Key Considerations for Designing Electronic Medical Records Systems

Transcription

1 Association for Information Systems AIS Electronic Library (AISeL) AMCIS 2007 Proceedings Americas Conference on Information Systems (AMCIS) December 2007 Key Considerations for Designing Electronic Medical Records Systems Edward Wright Case Western University Michel Avital University of Amsterdam Follow this and additional works at: Recommended Citation Wright, Edward and Avital, Michel, "Key Considerations for Designing Electronic Medical Records Systems" (2007). AMCIS 2007 Proceedings This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in AMCIS 2007 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 KEY CONSIDERATIONS FOR DESIGNING ELECTRONIC MEDICAL RECORDS SYSTEMS Edward Wright Case Western Reserve University Michel Avital University of Amsterdam Abstract Subsequent to earlier work investigating the slow adoption rate of Electronic Medical Record (EMR) technology, we investigated the influence of known antecedents of EMR adoption on post adoption success. Data collected from 388 physicians users suggests that ease of use of an EMR system is by far the most significant and substantive factor of enduring success regardless of physician age, gender, or practice size. Implications for the design of EMR systems are discussed. Keywords: Electronic Medical Records, EMR Design, EMR Success, Ease of Use Introduction Building on earlier studies investigating the adoption of Electronic Medical Record (EMR) technology, we investigated the possible effects of known antecedents of EMR adoption on post adoption success in an attempt to provide information that might promote faster diffusion of the technology and greater realization of its benefits. In a 2006 report published by the Health Industry Distributors Association, EMR and More: The Changing Landscape, electronic medical record technology is described as a software system that allows physicians to digitally enter, view, analyze, and store clinical information on a computer database. Besides eliminating the need for a paper patient chart, EMR can automate patient billing, provide diagnosis cross checks, enable electronic delivery of drug prescriptions to the pharmacy, schedule laboratory testing, screen for potential drug interactions, and even enable data analysis to improve diagnostic screenings for morbidity. The use of electronic records in industries such as banking and travel is ubiquitous. However, in health care, the benefits of Information Technology remain unrealized. Most physicians continue to use paper patient charts and records. In a preliminary qualitative study investigating this low adoption phenomenon, we conducted interviews with family practice physicians to develop a conceptual understanding of the factors that play a critical role in EMR adoption. One striking finding was that respondents generally believed that the widespread use of electronic medical records is inevitable and at one point about to become ubiquitous. In other words, using EMR at one point of time was not seen a choice. This raised questions regarding the validity of intention to use, a key attribute in many technology adoption studies (Davis, 1989; Venkatesh and Davis, 2000), as a reliable predictor of adoption in the case of EMR systems. We also found support to earlier studies arguing that intention to use and intention to buy are somewhat different constructs (Quester and Smart, 1998) and found that career stage was an important moderating attribute. We also projected that EMR adoption within the next decade appears assured due to US physician demographics, expected retirements of late career stage physicians, and the high rate of acceptance by young physicians. However, although adoption is certainly important, it is not sustainable without long-term value. Reported results of EMR implementations vary widely (Carter, 2003) with many projects ending in failure (Goddard, 2000). Physician purchases of EMR systems do not guarantee successful use of the technology (Meijden et al., 2003). There are some examples of EMR adoption deemed disastrous with physician users described as very dissatisfied (Wager et al., 2001). Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

3 With questions regarding the validity of "intention to use" as a predictor of EMR adoption and the notion that EMR adoption alone is an insufficient end, we aimed to examine in the context of EMR which of the primary antecedents of technology adoption has an effect that goes beyond the initial adoption decision and has a significant impact on value and long-term sustainable outcomes. While the definition of success clearly depends upon the setting and the user s criteria (Meijden et al., 2003; Delone and McLean, 1992), in this study we adapt generic IS success constructs (Seddon 1997, DeLone and McLean, 1992) and measure improvements in physician productivity, quality of patient care, and overall satisfaction with EMR as outcome variables. Based on survey data from 388 physician users of EMR, we concluded that EMR system's ease of use is by far the most significant and substantive factor of enduring success in terms of perceived healthcare quality, productivity and satisfaction regardless of physician age, gender, or practice size. Furthermore, the availability of IT support has a small positive effect on satisfaction, and both Physician's innovativeness and social influence have a small positive effect on his or her perceived personal productivity and gains in medical practice quality. Next, we highlight the literature leading to our hypotheses, provide further details about the study, discuss the results and conclude with comments concerning the implications of our work. Technology Adoption Key Concepts and Frameworks Technology adoption by health care organizations is a well-studied phenomenon. Previous research has documented both the resistance to change and slowness in the adoption of new practices by physicians (Leonard, 2004; Triester, 1998). Generally, studies related to adoption of technology take one of three approaches: diffusion approach, domestication approach, or adoption approach, (Vigayan et. al., 2005). The diffusion approach focuses on the innovation and its various adoption stages (e.g., Rogers 1995). The domestication approach focuses on the adoption, use, and domestication of a technology in society with a particular focus on the societal consequences of technology domestication. Finally, the adoption approach applies social theories of decision making to explain adoption decisions. Related models, collectively called the Technology Adoption Theories (TAT), have been widely used to explain adoption of information technologies and typically suggest that an individual s intention to use a technology is a reliable predictor of successful adoption. Among the models building on the adoption approach, the Unified Theory of Acceptance and Use of Information Technology (UTAUT) (Venkatesh et. al., 2003) is often considered the definitive work both for its parsimony and its extensive consideration of prior theoretical foundations of technology adoption theory (Carlsson et al., 2006). UTAUT suggests three key determinants of intention to use a technology: Performance expectancy defined as the degree to which an individual believes that using the technology will help to improve job performance. Effort expectancy defined as the perceived degree of ease associated with using the technology. Social influence defined as the degree to which an individual perceives that important others believe he should use the new technology. The researchers confirmed two additional determinants of actual technology usage behavior: Intention to Use - defined as a prediction or plan to use a system. Facilitating Conditions defined as having available knowledge or resources to assist with system difficulties. Moderating influences included Gender, Age, Voluntariness of use, and Experience. With a few exceptions due to context, these constructs served as the basis of our hypothesized independent variables affecting adoption success. Based on the outcome measures adapted from IS literature (i.e., productivity, quality, and satisfaction), we concluded that performance expectancy would yield confounded results as a separate independent construct. We also considered other technology adoption factors from a number of studies in various domains including Computer Playfulness (Martocchio and Webster, 1992) and Job Fit (Thompson et al, 1991) before settling upon Innovativeness (Marcinkiewicz, 1993) as a plausible factor. Innovativeness is defined as a personality trait denoting an individual s willingness to change. Marcinkiewicz found that innovativeness was a strong predictor of new technology use in academic settings. Individuals rating high in innovativeness are reported to have a stronger intrinsic motivation to use new technologies (Wi et al., 2003). While not a surrogate for performance expectancy, we did construe that innovativeness conveyed a similar notion of positive feelings and open-mindedness toward a new technology. Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

4 Finally, because our survey population included only physicians who had already purchased an EMR system, we dismissed intention to use, a predictor of adoption behavior, from consideration in our model. We included moderation tests for Gender and Age, but were not able to obtain data for Voluntariness of use or Experience. We also tested for any moderating effects of practice size following a recent study arguing that small medical practices are expected to be the last to adopt EMR technology (Ford, E. et al., 2005). Information Systems Success Prior work has devoted much attention to the conceptualization (e.g., Delone and McLean, 1992) and empirical assessment (e.g., Sabherwal et al., 2006) of IS success. This literature provided a useable framework for evaluating EMR success factors and metrics in terms of productivity, quality and user satisfaction. Rai et al. (2002) viewed perceived usefulness as an IS system outcome construct related to the impact of an information technology on the individual such as improved personal productivity. Likewise, Sabherwal et al. (2006) defines perceived usefulness as the degree to which an individual believes that using the system enhances his or her productivity and job performance. In addition, based on reviewing the claims of leading EMR manufacturers and advice of an expert panel of physicians, we selected productivity as a success construct and define it as the perception of improvement in the physician s personal productivity due to the use of EMR technology. Fourteen previous studies of IS success cite quality as an IS success construct (Sabherwal et al., 2006) that can be characterized as the quality of the system in terms of reliability, east of use, and response time. Following the above methodology, we selected quality as another EMR success construct defined as the physician s belief or perception of improvement in medical practice quality due to the use of EMR technology. User satisfaction appears as an IS success factor in twenty-eight previous studies of IS success. Related terms include user information satisfaction, end-user satisfaction, computing satisfaction, and overall satisfaction (Sabherwal et al., 2006). We likewise rely upon satisfaction as an indication of EMR success defined as the physician s satisfaction with the overall EMR use experience. Conceptual Model Building on the Technology Adoption and IS Success literatures, we developed the conceptual model in Figure 1 to test the possible effects of EMR adoption predictors on post adoption success. The respective constructs and their definitions are summarized in Appendix A. and their hypothesized relationships are in Table 1. Figure 1. The Conceptual Model for Understanding EMR Success Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

5 Hypothesis H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 Table 1. Hypothesized Construct Relationships Definition Physician innovativeness has a positive effect on his or her personal productivity due to EMR. Physician innovativeness has a positive effect on his or her gains in medical practice quality due to EMR. Physician innovativeness has a positive effect on his or her satisfaction with EMR. Ease of Use has a positive effect on personal productivity due to EMR. Ease of Use has a positive effect on gains in medical practice quality due to EMR. Ease of Use has a positive effect on satisfaction with EMR. Facilitating conditions (the availability of IT support) has a positive effect on gains in personal productivity due to EMR. Facilitating conditions (the availability of IT support) has a positive effect on perceived gains in medical practice quality due to EMR. Facilitating conditions (the availability of IT support) has a positive effect on satisfaction with EMR. Social influence has a positive effect on physician productivity due to EMR. Social influence has a positive effect on gains in medical practice quality due to EMR. Social influence has a positive effect on satisfaction with EMR. Method Procedures for developing the survey instrument are discussed below followed by data collection methodology and results. Survey Development The survey instrument was developed building on measures that were validated in prior research and were adapted to EMR technology and the physician s office environment (see Appendix A). The previous work of Venkatesh et. al. (2003) with UTAUT was especially helpful. Seven-point Likert scales were used for all measures. The scales of age, gender and size of practice were multiple choice questions with responses offered in logical groupings (six age group choices, two choices for sex, and four choices for practice size). See Appendix A for a complete listing of items and scales. Survey Testing A sorting exercise was conducted with six health care professionals whereby possible items were matched to construct categories by each individual to assess items clarity and as a preliminary screening for internal consistency. This resulted in some changes in wording for a few items. Additional testing based on Item Response Theory (DeVellis, 2003) was conducted with six physicians. This resulted in further refinements and confirmation of the outcome variables: physician productivity, practice quality, and physician satisfaction. The instrument was subsequently placed online using a commercial survey host with a university server providing a portal to the site. Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

6 Data Collection We obtained the data through the cooperation of the American Academy of Family Physicians (AAFP). The AAFP's Center for Health Care Information Technology (CHiT) estimates that 30% of its 94,000 members are EMR users. The Center maintains a proprietary list of 2100 known users of EMR and the group was solicited by CHiT to take our online survey pertaining to their related experience. Out of 2100 attempts, solicitations were successfully sent, 312 responses were received in the first week, and another 76 were received after a reminder over the following ten days. Overall, 388 total responses were obtained, yielding a usable response rate of 19.3%. Demographic information related to age, gender, patient base and practice size (in terms of number of employees) was also obtained (Table 2). Descriptive statistics, reliability estimates and correlations among the seven variables are presented in Table 3 below: Table 2. Demographic Profile of the Respondents Age (in Years) Sex Number of Employees Number of Patients < Male 81.3 < Female > > > Table 3. Descriptive Statistics and Inter-Correlations Std Mean Dev IT EASE INNO PEER PROD QUAL SATIS Reliability IT Support (IT) Ease of Use (EASE) Innovative (INNO) Peer Pressure (PEER) Productivity (PROD) Quality (QUAL) Satisfaction (SATIS) Measures Validity Exploratory factor analysis using SPSS produced a pattern matrix with an acceptable structure with reasonably high loadings except for two marginal items related to Quality and IT Support with respective loading of.51 and.54. Cross-loadings using Promax rotation were minimal (Table 4). Subsequent confirmatory factor analysis using AMOS yielded corresponding results with reasonably high and statistically significant estimates and acceptable fit indices. Overall, both EFA and CFA provide an indication of convergent and discriminate validity of the measures. Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

7 Table 4. Pattern Matrix Analysis and Findings Structural equation modeling was used to test the overall fit of the hypothesized model and to estimate the effects of the paths related to the associated hypotheses. Furthermore, in consideration with concerns related to self-reported results (Podsakoff and Organ, 1986), we have attempted to control for common method variance using a variant of Harmon s one-factor test (Greene and Organ, 1973). All items maintained significance at p <.001 and no construct appeared tainted by multiple, large item degradations. Measurement Model Fit Using AMOS, the hypothesized model (Figure 1) was tested and, following minor adjustments based on modification indices, the model yielded acceptable overall fit statistics as follows: <² = 510 with degrees of freedom = 247, Comparative Fit Index (CFI) =.96, Standardized RMR =.0459, and Normed Fit Index (NFI) =.92. The model p-value was.000 indicating significant departure of the data from the model fit, however, due to our sample size, we discounted this measure (Fornell and Larcker, 1981) and rely upon the other indicators that suggest the hypothesized model provides an adequate representation of the variance covariance matrix of the study measures. Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

8 Structural Coefficients and Hypotheses Tests The estimated coefficients from this model are listed in Table 5 (see also Figure 2). Table 5. Standardized Path Coefficients Hypotheses 1-3 suggest that physicians with an innovative personality will have greater perceived success with EMR technology. Though significant, these values are not very substantive (N =.12 and N =.13 for Productivity and Quality respectively) and innovation has an insignificant effect on Satisfaction. An innovative personality has merely a minor effect upon EMR user success. Hypotheses 4 6 propose that the greater the ease of use of an EMR system, the greater the physician s perception of improved Productivity, practice Quality, and personal Satisfaction. The findings indicate that these relationships are highly significant and the coefficient values are substantially high (N =.81 for Productivity, N =.59 for Quality, and N =.72 for Satisfaction). Hypotheses 7 9 suggest that the availability of IT support will have a positive impact on perceived success with EMR technology. No significant relationships are identified for Quality outcomes or for Productivity gains. However, the presence of IT availability yielded a significant yet minor result related to physicians Satisfaction with EMR (N =.14). Upon reflection, we conjecture that the dependability of commercially available EMR software may be such that IT support is rarely required. Hypotheses purport that the greater the peer support for EMR, the greater the degree of perceived EMR success. Though significant for Productivity and Quality, these relationships are also somewhat weak or moderately weak (N=.10 and N=.18 Productivity and Quality respectively) and peer support has no significant effect on EMR Satisfaction. We split out the respondent data into two age groups physicians younger than 41 and those ages 41 and older and tested the model for invariance across the groups. Age did not have a moderating influence on any of the hypothesized relationships. We likewise performed moderation tests for gender and practice size, and subsequently found no moderation effects. With these findings, the results of our hypotheses testing are summarized in Table 6 and Figure 2. Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

9 Table 6. Results of Hypotheses Testing Hypothesis Definition Result H1 H2 H3 Physician innovativeness has a positive effect on his or her personal productivity due to EMR. Physician innovativeness has a positive effect on his or her gains in medical practice quality due to EMR. Physician innovativeness has a positive effect on his or her satisfaction with EMR. Significant with low effects Significant with low effects Not Significant H4 EMR ease of use has a positive effect on personal productivity due to EMR. Significant with strong effects H5 EMR ease of use has a positive effect on gains in medical practice quality due to EMR. Significant with strong effects H6 EMR ease of use has a positive effect on satisfaction with EMR. Significant with strong effects H7 The availability of IT support has a positive effect on gains in personal productivity due to EMR. Not Significant H8 The availability of IT support has a positive effect on perceived gains in medical practice quality due to EMR. Not Significant H9 The availability of IT support has a positive effect on satisfaction with EMR. Significant with low effects H10 Social influence has a positive effect on physician productivity due to EMR. Significant with low effects H11 Social influence has a positive effect on gains in medical practice quality due to EMR. Significant with low effects H12 Social influence has a positive effect on satisfaction with EMR. Not Significant Figure 2. Re-Specified Model for Understanding Success with EMR Technology Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

10 Discussion The objective of this study was to identify the factors that influence the success of EMR past the adoption phase. Examining earlier definitive technology adoption studies, we focused on ease of use, IT support, innovation, and social influence as possible predictors of EMR success. The findings suggest that ease of use is the most critical success factor of EMR system past the implementation phase in the context of our study. Although the relevance and critical role of ease of use is not new, earlier information technology adoption studies have not suggested that it is more important than any other factors in any particular context. While the acquisition of individual EMR systems by physicians is progressing (albeit slowly), the ability of networking EMRs and patients records remains a distant, unrealized ideal. With industry trade associations such as the Healthcare Industry Distributors Association (HIDA), the Healthcare Information and Management System Society (HIMSS), and the National Alliance for Health Information Technology (Alliance) promoting EMR technology; and the Department of Health and Human Services (HHS) charged by executive order to foster adoption, one has to wonder if substantive improvements in EMR ease of use would hasten its acceptance into the mainstream of healthcare. Furthermore, in pursuit of the government s goal of a national healthcare record for every American by 2014, ease of use considerations beyond individual EMR systems regarding networking and database interfacing need attention and study. The study suggests that physicians value ease of use as a product feature (and most likely also as a marketing message) as they consider the acquisition of EMR system. Vendors of EMR systems should pay attention given that more than 200 providers of EMR systems compete for market share in the US alone. This is especially relevant considering the anticipated consolidation of EMR manufacturers. Those companies that endure are likely to be the ones that make a product that earns a reputation for successful use. Ease of use not only influences the initial adoption decision, it has enduring effect upon subsequent use and outcomes. One limitation of our study is worthy of note. Only family practice physicians were respondents. Family practice physicians account for approximately 10% of US physicians. With substantial differences in training, practice methodology, and patient populations, studies in other areas of medical specialty are required to establish the general applicability of these findings. Despite this limitation, our results offer useful insight into the significant influence of ease of use as the most substantive attribute related to all family practice physician age groups and all outcome variables. Conclusion The National Coalition on Healthcare reports that although nearly 47 million Americans are uninsured, the US spends on healthcare per capita more than any industrialized nation that provides public health insurance to all their citizens. As with banking and the airline industries, the increased adoption and use of electronic digital record technology could provide substantial cost savings for the benefit of everyone. Various interest groups medical associations, software vendors, governmental departments, and practitioner groups are promoting any number of scenarios for hastening adoption. This study suggests one clear success factor: the design requirements of EMR systems should highlight usability as an essential requirement and anyone involved in their development should ask how could we make EMR systems easier to use? References Carlsson, C., Carlsson, J., Hyvonen, K., Puhakainen, J., and Walden, P. Adoption of Mobile Devices/Services Searching for Answers with the UTAUT, Proceedings of the 39 th Annual Conference on System Sciences (HICCS 06) Track 6, 2006, p. 132a. Davis, F. D. Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology, MIS Quarterly (13:3), 1989, pp DeLone, W.H. and McLean, E.R. Information Systems Success: The Quest for the Dependent Variable, Information Systems Research (3:1), March 1992, pp DeVellis, R. F. Scale Development: Theory and Applications, 2 nd Edition, Sage Publication, Ford, E. Menachemi, N. Phillips, T. Predicting the Adoption of Electronic Health Records by Physicians: When Will Health Care Be Paperless? Journal of American Medical Informatics Association (13), 2005, pp Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

11 Fornell, C. and Larcker, D. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error, Journal of Marketing Research (18:1), 1981, pp Greene, C. and Organ, D. An Evaluation of Causal Models Linking the Received Role with Job Satisfaction, Administrative Science Quarterly (18:1), 1973, pp Goddard, B. Termination of a Contract to Implement an Enterprise Electronic Medical Record System, Journal of American Medical Informatics Association (7), 2000, pp Leonard, K.J. Critical Success Factors Relating to Healthcare s Adoption of New Technology: A Guide to Increasing the Likelihood of Successful Implementation, Healthcare (2:4), 2004, pp Marcinkiewicz, H.R. and Welliver, P.W. Procedures for Assessing Teachers Computer Use Based on Instructional Transformation, Proceedings of Selected Research and Development Presentations at the Convention of the Association for Educational Communications and Technology (15th, New Orleans, Louisiana, January 13-17, 1993, Martocchio, J. J. and Webster, J. Effects of Feedback and Cognitive Playfulness on Performance in Microcomputer Software Training, Personnel Psychology 45(3), Autumn, 1992, pp Meijden, M. van der, Tange, H. and Hasman, T. Determinants of Success of Inpatient Clinical Information Systems: A Literature Review. Journal of American Medical Informatics Association (10), 2003, pp Podsakoff, P. and Organ, D. Self-Reports in Organizational Research: Problems and Prospects, Journal of Management (12:4), 1986, pp Quester, P.G. and Smart, J. The Influence of Consumption Situation and Product Involvement Over Consumers Use of Product Attributes, Journal of Consumer Marketing (15:3), 1998, pp Rai, A., Lang, S. S., and Welker, R. B. "Assessing the Validity of IS Sucess Models: An Empirical test and Theoretical Analysis," Information Systems Research 13(1), March 2002, pp Rogers, E.M. Diffusion of Innovations, 4 th Edition, New York, New York: The Free Press, Sabherwal, R., Jeyaraj, A., and Chowa, C. Information System Success: Individual and Organizational Determinants, Management Science. (52:12), 2006, pp Seddon, PB. A Respecification and Extension of the DeLone and McLean Model of IS Success, Information Systems Research (8:3), 1997, p Thompson, R. L., Higgins, C. A., and Howell, J. M. Personal Computing: Toward a Conceptual Model of Utilization, MIS Quarterly (15:1), 1991, pp Treister, N.W. Physician Acceptance of New Medical Information Systems: The Field of Dreams, Physician Executive, May/June, 1998, pp Venkatesh, V. and Davis, F. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies, Management Science (46), 2000, pp Venkatesh, V., Morris, M., and Davis, F. User Acceptance of IT, MIS Quarterly (27:3), 2003, pp Vigayan, P., Perumal, V., and Shanmugam, B. Multimedia Bank and Technology Acceptance Theories, Journal of Internet Banking, 2005, Wager, K. A., Lee, F., and White, A. Life After a Disastrous Electronic Medical Record Implementation: One Clinic s Experience. Idea Group Publishing, Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August

12 Appendix A. Items and Constructs Operational Measures Used for Study Constructs Unless otherwise noted, we measured the following items on a seven-point Likert scale (from strongly disagree to strongly agree ). The items marked with [0] were removed from analyses because of poor internal consistency with their respective scales. Ease of Use (Venkatesh et. al., 2003) 1. Our Electronic Healthcare Records system (EHR) is easy to use. 2. My interaction with our EHR system is clear and understandable. 3. Using the EHR system helps me to do my job with less effort. [0] 4. Learning to use the system is easy for me. 5. I believe it is easy to get tour EHR system to do what I want it to do. Innovativeness (Marcinkiewicz, 1993; Hurt, 1977) 1. I am often one of the first people in my group to try something new. 2. I find it stimulating to be original in my thinking and behavior. 3. I feel that a new way of doing things may be the best. 4. I am challenged by unanswered questions. 5. I often find myself embracing new ideas. Social Influence (Venkatesh et. al., 2003) 1. Physicians who use EHR systems have more prestige than those who do not.[0] 2. Physicians who use EHR systems have a high profile. 3. Having an EHR system is a status symbol in my area of practice. 4. People who are important to me think that I should use EHR. [0] Facilitating Conditions (Venkatesh et. al., 2003) 1. Guidance in the use of the EHR system is available to me. 2. A specific person (or group) is available on site to provide assistance with system difficulties. 3. I can get the help I need whenever there is a problem with our EHR system. Productivity (Sabherwal et al., 2006) 1. Our EHR system allows me to be more efficient in my practice of medicine. 2. The EHR system is a valuable aid in the performance of my job. 3. Our EHR system improves the reliability of patient care. [0] 4. Use of EHR increases the effectiveness of performing routine tasks. 5. My personal productivity has increased as a result of EHR technology. [0] Quality (Sabherwal et al., 2006) 1. The use of EHR helps to reduce the number of patient billing errors. 2. EHR technology has increased the quality of patient care in my practice. 3. Our EHR system improves the reliability of patient care. 4. The use of EHR increases patient satisfaction with the quality of their care. Satisfaction (Sabherwal et al., 2006) 1. Our EHR system meets my needs. [0] 2. Our EHR system is dependable. [0] 3. Purchasing our EHR system was a wise decision. 4. The benefits of this EHR system have been well worth the cost. 5. Our system is exactly what is needed for EHR. [0] 6. If I had to do it over again, I would make the same choice about EHR. [0] Construct Definition The perceived degree of ease associated with using the physician s EMR system. A personality trait denoting one s willingness to change. The degree to which the physician perceives that important others believe he should use an EMR system. The physician s perception of having IT resources available to assist with EMR system difficulties. The physician s perception of improvement in his or her personal, work related productivity. The physician s perception of improvements in the quality of patient care due to the use of electronic medical record technology. The physician s satisfaction with the overall EMR use experience. Proceedings of the Thirteenth Americas Conference on Information Systems, Keystone, Colorado, August