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Factors Influencing AI Implementation Decision in Indian Healthcare Industry: A Qualitative Inquiry

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Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 617))

Abstract

Recently, Artificial Intelligence has started showing up in the realm of health care innovations with researchers exploring its potential for healthcare organisations. Since healthcare possess industry specific features, the context and challenges of exploring AI adoption in healthcare is different than other industries. This study intends to conduct grounded theory to review the strategic, cultural, environmental and operational factors towards adoption of AI technology in Indian hospitals. The study uses purposive sampling to conduct semi-structured in-depth interviews of the decision makers of various healthcare organizations across the country. The present study would contribute to the existing literature on the impact of disruptive technology on healthcare as it would be a comprehensive study assessing the determinants of adoption in hospitals.

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References

  1. Kakkad, V., Patel, M., Shah, M.: Biometric authentication and image encryption for image security in cloud framework. Multiscale Multidiscip. Model. Exp. Des. 2(4), 233–248 (2019). https://doi.org/10.1007/s41939-019-00049-y

    Article  Google Scholar 

  2. Talaviya, T., Shah, D., Patel, N., Yagnik, H., Shah, M.: Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif. Intell. Agric. (2020). https://doi.org/10.1016/j.aiia.2020.04.002

    Article  Google Scholar 

  3. Darko, A., Chan, A.P., Adabre, M.A., Edwards, D.J., Hosseini, M.R., Ameyaw, E.E.: Artificial intelligence in the AEC industry: scientometric analysis and visualization of research activities. Autom. Constr. 112, 103081 (2020)

    Article  Google Scholar 

  4. Andoni, M., et al.: Blockchain technology in the energy sector: a systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 100, 143–174 (2019)

    Article  Google Scholar 

  5. Singh, S., Sharma, P.K., Yoon, B., Shojafar, M., Cho, G.H., Ra, I.H.: Convergence of blockchain and artificial intelligence in IoT network for the sustainable smart city. Sustain. Cities Soc. 63, 102364 (2020)

    Article  Google Scholar 

  6. Alsamhi, S.H., Ma, O., Ansari, M.S.: Survey on artificial intelligence based techniques for emerging robotic communication. Telecommun. Syst. 72(3), 483–503 (2019). https://doi.org/10.1007/s11235-019-00561-z

    Article  Google Scholar 

  7. Lee, J., Davari, H., Singh, J., Pandhare, V.: Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manuf. Lett. 18, 20–23 (2018)

    Article  Google Scholar 

  8. Academy of Medical Royal College, Artificial Intelligence in Healthcare (2019). http://www.aomrc.org.uk/wpcontent/uploads/2019/01/Artificial_intelligence_in_healthcare_0119.pdf. Accessed 20 Sept 2020

  9. Frost and Sullivan.: From $600 M to $6 Billion, Artificial Intelligence Systems Poised for Dramatic Market Expansion in Healthcare (2016). http://ww2.frost.com/news/press-release/600-m-6-billion-artificial-intelligence-systems-poised-dramatic-market-expansion-healthcare. Accessed 24 Sept 2020

  10. Gao, F., Thiebes, S., Sunyaev, A.: Rethinking the meaning of cloud computing for health care: a taxonomic perspective and future research directions. J. Med. Internet Res. 20(7), e10041 (2018)

    Article  Google Scholar 

  11. Schönberger, D.: Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. Int. J. Law Inf. Technol. 27(2), 171–203 (2019)

    Article  Google Scholar 

  12. Gao, F., Sunyaev, A.: Context matters: a review of the determinant factors in the decision to adopt cloud computing in healthcare. Int. J. Inf. Manage. 48, 120–138 (2019)

    Article  Google Scholar 

  13. Kuo, M.H.: Opportunities and challenges of cloud computing to improve health care services. J. Med. Internet Res. 13(3), e67 (2011)

    Article  Google Scholar 

  14. Wang, Y., Kung, L., Byrd, T.A.: Big data analytics: understanding its capabilities and potential benefits for healthcare organizations. Technol. Forecast. Soc. Chang. 126, 3–13 (2018)

    Article  Google Scholar 

  15. Tsai, J.M., Cheng, M.J., Tsai, H.H., Hung, S.W., Chen, Y.L.: Acceptance and resistance of telehealth: the perspective of dual-factor concepts in technology adoption. Int. J. Inf. Manage. 49, 34–44 (2019)

    Article  Google Scholar 

  16. Varabyova, Y., Blankart, C.R., Greer, A.L., Schreyögg, J.: The determinants of medical technology adoption in different decisional systems: a systematic literature review. Health Policy 121(3), 230–242 (2017)

    Article  Google Scholar 

  17. Martins, S.M., Ferreira, F.A., Ferreira, J.J., Marques, C.S.: An artificial-intelligence-based method for assessing service quality: insights from the prosthodontics sector. J. Serv. Manag. 31(2), 291–312 (2020)

    Article  Google Scholar 

  18. Kelly, C.J., Karthikesalingam, A., Suleyman, M., Corrado, G., King, D.: Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17(1), 195 (2019)

    Article  Google Scholar 

  19. Dwivedi, Y.K., et al.: Artificial intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 101994 (2019)

    Google Scholar 

  20. Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., Abedi, V.: Artificial intelligence transforms the future of health care. Am. J. Med. 132(7), 795–801 (2019)

    Article  Google Scholar 

  21. Luh, J.Y., Thompson, R.F., Lin, S.: Clinical documentation and patient care using artificial intelligence in radiation oncology. J. Am. Coll. Radiol. 16(9), 1343–1346 (2019)

    Article  Google Scholar 

  22. Hamet, P., Tremblay, J.: Artificial intelligence in medicine. Metabolism 69, S36–S40 (2017)

    Article  Google Scholar 

  23. Wiljer, D., Hakim, Z.: Developing an artificial intelligence–enabled health care practice: rewiring health care professions for better care. J. Med. Imaging Radiat. Sci. 50(4), S8–S14 (2019)

    Article  Google Scholar 

  24. Bini, S.A.: Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care? J. Arthroplasty 33(8), 2358–2361 (2018)

    Article  Google Scholar 

  25. Bhattacharya, S., Singh, A., Hossain, M.M.: Strengthening public health surveillance through blockchain technology. AIMS Public Health 6(3), 326 (2019)

    Article  Google Scholar 

  26. Yu, K.H., Beam, A.L., Kohane, I.S.: Artificial intelligence in healthcare. Nat. Biomed. Eng. 2(10), 719–731 (2018)

    Article  Google Scholar 

  27. Zengul, F.D., Weech-Maldonado, R., Ozaydin, B., Patrician, P.A., O’Connor, S.J.: Longitudinal analysis of high-technology medical services and hospital financial performance. Health Care Manage. Rev. 43(1), 2–11 (2018)

    Article  Google Scholar 

  28. Ye, T., et al.: Psychosocial factors affecting artificial intelligence adoption in health care in China: Cross-sectional study. J. Med. Internet Res. 21(10), e14316 (2019)

    Article  Google Scholar 

  29. Cubric, M.: Drivers, barriers and social considerations for AI adoption in business and management: a tertiary study. Technol. Soc. 62, 101257 (2020)

    Article  Google Scholar 

  30. Zayyad, M.A., Toycan, M.: Factors affecting sustainable adoption of e-health technology in developing countries: an exploratory survey of Nigerian hospitals from the perspective of healthcare professionals. PeerJ 6, e4436 (2018)

    Article  Google Scholar 

  31. Reddy, S., Fox, J., Purohit, M.P.: Artificial intelligence-enabled healthcare delivery. J. R. Soc. Med. 112(1), 22–28 (2019)

    Article  Google Scholar 

  32. Maita, A.R.C., Martins, L.C., Paz, C.R.L., Peres, S.M., Fantinato, M.: Process mining through artificial neural networks and support vector machines. Bus. Process Manag. J. 21(6), 1391–1415 (2015)

    Article  Google Scholar 

  33. Merkert, J., Mueller, M., Hubl, M.: A survey of the application of machine learning in decision support systems (2015)

    Google Scholar 

  34. Jiang, F., et al.: Artificial intelligence in healthcare: past, present and future. Stroke Vasc. Neurol. 2(4), 230–243 (2017)

    Article  Google Scholar 

  35. Rao, A.S., Verweij, G.: Sizing the prize: what’s the real value of AI for your business and how can you capitalise. PwC Publication, PwC (2017)

    Google Scholar 

  36. Memeti, S., Pllana, S., Binotto, A., Kołodziej, J., Brandic, I.: Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review. Computing 101(8), 893–936 (2018). https://doi.org/10.1007/s00607-018-0614-9

    Article  MathSciNet  Google Scholar 

  37. Paré, G., Trudel, M.C.: Knowledge barriers to PACS adoption and implementation in hospitals. Int. J. Med. Informatics 76(1), 22–33 (2007)

    Article  Google Scholar 

  38. Alhashmi, S.F., Salloum, S.A., Mhamdi, C.: Implementing artificial intelligence in the United Arab Emirates healthcare sector: an extended technology acceptance model. Int. J. Inf. Technol. Lang. Stud 3(3), 27–42 (2019)

    Google Scholar 

  39. Maalouf, N., Sidaoui, A., Elhajj, I.H., Asmar, D.: Robotics in nursing: a scoping review. J. Nurs. Scholarsh. 50(6), 590–600 (2018)

    Article  Google Scholar 

  40. Malhotra, R., Chug, A.: Software maintainability: systematic literature review and current trends. Int. J. Software Eng. Knowl. Eng. 26(08), 1221–1253 (2016)

    Article  Google Scholar 

  41. Laranjo, L., et al.: Conversational agents in healthcare: a systematic review. J. Am. Med. Inform. Assoc. 25(9), 1248–1258 (2018)

    Article  Google Scholar 

  42. Sun, T.Q., Medaglia, R.: Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov. Inf. Q. 36(2), 368–383 (2019)

    Article  Google Scholar 

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Correspondence to Prashant Gupta .

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Jain, V., Singh, N., Pradhan, S., Gupta, P. (2020). Factors Influencing AI Implementation Decision in Indian Healthcare Industry: A Qualitative Inquiry. In: Sharma, S.K., Dwivedi, Y.K., Metri, B., Rana, N.P. (eds) Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation. TDIT 2020. IFIP Advances in Information and Communication Technology, vol 617. Springer, Cham. https://doi.org/10.1007/978-3-030-64849-7_56

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  • DOI: https://doi.org/10.1007/978-3-030-64849-7_56

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  • Online ISBN: 978-3-030-64849-7

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