Investigating the Factors Related to the Usage Behavior of Employees Towards Electronic Health Records Considering the Role of Behavioral Intention
Journal of Medical Library and Information Science,
Vol. 3 (2022),
,
Page 1-9
https://doi.org/10.22037/jmlis.v3i.36231
Abstract
Introduction: The improving electronic health record becomes one of the essential goals of social security medical centers. The present study aims to investigate the factors related to the usage behavior of employees towards electronic health records considering the role of behavioral intention in Social Security Medical centers of Khorasan Razavi Province, Iran.
Methods: The present correlational research is conducted in 2020. The statistical population was the staff of the social security medical centers (N=180) using the counting method. The data collection tool was a standard questionnaire that its validity and reliability had been verified in the previous studies. The data were analyzed by SPSS and SMART PLS software.
Results: Hope for effort (path coefficient: 0.240 and t-statistic: 2.984), social impact (path coefficient: 0.194 and t-statistic: 2.453), facilitation of conditions (path coefficient: 0.150 and t-statistic: 2.005), and personal innovation in information technology (path coefficient: 0.225 and t-statistic: 3.005), positively and significantly affected behavioral intention. Also, behavioral intention (path coefficient: 0.462 and t-statistic: 7.495) positively and significantly affected staff’s usage behavior in e-health record acceptance. This is while the expected performance (path coefficient: 0.048 and t-statistic: 0.548) and resistance to change (path coefficient: 0.106 and t-statistic: 1.690) didn't significantly affect behavioral intention.
Conclusion: Considering the positive effect of hope for effort and social impact and facilitation of conditions and personal innovation in information technology, and behavioral intentions on the behavior in e-health record acceptance, it is recommended that Social Security Medical centers of Khorasan Razavi to train and strengthen the above-mentioned factors.
- Behavioral Intention
- Usage behavior of employees
- Electronic health record
- Social security
- Medical centers
- Iran
How to Cite
References
Bigdeli AZ, Kamal MM, De Cesare S. Electronic information sharing in local government authorities: Factors influencing the decision-making process. Int J Inf Manage. 2013;33(5):816–830. doi: 10.1016/J.IJINFOMGT.2013.05.008
Shahbahrami A, Moayed Rezaie S, Hafezi M. Effective factors in acceptance of electronic health record from employees point of view. J Guilan Univ Med Sci. 2016;24(96):50–60. Available from: https://journal.gums.ac.ir/article-1-1101-en.html. [In Persian].
Hossain A, Quaresma R, Rahman H. Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study. Int J Inf Manage. 2019;44:76–87. doi: 10.1016/J.IJINFOMGT.2018.09.016
Gagnon MP, Ghandour EK, Talla PK, Simonyan D, Godin G, Labrecque M, et al. Electronic health record acceptance by physicians: Testing an integrated theoretical model. J Biomed Inform. 2014;48:17–27. doi: 10.1016/J.JBI.2013.10.010
Haried P, Claybaugh C, Dai H. Evaluation of health information systems research in information systems research: A meta-analysis. Health Informatics J. 2019;25(1):186–202. doi: 10.1177/1460458217704259
Hoque R, Sorwar G. Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. Int J Med Inform. 2017;101:75–84. doi: 10.1016/J.IJMEDINF.2017.02.002
Wu JH, Kao HY, Sambamurthy V. The integration effort and E-health compatibility effect and the mediating role of E-health synergy on hospital performance. Int J Inf Manag J Inf Prof. 2016;36(6):1288–1300. doi: 10.1016/J.IJINFOMGT.2016.09.002
Wu YL, Tao YH, Yang PC. The use of unified theory of acceptance and use of technology to confer the behavioral model of 3G mobile telecommunication users. J Stat Manag Syst. 2008;11(5):919–949. doi: 10.1080/09720510.2008.10701351
Renner AL, Swart JC. Patient Core Data Set. Standard for a longitudinal health/medical record. Comput Nurs. 1997;15(2):7–13. doi: 10.1197/jamia.M2573
Simborg DW. Promoting electronic health record adoption. Is it the correct focus? J Am Med Informatics Assoc. 2008;15(2):127–9. doi: 10.1197/JAMIA.M2573
Homburg C, Giering A. Personal characteristics as moderators of the relationship between customer satisfaction and loyalty - An empirical analysis. Psychol Mark. 2000;18(1):43–66. doi: 10.1002/1520-6793(200101)18:1<43::AID-MAR3>3.0.CO;2-I
Al-Qeisi KI. Analyzing the use of UTAUT model in explaining an online behaviour: Internet banking adoption [dissertation on the Internet]. Brunel University ; 2009 [cited 2022 Apr 9]. Available from: https://bura.brunel.ac.uk/handle/2438/3620
Venkatesh V, Davis FD. Theoretical extension of the Technology Acceptance Model: Four longitudinal field studies. Manage Sci. 2000;46(2):186–204. doi: 10.1287/MNSC.46.2.186.11926
Venkatesh V, Morris MG, Davis GB. User Acceptance of Information Technology Towards a Unified View. MIS quarterly [Internet]. 2003 [cited 2022 Apr 10]; 27(3):425-78 Available from: https://www.jstor.org/stable/pdf/30036540
Wong CH, Lee HS, Lim YH, Chua BH, Chai BH, Han Tan GW. Predicting the consumers’ intention to adopt mobile shopping: An emerging market perspective. Int J Netw Mob Technol [Internet]. 2012 [cited 2022 Apr 10];3(3):24–39. Available from: http://ur.aeu.edu.my/510/1/Predicting the Consumers’ Intention to Adopt Mobile-2-17.pdf
Wong CH, Tan Han GW, Tan BI, Ooi KB. Mobile advertising. Telemat Informatics. 2015;32(4):720–34. doi: 10.1016/J.TELE.2015.03.003
June L, Chun Sheng Y, Chang L, James E. Yao. Technology acceptance model for wireless Internet. Internet Res. 2003;13(3):206–22. doi: 10.1108/10662240310478222
Mohammadi J, Nadaf M, Safarian S. Explaining the effect of organizational trust and organizational health on resistance to change in the government employees by considering the mediated role of organizational commitment (The case: Municipality of Ahvaz). J Public Adm [Internet]. 2017[cited 2022 Apr 10];9(1):85–106. Available from: https://jipa.ut.ac.ir/article_63091.html. [In Persian].
Self DR. Organizational change - Overcoming resistance by creating readiness. Dev Learn Organ. 2007;21(5):11–3. doi: 10.1108/14777280710779427
Abbasi R, Khajouei R, Sadeqi Jabali M. Timeliness and accuracy of information sharing from hospital information systems to electronic health record in Iran. J Heal Adm [Internet]. 2019 [cited 2022 Apr 10];22(2):28–40. Available from: http://jha.iums.ac.ir/article-1-2891-en.html. [In Persian].
Rajaeian E, Peikari H, Zamani N. Factors affecting the intention of adoption of health information technology in hospitals affiliated with social security in Isfahan using the model of Don Bale et al. (2012). Q J Nurs Manag [Internet]. 2018 [cited 2022 Apr 10];7(2):49–59. Available from: http://ijnv.ir/browse.php?a_id=554&sid=1&slc_lang=en&ftxt=0. [In Persian].
Hajizadeh E, Asghari M. Statistical methods and analyses in health and biosciences a research methodological approach. Tehran Jahade Daneshgahi Publ. 2011. [In Persian].
Davari A, Rezazadeh A. Structural equation modeling with PLS. Tehran Jahade Daneshgahi Publ. 2013. [In Persian].
Shiferaw KB, Mehari EA. Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics Med Unlocked. 2019;17. doi: 10.1016/J.IMU.2019.100182
Davarpsand T. The role of factors affecting the acceptance of internet banking services by customers (case study: National Bank of Rasht). University of Tehran.; 2015. [In Persian].
Harle CA, Gruber LA, Dewar MA. Factors in medical student beliefs about electronic health record use. Perspect Heal Inf Manag [Internet]. 2014 [cited 2022 Apr 10];11(Winter). Available from: /pmc/articles/PMC3995485/. PMID: 24808813
Kowitlawakul Y, Chan SWC, Pulcini J, Wang W. Factors influencing nursing students’ acceptance of electronic health records for nursing education (EHRNE) software program. Nurse Educ Today. 201;35(1):189–94. doi: 10.1016/J.NEDT.2014.05.010
Agarwal R, Prasad J. A conceptual and operational definition of personal innovativeness in the domain of information technology. Inf Syst Res. 1998;9(2):204–15. doi: 10.1287/isre.9.2.204
Guo X, Sun Y, Wang N, Peng Z, Yan Z. The dark side of elderly acceptance of preventive mobile health services in China. Electron Mark. 2013;1(23):49–61. doi: 10.1007/S12525-012-0112-4
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