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As Part of a Research Study a Physician Plans to Review Medical Records to Explore Factors

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Exploring disquisitional factors influencing physicians' acceptance of mobile electronic medical records based on the dual-cistron model: a validation in Taiwan

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Abstract

Background

With respect to information direction, most of the previous studies on the acceptance of healthcare information technologies were analyzed from "positive" perspectives. However, such acceptance is always influenced by both positive and negative factors and information technology is necessary to validate both in lodge to get a complete understanding. This report aims to explore physicians' credence of mobile electronic medical records based on the dual-cistron model, which is comprised of inhibitors and enablers, to explain an individual's engineering usage. Following an earlier healthcare report in the U.s., the researchers conducted a like survey for an Eastern land (Taiwan) to validate whether perceived threat to professional autonomy acts as a critical inhibitor. In addition, perceived mobility, which is regarded as a disquisitional feature of mobile services, was also evaluated as a common antecedent variable in the model.

Methods

Physicians from three branch hospitals of a medical group were invited to participate and complete questionnaires. Partial least squares, a structural equation modeling technique, was used to evaluate the proposed model for explanatory power and hypotheses testing.

Results

158 valid questionnaires were nerveless, yielding a response rate of 33.xl%. Every bit expected, the inhibitor of perceived threat has a significant impact on the physicians' perceptions of usefulness besides as their intention to utilise. The enablers of perceived ease of utilize and perceived usefulness were besides pregnant. In improver, as expected, perceived mobility was confirmed to take a significant impact on perceived ease of apply, perceived usefulness and perceived threat.

Conclusions

It was confirmed that the dual-factor model is a comprehensive method for exploring the acceptance of healthcare data technologies, both in Western and Eastern countries. Furthermore, perceived mobility was proven to be an effective antecedent variable in the model. The researchers believe that the results of this study will contribute to the research on the acceptance of healthcare information technologies, specially with regards to mobile electronic medical records, based on the dual-factor viewpoints of academia and practice.

Peer Review reports

Groundwork

It is a mutual business organization for industry, regime, and academia to ameliorate the quality of medical services, increase the safety of patients, and reduce medical costs through the apply of information technologies, which likewise enhance competitiveness. Since the national health insurance organisation was launched in Taiwan in 1995, medical institutions take get more active in introducing a variety of technologies in order to get fee payments from the Bureau of National Health Insurance quickly and correctly. These technologies are related to healthcare data, such equally computerized physician gild entry systems (CPOE), medication administration systems, and clinical back up systems. Every bit these information technologies have evolved, innovative applications in the healthcare industry take become a global success. In contempo years, the government of Taiwan has made great efforts to promote the development of electronic medical records (EMR), invested huge sums in subsidies, and instigated another great leap forward in healthcare information technology. For instance, medical coaching institutions have introduced ISO27001 information security certification and an electronic signature system, and the National Commutation Eye of Electronic Medical Records has been established.

However, although medical institutions accept introduced many new types of technology and systems and take spent big sums of money in the process of bringing near different levels of change to healthcare do, questions and doubts remain every bit to whether they accept yielded the expected benefits. For example, a study by Lærum and colleagues [i] discovered that physicians only used a small pct of the functions constituting an EMR system. A discussion on the introduction of healthcare information technology (Hit) showed that the feet of healthcare professionals was always an of import influencing gene [2]. During the introduction of innovation technologies, healthcare professionals need to non merely change their working customs merely also learn to adapt, which has an bear on on their work. Equally a result, the resistance of physicians to new technologies has long been considered a common problem during the introduction of healthcare information systems in medical institutions [three,4].

With respect to information direction, most of the previous studies on the credence or adoption of innovation technologies were carried out from "positive" perspectives, such as perceived usefulness and perceived ease of utilize, with regards to the Technology Acceptance Model (TAM) [5]. Simply a few studies have adopted systematic methods (such as model validation) to discuss the negativity of users toward innovation technologies, such every bit perceived threats [six], innovation resistance [seven], and technophobia [8]. The situation is the same in the healthcare field [nine-fourteen]. As the application of information technologies is always influenced by both positive and negative factors, it is necessary to validate both to gain a consummate understanding. This is the basic concept of the dual-factor model, considering both positive and negative factors. Based on this concept, Walter and Lopez [15] introduced the new negative factor of "perceived threat to professional autonomy" ("perceived threat" for short) based on the TAM in gild to talk over US physicians' acceptance of EMR and Clinical Decision Support (CDS). They discovered that "perceived threat" influenced physicians' perceptions of the usefulness of information technologies as well every bit their intention to utilise these technologies. This study believes that it is worthwhile to validate the model proposed by Walter and Lopez'south [15] enquiry model for Eastern countries and confirm the general explanatory ability of the model.

Data and advice technologies are well developed in Taiwan [16], and the development of EMR in hospitals is too comparatively mature [17]. Consequently, information technology is proper for this study to choose Taiwanese physicians as the research subjects to validate the research model proposed in Walter and Lopez's written report [fifteen]. Since Taiwan started to promote its national health insurance system in 1995, traditional (desktop and wired) EMR has been commonly used in hospitals [17]. In addition, mobile healthcare (also known as m-Health and m-Healthcare) is considered to have significant benefits and is in the phase of initial evolution [18]. Nevertheless, presenting ubiquitous services to healthcare professionals is not like shooting fish in a barrel. A cardinal challenge is progressing grand-Health approaches from pilot projects to wider implementions whilst properly engaging healthcare professionals in the procedure [eighteen]. Developers of these projects demand to expend substantial endeavor and resources to ensure mobile service support. Thus, understanding the factors that influence healthcare professionals' usage of mobile services is important to the development of mobile electronic medical records (MEMR). Therefore, ii research questions are presented in this study: 1) Is the dual-factor model proposed by Walter and Lopez [15] applicable for evaluating physicians' acceptance of MEMR in Eastern countries? two) Could the characteristic of "perceived mobility" become a valuable antecedent variable for each of the inhibitors and enablers in Walter and Lopez's model [xv]?

Mobile electronic medical records

Various definitions for computer-based patient records accept been advanced e.g., [19,20]. A consentaneous definition of EMR provided in the US National Alliance for Health Information Applied science Report to the Office of the National Coordinator for Health Information Technology [21] states that "EMR is an electronic record of the health-related information on an individual that tin can be created, gathered, managed, and consulted by authorized clinicians and staff within 1 healthcare arrangement." In Taiwan, the cadre law source for electronic medical records is the Medical Intendance Act, which was promulgated on November 24, 1986. According to updated Article 69 of the Medical Care Act, "medical records that are generated and stored in the form of electronic files are exempted from generating newspaper versions, and their qualifications, generating methods, content, and other matters to follow shall exist determined by the competent central authority". In the past ten years, in order to speed up the evolution of electronic medical records, the Department of Health has undertaken several measures, such as revising the related laws and regulations, generating standards, providing technological support, strengthening data security, and providing subsidies. It has likewise ensured the implementation of paperless medical records during hospital accreditation and healthcare inspections so as to encourage a hospital's initial intention to use electronic medical records.

Due to the flourishing of wireless communication networks and the rapid development of handheld electronic devices, the mobility and "wirelessness" of electronic medical records have go more feasible. Ying believed that the Mobile Physician Guild Entry should focus on certain desired features, such as loftier-yield orders, a uncomplicated interface, and mobility [22]. A Wireless Wellness Outcomes Monitoring Arrangement (WHOMS) was developed and tested with cancer patients using mobile phones and the results suggested that such a mobile system has the potential to find patient-suffering before and enable the start of well-timed intervention [23]. Wu and colleagues pointed out that physicians who used PDA to conduct out Computerized Dr. Order Entry (CPOE) felt it was necessary to raise the response speed of the organisation, simplify operations, and meliorate the display, and then on [24]. Hsieh and colleagues discussed the thought that the adoption of Mobile Electronic Medication Administration Records can reduce human error and enhance medication safety [25].

The aforementioned studies were more than concerned with the application, advantages, and disadvantages of the mobile electronic medical record, and seldom gave a articulate definition of a mobile electronic medical tape. Therefore, referring to the definition by Hsu and colleagues [26], this study defines a "mobile electronic medical record" equally "an EMR that can be accessed and managed through mobile computers to help physicians deliver health care anytime and anywhere". During a md's clinical do, a mobile electronic medical record means the use of a mobile device (such as a tablet computer, laptop, mobile phone or PDA) that can be taken on rounds, used during inspections and consultations, used for enquiries about dr. orders and prescriptions, and for the functioning of other routine physician duties.

Prior research and hypotheses

The dual-factor model that influences the intention to employ technology

A rather high proportion of the studies on information systems (IS) discussed influences on the adoption, acceptance, and usage of such systems e.g., [5,27-31]. Many studies analyzed the belief in the system adoption, satisfaction with the organization, and other factors that would promote the success of the system, pb to positive attitude, and encourage usage. Still, there are comparatively fewer studies on the hindrances or inhibitions of system usage [32]. Cenfetelli believed that negative factors (inhibitors) and positive factors (enablers) were the external beliefs of users toward organisation features, which would influence users' decisions when adopting or refusing the system [32]. Therefore, it is necessary to consider these factors.

Walter and Lopez [15] posited that the perceived threat to professional person autonomy is a salient outcome belief affecting physician acceptance of an HIT. Including this negative cistron in the TAM [5], too as the core positive constructs of perceived ease of use and perceived usefulness, they proposed and validated a comprehensive model to explain physicians' acceptance of HITs in the U.s.. Their model suggests that perceived usefulness, perceived ease of use and the perceived threat of Hit are major determinants of behavioral intention. Furthermore, perceived usefulness is influenced past perceived threat and perceived ease of use.

The following sections volition hash out and comment on the positive and negative influencing factors in Walter and Lopez's [15] model. In addition this written report introduces and discusses "perceived mobility" as a potential antecedent variable in the model.

Perceived usefulness and perceived ease of use

The Applied science Credence Model (TAM) is one of the best known theories in the mod information arrangement enquiry field and is oft cited [33]. It was developed by Davis and colleagues in 1989 [5], referring to the Theory of Reasoned Activeness [34] and the Theory of Planned Behavior [35], and was used to explicate the relationship between engineering science and user beliefs [5]. The TAM inherits the basic thought of the Theory of Reasoned Action and states that internal beliefs will influence "attitude", which will farther influence the intention for usage. The intention for usage has a meaning and positive impact on the bodily use of the organisation.

Szajna [36] removed the variable of user's "mental attitude" from the original TAM and revised the model to conclude that the intention to utilize is strong enough to influence engineering science acceptance. The study divided the TAM into 2 models, before and afterwards the actual operation. The greatest contribution of the TAM lies in the introduction of two perceived beliefs, (perceived ease of apply and perceived usefulness) that influence the users' technology credence. These two constructs are the almost widely used positive factors.

Subsequently, Venkatesh and Davis [27] proposed TAM 2, and Venkatesh and Bala [29] proposed TAM 3. Both of these models are notwithstanding based on the two core beliefs of perceived ease of employ and perceived usefulness, but the difference is that they add broader external variables. The applicability of TAM to the HIT field has been widely validated [33], including the healthcare field [37]. This study therefore proposes the basic hypotheses of the TAM:

  • H1: Physicians' perceived usefulness positively influences their intention to use MEMRs.

  • H2: Physicians' perceived ease of use positively influences their intention to utilise MEMRs.

  • H3: Physicians' perceived ease of use positively influences their perceived usefulness of MEMRs.

Perceived threat

The acceptance of a new applied science is often mired by a reluctance to forsake the delivery to a previous work configuration and the perception of threat to connected job security (due east.g. loss of ability) [6].

Harvey [38] posited that physicians' resistance to the introduction of information technology is common, and that the primary outcome is the collection of data can exist threatening individually, as the potential for peer review or performance review by managers is obvious.

Physicians are particularly sensitive to changes in their working environment that will threaten their working autonomy [39,xl]. They experience uncomfortable knowing that other people can ascertain information about their care of their patients [41]. The so-called 'other people' too include the computer system. Therefore, physicians may be reluctant to use a estimator system [41] or have a tendency to deny the usefulness of the system.

Walter and Lopez [15] divers professional autonomy as professionals' having command over the weather, processes, procedures, or content of their piece of work according to their own collective and, ultimately, individual judgment in the application of their profession'due south body of knowledge and expertise. Thus the perceived threat of Information technology to professional autonomy (too abbreviated as perceived threat in this study) tin can be defined as the extent to which professionals perceive that Information technology systems will threaten their professional autonomy. In health care, physicians may perceive that MEMRs transgress their professional autonomy and are non useful due to the conventionalities that they tin can comport the best controlling for patient-care without MEMR assistance. Every bit a effect, they may express a depression intention to use MEMRs.

Walter and Lopez's study on The states physicians' usage of HITs [fifteen] confirmed that "perceived threat" had a negative influence on perceived usefulness, as well as on physicians' intention to use the organization. A similar study exploring consumers' health behavior intention also validated the causal relationship between perceived threat and usefulness [42]. Therefore, the following hypotheses are proposed:

  • H4: Physicians' perceived threat negatively influences their perceived usefulness of MEMRs.

  • H5: Physicians' perceived threat negatively influences their intention to use MEMRs.

Perceived mobility every bit an effective universal antecedent

Mobility enables users to receive and transmit information someday and anywhere. Calculator-supported collaborative piece of work and human/calculator interactions take provided an insight into the characteristics, requirements, and implications of mobile engineering use. This mobility (besides known as ubiquity) ways that, with the help of mobile terminals and networks, users tin can access mobile services, such every bit mobile cyberbanking, anytime and anywhere [43]. Compared with traditional e-commerce, mobile computing provides admission to information, communication, and services that are independent of time and place [44]. Thus, mobility, in the study past Mallat and colleagues [44], in the e-ticketing context is used to express the benefits of time and place, service access, and usage. Mobility is perceived to exist the most significant feature of a mobile service.

Huang and colleagues [45] explored user behavior in mobile learning and found that perceived mobility has a positive impact on perceived usefulness. Such a causal relationship has likewise been validated in other contexts, such as consumers' credence of mobile payment services [46], users' employment of mobile map services [47] and players' acceptance of mobile social network games [48]. In healthcare, mobility can express the aforementioned benefits for healthcare professionals as well. It frees them from spatial and temporal limitations and enables them to behave ubiquitous healthcare, peculiarly at the betoken of intendance. Of course, physicians may also realize that MEMRs are useful tools for care purposes. Thus, the post-obit hypothesis is proposed:

  • H6: Physicians' perceived mobility positively influences their perceived usefulness of MEMRs.

Due to the technical limitations of mobile devices, ease of apply becomes an imminent credence driver for mobile applications. Some of the limitations include small screen size, depression battery life and reduced input/output capabilities. This is especially true for mobile services, which compete with established solutions and thus need to provide benefits when information technology comes to ease of employ [46]. All the same, mobile services enable users to access information and people anytime and anywhere. Consequently, this feature enhances the ease of utilise of the service. Two recent studies regarding users' credence of mobile services have provided preliminary testify, which have confirmed the existence of the relationship betwixt perceived mobility and perceived ease of utilize [49,50]. In healthcare, mobility tin can be regarded every bit the primary advantage of MEMRs compared to traditional EMRs. If physicians perceive that MEMRs take higher mobility, it means that they tin can access MEMRs easier at whatsoever time and from anywhere, especially at the point of care. Thus, the following hypothesis is proposed:

  • H7: Physicians' perceived mobility positively influences their perceived ease of utilise of MEMRs.

Steinhubl [51] states that the level of exuberance for m-Health is driven by the convergence of three powerful forces, 1 of which is the need for more than precise and individualized medicine that enables physicians to have more than command and fourth dimension to complete their care work. In clinical practice, mobility of MEMRs enables the transmission of patient information to physicians without space or time limitations. When physicians take received or are inquiring almost important information, they are able to bear succeeding disposition and make decisions immediately, which will reduce the interference or threat to their control or the autonomy of their medical treatment. Although the academic field hasn't yet studied the influences of perceived mobility on perceived threat, this study believes that both of the variables should have a proper relationship in medical practice. Therefore, the following hypothesis is proposed:

  • H8: Physicians' perceived mobility negatively influences their perceived threat of MEMRs.

Methods

Research framework

The framework of this study was constructed by mainly referring to related theoretical ideas, such as the dual-factor model and the Technology Credence Model, and was used to hash out factors that influence a physician'south usage of medical information technologies. The research framework is shown in Figure one. The constructs and variables of this report come from the aforementioned literature. The operational definitions of the independent and dependent variables are shown in Table i.

Figure ane
figure 1

Research framework.

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Table 1 Operational definitions of variables

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The design of the questionnaire

The design of the questionnaire for this written report was based on related theories and questionnaires adult and validated past previous scholars. Therefore, it is believed that the questionnaire designed for this study has loftier reliability and validity. Firstly, the literature was reviewed to collect measuring tools that had been rigorously validated, and these were and then used as the foundation to develop the questionnaire for this study. The questionnaire was and so translated into Chinese, and the sentences modified to obtain the kickoff typhoon of the questionnaire. After that, three experts (a professor with a PhD in the medical information field, a professor with a PhD in the medical management field, and a clinical medico with a PhD) were asked to verify the draft and evaluate whether the sentences and their meanings were properly expressed (pretesting), and whether they could be merged for simplification. Finally, 3 potential users (physicians) were asked to complete the revised questionnaire as a pilot test, earlier the questionnaire for this study was finalized (Additional file one).

In this written report, the items for measuring perceived threat were adapted from Walter and Lopez's instrument [15], the items for measuring perceived mobility were adapted from Lee's instrument [53], while the instruments for perceived ease of employ, perceived usefulness, and behavioral intention were adapted from previous empirically validated studies [9,5,52]. The variables and respective measurement items of this study, equally well as reference resource, are shown in Table 2.

Table ii Measurement items of the variables and reference resources

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Written report subjects and upstanding considerations

The study subjects are the entire roster of physicians from three co-operative hospitals of a medical heart (i is a infirmary at medical center level, one is a infirmary at regional level, and 1 is a infirmary at commune level), including resident physicians, attending physicians, and concierge physicians. In Taiwan, the hospital accreditation system, as implemented in 1978 by Taiwan's Ministry of Education and Department of Health (DOH), issues a level of accreditation, determined by the size, capabilities, and performance quality of a infirmary. Based on these accreditation rules, the three primary levels are categorized as medical center, regional infirmary and district hospital. Thus, the study subjects chosen are fully representative of the medical system in Taiwan.

In order to protect the rights and privacy of the participants, appropriate upstanding approval for this study was obtained from the Institutional Review Board of the hospital (Chi Mei Medical Center) earlier the questionnaires were officially distributed.

Results

Descriptive statistics

A total of 158 valid questionnaires were collected from the three hospitals. As the total number of physicians beyond the 3 hospitals is 473, the recovery rate of valid questionnaires was 33.40%. Among these questionnaires, 71.52% (113 copies) were from the medical center, with male respondents accounting for the majority (81.65%, 129 copies); the proportion from the Department of Internal Medicine was the highest (37.97%, threescore copies) (see Tabular array 3). Table four shows that the respondents, with respect to MEMRs, gave extremely high approving for the perceived ease of use, usefulness, compatibility, and relevance to work (mean > iv). The respondents expressed a insufficiently low level of perceived threat regarding MEMRs (2 < mean < 3).

Table 3 Descriptive statistics of the respondents

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Table 4 Descriptive statistics of the criteria for determining the quality of the responses

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Reliability and validity

Cronbach's α for each of the constructs was greater than 0.9, exceeding the suggested cutting-off value of 0.7, and the composite reliability (CR) of all constructs exceeded the suggested cut-off value of 0.6. These results all indicated that the measurements satisfied the reliability criteria [54]. Fornell and Larcker [55] suggested using the average variance extracted (AVE) as a measure of convergent validity. Table 4 demonstrates that the AVEs ranged between 0.81 and 0.88, exceeding the cut-off value of 0.5 [55], suggesting satisfactory convergent validity. Additionally, Table 5 shows that none of the construct intercorrelations exceeded the square root of the AVE of the constructs, establishing discriminant validity [55]. Overall, all of the constructs in this study exhibited sufficient convergent and discriminant validity.

Table v Correlation matrix

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The partial least squares (PLS) technique was used in this study to evaluate the measurement and structural models [56]. The principal component analysis of the PLS was performed to ensure the unidimensionality of the iii constructs PU, PEOU, and PT. The factor loadings of all of these items were equal to or greater than 0.86, which exceeded the cut-off of 0.7 suggested by Fornell and Larcker [55]. These were significantly associated with but one latent variable, indicating conformance to unidimensionality [57].

According to the PLS path modelling construction, the measurement model, structural model and overall model need to exist validated with three unlike fit indices, namely the communality index, the redundancy alphabetize and the Goodness of Fit (GoF) index [56]. GoF is employed to gauge the overall fit of the PLS model, which is computed equally the geometric mean of the average communality and the average R square. The redundancy alphabetize represents the amount of variance in an endogenous construct explained by its independent latent variables. High back-up means a high ability to predict. A good value for the AVE index (equal to the communality alphabetize in the PLS analysis) is at least 0.50, which means that 50% or more than of the variance is accounted for. GoF is normed between 0 and ane, where a college value represents better path model estimations. For this model, the average back-up value is 0.26, the average communality value is 0.84, and the GoF value is 0.56, which exceeds the cut-off value in comparing with the baseline value every bit GoFsmall =0.1, GoFmedium =0.25, GoFlarge =0.36 [58]. These indices bespeak that this model has substantial predictive power.

Hypotheses testing

The statistical significance of the parameters in the structural model was tested using the bootstrapping resampling process of the PLS method. SmartPLS® ii.0 M3 software was used [59]. With a significance of 0.05 or better, the results revealed that the physicians' perceptions of PU and PEOU were positively associated with their behavioral intentions to use MEMR, while PT was negatively associated. These three factors jointly explained about 57.8% of the variance. PEOU and PT associated with the mutual antecedent of PM explained approximately 58.4% of the variance in PU. Surprisingly, PM was confirmed to be an effective antecedent that solely influenced PT and PEOU with a lighter explanatory power of 4.lxx% and a stronger explanatory power of 26.0%, respectively. These results support all the hypotheses proposed. Figure 2 presents the standardized path coefficients of the causal path.

Figure 2
figure 2

PLS path analysis results.

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Discussions and suggestions

Discussions

This report has discovered that physicians, mostly, take a loftier intention to use MEMRs (mean = iv.xv), which indicates that, in Taiwan, the evolution of k-Health has been widely accepted by healthcare professionals. Some other study targeting nursing staff as well demonstrated the same issue [26]. In Taiwan, due to the implementation of the national health insurance system, medical informatics has been well developed, and information technology is mutual for healthcare professionals to utilise information technologies to assist their piece of work. In add-on, the data and communication industry of Taiwan is quite avant-garde. All kinds of mobile facilities are available to and pop with the public. Therefore, physicians take a positive attitude toward the adoption of innovative technologies, such every bit MEMRs.

This report has verified the influence of perceived threat. That is, with respect to physicians' acceptance toward innovation technologies, apart from the positive factors of usefulness and ease of use, the threat perceived by physicians should not be neglected. This study result and the study result of Walter and Lopez [15] have provided strong support to demonstrate that this phenomenon is no different between Western and Eastern countries. The written report effect shows that perceived threat has a significant influence on perceived usefulness. This indicates that physicians feel that MEMRs will threaten their working autonomy because MEMRs enable other people to larn more information most the treatment of their patients. As a consequence, physicians may feel uncomfortable with this aspect and will then tend to refuse the usefulness of MEMRs, leading to a decrease in their intention to employ MEMRs.

With respect to positive factors, usefulness and ease of use have as well been verified to have a direct influence on physicians' intention to use MEMR, which conforms to the common feel generated from previous studies regarding individuals' credence of new technologies.

To summarize, physicians' intention to use MEMRs is nevertheless significantly and straight related to perceived ease of use and perceived usefulness. All the same, perceived threat, neglected past previous studies, has been proven past this study to have a negative influence on physicians' adoption of MEMRs.

Another purpose of this study was to hash out whether perceived mobility is the ancestor variable of all the positive and negative factors. The study result demonstrates that perceived mobility does have a significant influence on perceived usefulness, perceived ease of utilize, and perceived threat. However, it is worth noting that perceived mobility only has a slight influence on perceived threat. This implies that other obscure factors, such as patient autonomy [sixty,61], may be closely associated to a physician's perceived threat of professional autonomy. In clinical practice, a MEMR is able to transmit all kinds of useful and relevant data, such equally data reports for examinations and tests, without infinite or time limitations. With this on-demand information, physicians are able to consult the necessary information for timely determination-making. In this way, physicians will suffer less mental anxiety while still feeling they take sufficient autonomy and control over their medical practice, and the threat they may feel towards MEMRs will be reduced. Even so, the feature of mobility is not a primary concern for increasing physicians' professional autonomy.

Suggestions

First, this study suggests that, when developing MEMRs, information technology is better not to over-emphasize the nature of the technology'south innovation and automation, such every bit providing too many tips or guidelines, as it will make physicians experience that their expertise or autonomy to brand decisions regarding diagnosis is interfered with or challenged. As a upshot, it is suggested that, when developing MEMRs, it is necessary to safeguard the autonomy of medical practitioners. Otherwise, physicians may feel threatened and will then reject the system.

Secondly, MEMR developers should give sufficient consideration to the features (affect input, pocket-sized screen, etc.) of mobile facilities, and pay primary attention to providing basic and necessary functions. They should non allocate too many functions to a limited screen window while besides ensuring that physicians practice not have to switch pages likewise frequently. The organisation should be made easy to operate, and the tools of the MEMR should be hands controllable just by sliding and then as to avoid wrong character input.

3rd, this study suggests that industry, government, and academia should fully communicate with physicians to ensure MEMRs give full support to their medical practice while non hindering control over their medical decision-making. Otherwise, physicians' intention to employ MEMRs volition decrease.

Finally, regarding the mobility characteristic, this report suggests that, while developing MEMRs, manufacture, government, and academia should pay attention to the availability and readiness of the EMR infrastructure, including increasing the coverage, stability, and speed of wireless networks at places of work. Therefore, attending should be paid to the effective maintenance and availability of mobile facilities (such every bit updating operating systems to set bugs) and the readiness (such as sufficient power). Thus, the mobility of MEMRs volition be enhanced, which will increment physicians' perceived usefulness and perceived ease of employ, and reduce their perceived threat regarding MEMRs.

Determination

Contributions and implications

In contempo years, the development of innovation technology for m-Health has been a focus of attention. Although many clinical testing systems or bookish enquiry plans have proposed a number of interesting application prototypes, the applied science is all the same not usually used in medical practice. Also, every bit the development and maintenance of the application systems of MEMRs are expensive (such as costs for tablet devices, wireless networks, developing technologies and tools, user grooming, etc.), it is of import that the manufacture, authorities, and academia understand the inhibitors and enablers that will influence physicians' intention to use MEMRs during the development of chiliad-Health. This written report has discussed this research result using the dual-gene model and has obtained a high explanatory power. Information technology is valuable for accumulating inquiry experience of the dual-factor model in the healthcare field. 1 of the contributions of this study is confirmation that, both in Western and Eastern countries, the perceived threat to professional autonomy is an important factor with regards to perceived usefulness in the context of physicians' credence of HITs and has a significant, negative impact on their intention to employ HITs. This implies that excessive use of engineering science may adversely affect user perception, even for highly intelligent knowledge workers, such equally physicians. In addition, this study has also confirmed that usefulness and ease of use are all the same the two critical factors that influence healthcare professionals' intention to use HITs. This written report, therefore, reminds the healthcare industry, government, and academia to maintain attention on these ii factors. Another contribution of this study is to validate perceived mobility equally an constructive antecedent variable while modelling physicians' acceptance of mobile engineering science. This study calls for continuous exploration into perceived mobility in health care and other fields while initiating a mobile service. To summarize, it is worth stressing that previous studies have seldom discussed the influence of perceived mobility on perceived ease of use, perceived usefulness and perceived threat. Thus, this written report reveals both academic and applied values in the healthcare field.

Hereafter inquiry direction

Based on this written report, future researchers tin integrate other meaningful negative factors, such equally perceived risk [62], to enhance the explanatory power of the research model of this study. This report discovered that perceived mobility was an effective antecedent variable of the factors that influence the intention to use mobile services. All the same, the low explanatory power of perceived mobility on perceived threat highlights the fact that a comprehensive literature review is nonetheless necessary in order to identify additional critical factors for follow-up research. In improver to perceived mobility, other valuable antecedent variables may be and deserve to be explored in the hereafter. In addition, some studies have discovered that perceived mobility itself is directly related to the intention to use mobile services [45,63,46]. It is worthwhile testing this difference. Another urgent aspect to explore is nurses, the largest grouping of intendance workers, and their perceived threat relating to MEMRs, mainly when using mobile nurse stations. Across places of intendance, mobile technologies also present an opportunity to connect patients and health workers so equally to ameliorate the quality of care given at the indicate of intendance and reduce unnecessary referrals [64]. Therefore, patients' intention to accept MEMRs is too worthy of written report.

Limitations

Although this study attempted to be rigorous during the implementation, research limits may still be. Firstly, as the written report subjects are physicians from 3 hospitals under the same medical organization, the extrapolation validity of the study results may exist insufficient. Besides, as the questionnaires of this study were completed by means of self-reporting, each respondent may accept a different understanding or perception of the meanings of the questions, which may cause common method bias (CMB) or common method variance (CMV), and farther influence the study results.

References

  1. Lærum H, Ellingsen G, Faxvaag A: Doctors' apply of electronic medical records systems in hospitals: cross sectional survey. BMJ 2001, 323:1344–1348.

    Article  PubMed  PubMed Central  Google Scholar

  2. Ludwicke DA, Doucette J: Adopting electronic medical records in primary intendance: Lessons learned from health information systems implementation feel in vii countries. Int J Med Inform 2009, 78:22–31.

    Article  Google Scholar

  3. Kaplan B: The medical calculating "lag": Perceptions of barriers to the application of computers to medicine. Int J Technol Appraise Wellness Care 1987, three:123–136.

    CAS  Article  PubMed  Google Scholar

  4. Paré K, Sicotte C, Jacques H: The effects of creating psychological buying on physicians'acceptance of clinical information systems. J Am Med Inform Assoc 2006, 13:197–205.

    Article  PubMed  PubMed Central  Google Scholar

  5. Davis FD, Bagozzi RP, Warshaw PR: User credence of estimator technology: a comparison of ii theoretical models. Manage Sci 1989, 35:982–1003.

    Commodity  Google Scholar

  6. Ang J, Pavri F: A survey and critique of the impacts of information technology. Int J Inform Manage 1994, 14:122–133.

    Commodity  Google Scholar

  7. Timmons S: Nurses resisting it. Nurs Inq 2003, 10:257–269.

    Article  PubMed  Google Scholar

  8. Frideres JS, Goldenberg S, Disanto J, Fleming U: Technophobia: incidence and potential causal factors. Soc Indic Res 1983, thirteen:381–393.

    Article  Google Scholar

  9. Hu PJ, Chau PYK, Sheng ORL, Tam KY: Examining the technology acceptance model using physician acceptance of telemedicine engineering. J Manage Inform Syst 1999, 16:91–112.

    Article  Google Scholar

  10. Kuo KM, Liu CF, Ma CC: An investigation of the effect of nurses' technology readiness on the acceptance of mobile electronic medical record systems. BMC Med Inform Decis Mak 2013, 13:88.

    Article  PubMed  PubMed Central  Google Scholar

  11. Asua J, Orruño Eastward, Reviriego E, Gagnon M: Healthcare professional acceptance of telemonitoring for chronic intendance patients in primary intendance. BMC Med Inform Decis Mak 2012, 12:139.

    Article  PubMed  PubMed Cardinal  Google Scholar

  12. Zhang HP, Cocosila MP, Archer NP: Factors of Adoption of Mobile Information Technology by Homecare Nurses: A Technology Acceptance Model 2 Arroyo. Comput Inform Nurs 2010, 28:49–56.

    Article  PubMed  Google Scholar

  13. Aggelidis VP, Chatzoglou PD: Using a modified engineering acceptance model in hospitals. Int J Med Inform 2009, 78:115–126.

    Article  PubMed  Google Scholar

  14. Chang HC, Liu CF, Hwang HG: Exploring Nursing e-Learning Systems Success Based on Information Arrangement Success Model. Comput Inform Nurs 2011, 29:741–747.

    Article  PubMed  Google Scholar

  15. Walter Z, Lopez MS: Physician acceptance of information technologies: Part of perceived threat to professional autonomy. Decis Support Syst 2008, 46:206–215. 68.

    Commodity  Google Scholar

  16. World Economic Forum. The Global It Report 2013. URL: http://www3.weforum.org/docs/WEF_GITR_Report_2013.pdf. Accessed: 2014-04-05.

  17. Liu CF, Hwang HG, Chang HC: Due east-Healthcare Maturity in Taiwan. Telemed J E Wellness 2011, 17:569–573.

    Article  PubMed  Google Scholar

  18. Källander K, Tibenderana JK, Akpogheneta OJ, Strachan DL, Hill Z, ten Asbroek AH, Conteh L, Kirkwood BR, Meek SR: Mobile Wellness (mHealth) Approaches and Lessons for Increased Functioning and Retentiveness of Community Health Workers in Depression- and Middle-Income Countries: A Review. J Med Cyberspace Res 2013, fifteen:e17.

    Article  PubMed  PubMed Central  Google Scholar

  19. CPRI Work Grouping on CPR Description: Computer-based Patient Record Description of Content. Schaumburg, IL: Computer-based Patient Record Plant; 1996.

    Google Scholar

  20. Institute of Medicine: The Computer-Based Patient Record: An Essential Technology for Health Intendance. Washington, DC: National Academy Press; 1997.

    Google Scholar

  21. Section of Health and Human Services: The National Alliance for Health Information Technology report to the Office of the National Coordinator for Health Information technology on defining central health information technology terms. 2008.

    Google Scholar

  22. Ying A: Mobile Physician Order Entry. J Healthc Inf Manag 2003, 17:58–63.

    PubMed  Google Scholar

  23. Bielli Due east, Carminati F, La Capra Due south, Lina Thousand, Brunelli C, Tamburini M: A Wireless Wellness Outcomes Monitoring System (WHOMS): development and field testing with cancer patients using mobile phones. BMC Med Inform Decis Mak 2004, iv:7.

    Article  PubMed  PubMed Cardinal  Google Scholar

  24. Wu RC, Orr MS, Chignell Grand, Straus SE: Usability of a mobile electronic medical record epitome: a exact protocol analysis. Inform Health Soc Care 2008, 33:139–149.

    Commodity  PubMed  Google Scholar

  25. Hsieh SH, Hou IC, Cheng PH, Tan CT, Shen PC, Hsu KP, Hsieh SL, Lai F: Design and implementation of web-based mobile electronic medication administration record. J Med Syst 2010, 34:947–958.

    Article  PubMed  Google Scholar

  26. Hsu SC, Liu CF, Weng RH, Chen CJ: Factors influencing nurses' intentions toward the utilize of mobile electronic medical records. Comput Inform Nurs 2013, 31:124–132.

    Article  PubMed  Google Scholar

  27. Venkatesh V, Davis FD: A theoretical extension of the technology credence model: Four longitudinal field studies. Manage Sci 2000, 46(ii):186–204.

    Article  Google Scholar

  28. Venkatesh V, Morris MG, Davis GB, Davis FD: User credence of information technology: Toward a unified view. MIS Quart 2003, 27:425–478.

    Google Scholar

  29. Venkatesh V, Bala H: Technology acceptance model 3 and a research agenda on interventions. Decision Sci 2008, 39:273–315.

    Article  Google Scholar

  30. DeLone WH, McLean ER: Data systems success: the quest for the dependent variable. Inform Syst Res 1992, three:60–95.

    Article  Google Scholar

  31. Delone WH, McLean ER: The DeLone and McLean Model of Information Systems Success: a ten-year update. J Manage Inform Syst 2003, 19:ix–thirty.

    Google Scholar

  32. Cenfetelli RT: Inhibitors and enablers as dual gene concepts in applied science usage. J Assoc Inf Syst 2004, 5:472–492.

    Google Scholar

  33. Lee Y, Kozar KA, Larsen KRT: The technology credence model: past, present, and future. Communications of the Clan for Information Systems 2003, 12:752–780.

    Google Scholar

  34. Fishbein M, Ajzen I: Belief, attitude, intention and behavior: an introduction to theory and research. Reading, MA: Addison-Wesley; 1975.

    Google Scholar

  35. Ajzen I: From intention to actions: a theory of planned behavior. In Action-Control: From Cognition to Behavior. Edited by Kuhl J, Bechmann J. Heidelberg, Berlin, Germany: Springer; 1985.

    Google Scholar

  36. Szajna B: Empirical evaluation of the revised technology acceptance model. Manage Sci 1996, 42:85–92.

    Article  Google Scholar

  37. Holden RJ, Karsh BT: The Technology Credence Model: its by and its time to come in health care. J Biomed Inform 2010, 43:159–172.

    Commodity  PubMed  Google Scholar

  38. Harvey JD: Towards a convenient future. The impact of information technology inside primary health care. Int J Technol Assess Health Intendance 1989, 5:79–89.

    CAS  Article  PubMed  Google Scholar

  39. Dowswell G, Harrison Due south, Wright J: Clinical guidelines: attitudes, information processes and culture in English language primary care. Int J Health Programme M 2001, sixteen:107–124.

    CAS  Article  Google Scholar

  40. Hayward RSA, Moore KA: Canadian physicians' attitudes about and preferences regarding clinical practice guidelines. Tin Med Assoc J 1997, 156:1715–1723.

    CAS  Google Scholar

  41. Lowenhaupt M: Removing barriers to technology. Physician Exec 2004, 30:12–14.

    PubMed  Google Scholar

  42. Kim J, Park HA: Development of a health data technology acceptance model using consumers' health behavior intention. J Med Cyberspace Res 2012, 14:e133.

    Commodity  PubMed  PubMed Central  Google Scholar

  43. Zhou T: Examining mobile banking user adoption from the perspectives of trust and flow experience. Inf Technol Manag 2012, 13:27–37.

    Article  Google Scholar

  44. Mallat North, Rossi M, Tuunainen VK, Öörni A: The impact of use context on mobile services acceptance: the case of mobile ticketing. Inform Manage 2009, 46:190–195.

    Article  Google Scholar

  45. Huang JH, Lin Twelvemonth, Chuang ST: Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model. Electron Libr 2007, 25:585–598.

    CAS  Article  Google Scholar

  46. Schierz PG, Schilke O, Wirtz BW: Understanding consumer acceptance of mobile payment services: an empirical analysis. Electron Commer R A 2010, 9:209–216.

    Article  Google Scholar

  47. Park Eastward, Ohm J: Factors influencing users' employment of mobile map services. Telematics and Informatics 2014, 31:253–265.

    Commodity  Google Scholar

  48. Park E, Baek S, Ohm J, Chang HJ: Determinants of player acceptance of mobile social network games: An awarding of extended engineering science acceptance model. Telematics and Informatics 2014, 31:iii–15.

    Article  Google Scholar

  49. Park E, del Pobil AP: Extending the technology acceptance model in remote pointing engineering: identifying the role of perceived mobility and control. Sensor Rev 2013, 33:40–47.

    Article  Google Scholar

  50. Park Eastward, del Pobil AP: Engineering science acceptance model for the employ of tablet PCs. Wireless Pers Commun 2013, 73:1561–1572.

    Article  Google Scholar

  51. Steinhubl SR: Can mobile health technologies transform health care? JAMA 2013, 310:2395–2396.

    CAS  Article  PubMed  Google Scholar

  52. Davis FD: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 1989, xiii:319–340.

    Article  Google Scholar

  53. Lee T: The impact of perceptions of interactivity on customer trust and transaction intentions in mobile commerce. J Electron Commer Res 2005, half dozen:165–180.

    Google Scholar

  54. Henseler J, Ringle CM, Sinkovics RR: The use of partial least squares path modeling in international marketing. Adv Int Market 2009, xx:277–319.

    Google Scholar

  55. Fornell C, Larcker DF: Evaluating structural equation models with unobservable variables and measurement error. J Marketing Res 1981, 18:39–l.

    Article  Google Scholar

  56. Chin WW: How to write upward and report PLS analyses. In Handbook of Partial To the lowest degree Squares Concepts, Methods and Applications. 1st edition. Edited by Vinzi VE, Mentum WW, Henseler J, Wang H. Heidelberg, Berlin, Deutschland: Springer; 2010.

    Google Scholar

  57. O'Leary-Kelly SW, Vokurka RJ: The empirical assessment of construct validity. J Oper Manag 1998, sixteen:387–405.

    Article  Google Scholar

  58. Wetzels M, Schroder GO, Oppen VC: Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS Quart 2009, 33:177–195.

    Google Scholar

  59. Ringle CM, Wende S, Will A: SmartPLS 2.0 (beta). Hamburg, Germany: SmartPLS. URL: http://www.smartpls.de/forum/. Accessed: 2014-04-05.

  60. Pellegrino ED: Patient and doc autonomy: conflicting rights and obligations in the physician-patient relationship. J Contemp Health Law Policy 1994, 10:47–68.

    CAS  PubMed  Google Scholar

  61. Williams GC, McGregor HA, King D, Nelson CC, Glasgow RE: Variation in perceived competence, glycemic command, and patient satisfaction: relationship to autonomy back up from physicians. Patient Educ Couns 2005, 57:39–45.

    Article  PubMed  Google Scholar

  62. Featherman MS, Pavlou PA: Predicting e-services adoption: a perceived take chances facets perspective. Int J Hum-Comput 2003, 59:451–474.

    Article  Google Scholar

  63. Mallat Northward, Rossi Thousand, Tuunainen VK, Öörni A: An empirical investigation of mobile ticketing service adoption in public transportation. Pers Ubiquitous Comput 2008, 12:57–65.

    Article  Google Scholar

  64. World Wellness Arrangement: mHealth: New Horizons for Health Through Mobile Technologies. Geneva: WHO; 2011.

    Google Scholar

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Acknowledgements

The researchers admit the funding support for the inquiry projection from the National Science Council of Taiwan (No. NSC101-2410-H-041-001).

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Correspondence to Tain-Junn Cheng.

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The authors declare that they take no competing interests.

Authors' contributions

CF conceived of this study and participated in the design and carried out the study. TJ helped complete the questionnaire distribution and draft the manuscript. Both authors read and approved the last manuscript.

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Liu, CF., Cheng, TJ. Exploring disquisitional factors influencing physicians' acceptance of mobile electronic medical records based on the dual-gene model: a validation in Taiwan. BMC Med Inform Decis Mak xv, four (2015). https://doi.org/x.1186/s12911-014-0125-3

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Keywords

  • Dual-cistron model
  • Perceived threat
  • Perceived mobility
  • Mobile electronic medical records
  • Physicians

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