Literature DB >> 19615467

The technology acceptance model: its past and its future in health care.

Richard J Holden1, Ben-Tzion Karsh.   

Abstract

Increasing interest in end users' reactions to health information technology (IT) has elevated the importance of theories that predict and explain health IT acceptance and use. This paper reviews the application of one such theory, the Technology Acceptance Model (TAM), to health care. We reviewed 16 data sets analyzed in over 20 studies of clinicians using health IT for patient care. Studies differed greatly in samples and settings, health ITs studied, research models, relationships tested, and construct operationalization. Certain TAM relationships were consistently found to be significant, whereas others were inconsistent. Several key relationships were infrequently assessed. Findings show that TAM predicts a substantial portion of the use or acceptance of health IT, but that the theory may benefit from several additions and modifications. Aside from improved study quality, standardization, and theoretically motivated additions to the model, an important future direction for TAM is to adapt the model specifically to the health care context, using beliefs elicitation methods.

Entities:  

Mesh:

Year:  2009        PMID: 19615467      PMCID: PMC2814963          DOI: 10.1016/j.jbi.2009.07.002

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  78 in total

1.  The behavioral side of information technology.

Authors:  D R Dixon
Journal:  Int J Med Inform       Date:  1999-12       Impact factor: 4.046

2.  Telehospice: reasons for slow adoption in home hospice care.

Authors:  Pamela Whitten; Bree Holtz; Emily Meyer; Samantha Nazione
Journal:  J Telemed Telecare       Date:  2009       Impact factor: 6.184

3.  Factors influencing health information technology adoption in Thailand's community health centers: applying the UTAUT model.

Authors:  Boonchai Kijsanayotin; Supasit Pannarunothai; Stuart M Speedie
Journal:  Int J Med Inform       Date:  2009-02-03       Impact factor: 4.046

4.  Using a modified technology acceptance model in hospitals.

Authors:  Vassilios P Aggelidis; Prodromos D Chatzoglou
Journal:  Int J Med Inform       Date:  2008-08-03       Impact factor: 4.046

5.  Evaluation of a flowchart-based EHR query system: a case study of RetroGuide.

Authors:  Vojtech Huser; Scott P Narus; Roberto A Rocha
Journal:  J Biomed Inform       Date:  2009-06-26       Impact factor: 6.317

6.  Health IT acceptance factors in long-term care facilities: a cross-sectional survey.

Authors:  Ping Yu; Haocheng Li; Marie-Pierre Gagnon
Journal:  Int J Med Inform       Date:  2008-09-03       Impact factor: 4.046

7.  Managing change: an overview.

Authors:  N M Lorenzi; R T Riley
Journal:  J Am Med Inform Assoc       Date:  2000 Mar-Apr       Impact factor: 4.497

8.  The perception and intention to adopt female-focused healthcare applications (FHA): a comparison between healthcare workers and non-healthcare workers.

Authors:  Xue Lishan; Yen Ching Chiuan; Mahesh Choolani; Chan Hock Chuan
Journal:  Int J Med Inform       Date:  2008-09-09       Impact factor: 4.046

Review 9.  Adopting electronic medical records in primary care: lessons learned from health information systems implementation experience in seven countries.

Authors:  D A Ludwick; John Doucette
Journal:  Int J Med Inform       Date:  2008-07-21       Impact factor: 4.046

10.  Development and evaluation of nursing user interface screens using multiple methods.

Authors:  Sookyung Hyun; Stephen B Johnson; Peter D Stetson; Suzanne Bakken
Journal:  J Biomed Inform       Date:  2009-05-19       Impact factor: 6.317

View more
  365 in total

1.  Modeling nurses' acceptance of bar coded medication administration technology at a pediatric hospital.

Authors:  Richard J Holden; Roger L Brown; Matthew C Scanlon; Ben-Tzion Karsh
Journal:  J Am Med Inform Assoc       Date:  2012-06-03       Impact factor: 4.497

2.  Behavioral health providers' beliefs about health information exchange: a statewide survey.

Authors:  Nancy Shank
Journal:  J Am Med Inform Assoc       Date:  2011-12-18       Impact factor: 4.497

3.  From expert-derived user needs to user-perceived ease of use and usefulness: a two-phase mixed-methods evaluation framework.

Authors:  Mary Regina Boland; Alexander Rusanov; Yat So; Carlos Lopez-Jimenez; Linda Busacca; Richard C Steinman; Suzanne Bakken; J Thomas Bigger; Chunhua Weng
Journal:  J Biomed Inform       Date:  2013-12-12       Impact factor: 6.317

4.  What stands in the way of technology-mediated patient safety improvements?: a study of facilitators and barriers to physicians' use of electronic health records.

Authors:  Richard J Holden
Journal:  J Patient Saf       Date:  2011-12       Impact factor: 2.844

5.  Clinical benefits of electronic health record use: national findings.

Authors:  Jennifer King; Vaishali Patel; Eric W Jamoom; Michael F Furukawa
Journal:  Health Serv Res       Date:  2013-12-21       Impact factor: 3.402

6.  Understanding the mediating effects of relationship quality on technology acceptance: an empirical study of e-appointment system.

Authors:  Shih-Chih Chen; Shih-Chi Liu; Shing-Han Li; David C Yen
Journal:  J Med Syst       Date:  2013-10-19       Impact factor: 4.460

7.  Parental Perceptions of Displayed Patient Data in a PICU: An Example of Unintentional Empowerment.

Authors:  Onur Asan; Matthew C Scanlon; Bradley Crotty; Richard J Holden; Kathryn E Flynn
Journal:  Pediatr Crit Care Med       Date:  2019-05       Impact factor: 3.624

8.  Physiotherapists' and Physiotherapy Students' Perspectives on the Use of Mobile or Wearable Technology in Their Practice.

Authors:  Jenna Blumenthal; Andrea Wilkinson; Mark Chignell
Journal:  Physiother Can       Date:  2018       Impact factor: 1.037

9.  Artificial pancreas (AP) clinical trial participants' acceptance of future AP technology.

Authors:  Wendy C Bevier; Serena M Fuller; Ryan P Fuller; Richard R Rubin; Eyal Dassau; Francis J Doyle; Lois Jovanovič; Howard C Zisser
Journal:  Diabetes Technol Ther       Date:  2014-05-08       Impact factor: 6.118

10.  Impact of electronic health record technology on the work and workflow of physicians in the intensive care unit.

Authors:  Pascale Carayon; Tosha B Wetterneck; Bashar Alyousef; Roger L Brown; Randi S Cartmill; Kerry McGuire; Peter L T Hoonakker; Jason Slagle; Kara S Van Roy; James M Walker; Matthew B Weinger; Anping Xie; Kenneth E Wood
Journal:  Int J Med Inform       Date:  2015-04-15       Impact factor: 4.046

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.