Investigating Patients’ Intention to Continue Using Teleconsultation to Anticipate Postcrisis Momentum: Survey Study

Background The COVID-19 crisis has drastically changed care delivery with teleconsultation platforms experiencing substantial spikes in demand, helping patients and care providers avoid infections and maintain health care services. Beyond the current pandemic, teleconsultation is considered a significant opportunity to address persistent health system challenges, including accessibility, continuity, and cost of care, while ensuring quality. Objective This study aims at identifying the determinants of patients’ intention to continue using a teleconsultation platform. It extends prior research on information technology use continuance intention and teleconsultation services. Methods Data was collected in November 2018 and May 2019 with Canadian patients who had access to a teleconsultation platform. Measures included patients’ intention to continue their use; teleconsultation usefulness; teleconsultation quality; patients’ trust toward the digital platform, its provider. and health care professionals; and confirmation of patients’ expectations toward teleconsultation. We used structural equation modeling employing the partial least squares component-based technique to test our research model and hypotheses. Results We analyzed a sample of 178 participants who had used teleconsultation services. Our findings revealed that confirmation of expectations had the greatest influence on continuance intention (total effects=0.722; P<.001), followed by usefulness (total effects=0.587; P<.001) and quality (total effects=0.511; P<.001). Usefulness (β=.60; P<.001) and quality (β=.34; P=.01) had direct effects on the dependent variable. The confirmation of expectations had direct effects both on usefulness (β=.56; P<.001) and quality (β=.75; P<.001) in addition to having an indirect effect on usefulness (indirect effects=0.282; P<.001). Last, quality directly influenced usefulness (β=.34; P=.002) and trust (β=.88; P<.001). Trust does not play a role in the context under study. Conclusions Teleconsultation is central to care going forward, and it represents a significant lever for an improved, digital delivery of health care in the future. We believe that our findings will help drive long-term teleconsultation adoption and use, including in the aftermath of the current COVID-19 crisis, so that general care improvement and greater preparedness for exceptional situations can be achieved.

[1]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[2]  Vinita Magoon Operationalizing Virtual Visits During a Public Health Emergency. , 2020, Family practice management.

[3]  Stefano Omboni,et al.  Telemedicine During the COVID-19 in Italy: A Missed Opportunity? , 2020, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[4]  Charles R Doarn,et al.  Telemedicine and the COVID-19 Pandemic, Lessons for the Future. , 2020, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[5]  J. Thornton Covid-19: A&E visits in England fall by 25% in week after lockdown , 2020, BMJ.

[6]  K. Schulman,et al.  Covid-19 and Health Care's Digital Revolution. , 2020, The New England journal of medicine.

[7]  Saif S. Khairat,et al.  Interpreting COVID-19 and Virtual Care Trends: Cohort Study , 2020, JMIR public health and surveillance.

[8]  Abbas Sheikhtaheri,et al.  Teleoncology for children with cancer: A scoping review on applications and outcomes , 2020, Int. J. Medical Informatics.

[9]  C. Diver,et al.  The Use of Patient-Facing Teleconsultations in the National Health Service: Scoping Review , 2020, JMIR medical informatics.

[10]  C. Ivory,et al.  Social, Organizational, and Technological Factors Impacting Clinicians’ Adoption of Mobile Health Tools: Systematic Literature Review , 2019, JMIR mHealth and uHealth.

[11]  M. Calvo,et al.  Teleconsultation: an Integrative Review of the Doctor-Patient Interaction Mediated by Technology , 2020 .

[12]  F. Mold,et al.  Electronic Consultation in Primary Care Between Providers and Patients: Systematic Review , 2019, JMIR medical informatics.

[13]  Carol K. Kane,et al.  The Use Of Telemedicine By Physicians: Still The Exception Rather Than The Rule. , 2018, Health affairs.

[14]  Min Zhang,et al.  Central or peripheral? Cognition elaboration cues' effect on users' continuance intention of mobile health applications in the developing markets , 2018, Int. J. Medical Informatics.

[15]  Guy Paré,et al.  Diffusion of the Digital Health Self-Tracking Movement in Canada: Results of a National Survey , 2018, Journal of medical Internet research.

[16]  Trisha Greenhalgh,et al.  Real-World Implementation of Video Outpatient Consultations at Macro, Meso, and Micro Levels: Mixed-Method Study , 2018, Journal of medical Internet research.

[17]  Guy Paré,et al.  Transparency in literature reviews: an assessment of reporting practices across review types and genres in top IS journals , 2017, Eur. J. Inf. Syst..

[18]  H. Boateng,et al.  Examining customers’ continuance intentions towards internet banking usage , 2017 .

[19]  LiuXuan,et al.  Why and When do Patients Use e-Consultation Services? The Trust and Resource Supplementary Perspectives. , 2017 .

[20]  M. Rosenbaum,et al.  How Socioeconomic Status Affects Patient Perceptions of Health Care: A Qualitative Study , 2017, Journal of primary care & community health.

[21]  Jian Mou,et al.  Trust and online consumer health service success , 2017 .

[22]  Aliza S. Gordon,et al.  Virtual Visits for Acute, Nonurgent Care: A Claims Analysis of Episode-Level Utilization , 2017, Journal of medical Internet research.

[23]  Michael Groß Impediments to mobile shopping continued usage intention: A trust-risk-relationship , 2016 .

[24]  Mohammad Alamgir Hossain Assessing m-Health success in Bangladesh: An empirical investigation using IS success models , 2016, J. Enterp. Inf. Manag..

[25]  Craig Leth-Steensen,et al.  Testing Mediation in Structural Equation Modeling , 2016, Educational and psychological measurement.

[26]  Younghoon Chang,et al.  Determinants of continuance intention to use the smartphone banking services: An extension to the expectation-confirmation model , 2016, Ind. Manag. Data Syst..

[27]  Jaehee Cho,et al.  The impact of post-adoption beliefs on the continued use of health apps , 2016, Int. J. Medical Informatics.

[28]  Geoffrey S. Hubona,et al.  Using PLS path modeling in new technology research: updated guidelines , 2016, Ind. Manag. Data Syst..

[29]  Ali Nabavi,et al.  Information Technology Continuance Intention: A Systematic Literature Review , 2016, Int. J. E Bus. Res..

[30]  Xuequn Wang,et al.  The Integrated User Satisfaction Model: Assessing Information Quality and System Quality as Second-order Constructs in System Administration , 2016, Commun. Assoc. Inf. Syst..

[31]  Anol Bhattacherjee,et al.  A unified model of IT continuance: three complementary perspectives and crossover effects , 2015, Eur. J. Inf. Syst..

[32]  A. Ribeiro,et al.  Factors associated with the use of a teleconsultation system in Brazilian primary care. , 2015, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[33]  Alfredo Pérez-Rueda,et al.  Determinants of multi-service smartcard success for smart cities development: A study based on citizens' privacy and security perceptions , 2015, Gov. Inf. Q..

[34]  Jinyoung Min,et al.  The distinct roles of dedication-based and constraint-based mechanisms in social networking sites , 2015, Internet Res..

[35]  Gurpreet Dhillon,et al.  A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research , 2014, Inf. Syst. Res..

[36]  Isabel de la Torre Díez,et al.  Privacy and Security in Mobile Health Apps: A Review and Recommendations , 2014, Journal of Medical Systems.

[37]  Xingqiong Meng,et al.  Video Consultation Use by Australian General Practitioners: Video Vignette Study , 2013, Journal of medical Internet research.

[38]  Jiming Wu,et al.  Effects of Extrinsic and Intrinsic Motivators on Using Utilitarian, Hedonic, and Dual-Purposed Information Systems: A Meta-Analysis , 2013, J. Assoc. Inf. Syst..

[39]  Shahriar Akter,et al.  Continuance of mHealth services at the bottom of the pyramid: the roles of service quality and trust , 2012, Electronic Markets.

[40]  Ephraim R. McLean,et al.  Information Systems Success: The Quest for the Dependent Variable , 1992, Inf. Syst. Res..

[41]  Ned Kock,et al.  Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations , 2012, J. Assoc. Inf. Syst..

[42]  D. Straub,et al.  Editor's comments: a critical look at the use of PLS-SEM in MIS quarterly , 2012 .

[43]  Kenneth A. Bollen,et al.  Evaluating Effect, Composite, and Causal Indicators in Structural Equation Models , 2011, MIS Q..

[44]  Straub,et al.  Editor's Comments: An Update and Extension to SEM Guidelines for Administrative and Social Science Research , 2011 .

[45]  Shahriar Akter,et al.  Service quality of mHealth platforms: development and validation of a hierarchical model using PLS , 2010, Electron. Mark..

[46]  Jason Bennett Thatcher,et al.  Conceptualizing and testing formative constructs: tutorial and annotated example , 2009, DATB.

[47]  Christian Lovis,et al.  White Paper: Patient-centered Applications: Use of Information Technology to Promote Disease Management and Wellness. A White Paper by the AMIA Knowledge in Motion Working Group , 2008, J. Am. Medical Informatics Assoc..

[48]  J. Sweeney,et al.  A Hierarchical Model of Health Service Quality , 2007 .

[49]  Paul A. Pavlou,et al.  Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal-Agent Perspective , 2007, MIS Q..

[50]  Judy A. Siguaw,et al.  Formative versus Reflective Indicators in Organizational Measure Development: A Comparison and Empirical Illustration , 2006 .

[51]  Kar Yan Tam,et al.  Understanding Continued Information Technology Usage Behavior: A Comparison of Three Models in the Context of Mobile Internet , 2006, Decis. Support Syst..

[52]  Cheryl Burke Jarvis,et al.  The problem of measurement model misspecification in behavioral and organizational research and some recommended solutions. , 2005, The Journal of applied psychology.

[53]  D. Gefen,et al.  Consumer trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services , 2004 .

[54]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[55]  Ephraim R. McLean,et al.  The DeLone and McLean Model of Information Systems Success: A Ten-Year Update , 2003, J. Manag. Inf. Syst..

[56]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[57]  M. Lindell,et al.  Accounting for common method variance in cross-sectional research designs. , 2001, The Journal of applied psychology.

[58]  J. H. Davis,et al.  An Integrative Model Of Organizational Trust , 1995 .

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