital applications (Table 2). The level of significance 
was set at p < 0.05 for all levels. Age appeared to have a 

FIGURE 4. Reliability levels of the USE questionnaire.

FIGURE 2. Radar diagram of the 4 dimensions of the USE 
questionnaire.

FIGURE 3. Levels of agreement by dimension and overall in 
the USE questionnaire.

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 16

significant effect on the overall evaluation of usability 
and ease of use of digital applications F(3.220) = 3.05, p 
= 0.029. Post hoc comparisons using Hochberg’s GT2 test 
indicated that the mean value of age 36–45 (M = 5.33, SD 
= 0.95) (Figure 5) differed significantly from that of age 
46–55 (M = 5.70, SD = 0.90). However, the mean value of 
ages 25–35 (M = 5.66, SD = 0.72) and 55+ (M = 5.82, SD 
= 0.57) did not differ significantly from the other ages 
(Table 3).

The level of education appeared to have no significant 
effect on the overall evaluation of the usability and ease 
of use of digital applications F(4.219) = 0.82, p > 0.05. 
Accordingly, the address to which the health profession-
als belong appeared to have no significant effect on the 
overall evaluation of the usability and ease of use of the 
digital applications F(4.219) = 1.22, p > 0.05, as well as 
the Ministry of Health to which the health professionals 
belong F(2.221) = 0.38, p > 0.05.

TABLE 1. Results of independent samples t-tests for the effect of gender, Hospital & position of responsibility on the dimensions of 
the USE questionnaire.

Levene’s 
Test 
for 

Equality 
of 

Variances

t-test for Equality
 of Means

95% Confidence 
Interval of the 

Difference

F value Significance T value
Degrees 

of 
freedom

Sig. 
(2-tailed)

Mean 
Difference

Std. 
Error 

Difference
Lower Upper

GENDER

USE 0.901 0.344 0.199 222 0.843 0.02634 0.13247 −0.23472 0.28740

USEFULNESS 0.358 0.550 0.096 222 0.924 0.01354 0.14162 −0.26555 0.29264

EASE of USE 2.165 0.143 0.275 222 0.784 0.03915 0.14235 −0.24138 0.31968

EASE of 
LEARNING 0.107 0.744 0.691 222 0.490 0.10266 0.14860 −0.19020 0.39551

SATISFACTION 0.412 0.521 −0.317 222 0.751 −0.04999 0.15751 −0.36039 0.26042

HOSPITAL

USE 0.009 0.923 −0.728 222 0.467 −0.08743 0.12007 −0.32405 0.14920

USEFULNESS 0.246 0.620 −1.426 222 0.155 −0.18237 0.12793 −0.43448 0.06974

EASE of USE 1.176 0.279 −0.379 222 0.705 −0.04900 0.12915 −0.30351 0.20551

EASE of 
LEARNING 0.036 0.849 −0.110 222 0.913 −0.01483 0.13498 −0.28085 0.25118

SATISFACTION 0.107 0.744 −0.725 222 0.469 −0.10350 0.14278 −0.38489 0.17789

RESPONSIBILITY

USE 1.149 0.285 0.812 222 0.418 0.11519 0.14184 −0.16433 0.39471

USEFULNESS 3.583 0.060 1.778 222 0.077 0.26815 0.15078 −0.02900 0.56529

EASE of USE 0.842 0.360 0.912 222 0.363 0.13898 0.15237 −0.16130 0.43925

EASE of 
LEARNING 0.287 0.593 −0.438 222 0.662 −0.06979 0.15944 −0.38399 0.24441

SATISFACTION 3.479 0.063 0.732 222 0.465 0.12343 0.16871 −0.20906 0.45591

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17 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

Ease of Use

Age appeared to have a significant effect on the ease of 
use dimension of digital applications F(3.220) = 3.26, p = 
0.022. Post hoc comparisons using Hochberg’s GT2 test 
indicated that the mean value of age 36–45 (M = 5.18, SD 
= 1.04) differed significantly from that of age 46–55 (M 
= 5.58, SD = 0.94) (Table 4). However, the mean value of 
the ages 25–35 (M = 5.50, SD = 0.78) and 55+ (M = 5.76, 
SD = 0.64) did not differ significantly from the other ages 
(Figure 6).

TABLE 2. Results of the one-way ANOVA for the effect of age, address, grade & HSE on the dimensions of the USE questionnaire.
Sum of 
Squares

Degrees
Of freedom

Mean 
Square F value Significance

Between 
Groups

DIRECTORATE

USE 3.842 4 0.961 1.219 0.304

USEFULNESS 5.839 4 1.460 1.633 0.167

EASE of USE 4.013 4 1.003 1.100 0.358

EASE of LEARNING 1.064 4 0.266 0.263 0.901

SATISFACTION 10.547 4 2.637 2.417 0.050

AGE

USE 7051 3 2.350 3.053 0.029

USEFULNESS 5917 3 1.972 2.217 0.087

EASE of USE 8679 3 2.893 3.263 0.022

EASE of LEARNING 10.569 3 3.523 3.658 0.013

SATISFACTION 5.534 3 1.845 1.664 0.176

EDUCATION

USE 2.592 4 0.648 0.816 0.516

USEFULNESS 2.408 4 0.602 0.662 0.619

EASE of USE 2.246 4 0.562 0.610 0.656

EASE of LEARNING 4.754 4 1.188 1.195 0.314

SATISFACTION 4.317 4 1.079 0.964 0.428

HEALTH REGION

USE 0.610 2 0.305 0.383 0.682

USEFULNESS 2.192 2 1.096 1.214 0.299

EASE of USE 0.487 2 0.244 0.265 0.768

EASE of LEARNING 0.557 2 0.278 0.277 0.758

SATISFACTION 0.931 2 0.466 0.414 0.662

FIGURE 5. Results of the mean of usability and ease of USE by age.

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TABLE 3. Results of Hochberg’s GT2 test for the effect of age on the usability of digital applications.

AGE Mean Difference Standard Error Significance 95% Confidence 
Interval

Lower Bound Upper Bound

25–35 36–45 0.32622 0.16825 0.281 −0.1203 0.7727

46–55 −0.03373 0.16732 1.000 −0.4778 0.4103

55+ −0.15750 0.29873 0.996 −0.9503 0.6353

36–45 25–35 −0.32622 0.16825 0.281 −0.7727 0.1203

46–55 −0.35995 0.13344 0.044 −0.7141 −0.0058

55+ −0.48372 0.28116 0.418 −1.2299 0.2624

46–55 25–35 0.03373 0.16732 1.000 −0.4103 0.4778

36–45 −0.35995 0.13344 0.044 0.0058 0.7141

55+ −0.12377 0.28061 0.998 −0.8685 0.6209

55+ 25–35 0.15750 0.29873 0.996 −0.6353 0.9503

36–45 0.48372 0.28116 0.418 −0.2624 1.2299

46–55 0.12377 0.28061 0.998 −0.6209 0.8685

TABLE 4. Results of Hochberg’s GT2 test for the effect of age on ease of use of digital applications.

AGE Mean Difference Standard Error Significance 95% Confidence 
Interval

Lower Bound Upper Bound

25–35 36–45 0.31578 0.18056 0.398 −0.1634 0.7950

46–55 −0.08512 0.17957 0.998 −0.5617 0.3914

55+ −0.26488 0.32060 0.957 −1.1157 0.5859

36–45 25–35 −0.31578 0.18056 0.398 −0.7950 0.1634

46–55 −0.40090 0.14321 0.033 −0.7810 −0.0208

55+ −0.58065 0.30174 0.289 −1.3814 0.2201

46–55 25–35 008512 0.17957 0.998 −0.3914 0.5617

36–45 0.40090 0.14321 0.033 0.0208 0.7810

55+ −0.17975 0.30115 0.992 −0.9790 0.6195

55+ 25–35 0.26488 0.32060 0.957 −0.5859 1.1157

36–45 0.58065 0.30174 0.289 −0.2201 1.3814

46–55 0.17975 0.30115 0.992 −0.6195 0.9790

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Ease of Learning

Age appeared to have a significant effect on the ease 
of learning dimension of digital applications F(3.220) = 
3.66, p = 0.013. Post hoc comparisons using Hochberg’s 
GT2 test indicated that the mean value of age 25–35 (M 
= 5.86, SD = 0.83) differed significantly from that of age 
36–45 (M = 5.34, SD = 1.05) (Table 5). However, the mean 
values of ages 46–55 (M = 5.72, SD = 1.02) and 55+ (M = 
5.90, SD = 0.50) did not differ significantly from the other 
ages (Figure 7).

However, the level of education did not seem to have 
a significant effect on the dimension of ease of learning 
of digital applications F(4.219) = 1.19, p > 0.05, as well 
as the address to which the health professionals belong 
F(4.219) = 0.26, p > 0.05 and the HSE to which they belong 
F(2.221) = 0.28, p > 0.05.

The level of education did not appear to have a significant 
effect on the dimension of ease of use of digital applica-
tions F(4.219)= 0.61, p > 0.05, as well as the directorate 
to which the health professionals belong F(4.219) = 1.10, 
p > 0.05 and the HR to which health professionals belong 
F(2.221) = 0.26, p > 0.05.

FIGURE 6. Results of the mean of EASE of USE dimension by age.

TABLE 5. Results of Hochber’s GT2 test for the effect of age on the ease of learning digital applications.

AGE Mean Difference Standard Error Significance 95% Confidence 
Interval

Lower Bound Upper Bound

25–35 36–45 0.51213 0.18818 0.041 0.0127 1.0115

46–55 0.13182 0.18715 0.980 −0.3648 0.6285

55+ −0.05284 0.33413 1.000 −0.9396 0.8339

36–45 25–35 −0.51213 0.18818 0.041 −1.0115 −0.0127

46-55 −0.38031 0.14925 0.067 −0.7764 0.0158

55+ −0.56497 0.31447 0.367 −1.3995 0.2696

46–55 25–35 −0.13182 0.18715 0.980 −0.6285 0.3648

36–45 0.38031 0.14925 0.067 −0.0158 0.7764

55+ −0.18466 0.31386 0.992 −1.0176 0.6483

55+ 25–35 0.05284 0.33413 1.000 −0.8339 0.9396

36–45 0.56497 0.31447 0.367 −0.2696 1.3995

46–55 0.18466 0.31386 0.992 −0.6483 1.0176

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Satisfaction

Age appeared to have no significant effect on the di-
mension of satisfaction using digital applications F(3.220) 
= 1.66, p > 0.05. Accordingly, the level of education did 
not seem to have a significant effect on the dimension of 
satisfaction with the use of digital applications F(4.219) = 
0.96, p > 0.05, as well as the HR to which the health profes-
sionals belong F(2.221) = 0.41, p > 0.05. The directorate to 
which the health professionals belong, however, appeared 
to have a marginally significant effect on the dimension of 
satisfaction with the use of digital applications F(4.219) 
= 2.41, p = 0.05 (Table 3). Post hoc comparisons using 
Hochberg’s GT2 test, however (Table 6), did not indicate 
that the mean value of the Nursing Division (M = 5.54, SD 
= 1.02) differed significantly from that of the Administra-
tive Division (M = 5.13, SD = 1.14), the Medical Division 
(M = 5.15, SD = 1.12) of the IT Department (M = 5.64, SD 
= 0.92) and the other Departments (M = 5.90, SD = 0.91).

DISCUSSION

The digital maturation of healthcare professionals is a 
natural process, but it will not happen automatically and 
without appropriate guidance.21,22 The adoption of new 
digital technologies is a complex process with many factors 
influencing at the individual level, such as perceptions of 
ease of use and learning, usefulness, and satisfaction of 
use. Many negative and positive emotions are stimulated 
by them and affect this process.23

FIGURE 7. Results of the mean of the dimension of EASE of 
LEARNING by age.

It appears that the effort they are expected to put into 
learning and properly using digital technologies is often 
cited as a key factor affecting the motivation of health 
workers to adopt them.24 Healthcare workers can be 
empowered, adopt and use new digital technologies in 
environments where they align with their needs, work-
load, training, and skills. In turn, new digital technologies 
can empower health workers and equip them with skills 
and the necessary confidence when they are perceived 
as useful and easy to use and learn, in environments that 
enhance end-user recognition.25 While other professionals 
may decide to engage with new technologies or at least 
experiment with them more easily, healthcare profession-
als are more likely to demand greater levels of utility and 
ease of use to increase the appropriateness of their care, 
as they appear particularly wary of streamlining. of their 
use.26 Generally, in the hospital setting user acceptance 
theories do not represent the ultimate explanations for 
individual behaviors. The core features of professional 
functioning require both institutional compliance and a 
requirement for autonomous decision-making.27

Various organizational, cultural, and technological factors 
influence how people perceive the concept of usefulness 
and ease of use. But when individual decision-making is 
largely shaped by them, professionals embedded in the 
same institutional framework should exhibit isomorphic 
perceptions of the usefulness and ease of use of new 
practices or technologies, which may have also appeared 
in our results. After all, the existence of heterogeneous 
perceptions in a very strictly institutionalized environ-
ment such as that of health services would constitute, as 
it is traditionally considered, a paradox.27 In this regard, 
a form of dominantly imitative (and not coercive or 
normative) isomorphism seems to appear28, probably 
also as a result of the informative video. Despite the fact 
that professionals use the distinctness of their role and 
their knowledge as resistance to institutional pressures 
and make individual decisions about new technology, it 
seems that they are not completely unaffected by them.27

New digital technologies are promoted by early adopters 
in the workforce predominantly as significant advances in 
clinical suitability, and in particular in quality of service, 
stability, and reliability. At the same time, however, they 
are promoted by managers and policymakers as sources 

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21 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

Otherwise, healthcare professionals who have con-
siderable power and resistance to managers and other 
professional groups, and are variously shielded from other 
social pressures and obligations outside their group, will 
not commit to and adopt the effort to digital transforma-
tion or they may even sabotage it. As a consequence, even 
the managers and promoters of the new technologies, 
who carry the institutional idea of spreading their use, 
will distance themselves as is usually the case or will 
be completely subordinated by the intermediate users 
(health professionals) in order to avoid ruptures and 

TABLE 6. Results of Hochberg’s GT2 test for the effect of management on the satisfaction of using digital applications.

Directorate Directorate Mean Difference Standard Error Significance 95% Confidence 
Interval

Lower Bound Upper Bound

MEDICINE NURSING −0.39403 0.18579 0.298 −0.9192 0.1312

ADMIN 0.01539 0.26847 1.000 −0.7435 0.7743

IT −0.49652 0.45658 0.960 −1.7872 0.7941

OTHER −0.74890 0.31520 0.168 −1.6399 0.1421

NURSING MEDICINE 0.39403 0.18579 0.298 −0.1312 0.9192

ADMIN 0.40942 0.23102 0.550 −0.2436 1.0625

IT −0.10248 0.43561 1.000 −1.3339 1.1289

OTHER −0.35487 0.28398 0.905 −1.1576 0.4479

ADMIN MEDICINE −0.01539 0.26847 1.000 −0.7743 0.7435

NURSING −0.40942 0.23102 0.550 −1.0625 0.2436

IT −0.51190 0.47678 0.963 −1.8596 0.8358

OTHER −0.76429 0.34381 0.239 −1.7361 0.2076

IT MEDICINE 0.49652 0.45658 0.960 −0.7941 1.7872

NURSING 0.10248 0.43561 1.000 −1.1289 1.3339

ADMIN 0.51190 0.47678 0.963 −0.8358 1.8596

OTHER −0.25238 0.50457 1.000 −1.6787 1.1739

OTHER MEDICINE 0.74890 0.31520 0.168 −0.1421 1.6399

NURSING 0.35487 0.28398 0.905 −0.4479 1.1576

ADMIN 0.76429 0.34381 0.239 −0.2076 1.7361

IT 0.25238 0.50457 1.000 −1.1739 1.6787

of efficiency, standardization, and continuous monitoring. 
These rationales are often perceived as a managerial intru-
sion into the unaffected exercise of professional practice 
and are met with suspicion and skepticism.27 Essentially, 
therefore, employees should be given a sense of control 
over how the digital transformation will take place, 
demonstrating that new technologies are introduced as a 
means of enhancing rather than canceling them, in order 
to do much better and more easily what they already do 
exceptionally well. 29

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J Global Clinical Engineering Vol.6 Special Issue 6: 2024 22

confrontations.27 This can be fatal not only for the qual-
ity but also for the sustainability of the health services 
of the future.

Age appears to influence health professionals’ self-
efficacy. Usually, the aging workforce will bring about 
adverse effects for the near future of the health services 
provided, as their physical capabilities begin to decline 
and they will be constantly called upon to apply new 
digital technologies for which they will have little or zero 
knowledge.23,30 Of particular concern is the fact that the 
older workforce typically holds positions of responsibility. 
Equally worrying in our findings is the fact that the 36–45 
age group appears to have the least positive perceptions 
of ease of use and learning, with potential interest in their 
disengagement from the digital transformation project, 
despite the fact that they will inevitably be the dominant 
group that will be called upon to implement and manage it.

Gender, knowledge, and position of responsibility despite 
the fact that they are determining factors of the relative 
readiness and utilization of new digital technologies, did 
not seem to influence the perception of usefulness and 
ease of use and learning and indirectly the degree of their 
adoption. However, increasing the awareness, knowledge, 
and skills of health professionals in these technologies 
before their implementation is necessary to increase 
their adoption.31

Our findings also showed that professionals with a 
lower level of knowledge of new digital technologies 
show a higher perceived ease of use and learning as well 
as their usefulness, than expected. This, despite the fact 
that it may act as an aid to their adoption, does not au-
tomatically constitute the achievement of an improved 
capacity on their part. The self-confidence and belief of 
health professionals should be activated and effectively 
increased in order to achieve high levels of self-efficacy.25 
Health professionals, regardless of specialty, show positive 
perceptions of both the usefulness and the ease of use and 
learning of digital applications. This does not fully agree 
with corresponding findings that state that nurses can be 
characterized as laggards in the adoption of technology 
both in their personal life and in their workplace23, or the 
strongly negative attitude of doctors.26 

CONCLUSION

In conclusion, the optimal application of personaliza-
tion, work needs, and technology will enable increased 
adoption of new digital technologies.

An in-depth understanding of users’ opinions and 
perceptions about the usability of new digital technol-
ogy applications is essential for their effective adoption 
and their successful integration into the health services 
provided. These views and perceptions are complex and 
each user group has unique professional priorities and 
roles, which should be taken into account by decision-
makers to increase adoption.32

Acceptance of digital solutions and innovative medi-
cal technologies from all (intermediate and end users) 
is based on understanding their concerns and insecuri-
ties. The process will take time because people accept 
change at different rates. Therefore, the development of 
an extensive user community for the full and successful 
implementation of e-Health is less likely in the immediate 
and short term. However, this should not hinder the push 
for digital transformation in health services.26

CLINICAL ADJUSTMENTS

Recognizing the particularities and the necessity of 
immediately starting the digital transformation in health 
services, an integrated framework for its operation should 
be formed in our country as elsewhere.33, 34 Initially, inde-
pendent digital transformation offices should be created 
which will report directly to the general administration or 
the board. The main concern of these offices should initially 
be the awareness and information of the organization's 
employees about the necessity but also the real benefits 
that the employees will get from its implementation. On 
a second level, they should act as gatekeepers to help 
create and ensure that a single strategy is implemented 
across the length and breadth of the organization. This 
can be made possible as they will act as the intermediate 
coordinating link of all collaborative teams that will be 
involved in any digital transformation project. Administra-
tors of these offices should be clinical professionals with 
at least ten years of experience who have demonstrated 
an increased interest in digital technologies (something 

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23 J Global Clinical Engineering Vol.6 Special Issue 6: 2024

8. Mirković, V., Lukić, J., Lazarević, S., et al. Key charac-
teristics of the organizational structure that supports 
digital transformation. In Proceedings of the 24th 
International Scientific Conference Strategic Manage-
ment and Decision Support Systems in Strategic Man-
agement. Subotica, Serbia, 17–18 May, 2024. https://
doi.org/10.46541/978-86-7233-380-0_46.

9. Chirkunova, E.K., Khmeleva, G.A., Koroleva, E.N., et al. 
Regional Digital Maturity: Design and Strategies. In 
International Scientific Conference “Digital Transforma-
tion of the Economy: Challenges, Trends, New Opportu-
nities”. Samara, Russia; 26–27 April, 2019; Springer: 
Cham, Switzerland, 2019, pp. 205–213. https://doi.
org/10.1007/978-3-030-27015-5_26.

10.  Sturt, J., Huxley, C., Ajana, B., et al. How does the use of 
digital consulting change the meaning of being a patient 
and/or a health professional? Lessons from the Long-
term Conditions Young People Networked Communica-
tion study. Digit Health. 2020;6:2055207620942359. 
https://doi.org/10.1177/2055207620942359.

11. Bendor-Samuel, P. Digital transformation: 3 change manage-
ment mistakes to avoid. The Enterprisers Project. Available 
online: https://enterprisersproject.com/article/2019/10/
digital-transformation-3-change-management-mistakes.

12. Eriksson, N. Hospital management from a high reliability 
organizational change perspective: A Swedish case on 
Lean and Six Sigma. Int J Public Sect Ma. 2017;30(1):67–
84. https://doi.org/10.1108/IJPSM-12-2015-0221.

13. Dror, N. CIOs, Here’s How to Plan Digital Transformation. 
Oracle University Blog. Available online: https://blogs.oracle.
com/oracleuniversity/planning-digital-transformation.

14. Kreutzer, R.T., Neugebauer, T., Pattloch, A. Digital busi-
ness leadership. Springer: Berlin, Germany; 2018. Avail-
able online: https://content.e-bookshelf.de/media/
reading/L-11079574-afba41d34e.pdf.

15. Lund, A.M. Measuring usability with the USE question-
naire. Usability Interface. 2001;8(2):3–6. Available online: 
https://www.researchgate.net/publication/230786746_
Measuring_Usability_with_the_USE_Questionnaire.

16. Brown, J.D. Likert items and scales of measurement. 
Statistics. 2011;15(1):10–14. Available online: https://
www.ders.es/likert.pdf.

equivalent to NHS CCIO’s).34 The main concern of health 
policymakers should be to encourage the development of 
an integrated educational framework, both with the sys-
tematic restructuring of the detailed curriculum of health 
professions, but also with the creation of specialization 
programs at the postgraduate level that will support the 
development of the existing human potential and skills. 
The immediate, relevant formation of this potential can be 
performed by developing the Boot camps method (under 
the auspices of the Ministry of Health in cooperation with 
the local educational institutions), a practice followed with 
great success by all major organizations for the short and 
intensive training of their newly recruited executives.

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	Editor’s Corner
	Biomedical Technology and Clinical Engineering in Greece after the Pandemic: Highlighted Works from the Panhellenic Conference of Biomedical Technology
	Aris Dermitzakis1,2,*, Vasiliki Zilidou1,3, Eleftheria Vellidou1,4, Alkinoos Athanasiou1,3

	Digital Transformation Management in Health Services: Health Professionals Perceptions as an Implementation Factor
	Theodoros S. Tanis*, Chryssoula Chatzigeorgiou, Ioanna Simeli, and Evangelia Stalika

	Validating the ID-GAMING e-Training Toolkit for People with Intellectual Disabilities in Greece
	Niki Pandria*, Anastasia Barboudi, Vasileia Petronikolou, Panagiotis Antoniou and Panagiotis D. Bamidis

	Novel Functional Electrical Stimulation Parameter Optimization for Neurorehabilitation Using Both Conventional and AI Techniques 
	Arsenios Arsenidis1, Alexandros Moraitopoulos2, Alkinoos Athanasiou2, Alexandros Vildiridis3, Panagiotis Bamidis2, Petros Stefaneas4 and Alexandros Astaras5 

	Leveraging Web Scraping and API Integration for Efficient Medical Device Data Management 
	Agapi Konstantina Liontou1,*, Spilios Zisimopoulos2 and Aris Dermitzakis1 

	Human Muscle State Machine Using Electromyography Classification with Machine Learning 
	George Lyssas1,*, Konstantinos Mitsopoulos1, Dimitris Zantzas2, Anestis Kalfas2, Panagiotis D. Bamidis1 

	Kinematic and Dynamic Analysis of Lower Limb Movement: Towards the Design of a Wearable Rehabilitation Assistant Device 
	Filippos Margaritis1,*, Konstantinos Mitsopoulos1, Kostas Nizamis2, Alkinoos Athanasiou1 and Panagiotis D. Bamidis1 

	A Novel Dermatological Diagnosis Support Device Based on Electrical Impedance Spectroscopy 
	Alexandros Moraitopoulos1,*, Konstantinos Mitsopoulos1, Christina Kemanetzi2, Panagiotis Bamidis1 and Alexandros Astaras3

	Software Skills Identification: A Multi-Class Classification on Source Code Using Machine Learning
	Dimitris Bamidis, Ilias Kalouptsoglou, Apostolos Ampatzoglou, Alexandros Chatzigeorgiou*

	Improvement of Aortic Valve Stenosis Classification in Patients Through Computational Fluid Dynamics Model
	Ioannis Makropoulos, Dimitris Zantzas, Vasilis Gkoutzamanis, Anestis Kalfas*

	Kinematic and Dynamic Analysis of the Human Hand’s Articulation for Wearable Soft-Robotic Device Applications
	Paschalina-Danai Sarra, Vasiliki Fiska, Konstantinos Mitsopoulos, Diamanto Mylopoulou, and Panagiotis D. Bamidis*

	Deep Learning Classification of Epileptic Magnetoencephalogram 
	Andreas Stylianou1, Lefteris Koumakis2, Maria Hadjinicolaou3, Adam Adamopoulos1,* and Alkinoos Athanasiou4


