Technology Acceptance in Healthcare: A Systematic Review

: Understanding the factors affecting the use of healthcare technologies is a crucial topic that has been extensively studied, specifically during the last decade. These factors were studied using different technology acceptance models and theories. However, a systematic review that of ‐ fers extensive understanding into what affects healthcare technologies and services and covers dis ‐ tinctive trends in large ‐ scale research remains lacking. Therefore, this review aims to systematically review the articles published on technology acceptance in healthcare. From a yield of 1768 studies collected, 142 empirical studies have met the eligibility criteria and were extensively analyzed. The key findings confirmed that TAM and UTAUT are the most prevailing models in explaining what affects the acceptance of various healthcare technologies through different user groups, settings, and countries. Apart from the core constructs of TAM and UTAUT, the results showed that anxiety, computer self ‐ efficacy, innovativeness, and trust are the most influential factors affecting various healthcare technologies. The results also revealed that Taiwan and the USA are leading the research of technology acceptance in healthcare, with a remarkable increase in studies focusing on telemed ‐ icine and electronic medical records solutions. This review is believed to enhance our understanding through a number of theoretical contributions and practical implications by unveiling the full po ‐ tential of technology acceptance in healthcare and opening the door for further research opportuni ‐ ties.


Introduction
Technology acceptance is defined as opposite to the term rejection, where it signifies the positive decision toward using an innovative solution [1,2].Technology acceptance is concerned with the psychological status of a person regarding the intention to use a specific technology [3].A user's acceptance of technology is significant at any time and not only at the design phase or directly after implementation.Non-stop changes will occur in the information systems, their designs, working environments, and potential users.Users' needs may also differ due to these changes and other social or cultural issues [4].
There is no doubt on how information technologies have proliferated in the healthcare sector [5].Information technologies are important to enhance the quality of healthcare services and improve patients' satisfaction.Moreover, the staff using the technology in the healthcare domain is an essential issue, since information technologies play a vital role in increasing their work efficiency and effectiveness [6].That is why it is crucial to determine and understand how people react to the emergence of new technologies.The low levels of acceptance for particular information technology can lead to failure or delay in implementing that technology.Additionally, the lack of acceptance of technology in healthcare can negatively impact its key objectives [7].
Over the years, the acceptance of different information technologies and applications has been explored in the healthcare field.These technologies include internet-based health websites [8], picture archiving and communication systems (PACs) [9], mobile applications [7], telemedicine technologies, and electronic health records [10].As is the case with other technologies, healthcare technologies were examined using different technology acceptance models and theories.This is because those theories and models offer a better understanding of the users' behaviors toward a specific technology or service through the factors underpinning them [11].It is believed that the identification of these factors would enhance the effectiveness of healthcare technologies by allowing scholars to investigate the technical, social, and cultural aspects and understand the correlation between those factors and users' readiness to use healthcare systems.Therefore, this study aims to systematically review the studies that empirically evaluated the different technologies in healthcare in relation to technology acceptance models and theories.Stemming from this aim, the authors intend to answer the following research questions: RQ1.What are the prevailing technology acceptance models and theories explored in the healthcare domain?
RQ2.What are the key factors affecting technology acceptance in the healthcare domain?
RQ3.What are the primary confirmed relationships among the influential factors in the past studies?
RQ4.What are the leading information technologies studied and their relationships with countries and participants?RQ5.How are the reviewed studies distributed across the regions and countries of technology implementation?
RQ6.What is the progress of technology acceptance studies in healthcare?

Literature Review
During the last three decades, various theoretical models and their extensions have been designed to understand the acceptance levels and individuals' behaviors toward different technologies in various disciplines [6].These models introduced different factors to understand their effect on the user's acceptance of technology.Those theories include but are not limited to the theory of reasoned action (TRA) [12], the technology acceptance model (TAM) [13][14][15], extensions of TAM [16,17], the unified theory of acceptance and use of technology (UTAUT) [18], social cognitive theory (SCT) [19,20], the theory of interpersonal behavior (TIB) [21], the perceived characteristics of innovating theory [22], the theory of planned behavior (TPB) [23], the model of PC utilization [24], the motivational model [25], innovation diffusion theory (IDT) [26], and Igbaria's model [27].
Among the aforementioned theories and models, the UTAUT is known as the most relevant [28] and the most actively used model in technology acceptance studies in the healthcare domain [28,29].Apart from the healthcare domain, TAM is also recognized as the gold standard model across several technologies [30][31][32].On the other hand, UTAUT has shown 20-30% better explanatory power than TAM, which means 40-50% of the explanatory power regarding the behavioral intention of end-users [18,31].
Several reviews were conducted to analyze the technology acceptance models and their related constructs/factors in healthcare.It is impossible to ignore those reviews.As seen in Table 1, the review studies have mainly discussed one specific technology acceptance model except for two review studies [33,34].Besides, only one study focused on the classification of studies based on the examined technologies, participants, and country of implementation [6].For instance, telehealth solutions were mainly studied from the perspective of older populations [35], with little attention paid to the developing countries.There is an increasing number of healthcare services, which has resulted from the increment of population ages [36,37].To make it distinct, this review provides a broader view for understanding healthcare technologies and identifies the potential gaps in technology acceptance in healthcare.
It is beneficial to have a general review exploring multiple technology acceptance models instead of focusing on one acceptance model (e.g., TAM).Additionally, reviewing different information technologies instead of only one technology (e.g., electronic medical records) is essential to recognize a plethora or gap in the research.Therefore, this review study attempts to present a fresh overview of the literature of technology acceptance in the healthcare domain by classifying the collected studies based on the utilized technology acceptance models, the studied information technologies, participants, and countries of implementation.Additionally, this study aims to identify the prevailing acceptance models, the most utilized factors, and the most confirmed relationships to address the literature gaps and assist further research in building integrated models for technology acceptance in the healthcare domain.
As an example for the included studies, Tubaishat [38] has studied the acceptance of electronic health records (EHRs) through a self-administered questionnaire filled by 1539 nurses from 15 hospitals in Jordan.The utilized research model was the original TAM.A multiple linear regression analysis was used to explore nurses' perceptions regarding the ease of use and usefulness of the solution.It was found that the intention to use is influenced by the perceived ease of use and perceived usefulness.The study was limited to nurses without including other medical staff, such as physicians, pharmacists, or laboratory staff.
Hadadgar et al. [39] have explored 146 general practitioners' (GPs) intention to use the e-learning continuing medical education (e-CME).Based on the theory of planned behavior (TPB), the results revealed that attitudes and perceived behavioral control factors significantly influence the intention to use the e-CME solution.The study included only one user group (i.e., GPs), with a limited sample compared to the optimum sample for factor analysis.Further, Perlich et al. [29] have discussed the acceptance of interactive documentation systems by therapists and patients in an addiction therapy center in Germany.The study relied on extending the UTAUT model with the attitude construct.The key results indicated that attitude is the strongest predictor of intention to use.The study also aims to identify the prevailing acceptance models, most utilized factors, and the most confirmed relationships to address the literature gaps and help to build integrated models for technology acceptance in the healthcare domain.

Materials and Methods
This review is based on the findings from studies published in digital journals and databases to discuss and empirically explore technology acceptance in healthcare.A review of the previous relevant literature is a vital phase of any scientific study [46].Generally, reviews can simplify and extend the theory development, filling gaps in research, or close areas where a profusion of research exists [47].A systematic review is helpful to make researchers more familiar with the research topic [48].Systematic reviews are different from traditional or narrative reviews, since systematic reviews are more rigorous and provide a well-defined approach to review a particular subject area [49].
As presented in Figure 1, the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) have been applied to conduct this review [50,51].Using the PRISMA helps in demonstrating the flow of information through the different phases of the review [52].It also depicts the number of articles identified, included, and excluded and the rationale behind the excluded articles.The methods used to identify and collect the relevant studies in this review included different phases: define the inclusion/exclusion criteria, determine the sources and digital databases, specify the search strategies, and analyze the retrieved studies.

Inclusion/Exclusion Criteria
The inclusion and exclusion criteria are defined to set the selection rules for studies before the analysis phase (see Table 2).The specified criteria are crucial to decide whether the study is valid to be included in the analysis and ensure consistency in the reviewed studies.

ID Inclusion Criteria Exclusion Criteria
1 The objective of the study should be related to the application of technology acceptance theories in healthcare.
The study is related to applying technology acceptance or adoption but not in healthcare (e.g., banking).

2
The research model and its related hypotheses were empirically evaluated.
The research model was evaluated using a qualitative method or not even evaluated. 3 The study must be a journal article, conference paper, book chapter, Ph.D. dissertation, or master's thesis.
The study is a review, position paper, editorial, etc.

4
The study must be published in the English language.
The study is published in languages other than English.

Data Sources and Search Strategy
The studies have been identified by exploring six digital databases, including Pub-Med, IEEE Xplore, ACM digital library, Springer, Science Direct, and Google Scholar.The selected databases were searched to collect studies that have been published between January 2010 and December 2019 (10 years), where the search was conducted in January 2020.A search strategy was developed using specific search keywords, as presented in Table 3.By following the developed search keywords and strategy, the initial search results showed a total number of 1768 studies, as seen in Figure 1.In that, the inclusion and exclusion criteria were applied, and the refinement stages as per the PRISMA were followed.The analysis of the collected studies was carried out by the first and third authors of this study by analyzing each article independently.The differences in analyzing the studies between the two authors were resolved through discussion and further review of the disputed studies.Accordingly, a total number of 142 studies were recognized as valid to be included in the analysis.

Data Abstraction and Analysis
All citations have been downloaded into Mendeley reference manager [53].The characteristics of the research methodology have been coded to include (i) the studied technology acceptance model, (ii) the included factors in the study, (iii) the confirmed relationships between the factors as hypothesized in the research model (main findings), (iv) types of the studied information technologies, (v) participants, (vi) digital library (database), (vii) year of publication, and (viii) country (direction of research).The filtration process for the studies started by quickly screening the title and abstract.If the study passes this round, the full paper will be obtained and recorded in a different folder for the full and final round of review.The data were extracted through three stages.The first phase determines the theory used to explore the factors impacting specific technology acceptance in healthcare.The second phase categorizes the studies based on the publication year, publication type, and country of implementation.The third stage extracts the studied constructs, understands the developed hypotheses, and analyzes the findings.
A total of 1768 studies were retrieved from the digital libraries, as seen in Figure 1.After the removal of 549 duplicates, 1219 publications were sent out to the screening process.The titles and abstracts were assessed for the 1219 publications.The results of screening confirmed the exclusion of 916 records due to their incompatibility with the inclusion criteria.The full texts of 303 studies were then scanned to ensure their relevance to the subject of this study.The final number was 142 studies, which were found eligible to be analyzed and included in the study (Table A2 in Appendix B).

Quality Assessment
It is crucial to assess the quality of the collected studies [54].Therefore, a quality assessment checklist was designed to include seven items to evaluate the quality of the eligible research studies (N = 142).As seen in Table 4, the checklist had no intention to criticize the work of any researcher [49].The designed checklist was conformed to what was suggested in prior research [49,55,56].The checklist is based on a 3-point scale from 0 to 1, where 0 means "no", 0.5 "partially", and 1 "yes".The results of the quality assessment can be seen in Table A1 in Appendix A. In general, all the included studies have passed the quality assessment and are considered valid to be further analyzed.

Results
The results of the review provided a detailed analysis of the recent literature on technology acceptance in healthcare.The comprehensive summary for all the included studies can be found in Table A2 in Appendix B. According to the analyzed 142 studies, the findings of the study can be summarized based on the six research questions.

Prevailing Technology Acceptance Models and Theories in the Healthcare Domain
As mentioned earlier, many technology acceptance models have been discussed in different domains, including healthcare [57].In Table A2, the authors have classified the studies based on the studied acceptance model.As seen in Figure 2, the TAM, its extensions, and modifications are leading the research of technology acceptance in healthcare (N = 76) [58][59][60][61].It was also found that several studies (N = 21) have discussed the integration between TAM and other technology acceptance models (e.g., UTAUT, TPB) [62][63][64].The analysis also shows that the UTAUT and its extensions were widely employed to explore the user's acceptance of technology in healthcare (N = 26) [65,66].Further, the results showed that the number of studies related to the employment of the TPB model is reasonable (N = 12).

Key Factors Affecting Technology Acceptance in the Healthcare Domain
For being the key constructs of the TAM, perceived ease of use (N = 98) and perceived usefulness (N = 105) have been explored and utilized in many studies to assess the acceptance of various technologies in healthcare [60,[67][68][69].With evidence from 125 different studies, the analysis indicated that behavioral intention to use technology is the most used factor in evaluating the acceptance of different technologies in healthcare (see Figure 3).Although such a result is expected, it is significant to confirm the need for behavioral intention within the theory and practice of technology acceptance.Another aspect that needs to be considered is the user's performance and the related expected positive gain that has been investigated extensively, as per the findings in Figure 3.A similar case with the perceived ease of use factor and its equivalent effort expectancy appeared in the analysis for 98 and 24 times, respectively.
Apart from the factors of TAM and UTAUT acceptance models, the results showed that other factors had been extensively utilized to understand the acceptance of technology in healthcare.These factors include anxiety (N = 19) and computer self-efficacy (N = 32) from the social cognitive theory [1,19,20], innovativeness (N = 10) [70], and trust (N = 18) [71] as external factors.

Main Confirmed Relationships among the Influential Factors
The classification analysis in this study included an investigation for the most confirmed hypotheses as per the recent literature.Those hypotheses were developed as a part of the proposed models within various studies, confirmed by several scholars, and considered significant for technology acceptance in the healthcare domain.It is crucial to understand those common hypotheses to let researchers understand the potential correlation between the factors within the model.Similar to the determination of key factors, understanding the potential significant correlations can help to develop and enhance acceptance theories based on the findings of previous studies [72].
As seen in Figure 4, the most confirmed hypotheses were the significant correlation between the "perceived usefulness" and the behavioral intention to use a specific technology (N = 61) and between the "perceived ease of use" and "perceived usefulness" (N = 59).In general, the results confirmed the key relationships as hypothesized in TAM and UTAUT models.On the other hand, we cannot disregard the extensive impact of social influence, trust, anxiety, innovativeness, and computer self-efficacy factors on technology acceptance in healthcare.In other words, the frequency in Figure 4 presents the number of studies that have confirmed the significance of each hypothesis.

Main Information Technologies and Their Relationships with Countries and Participants
Figure 5 presents the distribution of the studied information technologies in the reviewed studies.As suggested by Rahimi et al. [6], the categorization of information technologies was performed based on the Medical Subject Headings (MeSH) thesaurus [73].With more than 48% (N = 69), it is clear that prior research is mainly dominated by five main categories, including telemedicine solutions, HIT systems in general, cloud computing applications, mobile applications, and electronic health records (e.g., health information solutions and electronic medical records).By having a quick look at the analysis in Table 5, it seems that the classification of technologies across the countries is equally distributed, with a slight notable difference in telemedicine and cloud computing.Telemedicine was mainly studied in Taiwan and the USA, while cloud computing was primarily studied in Taiwan.Figure 6 presents the distribution of studies according to the participants (user groups).With almost 56% of the total participants, physicians (N = 30), nurses (N = 24), and healthcare professionals in general (N = 26) attracted the attention of scholars to understand their technology acceptance.In terms of technology type and participants, we observed that the focus is scattered with little attention to study the acceptance of electronic health records by the same leading user groups (see Table 6).Additionally, there are efforts to understand the acceptance of patients and the general population as nonhealthcare workers for various technologies, including telemedicine, mobile applications, cloud computing, and wearable electronic devices.

Distribution of Studies across Regions and Countries
This review also determined the origin country and the region for each analyzed study.As per Figure 7, the majority of publications were conducted in Asia (N = 76), with 53.5% of the whole analyzed studies.Taiwan recorded 20.27% (N = 30) of the entire analyzed studies, as seen in Table 7. Further, the USA as a first runner-up is doing well, with 22 empirical studies (14.86%) to assess technology acceptance in healthcare.As shown in Figure 8, the geographic heat map indicates that there are no publications conducted in the Central and South American regions.The rest of the statistics related to country and region are illustrated in Table 7 and Figures 7 and 8.

Progress of Technology Acceptance Studies in Healthcare
The analyzed studies in the inspected period were categorized according to the year of publication, as presented in Figure 9.The studies are reflected through more or less constant frequency in the last decade, with peaks in 2013, 2015, and 2016.There is a remarkable drop in the number of studies from 2017, which can maximize the gap in the technology acceptance literature, especially with the ongoing boom in information technologies.

Discussion
The results of this review are believed to add a thorough understanding of the literature on technology acceptance in healthcare.The fundamental goal of this study was to review the empirical studies and analyze the results to understand the research situation of technology acceptance in the healthcare sector.This review covered the studies conducted in the recent decade to explore the acceptance of different technologies using different acceptance theories, various factors, and different healthcare organizations or settings.Figure 10 represents the mind map for the results summary.Concerning the study characteristics, the analysis classified the studies according to the studied model to address the prevailing technology acceptance models in the healthcare domain.The TAM, its extensions, and modifications are leading the research of technology acceptance in healthcare.It was also found that several studies have discussed integrated models.In general, the main aim of the integration in those studies was to improve the explanatory power of the TAM model.These results align with what was proposed by [47] regarding the power of TAM in investigating the user's acceptance of technology in general.Moreover, the UTAUT and its extensions were widely employed to explore the user's acceptance of several healthcare technologies.This observation is compatible with the conclusions of prior research [28,29].Additionally, the results showed that the number of studies, including the TPB model, is reasonable.These findings confirm the importance of studying various models as performed by [18,31], to better understand technology acceptance and facilitate building more unified models [74].This study also explored the key factors that were extensively employed in the recent literature to understand the acceptance of various healthcare technologies.The results showed that behavioral intention was utilized 125 times in the reviewed studies.This finding is significant to confirm the need for behavioral intention within the theory and practice of technology acceptance.Consequently, providers of information technologies and healthcare organizations have to focus on the users' intention to enhance the level of acceptance, regardless of whether they are professional staff or patients.Perceived ease of use and perceived usefulness have been explored in numerous studies to assess the acceptance of various technologies in healthcare [60,[67][68][69].These two factors are the core of the TAM.Other studies have confirmed that these constructs could explain about 40% of users' acceptance and intention to use specific technologies [33] in various domains, including healthcare [30,75,76].Instead, the UTAUT was found to extend the explanatory power by 20% to 30% more than TAM regarding user's behavior intention [31].The capability of UTAUT to explain the intention to use specific technology can reach 70%, especially with the injection of facilitating conditions and social influence factors, with age, gender, experience, and voluntariness as moderators [33].The TAM, UTAUT, and their constructs are robust theories to understand the acceptance of various technologies through different users.
The analysis revealed that the user's performance and its related expected positive gain had been investigated extensively.Those expected positive performance gains are linked with the perceived usefulness factor and its equivalent performance expectancy [9,18,31].This is also applied to the perceived ease of use and its identical factor, effort expectancy.These results indicate that it is mandatory to extend the levels of convenience in information technologies and make them more user friendly.In addition, the clear presence of the facilitating conditions factor and its equivalent factors "compatibility" and "perceived behavioral control" confirm the users' need for support and motivation to accept and use information technologies in healthcare.Additionally, scholars have not missed the importance of exploring innovativeness, computer self-efficacy, trust, and anxiety factors.A user will not use technology if he/she does not trust the technology or its creator.Similarly, it sounds reasonable to address users' innovativeness and confidence to use information technology without fear of making mistakes.
With a link to the extensively studied factors, the analysis investigated the most confirmed hypotheses in the recent literature.It is crucial to understand those common hypotheses to let researchers understand the potential correlations between the factors within a specific model.The determination of confirmed hypotheses is essential to understand the possible significant correlations between constructs and assist researchers in developing or enhancing acceptance theories based on the findings of other scholars.The recognition of the factors and their confirmed correlations can provide a better view for decision makers and help them determine the technology's strengths and weaknesses, enhancing its level of acceptance [77].
The results found that perceived usefulness and ease of use encourage behavioral intention in healthcare.Such a result suggests that users' behavioral intention is mainly influenced by their spent efforts to use a specific technology and their belief regarding the expected benefits from using that technology [9,78].Additionally, the results exposed that attitude toward using technology in healthcare is widely influenced by the expected performance results and effort expectancy.This implies that the end-users have a positive attitude regarding using a specific technology to improve their work efficiency [31,79].It is essential to implement user-friendly solutions in healthcare to expand the positive attitude toward technology adoption [31,61].The relationship between social influence and both behavioral intention and perceived usefulness was extensively confirmed.This correlation suggests that users' behavioral intention to use technology is significantly influenced by their social groups and beliefs regarding the expected enhancement in performance.
Regarding the studied information technologies, the analysis classified them by type and directions of countries to explore the booming topics in specific regions and countries.This can signify a lack or plethora in the literature regarding a particular technology or country.The classification of technologies can enable scholars to have a look for other technology solutions in healthcare.The results showed that telemedicine and electronic health records were the most studied technologies in general.This observation indicates that there is still room to explore the acceptance of these technologies in different countries and settings, especially that there is no specific country to lead the research.
In general, the results indicated that specific technologies dominate the literature, but this conclusion is deceptive, since the literature is scattered in terms of technology use per country.There is still a gap in discovering the factors that impact the acceptance of many information technology solutions in healthcare.Those solutions can fail due to the uncertainty of adoption enablers, barriers, and users' acceptance.It is, therefore, recommended to conduct more research on the technologies that are not covered or neglected, such as picture archiving and communication systems (PACs) [9] and robotics [80].
Concerning the distribution of the participants across the technologies type, the results indicated that prior research focused on the healthcare workers (e.g., physicians, nurses, and healthcare professionals) to study their acceptance of different technologies.This result can be misleading when the technology type is added.The reviewed studies could not confirm a clear focus except for the electronic health records by the aforementioned leading participants, which remains a research gap.Hence, further research may consider this prospective gap and try to discover the acceptance of other technologies by various user groups.Moreover, the literature witnessed extensive work to explore the acceptance of telemedicine, mobile applications, cloud computing, and wearable electronic devices by patients and the general population as non-healthcare workers.This finding can be explained by the need to understand the influence of innovativeness, trust, and anxiety on regular users' acceptance.For instance, a user needs to be innovative to try a new smartwatch or mobile application without fear of making mistakes and trust that the technology will not make his/her data public or breach the confidentiality terms.
Addressing the origin of publications can help to recognize a research gap in a specific country or region within particular subject areas.It helps to improve the research directions and create extra motivations for researchers.The results showed no publications regarding technology acceptance in healthcare within the Central and South American regions.This provides a research gap that is required to be filled by the researchers in these regions.This result can also indicate that technology implementation in the healthcare domain is rare in these two regions.By looking into the developing regions, Arab and African countries need to expand the research in technology acceptance.Despite the advanced healthcare services and the increasing use of information technologies across many Arab countries, the lack of technology acceptance research exists, specifically in the healthcare domain.
Taiwan recorded 20.27% of the analyzed studies, which makes up almost 40% of the total number of studies in Asia.This might be an outcome for the well-established healthcare systems in Taiwan [81].In contrast, China and South Korea's results are shocking compared to the boom in information technologies in these two countries.These results could be a gap that referred to the language with no assurance, especially that many scholars are publishing their research using their mother-tongue languages.Therefore, more research studies can be conducted to understand the enablers and barriers to adopting various healthcare technologies in China and South Korea.
Regarding the years of publication, the results indicated a fluctuation in the number of studies per year.The number of research articles has increased from 4 studies in 2010 to an average of 17 studies from 2012 to 2018.The hike could refer to the increased focus on telemedicine, electronic health records, cloud computing, and mobile applications.With 27 studies conducted in Taiwan and 17 in the USA, both countries have significantly encouraged the observed increase.Finally, the remarkable drop in the number of studies from 2017 to 2019 does not support technology acceptance literature.The current need to adopt new technologies and improve healthcare services opens the door for more studies to explain technology acceptance.It is expected that the number of studies will increase due to the outbreak of COVID-19 that was identified in December 2019 in China and has resulted in the deaths of thousands of human beings worldwide [82,83].

Conclusions
This study aimed to systematically provide an overview of the studies published on technology acceptance in healthcare.The study provided a classification analysis that includes the studied technology acceptance models, the influential factors, the confirmed relationships among those factors, the types of the studied information technologies, participants, year of publication, and countries (direction of research).Following the PRISMA guidelines, 1768 published studies were reviewed, and 142 studies were found to be valid and included in the statistical analysis.According to the findings, it is clear that TAM and UTAUT are the prevailing technology acceptance models.Additionally, the analysis found that the constructs of TAM and UTAUT were the most utilized factors to understand the acceptance of technology in healthcare.Moreover, other factors were extensively studied including, computer anxiety, computer self-efficacy, innovativeness, and trust.Overall, room is still available to integrate various technology acceptance models or add other factors to the current models to produce more robust and valid acceptance models.
On the other hand, some technology solutions were found to be dominant, including electronic health records, telemedicine, and mobile applications.In general, the results were scattered in terms of the research directions (technology country).Healthcare workers (i.e., physicians, nurses, and healthcare professionals) were the main focus of the reviewed studies.Patients' technology acceptance was only discussed in around 10% of the reviewed studies.In addition, the reviewed studies were mainly conducted in Taiwan and the USA, with minimum research articles in Arab and African countries.

Theoretical Contributions
As per the conducted classification analysis, the study provided multiple contributions to technology acceptance models and theories, especially in healthcare.This systematic review is believed to add a significant contribution to the existing literature for several reasons.First, it analyzed all the technology acceptance models instead of focusing on one model or theory (e.g., TAM).Second, this study included only the empirically evaluated acceptance models, their extensions, and integrations.Third, the study reviewed different information technologies instead of considering only one technology (e.g., electronic medical records).Fourth, studies with different settings and types of users were included in the review.Other healthcare professionals such as nurses, pharmacists, and clinical technicians are using the information technologies and playing a critical role in the success of those technologies.Fifth, the considered studies in the review were published in the recent decade (2010-2019), which provides a fresh overview of the literature.

Practical Implications
The study provides various practical implications for the healthcare domain.First, this review differs from the other reviews by including various technology acceptance models, various technologies, and various users.This diversity is valuable for other researchers and decision makers in different research areas, countries, and settings.For instance, virtual clinics can have great potential through telemedicine, cloud computing solutions, and mobile applications.Decision makers need to provide the necessary support for implementing these solutions to help physicians and healthcare professionals in providing many healthcare services (e.g., consultation, follow-up) without meeting the patient, especially in rural areas.
Second, the review shows a gap in the new technology trends in the healthcare sector.The decision makers and IT corporations should employ Internet of Medical Things (IoMT) and virtual reality (VR) solutions.IoMT can help to digitize the process, develop resource allocation, and provide real-time data to drive decisions.Virtual reality solutions can help to train resident physicians and young nurses to feel integrated with situations they may face in reality.Additionally, such augmented solutions can enable the physicians to access the patients' reports without leaving their current location, and using hands-free mode (voice commands).
Third, we believe that the results would assist policy makers in reviewing the current regulations and policies concerning data confidentiality and privacy.Additionally, these regulations should be announced and published.End-users need to be educated and aware of their roles and responsibilities to enhance their acceptance by improving the levels of trust and anxiety.
Fourth, information technology corporations (system analysts and developers) and healthcare organizations can utilize the findings related to the influential factors as a type of lessons learned.Consequently, this review can help to improve the currently implemented solutions and consider enhancements in future technology to be more userfriendly and innovative.Using information technology solutions with fewer efforts can encourage end-users to gain the maximum benefits without fear of making mistakes.
Fifth, the review addressed gaps in the technology acceptance literature by considering the regions of implementation.It has been observed that inadequate attention is paid to implementing cloud computing, telemedicine, and medical informatics applications in developing countries.Therefore, IT corporations need to concentrate on Arab and African countries, as there is potential to implement those new information technologies within the healthcare sector in these countries.

Limitations and Future Work
This systematic review was limited to particular digital libraries and databases to collect the research studies (i.e., PubMed, IEEE Xplore, Springer, ACM, Science Direct, and Google Scholar).Therefore, these digital libraries might not provide a complete picture for all empirical studies published on technology acceptance in healthcare.Future research may extend this review by including studies from other digital libraries, such as CINAHL, Cochrane, Scopus, Sage, and Web of Science.Additionally, this review has covered only empirical quantitative studies.Further reviews might consider qualitative studies.

Figure 2 .
Figure 2. Most studied technology acceptance models.

Figure 3 .
Figure 3. Key factors affecting technology acceptance in healthcare.

Figure 4 .
Figure 4.The most confirmed hypotheses in the reviewed literature.

Figure 5 .
Figure 5. Distribution of studies in terms of technology type.

Figure 6 .
Figure 6.Distribution of studies in terms of participants.

Figure 7 .
Figure 7. Publications statistics per region.Mixed: conducted in two different regions.

Figure 8 .
Figure 8. Geographic chart for the studies included in this review.

Figure 9 .
Figure 9. Frequency of studies per year.

Figure 10 .
Figure 10.Mind map for the results summary.

Table 1 .
Previous review studies on technology acceptance in healthcare.

Table 2 .
Inclusion and exclusion criteria.

Table 3 .
Summary of search keywords.
Nurse OR Patient) AND ("Intention to use" OR "Actual use") 4 ("Technology Adoption") AND (Healthcare OR Health OR Medical OR Physician OR Nurse OR Patient) AND ("Intention to use" OR "Actual use")

Table 5 .
Technology types and directions of countries.

Table 6 .
Technology types and participants' groups.

Table 7 .
Top countries by publication frequency.

Table A1 .
Quality assessment results.

Table A2 .
Full list of the included publications.