Appl Clin Inform 2020; 11(02): 286-294
DOI: 10.1055/s-0040-1708838
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Factors Affecting Patients' Acceptance of and Satisfaction with Cloud-Based Telehealth for Chronic Disease Management: A Case Study in the Workplace

Yung-Yu Su*
1   Department of Long Term Care, National Quemoy University, Kinmen, Taiwan
,
Su-Tsai Huang*
2   Department of Nursing, Changhua Christian Hospital, Changhua, Taiwan
,
Ying-Hsun Wu
2   Department of Nursing, Changhua Christian Hospital, Changhua, Taiwan
,
Chun-Min Chen
3   Research Education and Epidemiology Center, Changhua Christian Hospital, Changhua, Taiwan
› Author Affiliations
Funding This work was founded in part by a grant from the Changhua Christian Hospital (105-CCH-IRP-150).
Further Information

Publication History

25 November 2019

20 February 2020

Publication Date:
15 April 2020 (online)

Abstract

Objective Understanding patients' acceptance of and satisfaction with telehealth use is important for workplace health promotion. In this study, we used a questionnaire to measure patients' usage behavior and satisfaction with cloud-based telehealth services in the workplace. We empirically investigated the factors that influence patients' usage and satisfaction based on data collected from 101 participants.

Methods As its main research framework, this study utilized a revised version of the technology acceptance model 2 that was based on the telehealth services provided for chronic disease management. Through integrating a cross-sectional research design with an author-developed structured questionnaire that was assessed using reliability and validity tests, an anonymous survey was conducted on selected participants. The proposed research model and hypotheses were validated through path analysis using SPSS.

Results We found that users believe telehealth services can promote their workplace health management; that job relevance, result demonstrability, and perceived ease of use (PEOU) positively affect the perceived usefulness (PU), which implies that cognitive instrumental processes have the most significant impact on the PU of cloud-based telehealth; and that both PEOU and PU positively affect the intention to use (IU), but PU has a bigger influence than PEOU on users' intentions to continue using telehealth. In particular, the IU and actual usage behavior were critical to the patients' satisfaction with telehealth services.

Conclusion This research contributes to the rapid developing field of technology acceptance research by examining workplace telemedicine engagement. Our results will provide researchers with useful advice and a user-centered strategy for promoting workplace health management, which benefits both health care providers and corporate managers.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects, and was reviewed by Human Research Protection Program of Changhua Christian Hospital (Institutional Review Board No. 160819).


* These authors contributed equally to the work.


 
  • References

  • 1 Baena-Díez JM, Peñafiel J, Subirana I. , et al; FRESCO Investigators. Risk of cause-specific death in individuals with diabetes: a competing risks analysis. Diabetes Care 2016; 39 (11) 1987-1995
  • 2 Wong E, Backholer K, Gearon E. , et al. Diabetes and risk of physical disability in adults: a systematic review and meta-analysis. Lancet Diabetes Endocrinol 2013; 1 (02) 106-114
  • 3 Shin JA, Lee JH, Lim SY. , et al. Metabolic syndrome as a predictor of type 2 diabetes, and its clinical interpretations and usefulness. J Diabetes Investig 2013; 4 (04) 334-343
  • 4 Davis RM, Hitch AD, Salaam MM, Herman WH, Zimmer-Galler IE, Mayer-Davis EJ. TeleHealth improves diabetes self-management in an underserved community: diabetes TeleCare. Diabetes Care 2010; 33 (08) 1712-1717
  • 5 Glasgow RE, Christiansen SM, Kurz D. , et al. Engagement in a diabetes self-management website: usage patterns and generalizability of program use. J Med Internet Res 2011; 13 (01) e9
  • 6 Lin YH, Chen RR, Guo SH, Chang HY, Chang HK. Developing a web 2.0 diabetes care support system with evaluation from care provider perspectives. J Med Syst 2012; 36 (04) 2085-2095
  • 7 Kebede MM, Schuett C, Pischke CR. The role of continuous glucose monitoring, diabetes smartphone applications, and self-care behavior in glycemic control: results of a multi-national online survey. J Clin Med 2019; 8 (01) E109
  • 8 Hanlon P, Daines L, Campbell C, McKinstry B, Weller D, Pinnock H. Telehealth interventions to support self-management of long-term conditions: a systematic metareview of diabetes, heart failure, asthma, chronic obstructive pulmonary disease, and cancer. J Med Internet Res 2017; 19 (05) e172-e172
  • 9 Wu C, Wu Z, Yang L. , et al. Evaluation of the clinical outcomes of telehealth for managing diabetes: a PRISMA-compliant meta-analysis. Medicine (Baltimore) 2018; 97 (43) e12962
  • 10 Cho H, Iribarren S, Schnall R. Technology-mediated interventions and quality of life for persons living with HIV/AIDS. A systematic review. Appl Clin Inform 2017; 8 (02) 348-368
  • 11 Datillo JR, Gittings DJ, Sloan M, Hardaker WM, Deasey MJ, Sheth NP. “Is There An App For That?” Orthopaedic patient preferences for a smartphone application. Appl Clin Inform 2017; 8 (03) 832-844
  • 12 Quintana Y, Gonzalez Martorell EA, Fahy D, Safran C. A systematic review on promoting adherence to antiretroviral therapy in HIV-infected patients using mobile phone technology. Appl Clin Inform 2018; 9 (02) 450-466
  • 13 Magnus M, Sikka N, Cherian T, Lew SQ. Satisfaction and improvements in peritoneal dialysis outcomes associated with telehealth. Appl Clin Inform 2017; 8 (01) 214-225
  • 14 Wong AMK, Chang W-H, Ke P-C. , et al. Technology acceptance for an Intelligent Comprehensive Interactive Care (ICIC) system for care of the elderly: a survey-questionnaire study. PLoS One 2012; 7 (08) e40591-e40591
  • 15 Standing C, Standing S, McDermott M-L, Gururajan R, Kiani Mavi R. The paradoxes of telehealth: a review of the literature 2000–2015. Syst Res Behav Sci 2018; 35 (01) 90-101
  • 16 Zanaboni P, Wootton R. Adoption of telemedicine: from pilot stage to routine delivery. BMC Med Inform Decis Mak 2012; 12: 1
  • 17 Wade VA, Eliott JA, Hiller JE. Clinician acceptance is the key factor for sustainable telehealth services. Qual Health Res 2014; 24 (05) 682-694
  • 18 Greenhalgh T, Wherton J, Papoutsi C. , et al. Beyond adoption: a new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res 2017; 19 (11) e367
  • 19 Greenhalgh T. How to Implement Evidence-Based Healthcare. Hoboken, NJ, USA: Wiley Blackwell; 2018
  • 20 Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci 2008; 39(2): 273–315
  • 21 Rudin RS, Fanta CH, Predmore Z. , et al. Core components for a clinically integrated mHealth app for asthma symptom monitoring. Appl Clin Inform 2017; 8 (04) 1031-1043
  • 22 Couture B, Lilley E, Chang F. , et al. Applying user-centered design methods to the development of an mHealth application for use in the hospital setting by patients and care partners. Appl Clin Inform 2018; 9 (02) 302-312
  • 23 Dellifraine JL, Dansky KH. Home-based telehealth: a review and meta-analysis. J Telemed Telecare 2008; 14 (02) 62-66
  • 24 Venkatesh V, Davis F. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 2000; 46: 186-204
  • 25 Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 1989; 35 (08) 982-1003
  • 26 Nadri H, Rahimi B, Lotfnezhad Afshar H, Samadbeik M, Garavand A. Factors affecting acceptance of hospital information systems based on extended technology acceptance model: a case study in three paraclinical departments. Appl Clin Inform 2018; 9 (02) 238-247
  • 27 Chiu TM, Ku BP. Moderating effects of voluntariness on the actual use of electronic health records for allied health professionals. JMIR Med Inform 2015; 3 (01) e7-e7
  • 28 Downing CE. System usage behavior as a proxy for user satisfaction: an empirical investigation. Inf Manage 1999; 35 (04) 203-216
  • 29 Campbell DT, Cook TD. Quasi-Experimentation: Design & Analysis Issues for Field Settings. Chicago: Rand McNally College Publishing Company; 1979
  • 30 Ritzhaupt A, Dawson K, Cavanaugh C. An investigation of factors influencing student use of technology in K-12 classrooms using path analysis. J Educ Comput Res 2012; 46: 229-254
  • 31 Wright S. Correlation and causation. J Agric Res 1921; 20 (07) 557-585
  • 32 Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling 1999; 6 (01) 1-55
  • 33 McDonald RP, Ho M-HR. Principles and practice in reporting structural equation analyses. Psychol Methods 2002; 7 (01) 64-82
  • 34 Ketikidis P, Dimitrovski T, Lazuras L, Bath PA. Acceptance of health information technology in health professionals: an application of the revised technology acceptance model. Health Informatics J 2012; 18 (02) 124-134
  • 35 So CF, Chung JW. Telehealth for diabetes self-management in primary healthcare: a systematic review and meta-analysis. J Telemed Telecare 2018; 24 (05) 356-364
  • 36 Mettler T. . Post acceptance of electronic medical records: evidence from a longitudinal field study. Paper presented at: The 33rd International Conference on Information Systems; Orlando, USA; 2012
  • 37 Chang CP. The technology acceptance model and its application in a telehealth program for the elderly with chronic illnesses [in Chinese]. Hu Li Za Zhi 2015; 62 (03) 11-16
  • 38 Argaez Ed. . Internet World Stats statistics; 2019. Available at: https://www.internetworldstats.com/stats.htm . Accessed February 3, 2020
  • 39 Bech M, Kristensen MB. Differential response rates in postal and Web-based surveys in older respondents. Surv Res Methods 2009; 3 (01) 1-6
  • 40 Cook JV, Dickinson HO, Eccles MP. Response rates in postal surveys of healthcare professionals between 1996 and 2005: an observational study. BMC Health Serv Res 2009; 9: 160
  • 41 Hoyle RH. The structural equation modeling approach: basic concepts and fundamental issues. In: Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, CA: Sage Publications, Inc; 1995: 1-15