Technology Acceptance in Health Care: An Integrative Review of Predictive Factors and Intervention Programs☆

Abstract The acceptance and increasing utilization of technological innovations in health care are crucially beneficial for both health care professionals and patients during the diagnosis and treatment processes. The literature includes various intervention programs aiming to increase technology acceptance in this field. In this review, studies investigating the factors influencing the technology acceptance and recent interventions intending to enhance the technology usage in the field are covered. Generally, theory of planned behavior, technology acceptance model, diffusion of innovation theory and unified theory of technology acceptance cover the most distinguished concepts and constructs to understand attitudes towards technological innovations. Influencing factors may differ for health care professionals and patients. While perceived benefits of technological innovations may be the most distinctive factor for health care professionals, ease of use is of big importance for patients. Perceived ease of use is affected by personal norms and perceived control beliefs. Suspicions of confidentiality and privacy are strong influencing factors for refusing technology usage for patients. Considering all these factors are necessary while designing intervention programs to enhance technology acceptance in health care. In the conclusion, the paper discusses whether the intervention programs originate from previously covered theoretical concepts and constructs.

[1]  Cristina Rey-Reñones,et al.  Efficacy of a mobile application for smoking cessation in young people: study protocol for a clustered, randomized trial , 2013, BMC Public Health.

[2]  Tomas Escobar-Rodriguez,et al.  The acceptance of information technology innovations in hospitals: differences between early and late adopters , 2014, Behav. Inf. Technol..

[3]  J. Kane Technology-based interventions in health care , 2014, Epidemiology and Psychiatric Sciences.

[4]  Fred D. Davis Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology , 1989, MIS Q..

[5]  Arshia Khan,et al.  Comparison of Contemporary Technology Acceptance Models and Evaluation of the Best Fit for Health Industry Organizations. , 2011 .

[6]  Sibel Ertek Endokrinolojide Tele-Sağlık ve Tele-Tıp Uygulamaları , 2011 .

[7]  Mun Y. Yi,et al.  Understanding information technology acceptance by individual professionals: Toward an integrative view , 2006, Inf. Manag..

[8]  Sonia Livingstone,et al.  Taking risky opportunities in youthful content creation: teenagers' use of social networking sites for intimacy, privacy and self-expression , 2008, New Media Soc..

[9]  Xitong Guo,et al.  UNDERSTANDING THE ACCEPTANCE OF MOBILE HEALTH SERVICES: A COMPARISON AND INTEGRATION OF ALTERNATIVE MODELS , 2013 .

[10]  I. Ajzen,et al.  Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research , 1977 .

[11]  I. Ajzen The theory of planned behavior , 1991 .

[12]  D. Swendeman,et al.  Reliability and Validity of Daily Self-Monitoring by Smartphone Application for Health-Related Quality-of-Life, Antiretroviral Adherence, Substance Use, and Sexual Behaviors Among People Living with HIV , 2014, AIDS and Behavior.

[13]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[14]  R. Petrella,et al.  A lifestyle intervention supported by mobile health technologies to improve the cardiometabolic risk profile of individuals at risk for cardiovascular disease and type 2 diabetes: study rationale and protocol , 2013, BMC Public Health.

[15]  H. Mert,et al.  İmplante edilebilen kardiyoverter defibrilatörü olan hastanın eğitiminde teknoloji kullanımı , 2014 .

[16]  K. Mayer,et al.  Resilience as a Research Framework and as a Cornerstone of Prevention Research for Gay and Bisexual Men: Theory and Evidence , 2012, AIDS and Behavior.

[17]  Emiliano Rodriguez-Sanchez,et al.  Effectiveness of a smartphone application for improving healthy lifestyles, a randomized clinical trial (EVIDENT II): study protocol , 2014, BMC Public Health.

[18]  Mahmut Akbolat,et al.  Bilgi Teknolojileri ve Hastane Bilgi Sistemleri Kullanımı: Sağlık Çalışanları Üzerine Bir Araştırma , 2010 .

[19]  Pamela F. Wendt,et al.  Silver surfers: Training and evaluating internet use among older adult learners , 1999 .

[20]  Doç. Dr. Yasemin Özkan,et al.  Yaşlılıkta Teknolojik Yeniliklerin Kabulünü Etkileyen Sosyalizasyon Süreci , 2010 .

[21]  Deborah F Tate,et al.  The Efficacy of a Technology‐based System in a Short‐term Behavioral Weight Loss Intervention , 2007, Obesity.

[22]  Viswanath Venkatesh,et al.  Technology Acceptance Model 3 and a Research Agenda on Interventions , 2008, Decis. Sci..

[23]  Benoit Aubert,et al.  Towards a better understanding of the intention to use eHealth services by medical professionals: The case of developing countries , 2013 .

[24]  Monica C. Webb,et al.  Sexual behaviour and interest in using a sexual health mobile app to help improve and manage college students' sexual health , 2014 .

[25]  S le Cessie,et al.  Using internet technology to deliver a home-based physical activity intervention for patients with rheumatoid arthritis: A randomized controlled trial. , 2006, Arthritis and rheumatism.

[26]  Uma Kumar,et al.  Predicting mobile health adoption behaviour: A demand side perspective , 2014 .

[27]  Avci Yucel,et al.  Technology Acceptance Model: A Review of the Prior Predictors , 2013 .