AN EMPIRICAL STUDY ON THE INTENTIONS OF PHYSICIANS IN ADOPTING ELECTRONIC MEDICAL RECORDS WITH MODIFIED TECHNOLOGY ACCEPTANCE MODELS IN RURAL AREAS OF

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1 AN EMPIRICAL STUDY ON THE INTENTIONS OF PHYSICIANS IN ADOPTING ELECTRONIC MEDICAL RECORDS WITH MODIFIED TECHNOLOGY ACCEPTANCE MODELS IN RURAL AREAS OF TAIWAN Wei-Min Huang and Chiao-Ting Shih, Department of Healthcare Information Management, National Chung-Cheng University, Chiayi, Taiwan, R.O.C., Abstract Electronic medical records (EMR) solve many of the problems associated with paper records. Despite their advantages however, EMR systems have not been universally adopted by doctors, and their pattern of use is uncertain. This study combines theory on reasoned action with a technology acceptance model to determine which factors influence physician adaptation of EMR within rural areas of Taiwan. In particular, this study examines the role of external factors, such as professional autonomy, training, and computer self-efficacy. It finds that EMR use is not significantly influenced by feelings towards professional autonomy, but is positively related to levels of training and computer self-efficacy. Keywords - electronic medical record, professional autonomy, training, computer self-efficacy, reasoned action, technology acceptance model.

2 1. INTRODUCTION Paper-based patient record management faces many challenges. In addition to the temporal, spatial, and monetary constraints associated with continued record accumulation and compression over time, paper-based systems have limited functionality; many people cannot easily view the same record at the same time (Hwang et al. 2009). In order to resolve these problems, some hospitals use information technology to help them manage medical records, called electronic medical record (EMR) systems. These systems represent the main direction of modern medical care development: production of a real-time, shared, medical information system. Currently, the Taiwanese government is promoting the full implementation of electronic medical records in every hospital. Medical environments change frequently, which makes full acceptance of electronic medical records by hospitals and physicians an important issue. However, lately, most studies have focused largely on patient confidentiality and security(o Neill et al. 2009). Studies rarely examine other EMR-related issues. The main users of EMR are medical professionals or physicians, and the intentions behind their adaptation are fairly important(o Neill et al. 2009). Previous studies note that using these record systems is most advantageous in remote rural areas (O Neill et al. 2009), As a result, this study examines regional hospitals and physicians within remote rural areas in Taiwan (including islands). It combines the theory on reasoned action (TRA) and information technology acceptance model (TAM) to find what factors influence the adaptation of EMR by physicians. The results may provide valuable information for the medical industry and government in their implementation of EMR, and improve acceptance of EMR among physicians. 2. LITERATURE 2.1 Electronic Medical Records (EMR) "Medical records" refers to records of medical institutions and professionals engaged in medical work on patients, including records on various inspections, diagnoses and treatment processes(hwang et al. 2006). Electronic medical records originate from paper-based patient records, are often considered a part of larger data management systems, and are designed to store, manage, and query various medical data. They support medical professionals in their decision-making and improve operating efficiency, thus improving medical care quality(hersh 1995;Kazley & Ozcan 2007; Ayers et al. 2009; Al-Jafar 2002). Naturally, electronic medical records reduce printing costs, but they also act as a reference for medical research and help reduce the gap between the availability of urban and rural medical information. However, there are significant barriers to the implementation of EMS: high

3 initial import and maintenance costs, training for personnel, and a lack of consistency between systems at different hospitals. 2.2 Factors Influencing Physician Adoption of Electronic Medical Records In the information technology area, different groups have different cognitive biases, especially physician groups. As professionals, individual physicians have different personal knowledge that may affect their decision to accept information technology. Thus, focusing on the potential acceptance of information technology by physicians is important for successfully running medical institutions(chau & Hu 2002). In particular, there are four key factors, explained below Limits on Professional Autonomy: Physicians rely on their autonomy and authority to make decisions. Even in cases where a decision support system aids their choices, they may not necessarily want to leverage this technology, because they may perceive certain technological advances as a challenge and threat to their authority. Furthermore, such advances may weaken the independence of the physician. As a result, information technology implementation may carry with it unintended but significant negative effects(ilie et al. 2009; Berner et al. 2005; Walter & Lopez 2008) Training: Electronic medical records can substantially change hospital working environments. Every individual physician has a different expertise and background, and may in some cases lack the computer skills necessary to use an electronic medical information system. Likewise, hospitals may provide insufficient background knowledge on EMR and insufficient training (Ash 2000; Baron et al. 2005; Morton 2008). According to a Taiwan government electronic medical record survey, implementing proper training on EMR systems is difficult(department of health 2005). Previous studies have noted that training not only positively influences the usefulness of information technology(walter & Lopez 2008; Hu et al. 1999), but also specific training will positively impact information technology self-efficacy(staples et al. 1999) Computer Self-efficacy: Previous scholars define computer self-efficacy as a person who uses your computer's capabilities (Compeau & Higgins 1995). Along these lines, many researchers describe computer self-efficacy as the perception of the use, ability, and attitude of individuals towards computers(ma & Liu 2005; Venkatesh & Davis 1996) Physician Characteristics: In electronic medical records-related research, it is common to find basic demographic data

4 on physicians, including information on personal characteristics. Past research has found that physicians who are more experienced, younger, and are aware of computer operations will more likely accept electronic medical record systems (Menachemi et al. 2008; Menachemi et al. 2007). 2.3 Theory of Reasoned Action, TRA Fishbein & Ajzen (1975) s theory of reasoned action, widely used to forecast and explain individual behavior, can also be combined with theory and research from other areas, and can be used to specifically measure certain concepts(feeley 2003), mainly attitudes, subjective norms, behavior intentions, and actual behavior. Widespread in various behavioral fields, reasoned action theory is particularly used within the field of public health to study the intentions behind individual behavior(feeley 2003; Davis et al. 1989). In the field of subjective norms, there are a number of studies indicating that a physician s support from management, their physician-patient relationships, and their network of colleagues will affect the physician s intentions to use electronic medical records(darr et al. 2003; Miller & Sim 2004 ; Rouf 2007; Morton 2008 ; Beiter 2008; Ayers et al. 2009). 2.4 Technology Acceptance Model, TAM The technology acceptance model (TAM) was first proposed by Davis as a combination of TRA and theories of cost efficiency (Davis et al. 1989). TAM contains five main factors: perceived usefulness, perceived ease of use, attitude towards use, behavioral intention of use, and actual system use(hung et al. 2005). TAM effectively explains why an individual accepts information technology, and how the intended use of this technology and its perceived usefulness and ease of use will significantly affect a medical worker s continued use of the system(hu et al. 1999; Morton 2008; Ma & Liu 2005; Ilie et al. 2009). 3. METHODOLOGY This study combines TRA with TAM, carefully incorporating other external variables such as limits on professional autonomy, training, and computer self-efficacy. Together, this methodology follows the following framework. Figure 1 Study Model

5 Based on recommendations from the existing literature, this study limits the operational definition of several of its key concepts. For professional autonomy: when physicians use electronic medical records, they feel a perceived threat to their medical work processes and degree of control over work content. The operational definition of training is electronic medical records-related training, designed to enable physicians to learn how to use EMR information systems. Computer self-efficacy refers to a doctor s capabilities when using a computer to complete electronic medical information. Perceived usefulness refers to the degree to which a physician believes a new information system will improve his or her medical related performance. Perceived ease of use simply indicates the ease of which a physician uses electronic medical information systems. Attitude is defined as the positive or negative feelings of physicians towards the use of EMR systems. Subjective norms are factors that may influence the intent of a physician to use EMR systems, specifically the views of managers, colleagues, and patients. Lastly, behavior intention refers to physicians continuing to use electronic medical information system of their own subjective will. Given these variables and the existing literature, we test the following hypotheses: Hypothesis 1: Limits on professional autonomy may negatively impact the perceived usefulness of EMR. Hypothesis 2a: Physician acceptance of EMR training may positively impact the perceived usefulness of EMR. Hypothesis 2b: Physician acceptance of EMR training may positively impact the perceived ease of use of EMR. Hypothesis 2c: Physician acceptance of EMR training may positively impact EMR self-efficacy. Hypothesis 3a: Physician EMR self-efficacy may positively impact the perceived usefulness of EMR. Hypothesis 3b:Physician EMR self-efficacy may positively impact the perceived ease of use of EMR. Hypothesis 3c:Physician EMR self-efficacy may positively impact the intention of EMR adoption. Hypothesis 4: EMR perceived ease of use may positively impact the perceived usefulness of EMR. Hypothesis 5: EMR perceived ease of use may positively impact attitudes toward EMR adoption. Hypothesis 6: EMR perceived usefulness may positively impact attitudes toward EMR adoption. Hypothesis 7: EMR perceived usefulness may positively impact the intention to adopt EMR. Hypothesis 8: Attitudes toward adopting EMR may positively impact the intention of adopting EMR. Hypothesis 9: EMR subjective norms may positively impact the intention of adopting EMR. Table 1 Study Hypotheses List With the exception of computer self-efficacy, which is measured using a scale from zero to ten, the variables in this study are measured using a five-point Likert scale. Reference findings indicate that the use of EMR in remote rural areas is more beneficial than in larger cities (O Neill et al. 2009). Thus, this study examines the regional hospitals of rural

6 Taiwan and outlying islands. In doing so, we provided structured questionnaires, sent by mail, to each regional hospital. 4. DATA ANALYSIS 4.1 Sample Size and Response Rate The survey was deployed to 200 physicians in remote rural regional hospitals. From these, 60 usable responses were received, resulting in a net response rate of 30%. The results were imported and analyzed using PSS 12.0 and PLS 2.0. Regarding computer experience, 70% of respondents reported having ten years of experience or more with using computers, suggesting that the majority of physicians have a rich usage history. 4.2 Reliability and Validity of Research Constructs Using SPSS12.0, Cronbach s alpha reliability coefficients were computed to determine the internal consistency of all research constructs: Cronbach s alpha of 0.7 or above indicates high reliability, between 0.7 and 0.35 indicates ordinary reliability, and less than 0.35 indicates low reliability. Here, the analysis should remove lower related dimensions, until achieving a high reliability. Because each Cronbach s alpha was mostly above 0.7, the results indicate excellent scale reliability for most constructs (TABLEⅡ). The only exception is the limit on professional autonomy (Cronbach s alpha value 0.676). This is still within however, and thus it remains a generally reliable construct. Scale Cronbach s α Limit of Professional Autonomy Training Computer Self-efficacy Perceived Usefulness Perceived Ease of Use Attitude Subjective Norms Behavioral Intention Table 2 Scale Reliabilities Content validity is used for measuring the relevance of a tool s content(cooper & Schindler 2006). This study s questionnaire was compiled with the guidance of both the current literature and advice of EMR practice experts, who helped ensure question content was clear and held high content validity. Factor analysis was used to measure construction validity(cooper & Schindler 2006), however first KMO (Kaiser-Meyer-Olkin, sampling

7 relevance magnitude) was performed in order to ensure variables were suitable for factor analysis. A KMO value lower than 0.5 implies factor analysis may be inappropriate (Cooper & Schindler 2006). Here, the KMO result of is greater than 0.5, which suggests the data is suitable for factor analysis. Kaiser-Meyer-Olkin Measure of Sampling Adequacy..744 Bartlett's Test of Sphericity Approx. Chi-square Df 820 Sig..000 Table 3 Kaiser-Meyer-Olkin Measure (KMO) Previous studies have noted that if the sample size is only 60 to 70, load factors must be 0.65 or above to be fully accepted (Cooper & Schindler 2006). In this study eight constructs have acceptable load factors, while two constructs have insufficient load factors (professional autonomy and attitude). Thus, these constructs are removed during the following data analysis. 4.3 Structural Equation Modeling Analysis Path coefficient Partial least squares regression (PLS) applies to variables needed to predict huge, samples more small. In this study, the number of samples taken was not easy, so we use PLS complete path analysis to reduce errors. Furthermore, we adopt a bootstrap method for estimating p values. Table IV displays the results of this method for each of our hypotheses. Between Facets Path t-value p-value Coefficient (β) Subjective Norm -> Behavioral Intention ** Attitude -> Behavioral Intention *** Training -> Perceived Ease of Use *** Training -> Perceived Usefulness *** Training -> Computer self-efficacy * Perceived Ease of Use -> Attitude *** Perceived Ease of Use -> Perceived Usefulness *** Perceived Usefulness -> Behavioral Intention ** Perceived Usefulness -> Attitude *** Limit of professional autonomy -> Perceived Usefulness

8 Computer self-efficacy -> Behavioral Intention *** Computer self-efficacy -> Perceived Ease of Use *** Computer self-efficacy -> Perceived Usefulness * *p<0.1; **p<0.05; ***p<0.01(one-tailed test) Table 4 Standardized Total Effects PLS does not provide model fit indices; it only tests the entire mode of forecast level by the coefficient of determination R2 (0-1). In this study predictive tests are performed using an algorithm method. Figure 2 displays the path coefficient, R2, and p-value integration results. Table V presents the final results of the hypothesis tests. Figure 2 Standardized Total Effects Hypothesis Hypothesis 1: Limits on professional autonomy may negatively impact the perceived usefulness of EMR. Hypothesis 2a: Physician acceptance of EMR training may positively impact the perceived usefulness of EMR. Hypothesis 2b: Physician acceptance of EMR training may positively impact the perceived ease of use of EMR. Hypothesis 2c: Physician acceptance of EMR training may positively impact EMR self-efficacy. Hypothesis 3a: Physician EMR self-efficacy may positively impact the perceived usefulness of EMR. Hypothesis 3b:Physician EMR self-efficacy may positively impact the perceived ease of use of EMR. Hypothesis 3c:Physician EMR self-efficacy may positively impact the intention of adopting EMR. Hypothesis 4: EMR perceived ease of use may positively impact the perceived usefulness of Result No No

9 EMR. Hypothesis 5: EMR perceived ease of use may positive ly impact attitudes toward EMR adoption. Hypothesis 6: EMR perceived usefulness may positively impact attitudes toward EMR adoption. Hypothesis 7: EMR perceived usefulness may positively impact the intention to adopt EMR. Hypothesis 8: Attitudes toward adopting EMR may positively impact the intention of adopting EMR. Hypothesis 9: EMR subjective norms may positively impact the intention of adopting EMR. Table 5 Hypothesis Testing Results 5. CONCLUSIONS These results maintain enough explanatory power (R2 =78.4%) to help explain the intentions of physicians in adopting electronic medical record information systems. Among the hypotheses above, only hypotheses 1 and 3a were not accepted. Results from hypothesis 1 indicate that there is no relation between limits on professional autonomy and perceived usefulness, and hypothesis 3a indicates that computer self-efficacy negatively impacts perceived usefulness. This final section summarizes our findings and provides various policy suggestions. 5.1 Factors Affecting Computer Self-efficacy Our findings show that "training" has a significant positive effect on EMR self-efficacy. This means that if physicians are willing to adopt EMR and have received enough training on EMR, then their operation of EMR will improve. This finding is consistent with previous research (Staples et al. 1999), and suggests that medical institutions should provide comprehensive EMR training for individual physicians to increase their use of computer-based EMR information systems. 5.2 Factors Affecting Perceived Usefulness Our results also indicate that training and computer self-efficacy significantly influence the perceived usefulness of EMR, while limits on professional autonomy have no effect. This result somewhat conflicts with previous studies, but is consistent with Morton (2008). It indicates that physicians were not exposed to the actual medical decision-making process for implementation, and therefore were reporting their perceptions only (Morton 2008). Thus this study conjectures that Taiwan's EMR only act as a database pooling system; it does not replace the decision-making power of physicians, so physicians do not feel EMR will limit their autonomy to make medical decisions.

10 These results also suggest that computer self-efficacy has a significant negative effect on the perceived usefulness of EMR. This relationship was not hypothesized, and differs from past research. Chau (2001) indicated self-efficacy with technology significantly negatively influenced EMR perceived usefulness (Chau 2001). It explained that users employ technology systems mostly because of their ease of use, rather than their usefulness. Therefore this study contends that while Taiwanese physicians computer experience is rich, EMR system functionality is not recommended. Electronic medical record information system functionality should focus more on enhancing quality, speed, etc., and not expect to enhance physician EMR perceived usefulness. 5.3 Factors Affecting Perceived Ease of Use This study finds that training and computer self-efficacy significantly positively impact perceived ease of use. As a result, hospitals should consider providing appropriate and continued formal training according to rates of physician computer self-efficacy to reduce physicians stress and work loading when he or she is learning a new system. 5.4 Factors Affecting Attitudes Our results show perceived usefulness and perceived ease of use significantly positively influence attitudes towards the adoption of EMR information systems. If the physicians believe EMR information systems are useful and easy to use, physicians will have a positive attitude towards using these systems. Given this, when improving their EMR systems, the medical industry should especially strengthen system usefulness and ease of use, so as to attitudes towards their EMR systems and improve adoption. 5.5 Factors Affecting Intention of Adopting EMR These research findings indicate that four variables significantly positively impact the intention to adopt EMR. Among these, attitude has the most significant positive impact on adoption intentions. Therefore this study suggests that in order to enhance the intention to adopt EMR systems, hospitals should strengthen independent impact variables, including perceived usefulness, attitude, subjective norms, and computer self-efficacy. If hospitals can strengthen the positive feelings of physicians towards EMR and convince physicians of its usefulness, they will maintain a healthier attitude towards adoption. References Al-Jafar, A. E.. Factors affecting diffusion of the electronic medical record (EMR) from a physicians' perspective: The Kuwait experience. Dissertation Abstracts International, 2002, 5, 2284.

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