Literature DB >> 18768345

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

Ping Yu1, Haocheng Li, Marie-Pierre Gagnon.   

Abstract

BACKGROUND AND
PURPOSE: This study applied a modified version of the extended technology acceptance model (TAM2) to examine the factors determining the acceptance of health IT applications by caregivers in long-term care facilities. The antecedent variables, including social influence factors such as subjective norm and image were examined together with demographic variables including age, job level, long-term care work experience and computer skills in regard to their impact on caregivers' acceptance of health IT applications.
METHODS: A self-administered questionnaire was developed based on the validated items from TAM2. The data was collected in a cross-sectional survey using convenience sample. Confirmatory factor analysis and structural equation modelling techniques were used to validate our causal model.
RESULTS: Perceived usefulness, perceived ease of use and computer skills had significant positive impact, whereas image had significant negative impact on caregivers' intention to use health IT applications. Image, subjective norm and computer skills also indirectly impacted on intention through the mediating factor of ease of use. Ease of use, subjective norm and job level also determined perceived usefulness. The other demographic factors (including age and long-term care work experience) did not have any significant effect on caregivers' acceptance of a health IT application. Our model explains 34% of caregivers' intention to use an introduced IT application before any hands-on experience with the system established.
CONCLUSIONS: The planners and managers should ensure that a health IT application to be introduced into a long-term care facility is useful and easy to use. Effort should be focused on forming a positive social norm for the introduction of the new innovation and improving caregivers' computer skills. Securing the managers' and senior nurses' support for the innovation at the onset of the project is critical for success. Finally the caregivers appear to dislike the idea of increased IT ability will elevate their status.

Entities:  

Mesh:

Year:  2008        PMID: 18768345     DOI: 10.1016/j.ijmedinf.2008.07.006

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  36 in total

1.  Preparing Nursing Homes for the Future of Health Information Exchange.

Authors:  G L Alexander; M Rantz; C Galambos; A Vogelsmeier; M Flesner; L Popejoy; J Mueller; S Shumate; M Elvin
Journal:  Appl Clin Inform       Date:  2015-04-15       Impact factor: 2.342

2.  Using process visualizations to validate electronic form design.

Authors:  Jenna L Marquard; Yi You Mei
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

3.  Nurse Assistant Communication Strategies About Pressure Ulcers in Nursing Homes.

Authors:  Gregory L Alexander
Journal:  West J Nurs Res       Date:  2014-10-20       Impact factor: 1.967

4.  A Systematic Review of the Technology Acceptance Model in Health Informatics.

Authors:  Bahlol Rahimi; Hamed Nadri; Hadi Lotfnezhad Afshar; Toomas Timpka
Journal:  Appl Clin Inform       Date:  2018-08-15       Impact factor: 2.342

5.  Determinants of telemedicine acceptance in selected public hospitals in Malaysia: clinical perspective.

Authors:  Suhaiza Zailani; Mina Sayyah Gilani; Davoud Nikbin; Mohammad Iranmanesh
Journal:  J Med Syst       Date:  2014-07-20       Impact factor: 4.460

6.  Applying Electronic Medical Records in Health Care: Physicians' Perspective.

Authors:  Mohammadhiwa Abdekhoda; Maryam Ahmadi; Afsaneh Dehnad; Alireza Noruzi; Mahmodreza Gohari
Journal:  Appl Clin Inform       Date:  2016-05-11       Impact factor: 2.342

7.  Extending the Reach and Task-Shifting Ophthalmology Diagnostics Through Remote Visualisation.

Authors:  Mario E Giardini; Iain A T Livingstone
Journal:  Adv Exp Med Biol       Date:  2020       Impact factor: 2.622

8.  Factors Affecting Acceptance of Hospital Information Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments.

Authors:  Hamed Nadri; Bahlol Rahimi; Hadi Lotfnezhad Afshar; Mahnaz Samadbeik; Ali Garavand
Journal:  Appl Clin Inform       Date:  2018-04-04       Impact factor: 2.342

9.  Determining patient preferences for remote monitoring.

Authors:  Nuri Basoglu; Tugrul U Daim; Umit Topacan
Journal:  J Med Syst       Date:  2010-10-13       Impact factor: 4.460

Review 10.  The technology acceptance model: its past and its future in health care.

Authors:  Richard J Holden; Ben-Tzion Karsh
Journal:  J Biomed Inform       Date:  2009-07-15       Impact factor: 6.317

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.