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Healthcare system-use behavior: a systematic review of ITs determinants.

ABSTRACT

To understand patient and physician behavior, researchers have investigated the determinants of using healthcare information systems. Although this stream of research has produced important findings, it has yet to appreciably advance our understanding of system-use behavior in healthcare. To fill this gap, the current paper employs a systematic review to synthesize past research, reveal the key determinants of healthcare system usage, and illuminate a deeper understanding of the topic. This study thus helps healthcare researchers expand their baseline knowledge of these core determinants and conduct more fruitful future research on system-use behavior in healthcare.

Keywords: healthcare, healthcare information systems, system usage, system-use behavior, TAM, technology acceptance model

INTRODUCTION

Healthcare information systems refer to any system that captures, stores, manages, or transfers information related to healthcare. In the past ten years, healthcare researchers have invested tremendous efforts to understand patient and clinician system-use behavior. Not surprisingly then, a number of research models have been developed to identify the determinants of user acceptance of information technologies in healthcare. Many of those models are mainly based on the Technology Acceptance Model (TAM) due to its practical utility and predictive power (Davis et al, 1989). However, based on our knowledge, very few studies have systematically examined the determinants of system acceptance in healthcare.

To fill this gap, the current study uses a systematic review approach to synthesize past research and to investigate the determinants of user-behavior in healthcare. A systematic review approach is very useful here because the empirical studies included in the analysis offer a variety of results of interest, which are unlikely to be obtained from a single study. By doing so, the current study not only identify key factors for using healthcare information systems but also helps to better understand clinician and patient behavior. This particular topic is of profound theoretical and practical importance, but so far it has not been directly and systematically investigated.

METHODOLOGY

TAM, as shown in Figure 1, asserts that the intention to use or actual use of an information system is a function of perceived ease of use and perceived usefulness (Davis et al, 1989). To identify system-use predictors unique in the context of healthcare, the current study thus focuses on core determinants other than perceived ease of use and perceived usefulness.

[FIGURE 1 OMITTED]

To find studies, we searched bibliographic databases and electronic bibliographies in various sources in February 2016. Examples of the bibliographic databases are Business Source Premier, ScienceDirect, ABI/INFORM, and PubMed. We manually searched some journals whose back issues were unavailable in bibliographic databases. For the electronic searches, we used such key words as information system, healthcare, health care, management information systems, technology acceptance model (TAM), system use, system usage.

To locate additional studies, we also scanned bibliographies of the papers identified, and checked their potential for inclusion. This comprehensive search strategy enables us to locate as many studies as possible. Finally, studies were chosen if they satisfied these two criteria: (1) they study information systems in healthcare and (2) they investigate determinants of using information systems. In total, we scanned over 130 articles and found that 16 studies met the inclusion criteria and thus were included in this research.

DETERMINANTS IN PAST STUDIES

Perceived Compatibility

Past research has identified compatibility as a key motivator for using healthcare information technology. Rooted in Rogers' Innovation Diffusion Theory (IDT), compatibility is defined as "the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters" (Rogers, 1995, p. 250). No doubt, healthcare delivery is a complex work process involving many medical tests, different treatment techniques, discussion with colleagues, synthesis of information from multiple sources, and application of multiple treatments simultaneously. Healthcare information systems, designed by technical specialists, may sometimes be unable to adequately capture this complex process or may even force physicians to change their approach to practice, resulting a reluctance to use healthcare systems (Bhattacherjee & Hikmet, 2007). Therefore, compatibility is of great importance to healthcare technology adoption.

Extant healthcare studies have confirmed the critical role of compatibility, as perceived by physicians, in system-use behavior. For example, Lee et al. (2015) find that an individual's prior compatible experiences have a significant impact on his/her attitude toward using new healthcare information technology. Similarly, investigating technology usage in a private hospital setting, Ismail et al. (2012) also find a positive relationship between compatibility and attitude toward using new technology. Many other studies have also successfully integrated compatibility into their research models and have found that compatibility leads to preferable healthcare system adoption (Bhattacherjee & Hikmet, 2007; Rodriguez 2012). In an investigation of acceptance of e-prescriptions and automated medication-management systems in hospitals, Rodriguez et al. (2012) find that perceived compatibility has a significant effect on both perceived usefulness and perceived ease of use. The impact of compatibility on usefulness has also been confirmed by Bhattacherjee and Hikmet (2007) in their investigation of physicians' resistance toward healthcare information technology.

Trust

Trust is another important determinant of using healthcare information systems. According to Mayer et al. (1995), trust has been defined as "the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party" (p.712). Healthcare involves privacy issues, which concern patients, physicians, and primary care providers. The most important goal for primary care and technology providers is to secure healthcare information systems and prevent unauthorized people from accessing medical records and confidential information (AlHamad et al., 2014). Similarly, the main concern for patients is the trust of the healthcare information systems they are provided to use and the trust of the protection of their sensitive personal information. Therefore, in the context of healthcare, if patients and physicians trust an information system and its vendor, they are more likely to view the system as something they can safely gain from and thus use it more frequently (Lanseng & Andreassen 2007).

Focusing on Internet-based patient-physician communication applications, Klein (2007) find that patient trust in healthcare provider and in Internet-based application vendor has a significant effect on behavioral intention to use the healthcare information systems. In an investigation of patient privacy, AlHamad et al. (2014) argue that trust is one of the most important issues when dealing with patient privacy and suggest that the trust construct should be assessed in four dimensions: trust in protection of personal data, trust in healthcare information systems, trust in authorized access to records, and trust in system security. They further suggest that to alleviate patient privacy concerns, it is necessary to increase the trust between the patients and primary care provider and the trust between the patients and third parties who are involved in securing the networks and others. Investigating primary healthcare nurses' intention to use information technology, Hung et al. (2014) find that trust in primary health information system can significantly influence one's attitude toward using it.

Performance Expectancy

An information system may be of great importance to physicians because it can help them to better perform their daily job. Thus, performance expectancy is a vital variable to the understanding of attitude to and usage of healthcare information systems. Performance expectancy can be defined as the degree to which individuals believe that using an information system will benefit them in terms of improving job performance (2012). Researchers suggest that performance expectancy is closely related to these three factors: perceived usefulness, extrinsic motivation, and job fit (Jeng & Tzeng, 2012). In the context of healthcare, performance expectancy may involve helping patients and physicians to be more effective in monitoring daily physical conditions, making personal healthcare plans, and safely maintaining medical records (Gao et al., 2015).

Past research offers evidence to the important role of performance expectancy in healthcare system-use behavior. Investigating social influence on the use of clinical decision support systems, Jeng and Tzeng (2012) find that performance expectancy has a significant impact on behavior intention. Focusing on the factors associated with consumer's intention to adopt wearable technology in healthcare, Gao et al. (2015) also find that performance expectancy can significantly influence behavioral intention. Similarly, Sun and Lu (2014) provide empirical evidence that performance expectancy has a significant effect on behavioral intention to use healthcare website. In an investigation of issues of accepting the German electronic health card (eHC), Wirtz et al. (2012) have grounded their research on technology acceptance model and the relevant literature, and find that performance expectancy can indirectly affect the attitude toward using the system through perceived usefulness.

Perceived Risk

Healthcare information systems deal with sensitive medical records and personal information and thus perceived risk of system failure may be a big concern for patients and physicians. Therefore, it is very necessary and important to consider the influences of perceived risk on healthcare information system usage. Perceived risk can be defined as the probability of a negative consequence happening from engaging in a behavior (Jung & Berthon, 2009). Generally, individuals will perform risk-benefit analysis that takes into account for drivers and inhibitors of system-use behavior when they are asked to use healthcare information systems (Gao et al. 2015). Because healthcare systems may raise users' concern about privacy and system failure, the decision to adopt healthcare technology involves a highly salient risk-benefit analysis in which users will face the tradeoff between perceived benefits and perceived risks (Gao et al. 2015).

Rodriguez et al. (2012) suggest that perceived risk can be negatively linked to perceived usefulness and to perceived ease of use, and their empirical study provides strong evidence to the second link. In an attempt to develop a model for delivering successful online healthcare, Jung and Berthon (2009) suggest that perceived risk will significantly and negatively influence one's attitude toward using healthcare information systems. Drawing from the unified theory of acceptance and use of technology (UTAUT), perceived risk theory, and credibility theory of online health information, Sun and Lu (2014) also suggest that perceived risk is the key influencing factor of users' acceptance and usage of healthcare website. Similarly, in an empirical study of wearable technology acceptance in healthcare, Gao et al. (2015) find that perceived risk has a significant, negative effect on individual's intention to adopt medical wearable devices.

Perceived Threat

Past research suggests that physicians appear to be relatively independent in making decisions on technology acceptance. One reason for such independence may well be that they have specialized training and autonomy. Therefore, perceived threat to professional autonomy is likely to be a salient construct affecting physicians' acceptance of healthcare information systems. Perceived threat to professional autonomy can be defined as "the degree to which a person believes that using a particular system would decrease his or her control over the conditions, processes, procedures, or content of his or her work" [Walter & Lopez, 2008, p. 209]. Past research also suggests that professionals tend to resist using an information system if they perceive the system as threatening to their professional autonomy (Bhattacherjee & Hikmet, 2007).

Focusing on the usage of Electronic Medical Records (EMR) systems and Clinical Decision Support (CDS) systems, Walter and Lopez (2008) find that perceived threat to professional autonomy has a significant, negative direct impact on perceived usefulness of an information system and on behavioral intention to use that system. Presenting a theoretical model of physicians' resistance of using healthcare information technology, Bhattacherjee and Hikmet (2007) find that perceived threat can indirectly influence behavioral intention to use healthcare information systems through resistance to change, which is defined as "any conduct that serves to maintain the status quo in the face of pressure to alter the status quo" (Zaltman & Duncan, 1977, p. 63).

Credibility

Healthcare information systems provide a lot of useful information to patients and physicians. Consequently, users may visit the healthcare websites for different reasons. For example, users can visit a healthcare website to easily get some drug information or to find answers to some questions related to unspeakable health problems. Therefore, trust in this information is tightly related to the credibility of the information and/or healthcare provider. In an investigation of the credibility of online information, Fogg and Tseng (1999) simply define credibility as the degree of being trustworthy. This definition thus indicates that a credible person is the one who is trustable and credible information is the information that is trustable (Sun & Lu, 2014). Past research suggests that if a user perceives something or some person as credible, the user will have more positive attitude toward the thing and the person.

Studies in healthcare have also found a significant effect of credibility on system-use behavior. For example, Sun and Lu (2014) have found that credibility of online health information has a significant influence on both behavioral intention to use and actual usage of healthcare websites. In a review of the potential benefits and pitfalls of e-health, Jung and Berthon (2009) have developed a theory-based model of e-health acceptance, and suggested that credibility can impact both perceived usefulness and perceived risk and thus can indirectly influence attitude toward using healthcare information systems.

CONCLUSION

Employing a systematic review approach, the current study combines findings from many individual studies to investigate the determinants of using healthcare information systems. Thus, this study contributes to an integrated analysis of system-use behaviors in the field of healthcare. The current study finds that in the context of healthcare, the key determinants of system-use behavior are perceived compatibility, trust, performance expectancy, perceived risk, perceived threat, and credibility. All these findings, therefore, constitute an important step toward a better understanding of healthcare system-use behavior and facilitate future research to further investigate these key determinants in the context of healthcare.

Specifically, future research can apply these six determinants to various healthcare system use contexts to compare their predictive power and to find out which one is the most salient predictor. Future research can also compare their predictive power with that of perceived usefulness and perceived ease of use. Such a comparison will enable researchers to discover whether the predictive power will shift from perceived usefulness and perceived ease of use to these six determinants in the context of using healthcare information systems. Moreover, this research is based on 16 individual studies found from the literature and thus may have the limitation of small sample size. For this reason, future research is also needed to collect more healthcare system use behavior studies and investigate whether there are other critical determinants of using healthcare information systems.

REFERENCE

AlHamad, A. Omari, F., & AlHamad, A. (2014). Recommendation for Managing Patients' Privacy in an Integrated Health Information Network, Journal of IT and Economic Development, 5(1), 47-52.

Bhattacherjee, A., & Hikmet, N. (2007). Physicians' Resistance toward Healthcare Information Technology: A Theoretical Model and Empirical Test, European Journal of Information Systems, 16(6), 725-737.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models, Management Science, 35(8), 982-1003.

Fogg, B. J., & Tseng, H. (1999). The Elements of Computer Credibility, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM Press, Pittsburgh, 80-87.

Gao, Y., Li, H., & Luo, Y. (2015). An Empirical Study of Wearable Technology Acceptance in Healthcare, Industrial Management and Data Systems, 115(9), 1704-1723.

Hung, S., Tsai, J., & Chuang, C. (2014). Investigating Primary Health Care Nurses' Intention to Use Information Technology: An Empirical Study in Taiwan, Decision Support Systems, 57, 331--342.

Ismail, W., Kit, P., Buhari, N., & Muzaini, A. (2012). Acceptance of Smartphone in Enhancing Patient-Caregivers Relationship, Journal of Technology Management & Innovation, 7(3), 71-79.

Jeng, D., & Tzeng, G. (2012). Social Influence on the Use of Clinical Decision Support Systems: Revisiting the Unified Theory of Acceptance and Use of Technology by the Fuzzy DEMATEL Technique, Computers & Industrial Engineering, 62(3), 819-828.

Jung, M., & Berthon, P. (2009). Fulfilling the Promise: A Model for Delivering Successful Online Health Care, Journal of Medical Marketing, 9(3), 243-254.

Klein, R. (2007). An Empirical Examination of Patient-Physician Portal Acceptance, European Journal of Information Systems, 16(6), 751-760.

Klein, R. (2007). Internet-Based Patient-Physician Electronic Communication Applications: Patient Acceptance and Trust, e-Service Journal, 5(2), 27-52.

Kohnke, A., Cole, M. L., & Bush, R. (2014). Incorporating UTAUT Predictors for Understanding Home Care Patients' and Clinician's Acceptance of Healthcare Telemedicine Equipment, Journal of Technology Management & Innovation, 9(2), 29-41.

Lanseng, E. J., & Andreassen, T. W. (2007). Electronic Healthcare: A Study of People's Readiness and Attitude toward Performing Self-Diagnosis, International Journal of Service Industry Management, 18(4), 394-417.

Lee, C., Tsao, C., & Chang , W. (2015). The Relationship between Attitude toward Using and Customer Satisfaction with Mobile Application Services, Journal of Enterprise Information Management, 28(5), 680-697.

Mayer, R. C., Davis, J. H., & Schoorman, F.D. (1995). An Integration Model of Organizational Trust, Academy of Management Review, 20(3), 709-734.

Rodriguez,T. Lozano, P., & Alonso, M. (2012). Acceptance of E-Prescriptions and Automated Medication-Management Systems in Hospitals: An Extension of the Technology Acceptance Model, Journal of Infomation Systems, 26(1), 77-96.

Rogers, E. (1995). Diffusion of Innovations, New York: Free Press.

Sun, J., & Lu, J. (2014). An Empirical Study on User Acceptance of Healthcare Website, International Journal of Networking and Virtual Organisations, 14(1-2), 57-73.

Walter, Z., & Lopez, M. (2008). Physician Acceptance of Information Technologies: Role of Perceived Threat to Professional Autonomy, Decision Support Systems, 46(1), 206-215.

Wirtz, B., Mory, L., & Ullrich, S. (2012). EHealth In the Public Sector: An Empirical Analysis of the Acceptance of Germany's Electronic Health Card, Public Administration 90(3), 642-663.

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Jiming Wu

California State University, East Bay USA

Recommended Citation

Wu, Jiming (2016) "Healthcare System-Use Behavior: A Systematic Review of ITs Determinants," Journal of International Technology

and Information Management: Vol. 25 : Iss. 4 , Article 5.

Available at: htp://scholarworks.lib.csusb.edu/jitim/vol25/iss4/5

ABOUT THE AUTHOR

Jiming Wu

California State University, East Bay

jiming.wu@csueastbay.edu

USA
Table 1: Summary of the determinants found in past studies.

Determinant       Research Paper

                  Bhattacherjee and
                  Hikmet 2007
                  Lee, Tsao, and Chang
                  2015
Perceived         Jung and Berthon 2009
Compatibility     Wirtz, Mory, and
                  Ullrich 2012
                  Ismail, Kit, Buhari, and
                  Muzaini 2012
                  Rodriguez, Lozano, and
                  Alonso 2012
                  AlHamad, Omari, and
                  AlHamad 2014
                  Klein 2007

Trust             Hung, Tsai, and Chuang

                  Lanseng and
                  Andreassen 2007
                  Klein 2007
                  Jeng and Tzeng 2012
                  Gao, Li, and Luo 2015
Performance       Sun and Lu 2014
expectancy        Wirtz, Mory, and
                  Ullrich 2012
                  Kohnke, Cole, and Bush
                  2014
                  Jung and Berthon 2009
                  Gao, Li, and Luo 2015
                  Sun and Lu 2014
Perceived Risk
                  Rodriguez, Lozano, and
                  Alonso 2012
                  Walter and Lopez 2008
Perceived Threat
                  Bhattacherjee1 and
                  Hikmet 2007
Credibility       Sun and Lu 2014
                  Jung and Berthon 2009

Determinant       Findings





Perceived         Perceived compatibility has a significant impact on
Compatibility     perceived ease of use, perceived usefulness, and
                  attitude.

Trust             use, perceived usefulness, attitude, and behavioral
                  intention.

Performance       Performance expectancy has a significant impact on
expectancy        perceived usefulness and behavioral intention.

                  Perceived risk has a significant impact on perceived
Perceived Risk    ease of use and behavioral intention.

Perceived Threat
                  perceived usefulness and behavioral intention.

Credibility       Credibility has a significant impact on behavioral
                  intention.
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Date:Oct 1, 2016
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