Applying Electronic Medical Records in health care Physicians’ perspective

Abdekhoda, Mohammadhiwa and Ahmadi, Maryam and Dehnad, Afsaneh and Noruzi, Alireza and Gohari, Mahmodreza Applying Electronic Medical Records in health care Physicians’ perspective. Applied Clinical Informatics, 2016, vol. 7, n. 2, pp. 341-354. [Journal article (Paginated)]

This is the latest version of this item.

[img] Text
Applying Electronic Medical Records in health care.pdf - Accepted version
Download (483kB)

English abstract

Summary Background: In order to fulfill comprehensive interoperability and recognize the electronic medical records (EMRs’) benefits, physicians’ attitudes toward using and applying EMR must be recognized. Objectives: The purpose of this study was to present an integrated model of applying EMRs by physicians. Methods: This was a cross sectional study in which a sample of 330 physicians working in hospitals affiliated to the Tehran University of medical sciences (TUMS) was selected. Physicians’ attitudes toward using and accepting EMR in health care have been analyzed by an integrated model of two classical theories i.e. technology acceptance model (TAM) and diffusion of innovation (DOI). The model was tested using an empirical survey. The final model was tested by structural equation modeling (SEM) and represented by Analysis of Moment Structures (AMOS). Results: The results suggest that the hybrid model explains about 43 percent of the variance of using and accepting of EMRs (R2=0.43). The findings also evidenced that Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Relative Advantage, Compatibility, Complicatedness and Trainability have direct and significant effect on physicians’ attitudes toward using and accepting EMRs. But concerning observe ability, significant path coefficient was not reported. Conclusions: The integrated model supplies purposeful intuition for elucidates and anticipates of physicians’ behaviors in EMRs adoption. The study identified six relevant factors that affect using and applying EMRs that should be subsequently the major concern of health organizations and health policy makers.

Item type: Journal article (Paginated)
Keywords: Electronic medical record (EMR), physician, technology acceptance model (TAM), diffusion of innovation theory (DOI), structural equation modeling (SEM)
Subjects: B. Information use and sociology of information
B. Information use and sociology of information > BI. User interfaces, usability.
L. Information technology and library technology > LD. Computers.
L. Information technology and library technology > LT. Mobile devices
Depositing user: Dr. Mohammadhiwa Abdekhoda
Date deposited: 25 May 2016 07:18
Last modified: 25 May 2016 07:18
URI: http://hdl.handle.net/10760/29374

Available Versions of this Item

  • Applying Electronic Medical Records in health care Physicians’ perspective. (deposited 25 May 2016 07:18) [Currently Displayed]

References

1. Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC health services research 2010; 10(1): 231.

2. Carayon P et al. Implementation of an electronic health records system in a small clinic: the viewpoint of clinic staff. Behaviour & Information Technology 2009; 28(1): 5–20.

3. Abdekhoda M et al. Factors affecting information technology acceptance by health information management (HIM) staff of Tehran University Of Medical Sciences’ Hospitals based on the technology acceptance model (TAM) in 2011. Payavard Salamat 2013; 7(4): 287-298.

4. Abdekhoda M et al. Information technology acceptance in health information management. Methods of information in medicine 2014; 53(1): 14–20.

5. Chaudhry B. et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of internal medicine 2006; 144(10) :742.

6. Putzer GJ, Park Y. Are physicians likely to adopt emerging mobile technologies? Attitudes and innovation factors affecting smartphone use in the Southeastern United States. Perspectives in health information management/AHIMA, American Health Information Management Association 2012; 9(Spring).

7. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Management science 1989; 35(8): 982–1003.

8. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly 1989; Sep 1: 319–40

9. Venkatesh V, Davis FD. A theoretica l extension of the technology acceptance model: four longitudinal studies. Manage Sci 2000; 46(2): 186–204.

10. Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley Pub Co 1975.

11. Rogers EM. Diffusion of innovations: Free Pr 1995.

12. Koivunen M. Acceptance and use of information technology among nurses in psychiatric hospitals. 2009. Available at http://doria32-kk.lib.helsinki.fi/handle/10024/43661

13. Duyck P et al. User acceptance of a picture archiving and communication system – applying the unified theory of acceptance and use of technology in a radiological setting. Methods of information in Medicine 2008; 47(2): 149-156.

14. Gupta A. Exploring the acceptance and barriers to usage of information and communication technology by Irish occupational therapists. 2010.

15. Rogers EM. Diffusion of innovations. 5th Ed. New York: Free Press; 2003.

16. Lee TT. Nurses’ adoption of technology: application of Rogers’ innovation-diffusion model. Applied Nursing Research 2004; 17(4): 231-238.

17. Chew F, Grant W, Tote R. Doctors on-line: using diffusion of innovations theory to understand internet use. Family Medicine-Kansas City 2004; 36: 645-650.

18. Morton ME. Use and acceptance of an electronic health record: factors affecting physician attitudes [Retrieved From Proquest Dissertations And Theses Database. (Umi No. 3327272)]: Drexel University; 2008.

19. Legrisa P, Inghamb J, Collerettec P. Why do people use information technology? A critical review of the technology acceptance model. Information & Management 2003; 40(3): 191–204.

20. Gefen D, Karahanna E, Straub DW. Trust and TAM in online shopping: An integrated model. MIS quarterly 2003: 51–90.

21. Schepers J, Wetzels M. A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management 2007; 44(1): 90–103.

22. Ma Q, Liu L. The technology acceptance model: a meta-analysis of empirical findings. Journal of Organizational and End User Computing (JOEUC) 2004; 16(1): 59–72.

23. Lee Y, Kozar KA, Larsen KRT. The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems 2003; 12(50): 752-780.

24. Davis FD, Bagozzi RP, Warshaw PR. Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. Journal of Applied Social Psychology 1992; 22(14): 1111-1132.

25. Zhang N, Guo X, Chen G. IDT-TAM integrated model for IT adoption. Tsinghua Science & Technology 2008; 13(3): 306-311.

26. Tung F-C, Chang S-C, Chou C-M. An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International Journal of Medical Informatics 2008; 77(5): 324-335.

27. Ardis MA, Green JA. Successful introduction of domain engineering into software development. Bell Labs Technical Journal 1998; 3(3): 10–20.

28. Atkinson NL. Developing a questionnaire to measure perceived attributes of eHealth innovations. American Journal of Health Behavior 2007; 31(6): 612-621.

29. Völlink T, Meertens R, Midden CJ. Innovating ‘diffusion of innovation’theory: Innovation characteristics and the intention of utility companies to adopt energy conservation interventions. Journal of Environmental Psychology 2002; 22(4): 333-344.

30. Wu JH et al. Testing the technology acceptance model for evaluating healthcare professionals’ intention to use an adverse event reporting system. International Journal for Quality in Health Care 2008; 20(2): 123-129.

31. Murphy E. Issues in the adoption of broadband enabled learning. British journal of educational technology 2005; 36(3): 525-536.

32. Cho J, Park D, Lee HE. Cognitive factors of using health apps: Systematic analysis of relationships among health consciousness, health information orientation, eHealth literacy, and health app use efficacy. Journal of medical Internet research 2014; 16(5).

33. Conrad ED. Willingness to use IT innovations: A hybrid approach employing diffusion of innovations and technology acceptance models: Southern Illinois University Carbondale; 2009.

34. Kendall JD et al. Receptivity of Singapore’s SMEs to electronic commerce adoption. The Journal of Strategic Information Systems 2001; 10(3): 223-242.

35. Greer TH, Murtaza MB. Web personalization: The impact of perceived innovation characteristics on the intention to use personalization. Journal of Computer Information Systems 2003; 43(3): 50-55.

36. Faiers A, Cook M, Neame C. Towards a contemporary approach for understanding consumer behavior in the context of domestic energy use. Energy Policy 2007; 35(8): 4381-4390.

37. Wilkins MAJ. The health information manager as change agent in adopting electronic health record technology in hospitals: Capella University; 2009.

38. Nair SV. Benefits and security of electronic health record (EHR) use by pediatric staff: A technology acceptance model (TAM)-based quantitative study: Doctor of Philosophy thesis. CAPELLA UNIVERSITY; 2011.

39. Kowitlawakul Y. Technology acceptance model: Predicting nurses’ acceptance of telemedicine technology (eICU®). 2008.

40. Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. Journal of Biomedical Informatics 2010; 43(1): 159-172.

41. Subramanian GH. A replication of perceived usefulness and perceived ease of use measurement. Decision Sciences 1994; 25(5 6): 863-874.

42. Igbaria M, Iivari J. The effects of self-efficacy on computer usage. Omega 1995; 23(6): 587 605.

43. Pai FY, Huang KI. Applying the technology acceptance model to the introduction of healthcare information systems. Technological Forecasting and Social Change 2011; 78(4): 650-660.

44. Ortega Egea JM, Román González MV. Explaining physicians’ acceptance of EHCR systems: an extension of TAM with trust and risk factors. Computers in Human Behavior 2011; 27(1): 319-332.

45. Gagnon M-P et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. Journal of Medical Systems 2012; 36(1): 241-277.

46. Yu P, Li H, Gagnon M-P. Health IT acceptance factors in long-term care facilities: A cross-sectional survey. International Journal of Medical Informatics 2009; 78(4): 219-229.

47. Oh S, Ahn J, Kim B. Adoption of broadband Internet in Korea: the role of experience in building attitudes. Journal of Information Technology 2003; 18(4): 267-280.

48. Sanayei A, Ansari A, Ranjbarian B. A hybrid technology acceptance approach for using the E-CRM information system in clothing industry. International Journal of Information Science and Management (IJISM) 2012: 15–25.


Downloads

Downloads per month over past year

Actions (login required)

View Item View Item
Contact us
Send email Sponsors
Sponsor: AIMS Sponsor: D&P Sponsor: UniNa CAB Get connected
Facebook Facebook Twitter Twitter Linkedin LinkedIn RSS RSS