CC BY-NC-ND 4.0 · Methods Inf Med 2019; 58(S 02): e58-e71
DOI: 10.1055/s-0039-1695718
Original Article
Georg Thieme Verlag KG Stuttgart · New York

The Differing Effect of Gender and Clinical Specialty on Physicians' Intention to Use Electronic Medical Record

Hsin-Ginn Hwang
1   Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan
,
Bireswar Dutta
1   Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan
,
Hui-chuan Chang
2   Yuan General Hospital, Kaohsiung, Taiwan
› Author Affiliations
Further Information

Publication History

07 January 2019

05 July 2019

Publication Date:
09 September 2019 (online)

Abstract

Background The use of electronic medical record (EMR) is anticipated to bring benefits for patients, physicians, and organizations. But limited physicians' acceptance of EMR presents a serious threat to its effective implementation.

Objectives The current study incorporates technology acceptance model (TAM) with two antecedents, gender, and clinical specialty and one context-specific factor, financial incentives, to identify the factors that influence physicians' intention to use EMR in Taiwan.

Methods The survey methodology was used to collect data from the physicians, working in the regional hospital that had implemented EMR system. A total of 119 out of 213 questionnaires returned in a response rate of 56%. But four responses were considered ineffective due to missing values. The structural equation modeling (SEM) technique was employed to analyze the research framework.

Results The partial least squares (PLS) regression indicated that three factors perceived usefulness, financial incentives, and attitude toward using EMR significantly affect physicians' intention. But concerning perceived ease of use (PEOU), an insignificant path coefficient was reported. Additionally, regression analysis showed gender, and clinical specialty positively influenced physicians' intention to use EMR.

Discussion and Conclusions The proposed research framework contributes to the conclusive explanation for interpreting physicians' intention to use EMR. Physicians generally have a higher level of computer literacy. Therefore, the factor of PEOU could not be critical regarding adopting new health information technology (HIT). This study also brings perspectives from the gender, and clinical differences have primarily been missing in the literature of the physicians' intention to use HIT. In doing so, it infers how gender, and clinical specialty, may complement (and in some instances, reinforce) the influence of technological and attitudinal factors of HIT use. Thus, health care providers must take these factors into consideration in the development and validation of the theories regarding the intention to use EMR.

 
  • References

  • 1 Scott RE. e-Records in health--preserving our future. Int J Med Inform 2007; 76 (5,6): 427-431
  • 2 AlHazme RH, Haque SS, Wiggin H, Rana AM. The impact of health information technologies on quality improvement methodologies' efficiency, throughput and financial outcomes: a retrospective observational study. BMC Med Inform Decis Mak 2016; 16 (01) 154
  • 3 De Oliveira JF. The effect of the internet on the patient-doctor relationship in a hospital in the city of São Paulo. J Inf Syst Technol Manag 2014; 11 (02) 327-344
  • 4 Korgaonkar RB. Adoption of information system by Indian hospitals; challenges and roadmap. Int J Sci Eng Res 2014; 5 (02) 473-479
  • 5 Alhamid SM, Lee DXY, Wong HM. , et al. Implementing electronic handover: interventions to improve efficiency, safety and sustainability. Int J Qual Health Care 2016; 28 (05) 608-614
  • 6 Su CH, Li TC, Cho DY. , et al. Effectiveness of a computerised system of patient education in clinical practice: a longitudinal nested cohort study. BMJ Open 2018; 8 (05) e020621
  • 7 American Medical Association. Improving care: priorities to improve electronic health record usability. 2014 . Available at: https://pdfs.semanticscholar.org/807e/09d1277e3a6032c893ea47f445d06dc56f28.pdf?_ga=2.95001692.1959410284.1563782350-1889254951.1561091521 . Accessed July 22, 2019
  • 8 Johnson RJ III. A comprehensive review of an electronic health record system soon to assume market ascendancy: EPIC. J Health Commun 2016; 1 (04) 33-56
  • 9 Greenhalgh T, Russell J, Ashcroft RE, Parsons W. Why national eHealth programs need dead philosophers: Wittgensteinian reflections on policymakers' reluctance to learn from history. Milbank Q 2011; 89 (04) 533-563
  • 10 Esmaeilzadeh P, Sambasivan M. Health Information Exchange (HIE): A literature review, assimilation pattern and a proposed classification for a new policy approach. J Biomed Inform 2016; 64: 74-86
  • 11 Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci 2016; 11 (01) 146
  • 12 Boonstra A, Versluis A, Vos JFJ. Implementing electronic health records in hospitals: a systematic literature review. BMC Health Serv Res 2014; 14 (370) 370
  • 13 Gagnon MP, Ghandour K, Talla PK. , et al. Electronic health record acceptance by physicians: testing an integrated theoretical model. J Biomed Inform 2014; 48: 17-27
  • 14 Ajami S, Bagheri-Tadi T. Barriers for adopting electronic health records (EHRs) by physicians. Acta Inform Med 2013; 21 (02) 129-134
  • 15 O'Donnell A, Kaner E, Shaw C, Haighton C. Primary care physicians' attitudes to the adoption of electronic medical records: a systematic review and evidence synthesis using the clinical adoption framework. BMC Med Inform Decis Mak 2018; 18 (01) 101
  • 16 Sykes TA, Venkatesh V, Rai A. Explaining physicians' use of EMR systems and performance in the shakedown phase. J Am Med Inform Assoc 2011; 18 (02) 125-130
  • 17 Li J, Talaei-Khoei A, Seale H, Ray P, Macintyre CR. Health care provider adoption of eHealth: systematic literature review. Interact J Med Res 2013; 2 (01) e7
  • 18 Al-Mujaini A, Al-Farsi Y, Al-Maniri A, Ganesh A. Satisfaction and perceived quality of an electronic medical record system in a tertiary hospital in oman. Oman Med J 2011; 26 (05) 324-328
  • 19 Melas CD, Zampetakis LA, Dimopoulou A, Moustakis V. Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. J Biomed Inform 2011; 44 (04) 553-564
  • 20 Redd TK, Doberne JW, Lattin D. , et al. Variability in electronic health record usage and perceptions among specialty vs. primary care physicians. AMIA Annu Symp Proc 2015; 2015: 2053-2062
  • 21 Department of Health. 2003 Online health services promotion plan- 2003 result report. Available at: http://www.doh.gov.tw/CHT2006/DM/DM2_p01.aspx?class_no025&now_fod_list_no09009&level_no02&doc_no041589 . Accessed July 22, 2019
  • 22 Fong BYF, Cheung WY, Ho SW. , et al. Promoting electronic health record (eHR) sharing system in Hong Kong—what can we learn from Taiwan. Hong Kong Community College Working Paper Series 2015 9(03):
  • 23 Hwang HG, Han HE, Kuo KM, Liu CF. The differing privacy concerns regarding exchanging electronic medical records of internet users in Taiwan. J Med Syst 2012; 36 (06) 3783-3793
  • 24 Department of Health. 2012 . EMR Standard Management System. Available at: http://emr.doh.gov.tw/exProjects.aspx . Accessed July 22, 2019
  • 25 Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. Manage Inf Syst Q 2003; 27 (03) 425-478
  • 26 Peek STM, Wouters EJM, van Hoof J, Luijkx KG, Boeije HR, Vrijhoef HJM. Factors influencing acceptance of technology for aging in place: a systematic review. Int J Med Inform 2014; 83 (04) 235-248
  • 27 Klaus T, Blanton JE. User resistance determinants and the psychological contract in enterprise system implementations. Eur J Inf Syst 2010; 19 (06) 625-636
  • 28 Hu PJ, Chau PYK, Liu Sheng OR, Tam KY. Examining the technology acceptance model using physician acceptance of telemedicine technology. J Manage Inf Syst 1999; 16 (02) 91-112
  • 29 Chau PYK, Hu PJ. Examining a model of information technology acceptance by individual professionals: an exploratory study. J Manage Inf Syst 2002; 18 (04) 191-229
  • 30 Boonstra A, Broekhuis M. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions. BMC Health Serv Res 2010; 10: 231
  • 31 Yi MY, Jackson JD, Park JS, Probst JC. Understanding information technology acceptance by individual professionals: toward an integrative view. Inf Manage 2006; 43 (03) 350-363
  • 32 Chang I, Hwang HG, Hung WF, Li YC. Physicians' acceptance of pharmacokinetics-based clinical decision support systems. Expert Syst Appl 2007; 33 (02) 296-303
  • 33 Kijsanayotin B, Pannarunothai S, Speedie SM. Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model. Int J Med Inform 2009; 78 (06) 404-416
  • 34 Hoque MR. An empirical study of mHealth adoption in a developing country: the moderating effect of gender concern. BMC Med Inform Decis Mak 2016; 16 (51) 51
  • 35 Kim S, Lee KH, Hwang H, Yoo S. Analysis of the factors influencing healthcare professionals' adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Med Inform Decis Mak 2016; 16: 12
  • 36 Al-Adwan AS, Berger H. Exploring physicians' behavioural intention toward the adoption of electronic health records: an empirical study from Jordan. Int J Healthc Technol Manag 2015; 15 (02) 89
  • 37 Liu CF, Cheng TJ. Exploring critical factors influencing physicians' acceptance of mobile electronic medical records based on the dual-factor model: a validation in Taiwan. BMC Med Inform Decis Mak 2015; 15 (01) 4
  • 38 Price AP. A study of factors influencing physician adoption of electronic medical records technology. In: Grenoble Ecole de Management. 2010
  • 39 Podolny JM, James NB. Resources and relationships: social networks and mobility in the workplace. Am Sociol Rev 1997; 62 (05) 673-693
  • 40 Hing E, Burt CW. Are there patient disparities when electronic health records are adopted?. J Health Care Poor Underserved 2009; 20 (02) 473-488
  • 41 Gillard H, Howcroft D, Mitev N, Richardson H. Missing women: gender, ICTs, and the shaping of the global economy. Inf Technol Dev 2008; 14 (04) 262-279
  • 42 Moffat MO, Moffat KJ, Cano V. General practitioners and the Internet--a questionnaire survey of Internet connectivity and use in Lothian. Health Bull (Edinb) 2001; 59 (02) 120-126
  • 43 Schwirian PM, Malone JA, Stone VJ, Nunley B, Francisco T. Computers in nursing practice. A comparison of the attitudes of nurses and nursing students. Comput Nurs 1989; 7 (04) 168-177
  • 44 Menon AS, Greenwald S, Ma TJ, Kooshesh S, Duriseti R. Patient and physician willingness to use personal health records in the emergency department. West J Emerg Med 2012; 13 (02) 172-175
  • 45 Chan T, de Lusignan S, Brew S. Overcoming the barriers to using information systems. Nurs Times 2004; 100 (49) 44-46
  • 46 Lai TYY, Leung GM, Wong IOL, Johnston JM. Do doctors act on their self-reported intention to computerize? A follow-up population-based survey in Hong Kong. Int J Med Inform 2004; 73 (05) 415-431
  • 47 Loomis GA, Ries JS, Saywell Jr. RM, Thakker NR. If electronic medical records are so great, why aren't family physicians using them?. J Fam Pract 2002; 51 (07) 636-641
  • 48 Djalali S, Ursprung N, Rosemann T, Senn O, Tandjung R. Undirected health IT implementation in ambulatory care favors paper-based workarounds and limits health data exchange. Int J Med Inform 2015; 84 (11) 920-932
  • 49 Villalba-Mora E, Casas I, Lupiañez-Villanueva F, Maghiros I. Adoption of health information technologies by physicians for clinical practice: the Andalusian case. Int J Med Inform 2015; 84 (07) 477-485
  • 50 Holanda AA, do Carmo E Sá HL, Vieira AP, Catrib AM. Use and satisfaction with electronic health record by primary care physicians in a health district in Brazil. J Med Syst 2012; 36 (05) 3141-3149
  • 51 Chu RJ. How family support and Internet self-efficacy influence the effects of e-learning among higher aged adults: analyses of gender and age differences. Comput Educ 2010; 55 (01) 255-264
  • 52 Bhargava HK, Mishra AN. Electronic medical records and physician productivity: evidence from panel data analysis. Manage Sci 2014; 60 (10) 2543-2562
  • 53 Grinspan ZM, Banerjee S, Kaushal R, Kern LM. Physician specialty and variations in adoption of electronic health records. Appl Clin Inform 2013; 4 (02) 225-240
  • 54 Whitacre BE. The influence of the degree of rurality on EMR adoption, by physician specialty. Health Serv Res 2017; 52 (02) 616-633
  • 55 Grove DH, Patel V. Physician motivations for adoption of electronic health records. 2014 .Available at: https://www.healthit.gov/sites/default/files/oncdatabrief-physician-ehr-adoption-motivators-2014.pdf . Accessed July 22, 2019
  • 56 Clarke MA, Steege LM, Moore JL, Koopman RJ, Belden JL, Kim MS. Determining primary care physician information needs to inform ambulatory visit note display. Appl Clin Inform 2014; 5 (01) 169-190
  • 57 Kingma M. Can financial incentive influence medical practice?. Human Resources for Health Development Journal 1999; 3: 121-131
  • 58 National Health Statistics Group. Statistical abstract of the United States. 1999 . Available at: https://www.census.gov/library/publications/1999/compendia/statab/119ed.html . Accessed July 22, 2019
  • 59 Santerre RE, Neun SP. Health Economics: Theories, Insights, and Industry Studies. Orlando, FL: South-Western College Publishing; 2000
  • 60 Roski J, Jeddeloh R, An L. , et al. The impact of financial incentives and a patient registry on preventive care quality: increasing provider adherence to evidence-based smoking cessation practice guidelines. Prev Med 2003; 36 (03) 291-299
  • 61 Gosden T, Sibbald B, Williams J, Petchey R, Leese B. Paying doctors by salary: a controlled study of general practitioner behaviour in England. Health Policy 2003; 64 (03) 415-423
  • 62 Baron RJ, Fabens EL, Schiffman M, Wolf E. Electronic health records: just around the corner? Or over the cliff?. Ann Intern Med 2005; 143 (03) 222-226
  • 63 Marshall M, Harrison S. It's about more than money: financial incentives and internal motivation. Qual Saf Health Care 2005; 14 (01) 4-5
  • 64 Miller R, Sim I, Newman J. Electronic medical records: Lessons from small physician practices. 2003 . Available at: http://www.chcf.org/documents/ihealth/EMRLessonsSmallPhyscianPractices.pdf
  • 65 New EMR Adopter Funding Program. 2013 . Available at: www.ontariomd.ca/portal/server.pt/community/emr_funding/new_emradopters
  • 66 Burt CW, Sisk JE. Which physicians and practices are using electronic medical records?. Health Aff (Millwood) 2005; 24 (05) 1334-1343
  • 67 Conrad DA, Perry L. Quality-based financial incentives in health care: can we improve quality by paying for it?. Annu Rev Public Health 2009; 30: 357-371
  • 68 Weisbrod Burton A. The health care quadrilemma: an essay on technological change, insurance, quality of care, and cost containment. J Econ Lit 1991; 29 (02) 523-552
  • 69 Chandra A, Jonathan SS. Technology growth and expenditure growth in health care. Available at: https://www.nber.org/papers/w16953.pdf . Accessed July 22, 2019
  • 70 Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manage Inf Syst Q 1989; 13 (03) 319-340
  • 71 Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev 2007; 64 (06) 650-672
  • 72 Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010; 43 (01) 159-172
  • 73 Aggelidis VP, Chatzoglou PD. Using a modified technology acceptance model in hospitals. Int J Med Inform 2009; 78 (02) 115-126
  • 74 Kuo KM, Liu CF, Ma CC. An investigation of the effect of nurses' technology readiness on the acceptance of mobile electronic medical record systems. BMC Med Inform Decis Mak 2013; 13: 88
  • 75 Ryu S, Ho SH, Han I. Knowledge sharing behavior of physicians in hospitals. Expert Syst Appl 2003; 25 (01) 113-122
  • 76 Hung SY, Ku YC, Chien JC. Understanding physicians' acceptance of the Medline system for practicing evidence-based medicine: a decomposed TPB model. Int J Med Inform 2012; 81 (02) 130-142
  • 77 Miller RH, Sim I. Physicians' use of electronic medical records: barriers and solutions. Health Aff (Millwood) 2004; 23 (02) 116-126
  • 78 Vishwanath A, Scamurra SD. Barriers to the adoption of electronic health records: using concept mapping to develop a comprehensive empirical model. Health Informatics J 2007; 13 (02) 119-134
  • 79 Patel V, Abramson EL, Edwards A, Malhotra S, Kaushal R. Physicians' potential use and preferences related to health information exchange. Int J Med Inform 2011; 80 (03) 171-180
  • 80 Venkatesh V, Morris MG. Why don't men ever stop to ask for directions? gender, social influence, and their role in technology acceptance and usage behavior. Manage Inf Syst Q 2000; 24 (01) 115-139
  • 81 Nai L, Gill K. Gender and cultural differences in Internet use: A study of China and the UK. Comput Educ 2007; 48 (02) 301-317
  • 82 Nel J, Raleting T. Gender Differences in Non-Users' Attitude towards WIG Cellphone Banking 2010. Paper presented at the ANZMAC, University of Canterbury, Christchurch, New Zealand; 2010 . Available at: http://anzmac2010.org/proceedings/pdf/anzmac10Final00038.pdf
  • 83 Dutta B, Peng MH, Sun SL. Modeling the adoption of personal health record (PHR) among individual: the effect of health-care technology self-efficacy and gender concern. Libyan J Med 2018; 13 (01) 1500349
  • 84 Alipour J, Erfannia L, Karimi A, Aliabadi A. Electronic health record acceptance: a descriptive study in Zahedan, Southeast Iran. J Health Med Inform 2013 4. (02): Doi: 10.4172/2157-7420.1000120
  • 85 Chismar WG, Wiley-Patton S. Does the extended technology acceptance model apply to physicians. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on 2003 ;8
  • 86 Alharthi H, Youssef A, Radwan S, Al-Muallim S, Zainab AT. Physician satisfaction with electronic medical records in a major Saudi Government hospital. Journal of Taibah University Medical Sciences 2014; 9 (03) 213-218
  • 87 Lin C, Lin IC, Roan J. Barriers to physicians' adoption of healthcare information technology: an empirical study on multiple hospitals. J Med Syst 2012; 36 (03) 1965-1977
  • 88 Dünnebeil S, Sunyaev A, Blohm I, Leimeister JM, Krcmar H. Determinants of physicians' technology acceptance for e-health in ambulatory care. Int J Med Inform 2012; 81 (11) 746-760
  • 89 Lakbala P, Dindarloo K. Physicians' perception and attitude toward electronic medical record. Springerplus 2014; 3: 63
  • 90 Lakbala P, Lakbala M, Inaloo KD. Factors affecting electronic medical record acceptance by specialist physicians. Lecture Notes Information Theory 2014; 2 (04) 316-321
  • 91 Horan TA, Tulu B, Hilton B, Burton J. Use of Online Systems in Clinical Medical Assessments: An Analysis of Physician Acceptance of Online Disability Evaluation Systems. Proceedings of the 37th Hawaii International Conference on System Sciences – 2004
  • 92 Huang WM, Shih CT. An empirical study on the intentions of physicians in adopting electronic medical records with modified technology acceptance models in rural areas of Taiwan. Available at: https://pdfs.semanticscholar.org/6a34/b33bc9ec818acbbee24b2b8f6fa246efa9aa.pdf?_ga=2.66279534.1959410284.1563782350-1889254951.1561091521 . Accessed July 22, 2019
  • 93 Alasmary M, El Metwally A, Househ M. The association between computer literacy and training on clinical productivity and user satisfaction in using the electronic medical record in Saudi Arabia. J Med Syst 2014; 38 (08) 69
  • 94 Khorma OT, Baharom F, Mohd H. Construction of extended technology acceptance model of electronic medical records in jordan: the influence of doctors' self-efficacy and perceived behavioral control. Available at: https://pdfs.semanticscholar.org/8ecf/95986fa9c688c5332b52e33b85d242fa2d85.pdf?_ga=2.41661666.1959410284.1563782350-1889254951.1561091521 . Accessed July 22, 2019
  • 95 Liu HY, Liu CC, Shen TH. , et al. Pattern of Visits to Older Family Physicians in Taiwan. Int J Environ Res Public Health 2017; 14 (05) 499
  • 96 Taiwan Medical Association. 2017 http://www.tma.te/stats/indexNYearInfo.asp?/2017.html . Accessed July 22, 2019
  • 97 Hair JF, Anderson RE, Tatham RL, Black W. Multivariate Data Analysis. 5th ed. NJ: Prentice-Hall, Inc; 1998
  • 98 Hair JF, Ringle CM, Sarstedt M. PLS-SEM: indeed a silver bullet. J Mark Theory Pract 2011; 19 (02) 139-152
  • 99 Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two step approach. Psychol Bull 1998; 103 (03) 411-423
  • 100 Nunnally JC. Psychometric Theory. New York, NY: McGraw-Hill; 1967
  • 101 Streiner DL. Starting at the beginning: an introduction to coefficient alpha and internal consistency. J Pers Assess 2003; 80 (01) 99-103
  • 102 Bagozzi RP, Yi Y. On the evaluation of structural equation models. J Acad Mark Sci 1988; 16 (01) 74-94
  • 103 Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 1981; 18 (01) 39-50
  • 104 Taylor S, Todd P. Understanding information technology usage: A test of competing models. Inf Syst Res 2001; 6 (02) 144-176
  • 105 Cohen MF. Impact of the HITECH financial incentives on EHR adoption in small, physician-owned practices. Int J Med Inform 2016; 94: 143-154
  • 106 Pijpers GGM, Bemelmans TMA, Heemstra FJ, van Montfort KAGM. Senior executives' use of information technology. Inf Softw Technol 2001; 43 (15) 959-971
  • 107 Hsiao CJ, Decker SL, Hing E, Sisk JE. Most physicians were eligible for federal incentives in 2011, but few had EHR systems that met meaningful-use criteria. Health Aff (Millwood) 2012; 31 (05) 1100-1107
  • 108 National Health Care Purchasing Institute. Bailit Health Purchasing, LLC, Sixth Man Consulting, Inc. An Initiative of The Robert Wood Johnson Foundation. 2001 https://www.ncbi.nlm.nih.gov/nlmcatalog/101192311