Literature DB >> 28599820

Towards reinforcing telemedicine adoption amongst clinicians in Nigeria.

Kayode I Adenuga1, Noorminshah A Iahad2, Suraya Miskon3.   

Abstract

Telemedicine systems have been considered as a necessary measure to alleviate the shortfall in skilled medical specialists in developing countries. However, the obvious challenge is whether clinicians are willing to use this technological innovation, which has aided medical practice globally. One factor which has received little academic attention is the provision of suitable encouragement for clinicians to adopt telemedicine, in the form of rewards, motivation or incentives. A further consideration for telemedicine usage in developing countries, especially sub-Saharan Africa and Nigeria in particular, are to the severe shortage of available practising clinicians. The researchers therefore explore the need to positively reinforce the adoption of telemedicine amongst clinicians in Nigeria, and also offer a rationale for this using the UTAUT model. Data were collected using a structured paper-based questionnaire, with 252 physicians and nurses from six government hospitals in Ondo state, Nigeria. The study applied SmartPLS 2.0 for analysis to determine the relationship between six variables. Demographic moderating variables, age, gender and profession, were included. The results indicate that performance expectancy (p<0.05), effort expectancy (p<0.05), facilitating condition (p<0.01) and reinforcement factor (p<0.001) have significant effects on clinicians' behavioural intention to use telemedicine systems, as predicted using the extended UTAUT model. Our results showed that the use of telemedicine by clinicians in the Nigerian context is perceived as a dual responsibility which requires suitable reinforcement. In addition, performance expectancy, effort expectancy, facilitating condition and reinforcement determinants are influential factors in the use of telemedicine services for remote-patient clinical diagnosis and management by the Nigerian clinicians.
Copyright © 2017. Published by Elsevier B.V.

Entities:  

Keywords:  Incentives; Reimbursement; Reinforcement; Telemedicine

Mesh:

Year:  2017        PMID: 28599820     DOI: 10.1016/j.ijmedinf.2017.05.008

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


  15 in total

1.  Predicting Health Care Providers' Acceptance of a Personal Health Record Secure Messaging Feature.

Authors:  Consuela C Yousef; Teresa M Salgado; Ali Farooq; Keisha Burnett; Laura E McClelland; Laila C Abu Esba; Hani S Alhamdan; Sahal Khoshhal; Ibrahim Aldossary; Omar A Alyas; Jonathan P DeShazo
Journal:  Appl Clin Inform       Date:  2022-02-09       Impact factor: 2.342

2.  User acceptance of wearable intelligent medical devices through a modified unified theory of acceptance and use of technology.

Authors:  Zheng Yin; Jiayu Yan; Shengyu Fang; Dongbo Wang; Demin Han
Journal:  Ann Transl Med       Date:  2022-06

3.  Is Telemedicine our cup of tea? A nationwide cross-sectional survey regarding doctors' experience and perceptions.

Authors:  Laima Alam; Mafaza Alam; Amina Mannan Malik; Varqa Faraid
Journal:  Pak J Med Sci       Date:  2021 Sep-Oct       Impact factor: 1.088

4.  Willingness to Adopt Health Information Among Social Question-and-Answer Community Users in China: Cross-sectional Survey Study.

Authors:  PengFei Li; Lin Xu; Tingting Tang; Xiaoqian Wu; Cheng Huang
Journal:  J Med Internet Res       Date:  2021-05-21       Impact factor: 5.428

5.  Understanding Clinicians' Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks.

Authors:  Christine Jacob; Antonio Sanchez-Vazquez; Chris Ivory
Journal:  JMIR Mhealth Uhealth       Date:  2020-07-06       Impact factor: 4.773

6.  Tripartite Data Analysis for Optimizing Telemedicine Operations: Evidence from Guizhou Province in China.

Authors:  Jinna Yu; Tingting Zhang; Zhen Liu; Assem Abu Hatab; Jing Lan
Journal:  Int J Environ Res Public Health       Date:  2020-01-06       Impact factor: 3.390

7.  Determinants of the behavioral intention to use a mobile nursing application by nurses in China.

Authors:  Minghao Pan; Wei Gao
Journal:  BMC Health Serv Res       Date:  2021-03-12       Impact factor: 2.655

8.  Social, Organizational, and Technological Factors Impacting Clinicians' Adoption of Mobile Health Tools: Systematic Literature Review.

Authors:  Christine Jacob; Antonio Sanchez-Vazquez; Chris Ivory
Journal:  JMIR Mhealth Uhealth       Date:  2020-02-20       Impact factor: 4.773

9.  Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study.

Authors:  Tiantian Ye; Jiaolong Xue; Mingguang He; Jing Gu; Haotian Lin; Bin Xu; Yu Cheng
Journal:  J Med Internet Res       Date:  2019-10-17       Impact factor: 5.428

10.  Factors Impacting Clinicians' Adoption of a Clinical Photo Documentation App and its Implications for Clinical Workflows and Quality of Care: Qualitative Case Study.

Authors:  Christine Jacob; Antonio Sanchez-Vazquez; Chris Ivory
Journal:  JMIR Mhealth Uhealth       Date:  2020-09-23       Impact factor: 4.773

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.