Appl Clin Inform 2018; 09(03): 604-634
DOI: 10.1055/s-0038-1668091
Review Article
Georg Thieme Verlag KG Stuttgart · New York

A Systematic Review of the Technology Acceptance Model in Health Informatics

Bahlol Rahimi
1   Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
,
Hamed Nadri
1   Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
2   Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran
,
Hadi Lotfnezhad Afshar
1   Department of Health Information Technology, School of Allied Medical Sciences, Urmia University of Medical Sciences, Urmia, Iran
,
Toomas Timpka
3   Department of Computer and Information Sciences, Linköping University, Linköping, Sweden
4   Department of Medical and Health Sciences, Linköping University, Linköping, Sweden
› Author Affiliations
Funding None.
Further Information

Publication History

05 February 2018

24 June 2018

Publication Date:
15 August 2018 (online)

Abstract

Background One common model utilized to understand clinical staff and patients' technology adoption is the technology acceptance model (TAM).

Objective This article reviews published research on TAM use in health information systems development and implementation with regard to application areas and model extensions after its initial introduction.

Method An electronic literature search supplemented by citation searching was conducted on February 2017 of the Web of Science, PubMed, and Scopus databases, yielding a total of 492 references. Upon eliminating duplicates and applying inclusion and exclusion criteria, 134 articles were retained. These articles were appraised and divided into three categories according to research topic: studies using the original TAM, studies using an extended TAM, and acceptance model comparisons including the TAM.

Results The review identified three main information and communication technology (ICT) application areas for the TAM in health services: telemedicine, electronic health records, and mobile applications. The original TAM was found to have been extended to fit dynamic health service environments by integration of components from theoretical frameworks such as the theory of planned behavior and unified theory of acceptance and use of technology, as well as by adding variables in specific contextual settings. These variables frequently reflected the concepts subjective norm and self-efficacy, but also compatibility, experience, training, anxiety, habit, and facilitators were considered.

Conclusion Telemedicine applications were between 1999 and 2017, the ICT application area most frequently studied using the TAM, implying that acceptance of this technology was a major challenge when exploiting ICT to develop health service organizations during this period. A majority of the reviewed articles reported extensions of the original TAM, suggesting that no optimal TAM version for use in health services has been established. Although the review results indicate a continuous progress, there are still areas that can be expanded and improved to increase the predictive performance of the TAM.

Protection of Human and Animal Subjects

Not applicable.


 
  • References

  • 1 Blackwell G, Gordon B. The future of IT in healthcare. Inform Health Soc Care 2008; 33 (04) 211-326
  • 2 Peña-López I. Improving health sector efficiency: the role of information and communication technologies. OECD Health Policy Studies; 2010
  • 3 Scott RE. e-Records in health--preserving our future. Int J Med Inform 2007; 76 (5-6): 427-431
  • 4 Baker A. Crossing the quality chasm: a new health system for the 21st century. Br Med J 2001; 323 (7322): 1192
  • 5 Cooper JD. Organization, management, implementation and value of EHR implementation in a solo pediatric practice. J Healthc Inf Manag 2004; 18 (03) 51-55
  • 6 Rahimi B, Vimarlund V, Timpka T. Health information system implementation: a qualitative meta-analysis. J Med Syst 2009; 33 (05) 359-368
  • 7 Hackl WO, Hoerbst A, Ammenwerth E. “Why the hell do we need electronic health records?”. EHR acceptance among physicians in private practice in Austria: a qualitative study. Methods Inf Med 2011; 50 (01) 53-61
  • 8 Nadri H, Rahimi B, Timpka T, Sedghi S. The top 100 articles in the medical informatics: a bibliometric analysis. J Med Syst 2017; 41 (10) 150
  • 9 Anderson JG. Clearing the way for physicians' use of clinical information systems. Commun ACM 1997; 40 (08) 83-90
  • 10 Jha AK, Ferris TG, Donelan K. , et al. How common are electronic health records in the United States? A summary of the evidence. Health Aff (Millwood) 2006; 25 (06) w496-w507
  • 11 Poon EG, Jha AK, Christino M. , et al. Assessing the level of healthcare information technology adoption in the United States: a snapshot. BMC Med Inform Decis Mak 2006; 6 (01) 1
  • 12 Lorenzi NM, Novak LL, Weiss JB, Gadd CS, Unertl KM. Crossing the implementation chasm: a proposal for bold action. J Am Med Inform Assoc 2008; 15 (03) 290-296
  • 13 Rahimi B, Vimarlund V. Methods to evaluate health information systems in healthcare settings: a literature review. J Med Syst 2007; 31 (05) 397-432
  • 14 Catwell L, Sheikh A. Evaluating eHealth interventions: the need for continuous systemic evaluation. PLoS Med 2009; 6 (08) e1000126
  • 15 Black AD, Car J, Pagliari C. , et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med 2011; 8 (01) e1000387
  • 16 Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: Toward a unified view. Manage Inf Syst Q 2003; 27: 425-478
  • 17 Coiera E. Guide to Health Informatics. 3rd ed. London: CRC Press; 2015
  • 18 Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. Manage Inf Syst Q 1989; 13: 319-340
  • 19 Yousafzai SY, Foxall GR, Pallister JG. Technology acceptance: a meta-analysis of the TAM: part 1. J Model Manag 2007; 2 (03) 251-280
  • 20 Yarbrough AK, Smith TB. Technology acceptance among physicians: a new take on TAM. Med Care Res Rev 2007; 64 (06) 650-672
  • 21 Surendran P. Technology acceptance model: a survey of literature. Int J Business Soc Res 2013; 2 (04) 4
  • 22 Nadri H, Rahimi B, Lotfnezhad Afshar H, Samadbeik M, Garavand A. Factors affecting acceptance of hospital information systems based on extended technology acceptance model: a case study in three paraclinical departments. Appl Clin Inform 2018; 9 (02) 238-247
  • 23 Holden RJ, Karsh BT. The technology acceptance model: its past and its future in health care. J Biomed Inform 2010; 43 (01) 159-172
  • 24 Hale JL, Householder BJ, Greene KL. The theory of reasoned action. In: Dillard JP, Pfau M. , eds. The Persuasion Handbook: Developments in Theory and Practice. CA: SAGE Publications; 2002
  • 25 Legris P, John I, Pierre C. Why do people use information technology? A critical review of the technology acceptance model. Inf Manage 2003; 40 (03) 191-204
  • 26 Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 1989; 35 (08) 982-1003
  • 27 Lee Y, Kozar KA, Rt LK. The technology acceptance model: past, present, and future. Comm Assoc Inform Syst 2003; 12 (01) 50
  • 28 Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage Sci 2000; 46 (02) 186-204
  • 29 Sharp JH. Development, extension, and application: a review of the technology acceptance model. Director 2006; 5: 7
  • 30 King WR, Jun H. A meta-analysis of the technology acceptance model. Inf Manage 2006; 43 (06) 740-755
  • 31 Venkatesh V, Hillol B. Technology acceptance model 3 and a research agenda on interventions. Decis Sci 2008; 39 (02) 273-315
  • 32 Turner M, Barbara K, Pearl B, Stuart C, David B. Does the technology acceptance model predict actual use? A systematic literature review. Inf Softw Technol 2010; 52 (05) 463-479
  • 33 Hsiao CH, Chyan Y. The intellectual development of the technology acceptance model: a co-citation analysis. Int J Inf Manage 2011; 31 (02) 128-136
  • 34 Hoyt RE, Yoshihashi A, Bailey NJ. Health informatics: practical guide for healthcare and information technology professionals. Informatics Education; 2014: 533
  • 35 Turban E, David K, Jae L, Dennis V. Electronic Commerce: A Managerial Perspective 2002. Prentice Hall; 2002. . ISBN 0 13(975285):4
  • 36 Gagnon M-P, Desmartis M, Labrecque M. , et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst 2012; 36 (01) 241-277
  • 37 Strudwick G. Predicting nurses' use of healthcare technology using the technology acceptance model: an integrative review. Comput Inform Nurs 2015; 33 (05) 189-198 , quiz E1
  • 38 Ahlan AR, Isma'eel AB. An overview of patient acceptance of health information technology in developing countries: a review and conceptual model. Int J Inform Syst Project Management 2015; 3 (01) 29-48
  • 39 Garavand A, Mohseni M, Asadi H, Etemadi M, Moradi-Joo M, Moosavi A. Factors influencing the adoption of health information technologies: a systematic review. Electron Physician 2016; 8 (08) 2713-2718
  • 40 Zanaboni P, Lettieri E. Institutionalizing telemedicine applications: the challenge of legitimizing decision-making. J Med Internet Res 2011; 13 (03) e72
  • 41 Ventola CL. Mobile devices and apps for health care professionals: uses and benefits. P&T 2014; 39 (05) 356-364
  • 42 Hu PJ, Chau Patrick YK, Sheng Olivia RL, Yan TK. Examining the technology acceptance model using physician acceptance of telemedicine technology. J Manage Inf Syst 1999; 16 (02) 91-112
  • 43 Han S, Pekka M, Matti S, Markku K. Physicians' acceptance of mobile communication technology: an exploratory study. IJMC 2006; 4 (02) 210-230
  • 44 Hsiao C-H, Tang K-Y. Examining a model of mobile healthcare technology acceptance by the elderly in Taiwan. J Global Inform Tech Manag 2015; 18 (04) 292-311
  • 45 Day M, Demiris G, Oliver DP, Courtney K, Hensel B. Exploring underutilization of videophones in hospice settings. Telemed J E Health 2007; 13 (01) 25-31
  • 46 Chau PY, Jen-Hwa HP. Investigating healthcare professionals' decisions to accept telemedicine technology: an empirical test of competing theories. Inf Manage 2002; 39 (04) 297-311
  • 47 Kim J, Dellifraine JL, Dansky KH, Mccleary KJ. Physicians' acceptance of telemedicine technology: an empirical test of competing theories. Int J Inf Syst Change Manag 2010; 4 (03) 210-225
  • 48 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 (01) 12
  • 49 Manimaran S, Lakshmi KB. Development of model for assessing the acceptance level of users in rural healthcare system of Tamil Nadu, India. Technol Health Care 2013; 21 (05) 479-492
  • 50 Smith AD, Motley D. Operational and customer relationship management considerations of electronic prescribing among pharmacists. Int J Electron Healthc 2009; 5 (03) 245-272
  • 51 Liang H, Xue Y, Wu X. User acceptance of computerized physician order entry: an empirical investigation. Int J Healthc Inf Syst Inform 2006; 1 (02) 39-50 (IJHISI)
  • 52 Handayani PW, Hidayanto AN, Pinem AA, Hapsari IC, Sandhyaduhita PI, Budi I. Acceptance model of a Hospital Information System. Int J Med Inform 2017; 99: 11-28
  • 53 May C, Harrison R, Finch T, MacFarlane A, Mair F, Wallace P. ; Telemedicine Adoption Study Group. Understanding the normalization of telemedicine services through qualitative evaluation. J Am Med Inform Assoc 2003; 10 (06) 596-604
  • 54 de Bont A, Bal R. Telemedicine in interdisciplinary work practices: on an IT system that met the criteria for success set out by its sponsors, yet failed to become part of every-day clinical routines. BMC Med Inform Decis Mak 2008; 8 (01) 47
  • 55 Wager KA, Lee FW, Glaser JP. Health Care Information Systems: A Practical Approach for Health Care Management. New York: John Wiley & Sons; 2017
  • 56 Chang H. Evaluation framework for telemedicine using the logical framework approach and a fishbone diagram. Healthc Inform Res 2015; 21 (04) 230-238
  • 57 Novak TP, Hoffman DL, Adam D. The influence of goal-directed and experiential activities on online flow experiences. J Consum Psychol 2003; 13 (01) 3-16
  • 58 Deci EL, Ryan RM. Self-determination theory. Handbook of Theories of Social Psychol 2011; 1 (2011): 416-433
  • 59 Abdekhoda M, Ahmadi M, Gohari M, Noruzi A. The effects of organizational contextual factors on physicians' attitude toward adoption of Electronic Medical Records. J Biomed Inform 2015; 53: 174-179
  • 60 Darker CD, French DP, Longdon S, Morris K, Eves FF. Are beliefs elicited biased by question order? A theory of planned behaviour belief elicitation study about walking in the UK general population. Br J Health Psychol 2007; 12 (Pt 1): 93-110
  • 61 Barker DJ, van Schaik P, Simpson DS, Corbett WA. Evaluating a spoken dialogue system for recording clinical observations during an endoscopic examination. Med Inform Internet Med 2003; 28 (02) 85-97
  • 62 Chang P, Hsu YS, Tzeng YM, Hou IC, Sang YY. Development and pilot evaluation of user acceptance of advanced mass-gathering emergency medical services PDA support systems. Stud Health Technol Inform 2004; 107 (Pt 2): 1421-1425
  • 63 Chang P, Hsu YS, Tzeng YM, Sang YY, Hou IC, Kao WF. The development of intelligent, triage-based, mass-gathering emergency medical service PDA support systems. J Nurs Res 2004; 12 (03) 227-236
  • 64 Chen IJ, Yang KF, Tang FI, Huang CH, Yu S. Applying the technology acceptance model to explore public health nurses' intentions towards web-based learning: a cross-sectional questionnaire survey. Int J Nurs Stud 2008; 45 (06) 869-878
  • 65 Wilkins MA. Factors influencing acceptance of electronic health records in hospitals. Perspect Health Inf Manag 2009; 6 (05) 1f
  • 66 Marini SD, Hasman A, Huijer HA. Information technology for medication administration: assessing bedside readiness among nurses in Lebanon. Int J Evid-Based Healthc 2009; 7 (01) 49-58
  • 67 Van Schaik P, Bettany-Saltikov JA, Warren JG. Clinical acceptance of a low-cost portable system for postural assessment. Behav Inf Technol 2002; 21 (01) 47-57
  • 68 Huser V, Narus SP, Rocha RA. Evaluation of a flowchart-based EHR query system: a case study of RetroGuide. J Biomed Inform 2010; 43 (01) 41-50
  • 69 Cranen K, Veld RH, Ijzerman M, Vollenbroek-Hutten M. Change of patients' perceptions of telemedicine after brief use. Telemed J E Health 2011; 17 (07) 530-535
  • 70 Hung M-C, Jen WY. The adoption of mobile health management services: an empirical study. J Med Syst 2012; 36 (03) 1381-1388
  • 71 Aldosari B. User acceptance of a picture archiving and communication system (PACS) in a Saudi Arabian hospital radiology department. BMC Med Inform Decis Mak 2012; 12 (01) 44
  • 72 Noblin AM, Th WT, Myron F. Intention to use a personal health record: a theoretical analysis using the technology acceptance model. Int J Healthc Technol Manag 2013; 14 (1–2): 73-89
  • 73 Martínez-García A, Moreno-Conde A, Jódar-Sánchez F, Leal S, Parra C. Sharing clinical decisions for multimorbidity case management using social network and open-source tools. J Biomed Inform 2013; 46 (06) 977-984
  • 74 Monthuy-Blanc J, Bouchard S, Maïano C, Séguin M. Factors influencing mental health providers' intention to use telepsychotherapy in First Nations communities. Transcult Psychiatry 2013; 50 (02) 323-343
  • 75 Abdekhoda M, Ahmadi M, Dehnad A, Hosseini AF. Information technology acceptance in health information management. Methods Inf Med 2014; 53 (01) 14-20
  • 76 Cilliers L, Stephen F. User acceptance of telemedicine by health care workers: a case of the Eastern Cape province, South Africa. Electron J Inf Syst Dev Ctries 2014
  • 77 Ologeanu-Taddei R, David M, Hugues D. . Bourret R, eds. “Understanding the acceptance factors of an Hospital Information System: evidence from a French University Hospital.” AMIA Annual Symposium Proceedings; American Medical Informatics Association; 2015
  • 78 Money AG, Atwal A, Young KL, Day Y, Wilson L, Money KG. Using the Technology Acceptance Model to explore community dwelling older adults' perceptions of a 3D interior design application to facilitate pre-discharge home adaptations. BMC Med Inform Decis Mak 2015; 15 (01) 73
  • 79 Faruque MO, Holakouie Naieni K, Ardalan A, Ahmadnezhad E, Mohammadinia L. Feasibility assessment of using geoinformatics technology in disaster disease surveillance in a developing country, Iran. PLoS Curr 2015; 7: 7
  • 80 Kivekäs E, Enlund H, Borycki E, Saranto K. General practitioners' attitudes towards electronic prescribing and the use of the national prescription centre. J Eval Clin Pract 2016; 22 (05) 816-825
  • 81 Abdullah A, Liew SM, Hanafi NS. , et al. What influences patients' acceptance of a blood pressure telemonitoring service in primary care? A qualitative study. Patient Prefer Adherence 2016; 10: 99-106
  • 82 Hanauer DA, Wu DTY, Yang L. , et al. Development and empirical user-centered evaluation of semantically-based query recommendation for an electronic health record search engine. J Biomed Inform 2017; 67: 1-10
  • 83 Rawstorne P, Rohan J, Peter C. , eds. Issues in predicting and explaining usage behaviors with the technology acceptance model and the theory of planned behavior when usage is mandatory. Proceedings of the Twenty First International Conference on Information Systems. Association for Information Systems; 2000
  • 84 Handy J, Hunter I, Whiddett R. User acceptance of inter-organizational electronic medical records. Health Informatics J 2001; 7 (02) 103-107
  • 85 Chismar WG, Sonja W-P. , eds. Test of the technology acceptance model for the internet in pediatrics. Proceedings of the AMIA Symposium. American Medical Informatics Association; 2002
  • 86 Liang H, Xue Y, Anthony BT. PDA usage in healthcare professionals: testing an extended technology acceptance model. IJMC 2003; 1 (04) 372-389
  • 87 Liu L, Ma Q. The impact of service level on the acceptance of application service oriented medical records. Inf Manage 2005; 42 (08) 1121-1135
  • 88 Liu L, Ma Q. Perceived system performance: a test of an extended technology acceptance model. ACM SIGMIS Database 2006; 37 (2–3): 51-59
  • 89 Palm J-M, Isabelle C, Claude S, Patrice D. Determinants of user satisfaction with a clinical information system. AMIA Annual Symposium Proceedings; 2006:614–618. PubMed PMID: PMC1839744
  • 90 Kim D, Chang H. Key functional characteristics in designing and operating health information websites for user satisfaction: an application of the extended technology acceptance model. Int J Med Inform 2007; 76 (11-12): 790-800
  • 91 Wu J-H, Wang S-C, Lin L-M. Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int J Med Inform 2007; 76 (01) 66-77
  • 92 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. Int J Med Inform 2008; 77 (05) 324-335
  • 93 Lai T-Y, Larson EL, Rockoff ML, Bakken S. User acceptance of HIV TIDES—tailored interventions for management of depressive symptoms in persons living with HIV/AIDS. J Am Med Inform Assoc 2008; 15 (02) 217-226
  • 94 Wu J-H, Shen W-S, Lin L-M, Greenes RA, Bates DW. Testing the technology acceptance model for evaluating healthcare professionals' intention to use an adverse event reporting system. Int J Qual Health Care 2008; 20 (02) 123-129
  • 95 Yu P, Li H, Gagnon MP. Health IT acceptance factors in long-term care facilities: a cross-sectional survey. Int J Med Inform 2009; 78 (04) 219-229
  • 96 Dasgupta A, Sansgiry SS, Sherer JT, Wallace D, Sikri S. Application of the Extended Technology Acceptance Model in predicting pharmacists' intention to use personal digital assistants. J Am Pharm Assoc (2003) 2009; 49 (06) 792-796
  • 97 Ilie V, Craig VS, Parikh MA, Courtney JF. Paper versus electronic medical records: the effects of access on physicians' decisions to use complex information technologies. Decis Sci 2009; 40 (02) 213-241
  • 98 Trimmer K, Cellucci LW, Carla W, William W. Electronic medical records: TAM, UTAUT, and culture. Int J Healthc Inf Syst Inform 2009; 4 (03) 55-68
  • 99 Lin S-P, Yang HY. Exploring key factors in the choice of e-health using an asthma care mobile service model. Telemed J E Health 2009; 15 (09) 884-890
  • 100 Aggelidis VP, Chatzoglou PD. Using a modified technology acceptance model in hospitals. Int J Med Inform 2009; 78 (02) 115-126
  • 101 Hyun S, Johnson SB, Stetson PD, Bakken S. Development and evaluation of nursing user interface screens using multiple methods. J Biomed Inform 2009; 42 (06) 1004-1012
  • 102 Vishwanath A, Brodsky L, Shaha S. Physician adoption of personal digital assistants (PDA): testing its determinants within a structural equation model. J Health Commun 2009; 14 (01) 77-95
  • 103 Morton ME, Susan W. EHR acceptance factors in ambulatory care: a survey of physician perceptions. EHR Acceptance Factors in Ambulatory Care: A Survey of Physician Perceptions/AHIMA. American Health Information Management Association; 2010
  • 104 Zhang H, Cocosila M, Archer N. Factors of adoption of mobile information technology by homecare nurses: a technology acceptance model 2 approach. Comput Inform Nurs 2010; 28 (01) 49-56
  • 105 Stocker G. Technology Acceptance of Electronic Medical Records by Nurses. Webster Groves, MO: Webster University; 2010
  • 106 Lim S, Xue L, Yen CC. , et al. A study on Singaporean women's acceptance of using mobile phones to seek health information. Int J Med Inform 2011; 80 (12) e189-e202
  • 107 Schnall R, Bakken S. Testing the technology acceptance model: HIV case managers' intention to use a continuity of care record with context-specific links. Inform Health Soc Care 2011; 36 (03) 161-172
  • 108 Kowitlawakul Y. The technology acceptance model: predicting nurses' intention to use telemedicine technology (eICU). Comput Inform Nurs 2011; 29 (07) 411-418
  • 109 Egea JMO, González MVR. Explaining physicians' acceptance of EHCR systems: an extension of TAM with trust and risk factors. Comput Human Behav 2011; 27 (01) 319-332
  • 110 Hsiao J-L, Chang H-C, Chen RF. A study of factors affecting acceptance of hospital information systems: a nursing perspective. J Nurs Res 2011; 19 (02) 150-160
  • 111 Orruño E, Gagnon MP, Asua J, Ben Abdeljelil A. Evaluation of teledermatology adoption by health-care professionals using a modified Technology Acceptance Model. J Telemed Telecare 2011; 17 (06) 303-307
  • 112 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
  • 113 Pai F-Y, Kai-I H. Applying the technology acceptance model to the introduction of healthcare information systems. Technol Forecast Soc Change 2011; 78 (04) 650-660
  • 114 Jimoh L, Pate MA, Lin L, Schulman KA. A model for the adoption of ICT by health workers in Africa. Int J Med Inform 2012; 81 (11) 773-781
  • 115 Lu C-H, Hsiao J-L, Chen RF. Factors determining nurse acceptance of hospital information systems. Comput Inform Nurs 2012; 30 (05) 257-264
  • 116 Lakshmi KB, Rajaram M. Impact of information technology reliance and innovativeness on rural healthcare services: study of Dindigul district in Tamil Nadu, India. Telemed J E Health 2012; 18 (05) 360-370
  • 117 Jian W-S, Syed-Abdul S, Sood SP. , et al. Factors influencing consumer adoption of USB-based Personal Health Records in Taiwan. BMC Health Serv Res 2012; 12 (01) 277
  • 118 Escobar-Rodríguez T, Pedro M-L, Mercedes R-AM. Acceptance of e-prescriptions and automated medication-management systems in hospitals: an extension of the technology acceptance model. J Inf Syst 2012; 26 (01) 77-96
  • 119 Ketikidis P, Dimitrovski T, Lazuras L, Bath PA. Acceptance of health information technology in health professionals: an application of the revised technology acceptance model. Health Informatics J 2012; 18 (02) 124-134
  • 120 Chen R-F, Hsiao J-L. An empirical study of physicians' acceptance of hospital information systems in Taiwan. Telemed J E Health 2012; 18 (02) 120-125
  • 121 Kim J, Park HA. Development of a health information technology acceptance model using consumers' health behavior intention. J Med Internet Res 2012; 14 (05) e133
  • 122 Parra C, Jódar-Sánchez F, Jiménez-Hernández MD. , et al. Development, implementation, and evaluation of a telemedicine service for the treatment of acute stroke patients: teleStroke. Interact J Med Res 2012; 1 (02) e15
  • 123 Gagnon MP, Orruño E, Asua J, Abdeljelil AB, Emparanza J. Using a modified technology acceptance model to evaluate healthcare professionals' adoption of a new telemonitoring system. Telemed J E Health 2012; 18 (01) 54-59
  • 124 Wangia V. Testing an extended theoretical framework to explain variance in use of a public health information system. Online J Public Health Inform 2012; 4 (03) ojphi.v4i3.4238
  • 125 Wong AM, Chang W-H, Ke P-C. , et al. Technology acceptance for an Intelligent Comprehensive Interactive Care (ICIC) system for care of the elderly: a survey-questionnaire study. PLoS One 2012; 7 (08) e40591
  • 126 Holden RJ, Brown RL, Scanlon MC, Karsh BT. Modeling nurses' acceptance of bar coded medication administration technology at a pediatric hospital. J Am Med Inform Assoc 2012; 19 (06) 1050-1058
  • 127 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
  • 128 Asua J, Orruño E, Reviriego E, Gagnon MP. Healthcare professional acceptance of telemonitoring for chronic care patients in primary care. BMC Med Inform Decis Mak 2012; 12 (01) 139
  • 129 Kummer TF, Schäfer K, Todorova N. Acceptance of hospital nurses toward sensor-based medication systems: a questionnaire survey. Int J Nurs Stud 2013; 50 (04) 508-517
  • 130 Sedlmayr B, Patapovas A, Kirchner M. , et al. Comparative evaluation of different medication safety measures for the emergency department: physicians' usage and acceptance of training, poster, checklist and computerized decision support. BMC Med Inform Decis Mak 2013; 13 (01) 79
  • 131 Abu-Dalbouh HM. A questionnaire approach based on the technology acceptance model for mobile tracking on patient progress applications. J Comput Sci 2013
  • 132 Tavakoli N, Jahanbakhsh M, Shahin A, Mokhtari H, Rafiei M. Electronic medical record in Central Polyclinic of Isfahan oil industry: a case study based on technology acceptance model. Acta Inform Med 2013; 21 (01) 23-25
  • 133 Buenestado D, Elorz J, Pérez-Yarza EG. , et al. Evaluating acceptance and user experience of a guideline-based clinical decision support system execution platform. J Med Syst 2013; 37 (02) 9910
  • 134 Escobar-Rodriguez T, Bartual-Sopena L. The roles of users personal characteristics and organisational support in the attitude towards using ERP systems in a Spanish public hospital. Health Inf Manag 2013; 42 (01) 18-28
  • 135 Su S-P, Chung-Hung T, Hsu W-L. Extending the TAM model to explore the factors affecting intention to use telecare systems. JCP 2013; 8 (02) 525-532
  • 136 Alali H, Juhana S. Virtual communities of practice: the role of content quality and technical features to increase health care professionals' satisfaction. J Theoretical Appl Inform Tech 2013; 54 (02) 269-275
  • 137 Wang J, Tsai C, Wang S. Using telecare system to construct medication safety mechanisms for remote area elderly. J Chem Pharm Res 2013; 11 (05) 1-5
  • 138 Chen S-C, Liu SC, Li SH, Yen DC. Understanding the mediating effects of relationship quality on technology acceptance: an empirical study of e-appointment system. J Med Syst 2013; 37 (06) 9981
  • 139 Sicotte C, Taylor L, Tamblyn R. Predicting the use of electronic prescribing among early adopters in primary care. Can Fam Physician 2013; 59 (07) e312-e321
  • 140 Liu C-F, Tsai YC, Jang FL. Patients' acceptance towards a web-based personal health record system: an empirical study in Taiwan. Int J Environ Res Public Health 2013; 10 (10) 5191-5208
  • 141 Ma C-M, Chao C-M, Bor-Wen C. Integrating technology acceptance model and task-technology fit into blended e-learning system. J Appl Sci (Faisalabad) 2013; 13 (05) 736
  • 142 Escobar-Rodríguez T, Romero-Alonso MM. Modeling nurses' attitude toward using automated unit-based medication storage and distribution systems: an extension of the technology acceptance model. Comput Inform Nurs 2013; 31 (05) 235-243
  • 143 Huang J-C. Innovative health care delivery system–a questionnaire survey to evaluate the influence of behavioral factors on individuals' acceptance of telecare. Comput Biol Med 2013; 43 (04) 281-286
  • 144 Portela F, Filipe SM, Álvaro S, Fernando R, António A, José M. Adoption of pervasive intelligent information systems in intensive medicine. Proc Tech 2013; 9: 1022-1032
  • 145 Johnson MP, Kai Z, Rema P. Modeling the longitudinality of user acceptance of technology with an evidence-adaptive clinical decision support system. Decis Support Syst 2014; 57: 444-453
  • 146 Zhang X, Guo X, Guo F, Lai KH. Nonlinearities in personalization-privacy paradox in mHealth adoption: the mediating role of perceived usefulness and attitude. Technol Health Care 2014; 22 (04) 515-529
  • 147 Andrews L, Gajanayake R, Sahama T. The Australian general public's perceptions of having a personally controlled electronic health record (PCEHR). Int J Med Inform 2014; 83 (12) 889-900
  • 148 Gagnon M-P, Ghandour K, Talla PK. , et al. Electronic health record acceptance by physicians: testing an integrated theoretical model. J Biomed Inform 2014; 48: 17-27
  • 149 Hwang JY, Kim KY, Lee KH. Factors that influence the acceptance of telemetry by emergency medical technicians in ambulances: an application of the extended technology acceptance model. Telemed J E Health 2014; 20 (12) 1127-1134
  • 150 Tsai C-H. The adoption of a Telehealth system: the integration of extended technology acceptance model and health belief model. J Med Imag Health Inform 2014; 4 (03) 448-455
  • 151 Rho MJ, Choi IY, Lee J. Predictive factors of telemedicine service acceptance and behavioral intention of physicians. Int J Med Inform 2014; 83 (08) 559-571
  • 152 Tsai C-H. Integrating social capital theory, social cognitive theory, and the technology acceptance model to explore a behavioral model of telehealth systems. Int J Environ Res Public Health 2014; 11 (05) 4905-4925
  • 153 Horan TA, Bengisu T, Brian H, Jacqueline B. , eds. Use of online systems in clinical medical assessments: an analysis of physician acceptance of online disability evaluation systems. 2004 Proceedings of the 37th Annual Hawaii International Conference on System Sciences. IEEE; 2004
  • 154 Saigí-Rubió F, Torrent-Sellens J, Jiménez-Zarco A. Drivers of telemedicine use: comparative evidence from samples of Spanish, Colombian and Bolivian physicians. Implement Sci 2014; 9 (01) 128
  • 155 Steininger K, Barbara S. EHR acceptance among Austrian resident doctors. Health Policy Tech 2015; 4 (02) 121-130
  • 156 Basak E, Gumussoy CA, Calisir F. Examining the factors affecting PDA acceptance among physicians: an extended technology acceptance model. J Healthc Eng 2015; 6 (03) 399-418
  • 157 Al-Adwan AS, Hilary B. Exploring physicians' behavioural intention toward the adoption of electronic health records: an empirical study from Jordan. Int J Healthc Technol Manag 2015; 15 (02) 89-111
  • 158 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 2015; 35 (01) 189-194
  • 159 Michel-Verkerke MB, Stegwee RA, Spil Ton AM. The six P's of the next step in electronic patient records in the Netherlands. Health Policy Tech 2015; 4 (02) 137-143
  • 160 Lin H-C. The impact of national cultural differences on nurses' acceptance of hospital information systems. Comput Inform Nurs 2015; 33 (06) 265-272
  • 161 Gartrell K, Trinkoff AM, Storr CL, Wilson ML, Gurses AP. Testing the electronic personal health record acceptance model by nurses for managing their own health: a cross-sectional survey. Appl Clin Inform 2015; 6 (02) 224-247
  • 162 Carrera PM, Lambooij MS. Implementation of out-of-office blood pressure monitoring in the Netherlands: from clinical guidelines to patients' adoption of innovation. Medicine (Baltimore) 2015; 94 (43) e1813
  • 163 Sieverdes JC, Nemeth LS, Magwood GS. , et al. Patient-centered mHealth living donor transplant education program for African Americans: development and analysis. JMIR Res Protoc 2015; 4 (03) e84
  • 164 Song L, Park B, Oh KM. Analysis of the technology acceptance model in examining hospital nurses' behavioral intentions toward the use of bar code medication administration. Comput Inform Nurs 2015; 33 (04) 157-165
  • 165 Jeon E, Park HA. Factors affecting acceptance of smartphone application for management of obesity. Healthc Inform Res 2015; 21 (02) 74-82
  • 166 Alrawabdeh W, Adel S, Fayiz S. Factors affecting the implementation of the national programme for information technology in the national health services: the case of Lorenzo in the North, Midlands and East of England region. Am J Appl Sci 2015; 12 (01) 20
  • 167 Escobar-Rodríguez T, Lourdes B-S. Impact of cultural factors on attitude toward using ERP systems in public hospitals. Rev Contabilidad 2015; 18 (02) 127-137
  • 168 Briz-Ponce L, García-Peñalvo FJ. An empirical assessment of a technology acceptance model for apps in medical education. J Med Syst 2015; 39 (11) 176
  • 169 Lai Y-H, Huang F-F, Yang HH. A study on the attitude of use the mobile clinic registration system in Taiwan. Technol Health Care 2015; 24 (Suppl. 01) S205-S211
  • 170 Al-Nassar BAY, Khalid Ali R, Sana'a Nawaf A-N. Impact of computerised physician order entry in Jordanian hospitals by using technology acceptance model. Int J Inf Syst Change Manag 2016; 8 (03) 191-210
  • 171 Lin W-Y, Chou W-C, Tsai TH, Lin CC, Lee MY. Development of a wearable instrumented vest for posture monitoring and system usability verification based on the technology acceptance model. Sensors (Basel) 2016; 16 (12) 2172
  • 172 Suresh V, Prabhakar K, Santhanalakshmi K, Maran K. Applying technology acceptance (TAM) model to determine the factors of acceptance in out-patient information system in private hospital sectors in Chennai city. J Pharm Sci Res 2016; 8 (12) 1373-1377
  • 173 Ifinedo P. The moderating effects of demographic and individual characteristics on nurses' acceptance of information systems: a Canadian study. Int J Med Inform 2016; 87: 27-35
  • 174 Goodarzi H, Khatami SM, Javadzadeh H. , et al. User acceptance of picture archiving and communication system in the emergency department. Iran J Radiol 2016; 13 (02) e20102
  • 175 Abdekhoda M, Ahmadi M, Dehnad A, Noruzi A, Gohari M. Applying electronic medical records in health care physicians' perspective. Appl Clin Inform 2016; 7 (02) 341-354
  • 176 Strudwick G, Booth R, Mistry K. Can social cognitive theories help us understand nurses' use of electronic health records?. Comput Inform Nurs 2016; 34 (04) 169-174
  • 177 Hsiao J-L, Chen RF. Critical factors influencing physicians' intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model. BMC Med Inform Decis Mak 2016; 16 (01) 3
  • 178 Saigi-Rubió F, Jiménez-Zarco A, Torrent-Sellens J. Determinants of the intention to use telemedicine: evidence from primary care physicians. Int J Technol Assess Health Care 2016; 32 (1-2): 29-36
  • 179 Lin IC, Lin C, Hsu CL, Roan J, Yeh JS, Cheng YH. The usage behavior and intention stability of nurses: an empirical study of a nursing information system. J Nurs Res 2016; 24 (01) 48-57
  • 180 Ducey AJ, Coovert MD. Predicting tablet computer use: an extended Technology Acceptance Model for physicians. Health Policy Tech 2016; 5 (03) 268-284
  • 181 Holden RJ, Asan O, Wozniak EM, Flynn KE, Scanlon MC. Nurses' perceptions, acceptance, and use of a novel in-room pediatric ICU technology: testing an expanded technology acceptance model. BMC Med Inform Decis Mak 2016; 16 (01) 145
  • 182 Omar A, Johan E, Synnöve L. , eds. Evaluation of Electronic Prescribing Decision Support System at a Tertiary Care Pediatric Hospital: The User Acceptance Perspective. ITCH; 2017
  • 183 Kuo K-M, 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 (01) 88