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The Technology Acceptance Model: Its Past and Its Future in Health Care

Overview
Journal J Biomed Inform
Publisher Elsevier
Date 2009 Jul 21
PMID 19615467
Citations 559
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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.

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References
1.
Chismar W . Test of the technology acceptance model for the internet in pediatrics. Proc AMIA Symp. 2002; :155-9. PMC: 2244480. View

2.
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. 2006; 76(11-12):790-800. DOI: 10.1016/j.ijmedinf.2006.09.001. View

3.
Pare G, Sicotte C, Jacques H . The effects of creating psychological ownership on physicians' acceptance of clinical information systems. J Am Med Inform Assoc. 2005; 13(2):197-205. PMC: 1447539. DOI: 10.1197/jamia.M1930. View

4.
Koppel R, Wetterneck T, Telles J, Karsh B . Workarounds to barcode medication administration systems: their occurrences, causes, and threats to patient safety. J Am Med Inform Assoc. 2008; 15(4):408-23. PMC: 2442264. DOI: 10.1197/jamia.M2616. View

5.
Barker D, Van Schaik P, Simpson D, Corbett W . Evaluating a spoken dialogue system for recording clinical observations during an endoscopic examination. Med Inform Internet Med. 2003; 28(2):85-97. DOI: 10.1080/14639230310001600452. View