A total of 1140 studies were identified in the initial database search. Four additional studies were incorporated based on references from the existing studies, and 61 duplicate studies were subsequently removed. Utilizing the PRISMA procedure, the remaining 1083 studies were screened based on title and abstract, leading to the exclusion of 900 studies. Subsequently, the eligibility of the remaining 183 studies was assessed in full text, resulting in the inclusion of 28 studies in the review and the exclusion of 155 studies.
The 28studies used five different study designs, which all went through a quality assessment. Seventeen of the included studies were classed as having a high quality according to the MMAT checklist [34, 35, 36, 37, 38, 39, 40, 41, 42, 43, .44, 45, 46, 47, 48, 49, 50]
Four of the included studies was classed as having a moderate quality, [51, 52, 53, 54] Furthermore, seven of the included studies were classed as having a low quality, [55, 56, 57, 58, 59, 60, 61]
After a critical discussion with the two academic authors of this review, studies with a low quality were excluded in an attempt to use only information from high and moderate quality studies (See Appendix 2 for the full summary of MMAT Critical Appraisal Tool)
Characteristics of included studies.
In this systematic review a total of 21 studies were identified. among which 11 were qualitative [40, 39, 38, 40, 45, 52, 49, 43, 41, 50 48] and 4 quantitative [47, 54, 34, 46]), and 6 mixed methods studies [51, 35, 40, 42, 43 37] (Appendix 1).
These studies were published between 2017 and 2023. Three studies were from Canada, followed by two from Australia and also included studies from Finland, Nigeria, England, Taiwan and China, Cape Town, South Africa, German, Polish and Greek. The results are presented based on the four core constructs of UTAUT to discuss outcomes related to users' behavioural intention to use the technology. Which is Performance Expectancy, Effort Expectancy, Facilitating Conditions and Social Influence. Therefore, the number of themes is four and 2 sub-themes were extracted.
Theme 1. Performance Expectancy:
Performance expectancy is a key construct in the Unified Theory of Acceptance and Use of Technology, which aims to explain individuals' intentions to use technology in organizational settings [24]. Performance expectancy refers to the extent to which individuals believe that using a particular technology will help them perform their tasks more effectively and efficiently [24].
In this review, insights from seven studies [37, 34, 48, 54, 47, 51 and 40] collectively suggest a noteworthy connection between users' perceptions of enhanced performance and their inclination to embrace the technology. According to [37] a key driver of acceptability cited by frontline health workers (FHWs)was the perceived usefulness in improving the quality of health care provision.
Chang et al., [34] investigated the factors that affect nurses' intentions to use information systems for nursing. The results of the study confirmed the importance of performance expectancy in predicting the usage intentions of nursing information systems. The study also found that operational ease is more important to users in accepting societies, for example, China, indicating a need for tailored strategies.
[48] reported that health care professionals wanted to help patients achieve meaningful personal goals. However, health care professionals varied in how they pursued this aim, which influenced how they engaged with the digital tools. For example, according to Singh et al some health care professionals had a high or low behavioral intention and, for some, it changed over time. In the same way [54] found that both ability and motivation play had an important and positive role in the adoption and behavioral intention of digital health care by influencing performance expectancy. Health care professionals that perceived the digital health tools to be useful and relevant to their job had a significantly more positive attitude formation toward and intention to use the system. However, [54] also found that familiarity with online experience did not play a role in the perceptions and adoption digital health tools. Conversely [47] reported that the majority of health professionals had a positive attitude as a result of their experience, including high digital literacy, which played a positive role in the acceptance of digital tools.
[51] findings demonstrate that nurses face daily challenges with electronic medical records (eMRs) that integrate paper-based activities for efficient and timely care. Despite the push for a paperless environment, nurses realize the value of electronic systems considered as complementary methods with paper systems instead of opposing [51]. Similarly, [40] found that participants also valued the shared electronic documentation to inform and facilitate patient care, enabling more efficient work by reducing the time needed to complete documentation and through preventing mistakes and loss of patient data.
Theme 2. Effort Expectancy:
Effort expectancy is a crucial factor in the Unified Theory of Acceptance and Use of Technology. Effort expectancy refers to the perceived ease of use and the perceived effort required to use a specific technology [21]. It measures the user's perception of how complex or difficult it would be to use a technology. Effort Expectancy plays a key role in determining an individual's intention to use and accept technology [21].
Three studies in this review [53], [39] and [34] clarify that the degree of ease associated with the use the digital tools is an important factor in technology acceptance. Moreover [53] also found that effort expectancy lack of emphasis on usability, the complexity of use emerged as a significant barrier to acceptance contributed to unclear use digital tools.
[39], reported that the need for multiple logins and sign-outs, challenged users and led to a loss of productivity through wasted time and is exemplified in the quotes. ‘We still have different logins for different providers that means we waste time logging in etc.’ (Medical #483); ‘If you are not using the program, it shuts down so [you] have to repeatedly sign in throughout the day. While sitting at desk where I am using the program it will shut down. IT [Information Technology] department will not extend the time it is open. This makes the program very difficult and user unfriendly’ (Nurse #150); and ‘Loss of productivity. Takes 15 min to log in to all the necessary electronic systems and I can out-type the computer every time so need to slow down my typing to avoid errors’ (Medical #528). Moreover, [34] study found that information literacy has a positive impact on effort expectancies, promoting the belief that using nursing information systems can enhance performance and increase usage intentions.
Theme 3. Facilitating Conditions
In the Unified Theory of Acceptance and Use of Technology, facilitating conditions refer to the degree to which individuals perceive that they have the necessary organizational and technical infrastructure to use a specific technology. Facilitating conditions play an important role in the UTAUT model as they influence the intention to use a technology [62]. Facilitating conditions are important in the UTAUT model because they determine whether individuals have the necessary resources and support to effectively use a technology [62].
3.1 Insufficient Training
Insufficient training can act as a significant barrier to the adoption and acceptance of technology as was evident in several studies: [46, 52, 45, 35, 41, 44, 50, 42].
[46] reported that insufficient training in problem-solving in digital health issues, lead to low basic digital competency among health care professionals, particularly in routine problem-solving, safety, and communication. The majority lacked basic technical skills for hardware and software issues. Also, sex, educational status, profession type, and years of experience were significant factors which impacted behavioral Intention. Males were 3.9 times more likely to have higher digital competency and education level and positively correlated with digital competency also, younger professionals may be more receptive to changes in the working environment.
Participants in the study by [52] had limited exposure in professional training to digital health during their education. In addition, readiness challenges that related to infrastructure such as digital skill gaps and infrastructure deficiencies led to negative behavioural intention which resulted in poor penetration of digital health by [42].
According to [35] many care professionals had available resources and support for training on virtual care. A few did not initially have infrastructure available to them. A few statements also revealed that those who had previous experience with telemedicine use found the transition to be smooth. In the same context [41] found that the majority of nurses need training to manage digital tools and identification of personnel responsible for managing symptoms; that is facilitating conditions that empower nurses.
Jensen et al., [44] Factors influencing lower uptake were computer unavailability, staff allocation, low literacy, training time, and workload concerns.
Insufficient training and support can hinder the scale-up of digital tools, leading to negative usage outcomes and affecting their intended benefits [50]. Similarly, a study [42] found that inadequate training and lack of on-the-job support contributed to stress and feelings of incompetence, which can lead to resistance to information technology (IT) and technology adoption, both personally and professionally. These conditions may cause individuals to be late technology adopters.
3.2. Organizational and technical infrastructure
Five studies in this review clarify the importance of organizational and technical infrastructure to support the use of the digital system: [43, 38, 53, 47, 50].
[43] illustrates the issues faced by Health care workers (HCWs) during the introduction and maintenance of the eHealth system. They reported a number of technical issues, such as breakdowns of hardware, bugs in the software, and an erratic electricity supply which in turn effects user behaviors. [43] reported that HCWs found that the eHealth system did not provide flexibility. [38] emphasizes the importance of providing resources and opportunities for digital competence sharing to help create friendly and safe digital organizational atmosphere.
Privacy concerns have a significant impact on the acceptance of technology. According to findings, individuals are increasingly concerned about their privacy and the security of their personal information. This concern can lead to hesitation and reluctance in adopting new technologies. For example, [49] found that doubts about the privacy and security of data, as well as insufficient digital skills of users, can hinder technology acceptance. A study conducted by [53] found that a considerable proportion of participants had concerns about privacy issues related to technology for example, “can violate my own/my relatives’ privacy," 62.5% (n = 15) of participants agreed or strongly agreed with this statement [53]. This indicates that a considerable proportion of participants had concerns about privacy issues. These concerns included the fear of data privacy violations, which can negatively impact the attitude towards technology acceptance [53]. Furthermore, [47] found that a negative perception of security and privacy can lead to a less positive attitude towards technology acceptance. Overall, privacy concerns are a significant factor that influences individuals' attitudes and intentions towards accepting and adopting new technologies. The level of trust individuals has towards technology can greatly influence their attitudes and intentions to adopt new technology. Finally, [50] reported that healthcare workers raised concerns about the accuracy of data captured by DHT due to the lack of validation and calibration.
Theme 4. Social Influence
Social influence is one of the key constructs in the UTAUT model that refers to the degree to which an individual feels that it is important for others to believe they should use the new system. Two of the included studies mentioned to social influence [49] and [50].The main influence factors mentioned that convince the individual can encourage others to follow suit in healthcare were motivating healthcare workers and patients' healthcare providers by clarifying the added value of using a digital care platform, clear business case with vision, demonstrating effectiveness, using an implementation guide, and educating patients and health care providers about how to use digital health tools [49].In addition, Shared decision-making and patient-cantered care were play an important role in establishing perceived value that motivates the team to embrace technology [50].