Skip to main content
SpringerLink
Log in

Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS)

  • S.I.: BALCOR-2017
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Compared to the booming industry of AIMDSS, the usage of AIMDSS among healthcare professionals is relatively low in the hospital. Thus, a research on the acceptance and adoption intention of AIMDSS by health professionals is imperative. In this study, an integration of Unified theory of user acceptance of technology and trust theory is proposed for exploring the adoption of AIMDSS. Besides, two groups of additional factors, related to AIMDSS (task complexity, technology characteristics, and perceived substitution crisis) and health professionals’ characteristics (propensity to trust and personal innovativeness in IT) are considered in the integrated model. The data set of proposed research model is collected through paper survey and Internet survey in China. The empirical examination demonstrates a high predictive power of this proposed model in explaining AIMDSS adoption. Finally, the theoretical contribution and practical implications of this research are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. http://mt.sohu.com/20161222/n476678978.shtml.

  2. https://www.nanalyze.com/2016/02/enlitic-deep-learning-algorithms-for-medical-imaging/.

  3. http://fortune.com/2012/12/04/technology-will-replace-80-of-what-doctors-do/.

  4. http://www.modernhealthcare.com/article/20130216/MAGAZINE/302169974.

  5. https://www.hhnmag.com/articles/6561-ways-artificial-intelligence-will-transform-health-care.

  6. http://hn.people.com.cn/n2/2017/0519/c337651-30211488.html.

  7. https://www.forbes.com/sites/jenniferhicks/2017/05/16/see-how-artificial-intelligence-can-improve-medical-diagnosis-and-healthcare/#1afb31de6223.

  8. http://www.healthcareitnews.com/news/half-hospitals-adopt-artificial-intelligence-within-5-years.

  9. https://theconversation.com/ai-can-excel-at-medical-diagnosis-but-the-harder-task-is-to-win-hearts-and-minds-first-63782.

References

  • Abushanab, E., & Pearson, J. M. (2007). Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective. Journal of Systems & Information Technology, 9(1), 78–97.

    Google Scholar 

  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.

    Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Research in Nursing & Health, 14(2), 137–144.

    Google Scholar 

  • Alharbi, S. T. (2014). Trust and acceptance of cloud computing: A revised UTAUT model. In International conference on computational science and computational intelligence (Vol.2, pp. 131–134). IEEE.

  • Alshehri, M., Drew, S., & Alghamdi, R. (2013). Analysis of citizens acceptance for e-government services: Applying the UTAUT model.

  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

    Google Scholar 

  • Asher, H. B. (1983). Causal modeling (2nd ed.). Thousand Oaks: Sage Publications.

    Google Scholar 

  • Bansal, G., Zahedi, F. M., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138–150.

    Google Scholar 

  • Chang, I. C., Hwang, H. G., Hung, M. C., Lin, M. H., & Yen, D. C. (2007). Factors affecting the adoption of electronic signature: executives’ perspective of hospital information department. Decision Support Systems, 44(1), 350–359.

    Google Scholar 

  • Chaouali, W., Yahia, I. B., & Souiden, N. (2016). The interplay of counter-conformity motivation, social influence, and trust in customers’ intention to adopt internet banking services: the case of an emerging country. Journal of Retailing & Consumer Services, 28, 209–218.

    Google Scholar 

  • Chew, F., Grant, W., & Tote, R. (2004). Doctors on-line: using diffusion of innovations theory to understand internet use. Family Medicine, 36(9), 645.

    Google Scholar 

  • Chiu, C. M., Hsu, M. H., Lai, H., & Chang, C. M. (2012). Re-examining the influence of trust on online repeat purchase intention: The moderating role of habit and its antecedents. Decision Support Systems, 53(4), 835–845.

    Google Scholar 

  • Cimperman, M., Makovec, B. M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior-applying an extended utaut model. International Journal of Medical Informatics, 90, 22–31.

    Google Scholar 

  • Daft, R. L., Lengel, R. H., & Trevino, L. K. (1987). Message equivocality, media selection, and manager performance: implications for information systems. MIS Quarterly, 11(3), 355–366.

    Google Scholar 

  • Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.

    Google Scholar 

  • Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man–Machine Studies, 38(3), 475–487.

    Google Scholar 

  • Deng, Z., Mo, X., & Liu, S. (2014). Comparison of the middle-aged and older users’ adoption of mobile health services in China. International Journal of Medical Informatics, 83(3), 210.

    Google Scholar 

  • Dulle, F. W., & Minishi-Majanja, M. K. (2011). The suitability of the unified theory of acceptance and use of technology (UTAUT) model in open access adoption studies. Information Development, 27(1), 32–45.

    Google Scholar 

  • Er, O., Tanrikulu, A. Ç., & Abakay, A. (2015). Use of artificial intelligence techniques for diagnosis of malignant pleural mesothelioma. Dicle Medical Journal, 42(1), 5–11.

    Google Scholar 

  • Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115.

    Google Scholar 

  • Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. Cambridge: Academic Press, Inc.

    Google Scholar 

  • Fornell, C. (1982). A second generation of multivariate analysis. Santa Barbara: Praeger.

    Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

    Google Scholar 

  • Gagnon, M. P., Ngangue, P., Paynegagnon, J., & Desmartis, M. (2015). M-health adoption by healthcare professionals: a systematic review. Journal of the American Medical Informatics Association Jamia, 54(1), 334–336.

    Google Scholar 

  • Gallupe, R. B., Desanctis, G., & Dickson, G. W. (1988). Computer-based support for group problem-finding: An experimental investigation. MIS Quarterly, 12(2), 277–296.

    Google Scholar 

  • Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and tam in online shopping: an integrated model. MIS Quarterly, 27(1), 51–90.

    Google Scholar 

  • Gefen, D., Rose, G. M., Warkentin, M., & Pavlou, P. A. (2008). Cultural diversity and trust in it adoption: A comparison of potential e-voters in the USA and South Africa. Journal of Global Information Management, 13(1), 54–78.

    Google Scholar 

  • Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.

    Google Scholar 

  • Gu, Z., Wei, J., & Xu, F. (2015). An empirical study on factors influencing consumers’’ initial trust in wearable commerce. Data Processor for Better Business Education, 56(1), 79–85.

    Google Scholar 

  • Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2013). Multivariate data analysis. Technometrics, 30(1), 130–131.

    Google Scholar 

  • Hilliard, M. E., Hahn, A., Ridge, A. K., Eakin, M. N., & Riekert, K. A. (2014). User preferences and design recommendations for an mhealth app to promote cystic fibrosis self-management. JMIR mHealth & uHealth, 2(4), e44.

    Google Scholar 

  • Hoque, M. R. (2016). An empirical study of mhealth adoption in a developing country: The moderating effect of gender concern. BMC Medical Informatics and Decision Making, 16(1), 51.

    Google Scholar 

  • Hung, M. C., & Jen, W. Y. (2012). The adoption of mobile health management services: An empirical study. Journal of Medical Systems, 36(3), 1381.

    Google Scholar 

  • Ke, W., Liu, H., Wei, K. K., Gu, J., & Chen, H. (2009). How do mediated and non-mediated power affect electronic supply chain management system adoption? The mediating effects of trust and institutional pressures. Decision Support Systems, 46(4), 839–851.

    Google Scholar 

  • Kelman, H. C. (1958). Compliance, identification, and internalization: three processes of attitude change. Journal of Conflict Resolution, 2(1), 51–60.

    Google Scholar 

  • Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand’s community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78(6), 404–416.

    Google Scholar 

  • Kim, S., Lee, K. H., Hwang, H., & Yoo, S. (2016). Analysis of the factors influencing healthcare professionals’ adoption of mobile electronic medical record (EMR) using the unified theory of acceptance and use of technology (UTAUT) in a tertiary hospital. BMC Medical Informatics and Decision Making, 16(1), 1–12.

    Google Scholar 

  • Lai, I. K. W., Tong, V. W. L., & Lai, D. C. F. (2011). Trust factors influencing the adoption of internet-based interorganizational systems. Electronic Commerce Research and Applications, 10(1), 85–93.

    Google Scholar 

  • Lee, J. N., & Kim, Y. G. (1999). Effect of partnership quality on is outsourcing success: conceptual framework and empirical validation. Journal of Management Information Systems, 15(4), 29–61.

    Google Scholar 

  • Lee, J. K., & Rao, H. R. (2009). Task complexity and different decision criteria for online service acceptance: A comparison of two e-government compliance service domains. Decision Support Systems, 47(4), 424–435.

    Google Scholar 

  • Li, X., Hess, T. J., & Valacich, J. S. (2006). Using attitude and social influence to develop an extended trust model for information systems. ACM Sigmis Database, 37(2–3), 108–124.

    Google Scholar 

  • Li, X., Hess, T. J., & Valacich, J. S. (2008). Why do we trust new technology? A study of initial trust formation with organizational information systems. Journal of Strategic Information Systems, 17(1), 39–71.

    Google Scholar 

  • Lu, B., Zhang, T., Wang, L., & Keller, L. R. (2016). Trust antecedents, trust and online microsourcing adoption. Decision Support Systems, 85(C), 104–114.

    Google Scholar 

  • Luhmann, N. (1982). Trust and power. Chichester: Wiley.

    Google Scholar 

  • Maguire, R., Mccann, L., Miller, M., & Kearney, N. (2009). Nurse’s perceptions and experiences of using of a mobile-phone-based advanced symptom management system (ASYMS) to monitor and manage chemotherapy-related toxicity. European Journal of Cancer Care, 18(2), 156–164.

    Google Scholar 

  • Mayer, R. C., Davis, J. H. F., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734.

    Google Scholar 

  • Mcknight, D. H. (2005). Trust in information technology. The Blackwell encyclopedia of management. Vol. 7 management information systems. Malden: Blackwell Publications.

    Google Scholar 

  • Oliveira, T., Faria, M., & Thomas, M. A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management the Journal for Information Professionals, 34(5), 689–703.

    Google Scholar 

  • Or, C.K.L., Karsh, B. T., Severtson, D. J., Burke, L. J., Brown, R. L., & Brennan, P. F. (2011). Factors affecting home care patients’ acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association Jamia, 18(1), 51–59.

    Google Scholar 

  • Rogers, E. M. (2003). Diffusion of Innovations, (5th ed.). New York: Free Press Edition.

    Google Scholar 

  • Rotter, J. B. (1971). Generalized expectancies for interpersonal trust. American Psychologist, 26(5), 443–452.

    Google Scholar 

  • Terry, H.P., Hulsing, J., Grant, M., Powell, D., Mubayi, P., & Syed, W. (2016). AI, Machine learning and data Fuel the future of productivity. The Golden Sachs Group, Inc. November 14.

  • Toth-pal, E., Wårdh, I., Strender, L. E., & Nilsson, G. (2008). Implementing a clinical decision-support system in practice: A qualitative analysis of influencing attitudes and characteristics among general practitioners. Medical Informatics, 33(1), 39–54.

    Google Scholar 

  • Tornatzky, L. G., & Fleischer, M. (1990). Processes of technological innovation. Lexington, KY: Lexington Books.

    Google Scholar 

  • Tung, F. C., & Chou, C. C. M. (2008). An extension of trust and tam model with IDT in the adoption of the electronic logistics information system in his in the medical industry. International Journal of Medical Informatics, 77(5), 324.

    Google Scholar 

  • Venkatesh, V., Moris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

    Google Scholar 

  • Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: Incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555.

    Google Scholar 

  • Wallace, S., Clark, M., & White, J. (2012). ‘It’s on my iphone’: Attitudes to the use of mobile computing devices in medical education, a mixed-methods study. BMJ Open, 2(4), e001099.

    Google Scholar 

  • Wang, W., & Benbasat, I. (2005). Trust in and adoption of online recommendation agents. Journal of the Association for Information Systems, 6(3), 4.

    Google Scholar 

  • Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British Journal of Educational Technology, 40(1), 92–118.

    Google Scholar 

  • Wiggins, C., Trimmer, K., Beachboard, J., & Peterson, T. (2009). Prior experience and physicians’ intentions to adopt EMR. In Hawaii international conference on system sciences (pp. 1–9). IEEE Computer Society.

  • Wu, I. L., Li, J. Y., & Fu, C. Y. (2011). The adoption of mobile healthcare by hospital’s professionals: An integrative perspective. Decision Support Systems, 51(3), 587–596.

    Google Scholar 

  • Xue, L., Yen, C. C., Chang, L., Chan, H. C., Tai, B. C., Tan, S. B., Duh H. B. L., & Choolani, M. (2012). An exploratory study of ageing women’s perception on access to health informatics via a mobile phone-based intervention. International Journal of Medical Informatics, 81(9), 637–648.

    Google Scholar 

  • Yan, H., & Pan, K. (2015). Examining mobile payment user adoption from the perspective of trust transfer. International Journal of Networking and Virtual Organisations, 8(1), 117–130.

    Google Scholar 

  • Yang, H., Guo, X., & Wu, T. (2015). Exploring the influence of the online physician service delivery process on patient satisfaction. Decision Support Systems, 78(1), 113–121.

    Google Scholar 

  • Yang, Z., Kankanhalli, A., Ng, B. Y., & Lim, J. T. Y. (2013). Analyzing the enabling factors for the organizational decision to adopt healthcare information systems. Decision Support Systems, 55(3), 764–776.

    Google Scholar 

  • Yi, M. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350–363.

    Google Scholar 

  • Zhang, X., Guo, X., Lai, K. H., Guo, F., & Li, C. (2014). Understanding gender differences in m-health adoption: A modified theory of reasoned action model. Telemedicine Journal and e-Health: The Official Journal of the American Telemedicine Association, 20(1), 39–46.

    Google Scholar 

  • Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760–767.

    Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 71501058, 71601065, 71690235 and 71690230), and Innovative Research Groups of the National Natural Science Foundation of China (71521001). Panos M. Pardalos is partially supported by the project of “Distinguished International Professor by the Chinese Ministry of Education” (MS2014HFGY026).

Author information

Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Wenjuan Fan, Jingnan Liu & Shuwan Zhu

  2. Key Laboratory of Minister of Education on Process Optimization and Intelligent Decision-making, Hefei, China

    Wenjuan Fan

  3. Department of Industrial Systems and Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

Authors
  1. Wenjuan Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jingnan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuwan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Panos M. Pardalos
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenjuan Fan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, W., Liu, J., Zhu, S. et al. Investigating the impacting factors for the healthcare professionals to adopt artificial intelligence-based medical diagnosis support system (AIMDSS). Ann Oper Res 294, 567–592 (2020). https://doi.org/10.1007/s10479-018-2818-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-018-2818-y

Keywords

Search

Navigation