PREDICTING ACCEPTANCE OF ELECTRONIC MEDICAL RECORDS: WHAT FACTORS MATTER MOST?

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1 PREDICTING ACCEPTANCE OF ELECTRONIC MEDICAL RECORDS: WHAT FACTORS MATTER MOST? Shanan G. Gibson & Elaine D. Seeman College of Business, East Carolina University ABSTRACT The current paper compares the degree to which variables associated with three common theories, Davis s Technology Acceptance Model (TAM), Ajzen s Theory of Planned Behavior, and Moore and Benbasat s Innovation Diffusion Theory (IDT), successfully explain variance in medical personnel s acceptance of electronic medical records (EMR) technologies. All of the variables of interest were found to account for statistically significant amounts of variance; Attitude toward EMR (from the Theory of Planned Behavior) was the construct which predicted the greatest amount of variance in intentions to use EMR, followed by Usefulness (from TAM), and Relative Advantage (from IDT). Perceived Social Influence and Behavioral Control explained the least amounts of variance in usage intentions. INTRODUCTION At the 2005 Southeast Decision Sciences Institute conference, we presented a paper entitled The Theory of Planned Behavior and its Role in Technology Acceptance: Model Development Utilizing Computerized Physician Order Entry (CPOE) which marked the beginning of a study seeking to lay a framework for a new model of technology acceptance that incorporates the unique features of physicians and physician extenders, and the complex environment in which they work. At that juncture qualitative analysis of physician interviews supported our research assertion that the constructs associated with the Theory of Planned Behavior impacted the physicians acceptance (or lack thereof) of CPOE within the current environment. In 2006, a follow-up was provided that utilized simple descriptive statistics from a very small hospital sample as a preliminary step toward proving quantitative substantiation. Following this, in 2008, data was presented that indicated that the Theory of Planned Behavior did explain variance in medical personnel s stated intention to utilize a newly introduced Electronic Medical Records (EMR) system. Since that time, we have included an additional construct of interest, relative advantage provided by technology, and conducted more sophisticated analyses to determine which factors within the theories of interest do the best job of explaining variance in intention to adopt a newly implemented EMR system. The objective of this paper is to report these findings related to acceptance of EMR and substantiate the importance of considering factors beyond those associated with TAM when examining technology acceptance among medical professionals. The Electronic Medical Record integrates patient information systems so that patient demographic, financial and medical information can be collected, accessed, transmitted and stored in a readily available digital format (Hough, Chen, & Lin, 2005; Steele, Gardner, Chandra, & Dillon, 2007). EMR technology represents a movement from paper-based care activities toward outcome-focused, evidenced based processes (Mangalompalli, Rama, Muthivalian, Jain, & Parinam, 2007). This shift can be an agent for change and improvement by eliminating confusing or illegible hand-written order documentation, minimizing transcription errors and

2 fundamentally reducing clinical mistakes. Most importantly EMR technology allows physicians fast access to appropriate patient information allowing prompt diagnosis and treatment (Chao, Jen, Chi, & Lin, 2007). In critical situations, such quick access saves lives (Steele, Gardner, Chandra, & Dillon, 2007). While healthcare organizations recognize the advantages associated with the use of the EMR, adoption of the technology has been slow. To date, less than ten percent of American hospitals have implemented electronic medical record keeping as part of their technology strategy for health information (Gardner, 2007). Reasons for the slow deployment include expenses related to upgrading existing paper systems, funding for additional workstations and resources, and the challenges associated with achieving and maintaining physician buy-in and acceptance. According to John Hammergren, CIO of McKesson, It s really not a technological barrier. The systems are available and we can provide those interconnections. The issue is one of adoption. Are people really ready to do this? As long as it s easier to script it out and hand it to a voice-activated nurse, that s what the physician will do (Colvin, 2007). We have based our study on this issue-physician acceptance of the Electronic Medical Record. THEORETICAL BACKGROUND The Technology Acceptance Model Davis s (1989) Technology Acceptance Model (TAM) has been and remains an important and viable tool for researchers. Research based upon TAM has offered valuable insights into how and why individuals choose to accept or reject technology. However, many of the studies utilizing the TAM or some variation of the TAM have focused on general user populations working in varying occupational settings, and utilizing a wide spectrum of information technology solutions (Gefen & Straub, 1997; Taylor & Todd, 1995; Veiga, Floyd, & Dechant, 2001; Venkatesh & Morris, 2000). However, physicians and physician extenders (i.e. physician assistants and nurse practitioners) differ quite markedly from general users. They are highly educated, highly trained professionals, working in stressful and highly politicized environments. Given the complexity of the healthcare industry and its unique occupational dynamics, we feel that the TAM in and of itself, may not be an appropriate methodology for explaining technology acceptance as it applies to medical practitioners. The Theory of Planned Behavior Advocates of the Theory of Planned Behavior suggest that all behavior is motivated by individual decisions that are based on an individual s intention to perform that behavior. Intention to perform a behavior, in turn, is influenced by the individual s perceived control over the performance of that behavior, his or her attitude toward performing the behavior and his or her perception of social norms (pressure or approval from important referent individuals to perform a behavior). The Theory of Planned Behavior asserts that behavioral control reflects an individual s belief regarding the ease of performing or completing a task. Behavior control is similar to the Technology Acceptance Model s perceived ease of use construct. Indeed the TAM was derived in part from the Theory of Planned Behavior. However, the Theory of Planned Behavior incorporates the individual s past experience as well as a sense of control into choosing a behavior.

3 According to the Theory of Planned Behavior, individuals behave in accordance with their beliefs (Ajzen, 1988). This theory has considerable support for behaviors in medicine, education, business, and the general population. The Theory of Planned Behavior implies that doctors attitudes, their subjective norms and perceived behavioral control are positively related to their planned and actual behavior concerning the acceptance of new organizational technology operationalized as an Electronic Medical Records system. Indeed, prior research by Seeman and Gibson (2008) found that the constructs associated with the Theory of Planned Behavior did in fact explain variance in medical personnel s stated intention to utilize a newly implemented EMR system. Innovation Diffusion Theory Innovation diffusion theory (IDT) has been used to study the adoption of a variety of innovations, not all of which are technological (Rogers, 1995). Within information systems, Moore and Benbasat (1991) have utilized the characteristics of IDT to successfully predict technology acceptance. The constructs associated with IDT in the technological realm are: relative advantage, ease of use, image, visibility, compatibility, results, and voluntariness of use. Given the inclusion of both TAM and the Theory of Planned Behavior in the current study, and the high similarity of their constructs to those associated with IDT, the current study will include Relative Advantage as an additional construct anticipated to explain physician acceptance of EMR. Relative Advantage is defined as the degree to which an innovation is perceived as being better than its precursor (Moore and Benbasat, 1991, p. 195). METHODS Research Setting, Participants, and Procedures As part of an on-going, multi-phase research endeavor examining the implementation of electronic medical records, faculty associated with both a medical school from a large regional university and a large multi-physician practice were asked to complete an anonymous survey regarding their perceptions of EMR implementation at their respective locations. Completed surveys (57% male, 43% female) were received from 102 of the physicians that were invited to participate. The average age of physician participants was 42.4 years old, with an average of 13.8 years practicing medicine, 7.2 years at the current location, and 6.7 years in their current job position. Survey Instrument The survey instrument used for the current study was based on questions derived from Davis s TAM model (Davis, 1989), Ajzen s Planned Behavior model (Ajzen, 1988), and questions derived from Person X s Relative Advantages construct. Hence, participants responded to questions measuring the central constructs of the TAM: the perceived ease of use of EMR technologies and the perceived usefulness of EMR, questions measuring the central constructs of the Theory of Planned Behavior: perceived behavioral control, attitudes toward EMR technology, and perceived social pressure regarding EMR usage, and questions that considered the relative advantages offered by EMR versus the traditional written records systems. In all instances, respondents used a 7-point Likert-type scale where one was Not at All and seven was Very Much So.

4 To assess the criterion of technology acceptance, participants were asked to indicate the degree to which they concurred with a statement assessing their intention to utilize EMR technology in the future. This is highly consistent with previous technology acceptance studies that have utilized intention to use technology as indicative of technology acceptance. All survey items are shown in Table 1 grouped by construct. Analyses In order to examine the degree to which each of the constructs of interest explained variance in intentions to embrace EMR, a series of multiple regression procedures were conducted. Details of these analyses are described in the Results section. RESULTS Six distinct multiple regressions were conducted to determine how well each of the constructs of interest explained variance in physician acceptance of EMR technology. For the constructs associated with TAM, the regression equation with the perceived usefulness was significant, R 2 =.549, adjusted R 2 =.510, F (8, 101) = , p <.01. Likewise, the regression equation for perceived ease of use was also significant, R 2 =.503, adjusted R 2 =.471, F (6, 101) = 15.96, p <.01. Based on these results, perceived usefulness appears to provide more insight into why medical personnel embrace EMR technology. With regard to the Theory of Planned Behavior, the regression equation for Attitude toward EMR was significant, R 2 =.699, adjusted R 2 =.673, F (8, 101) = , p <.01. Both Social Influence (R 2 =..355, adjusted R 2 =.329, F (4, 101) = , p <.01) and Behavioral Control (R 2 =.343, adjusted R 2 =.309, F (5, 101) = , p <.01) were also significant, but each explained considerably less amounts of variance than other variables already considered. Lastly, the construct of Relative Advantage, from IDF, had a significant regression question, R 2 =.516, adjusted R 2 =.474, F (8, 101) = , p <.01. Table 2 summarizes the results above by listing each of the constructs of interest according to the amount of variance accounted for in EMR acceptance. Table 2. Constructs and Variance Accounted For Construct R 2 Adjusted R 2 Attitude toward EMR Usefulness Relative Advantage Ease of Use Social Influence Behavioral Control

5 Table 1. Survey Items TAM Items Perceived Ease of Use (α =.594) I find EMR flexible to interact with. I find EMR to be easy to use. I find it easy to get EMR to do what I need it to do in my patient care & management. It is easy for me to become skillful in use the EMR technology. Learning to operate EMR is easy for me. My interactions with EMR are clear and understandable. Perceived Usefulness (α =.859) The primary benefit of EMR is patient safety. EMR is related to a physician s ethical responsibility to do no harm. I find EMR useful for my patient care and management. Using EMR enhances my service effectiveness. Using EMR improves my patient care and management. Using EMR enables me to complete patient care more quickly. Using EMR increases my productivity in patient care. Theory of Planned Behavior Items Perceived Behavioral Control (alpha =.72) I know why EMR was/is being implemented at my organization. Individual physicians have the ability to influence the decisions regarding EMR. Individual physicians will influence the decisions regarding EMR. I have the knowledge necessary to use EMR. I have the resources necessary to use EMR. Perceived Social Influence (alpha =.35) Medical leadership believes that I/we should use EMR. My feelings of responsibility toward my patients influence me to use EMR. My peers think I/we should use EMR. The culture here embraces EMR technology. Attitudes Toward EMR (alpha =.87) EMR will be successfully implemented at other organizational locations. EMR is an appropriate tool for physicians to use. I like the idea of using EMR. I find EMR technology useful for my patient care & management. Using EMR is a good idea. Using EMR is pleasant Using the EMR system is a wise idea. I have embraced the EMR technology in my workplace. Innovation Diffusion Theory Items Relative Advantage (alpha =.95) EMR will lower my malpractice risk in the future. EMR will increase my overall effectiveness. EMR will increase my overall efficiency. EMR will increase my profitability. EMR will enable greater achievement or success in my work. EMR will increase the amount of autonomy & independence I experience at work. EMR will lead to greater amounts of recognition for my work. EMR improves my ability to build medical relationships with my patients.

6 DISCUSSION & FUTURE RESEARCH As pointed out by Hu et al. (1999), professionals might subtly differ in their acceptance of technology when compared with individuals in an ordinary business setting. While the advantages of using the Electronic Medical Record in physician decision making are clearly recognized, this study explores reasons beyond those constructs associated with TAM that explain the slow adoption of this technology. A major contribution of this research is the finding that constructs associated with both the Theory of Planned Behavior (Attitude toward EMR) and Innovation Diffusion Theory (Relative Advantage) could explain greater amounts of variance than does the TAM construct Perceived Ease of Use. Future research might consider how technology adoption by physicians is affected by other factors such as culture. For example, perceived usefulness appears more important in western culture while non-western cultures utilize ease of use more in determining intention and actual use (Schepers & Wetzel, 2007).. Given the diverse ethnicity of the physician population, studying the relationship of ethnicity to EMR acceptance would also be an important contribution. From an organizational perspective, the role of EMR as a source of medical organizational change (Jimmieson, Peach & White, 2008) offers an avenue of extending this research. For example, examining the role that the constructs from the Theory of Planned Behavior, Innovation Diffusion, and the Technology Acceptance Model might collectively play in assisting the implementation of this technological change would be a valuable implication for practice. Finally, Venkatesh and Balla, (2008) have suggested a need for research regarding the role of interventions in decision making that can lead to increased acceptance and better use of Information Technology. As the EMR moves from larger to smaller hospitals and practices, adoption decisions and intervention creates yet another research avenue. REFERENCES Ajzen, I. (1988). Attitudes, Personality and Behavior. Milton Keynes (UK): Open University Press. Chao, C., Jen, W., Chi, Y, and Lin, B. (2007). Improving patient safety with RFID and mobile technology, International Journal of Electronic Healthcare, Vol. 3, No. 2 pp Colvin, G. (2007). Wiring the Medical World, Fortune, February 19, 2007, Vol. 155, No. 3 pp Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), Ferren, A. (2002). Gaining MD Buy-In: Physician Order Entry. Journal of Healthcare Information Management, 16(2), 67. Gardner, Tom. Six Trends to Bank On Fortune, 6/25/2007, Vol. 155 Issue 12, p85-92

7 Gefen, D. & Straub, D. (1997). Gender Differences in the Perception and Use of An Extension to the Technology Acceptance Model. MIS Quarterly, 21(4), Hieb, B. & Handler, T. (2001). The Critical Role of Orders in the Delivery of Healthcare. Gardner Research AV Hough, C.B.H., Chen, J.C.H. and Lin, B. (2005). Virtual health/electronic medical record: current status and perspective, International Journal of Healthcare Technology and Management, Vol. 6, No. 3, pp Mangalompalli, A., Rama, C, Muthiyalian, R., Jain, A.K, and Parinam, A.M. (2007). High-end clinical domain information systems for effective healthcare delivery, International Journal of Electronic Healthcare, Vol. 3, No. 2 pp Moore, G.C., and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), pp Rogers, E. Diffusion of Innovations, Free Press, New York, Seeman, E. and Gibson, S. (2008). The theory of planned behavior and its role in technology acceptance of electronic medical records implementation. Proceedings of the 2008 SE DSI Annual Conference, Orlando, FL. Steele, R., Gardner, W., Chandra, D., and Dillon, T.S (2007). Framework and prototype for a secure XML-based electronic health records system, International Journal of Electronic Healthcare, Vol. 3, No. 2 pp Taylor, S. & Todd, P. (1995). Assessing IT Usage: The Role of Prior Experience. MIS Quarterly, 19(4), Veiga, J., Floyd, S., & Dechant, K. (2001). Towards Modeling the Effects of National Culture on IT Implementation and Acceptance. Journal of Information Technology, 16, Venkatesh, V. & Morris, M. (2000). Why Don t Men Ever Stop to Ask for Directions? Gender, Social Influence, and their Role in Technology Acceptance and Usage Behavior.MIS Quarterly, 24(1), Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3),

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