J Global Clinical Engineering Vol.6 Special Issue 6: 2024 12 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. http://www.globalce.org http://globalce.org http://globalce.org mailto: thtanis1@gmail.com https://creativecommons.org/licenses/by/4.0/ https://creativecommons.org/licenses/by/4.0/ 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 http://www.globalce.org http://globalce.org http://globalce.org 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. http://www.globalce.org http://globalce.org http://globalce.org 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. http://www.globalce.org http://globalce.org http://globalce.org 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 http://www.globalce.org http://globalce.org http://globalce.org 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. http://www.globalce.org http://globalce.org http://globalce.org J Global Clinical Engineering Vol.6 Special Issue 6: 2024 18 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 http://www.globalce.org http://globalce.org http://globalce.org 19 J Global Clinical Engineering Vol.6 Special Issue 6: 2024 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 http://www.globalce.org http://globalce.org http://globalce.org J Global Clinical Engineering Vol.6 Special Issue 6: 2024 20 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 http://www.globalce.org http://globalce.org http://globalce.org 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 http://www.globalce.org http://globalce.org http://globalce.org 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 http://www.globalce.org http://globalce.org http://globalce.org 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. 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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