Deutsch
Bibtex
Cite as text
@Select Types{,
Journal = "Band-1",
Title= "Intelligent Systems and Hospitals: Joint Forces in the Name of Health?",
Author= "Luisa Pumplun, Peter Buxmann",
Doi= "https://doi.org/10.30844/wi_2020_f8-pumplun",
Abstract= "In recent times, intelligent systems based on artificial intelligence have gained relevance for a variety of industries. Their potential is particularly high in healthcare, where they could be used for prevention, diagnosis and follow-up-care. However, their adoption requires that healthcare delivery organizations are able to integrate the new technology into their processes, systems, and values – a task, that most hospitals have not yet been able to accomplish. To learn more about this issue, we conducted a systematic literature search and found, that little is known regarding the specific aspects that influence hospitals to continuously adopt intelligent systems. Based on this finding and drawing on the TOE and NASSS framework, we want to conduct semi-structured expert interviews with hospital management and physicians. The aim of our research is to analyse the specific requirements of hospitals and thus contribute to a theoretical foundation of intelligent systems’ persistent adoption.
",
Keywords= "Artificial Intelligence, Intelligent Systems, Hospitals, Adoption, Implementation",
}
Luisa Pumplun, Peter Buxmann: Intelligent Systems and Hospitals: Joint Forces in the Name of Health?. Online: https://doi.org/10.30844/wi_2020_f8-pumplun (Abgerufen 26.12.24)
Open Access
Abstract
Abstract
In recent times, intelligent systems based on artificial intelligence have gained relevance for a variety of industries. Their potential is particularly high in healthcare, where they could be used for prevention, diagnosis and follow-up-care. However, their adoption requires that healthcare delivery organizations are able to integrate the new technology into their processes, systems, and values – a task, that most hospitals have not yet been able to accomplish. To learn more about this issue, we conducted a systematic literature search and found, that little is known regarding the specific aspects that influence hospitals to continuously adopt intelligent systems. Based on this finding and drawing on the TOE and NASSS framework, we want to conduct semi-structured expert interviews with hospital management and physicians. The aim of our research is to analyse the specific requirements of hospitals and thus contribute to a theoretical foundation of intelligent systems’ persistent adoption.
Keywords
Schlüsselwörter
Artificial Intelligence, Intelligent Systems, Hospitals, Adoption, Implementation
References
Referenzen
1. Agwunobi, A., Osborne, P.: Dynamic Capabilities and Healthcare: A Framework for Enhancing the Competitive Advantage of Hospitals. Calif. Manage. Rev. 58, 141–161 (2016).
2. McCarthy, J.: What is Artificial Intelligence?. Stanford (2007).
3. Rzepka, C., Berger, B.: User Interaction with AI-Enabled Systems: A Systematic Review of IS Research. In: Proceedings of the 39th International Conference on Information Systems. San Francisco, USA (2018).
4. Brynjolfsson, E., Mitchell, T.: What Can Machine Learning Do? Workforce Implications. Science 358, 1530–1534 (2017).
5. Jordan, M.I., Mitchell, T.M.: Machine Learning: Trends, Perspectives, and Prospects. Science 349, 255–260 (2015).
6. LeCun, Y., Bengio, Y., Hinton, G.: Deep Learning. Nature 521, 436–444 (2015).
7. Bouktif, S., Hanna, E.M., Zaki, N., Abu Khousa, E.: Ant Colony Optimization Algorithm for Interpretable Bayesian Classifiers Combination: Application to Medical Predictions. PLoS One 9, 1–15 (2014).
8. Dolz, J., Ken, S., Leory, H.-A., Reyns, N.: Supervised Machine Learning-Based Classification Scheme to Segment the Brainstem on MRI in Multicenter Brain Tumor Treatment Context. Int. J. Comput. Assist. Radiol. Surg. 11, 43–51 (2015).
9. Liu, D., Sepulveda, N., Zheng, M.: Artificial Neural Networks Condensation: A Strategy to Facilitate Adaption of Machine Learning in Medical Settings by Reducing Computational Burden. ArXiv cs.AI (2018).
10. Paschalidis, Y.: How Machine Learning Is Helping Us Predict Heart Disease and Diabetes. Harv. Bus. Rev. May (2017).
11. Kuan, R.: Adopting AI in Health Care Will Be Slow and Difficult. Harv. Bus. Rev. October (2019).
12. Alsheiabni, S., Cheung, Y., Messom, C.: Factors Inhibiting the Adoption of Artificial Intelligence at Organizational-level: A Preliminary Investigation. In: Proceedings of the 25th Americas Conference on Information Systems. Cancun, Mexico (2019).
13. Baier, L., Jöhren, F., Seebacher, S.: Challenges in the Deployment and Operation of Machine Learning in Practice. In: Proceedings of the 27th European Conference on Information Systems (ECIS). Stockholm, Sweden (2019).
14. DePietro, R., Wiarda, E., Fleischer, M.: The Context for Change: Organization, Technology and Environment. In: Tornatzky, L. and Fleischer, M. (eds.) The Process of Technological Innovation. pp. 152–175. Lexington Books, Lexington, USA (1990).
15. Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A’Court, C., Hinder, S., Fahy, N., Procter, R., Shaw, S.: 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. 19, e367 (2017).
16. Mayer, K.J.R., Sparrowe, R.T.: From the Editors Integrating Theories in AMJ Articles. Acad. Manag. J. 56, 917–922 (2013).
17. Chang, I.-C., Hwang, H.-G., Hung, M.-C., Lin, M.-H., Yen, D.C.: Factors Affecting the Adoption of Electronic Signature: Executives’ Perspective of Hospital Information Department. Decis. Support Syst. 44, 350–359 (2007).
18. Hung, S.-Y., Hung, W.-H., Tsai, C.-A., Jiang, S.-C.: Critical Factors of Hospital Adoption on CRM System: Organizational and Information System Perspectives. Decis. Support Syst. 48, 592–603 (2010).
19. Lee, C.-P., Shim, J.P.: An Exploratory Study of Radio Frequency Identification (RFID) Adoption in the Healthcare Industry. Eur. J. Inf. Syst. 16, 712–724 (2007).
20. Kuan, K.K.Y., Chau, P.Y.K.: A Perception-Based Model for EDI Adoption in Small Businesses Using a Technology-Organization-Environment Framework. Inf. Manag. 38, 507–521 (2001).
21. Aboelmaged, M.G.: Predicting E-Readiness at Firm-Level: An Analysis of Technological, Organizational and Environmental (TOE) Effects on E-Maintenance Readiness in Manufacturing Firms. Int. J. Inf. Manage. 34, 639–651 (2014).
22. Yang, Z., Sun, J., Zhang, Y., Wang, Y.: Understanding SAAS Adoption from the Perspective of Organizational Users: A Tripod Readiness Model. Comput. Human Behav. 45, 254–264 (2015).
23. Yen, P.-Y., McAlearney, A.S., Sieck, C.J., Hefner, J.L., Huerta, T.R.: Health Information Technology (HIT) Adaptation: Refocusing on the Journey to Successful HIT Implementation. JMIR Med. Informatics 5, e28 (2017).
24. Damschroder, L.J., Aron, D.C., Keith, R.E., Kirsh, S.R., Alexander, J.A., Lowery, J.C.: Fostering Implementation of Health Services Research Findings into Practice: A Consolidated Framework for Advancing Implementation Science. Implement. Sci. 4 (2009).
25. vom Brocke, J., Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Plattfaut, R., Cleven, A.: Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature Search Process. In: Proceedings of the 17th European Conference on Information Systems. Verona, Italy (2009).
26. Webster, J., Watson, R.T.: Analyzing the Past to Prepare for the Future: Writing a Literature Review. Manag. Inf. Syst. Q. 26, xiii–xxiii (2002).
27. Günther, W.A., Rezazade Mehrizi, M.H., Huysman, M., Feldberg, F.: Debating Big Data: A Literature Review on Realizing Value from Big Data. J. Strateg. Inf. Syst. 26, 191–209 (2017).
28. Russell, S., Norvig, P.: Artificial Intelligence – A Modern Approach. Pearson Education, Boston, USA (2016).
29. Allen, B.: How Structured Use Cases Can Drive the Adoption of Artificial Intelligence Tools in Clinical Practice. J. Am. Coll. Radiol. 15, 1758–1760 (2018).
30. Moore, K.L., Kagadis, G.C., McNutt, T.R., Moiseenko, V., Mutic, S.: Vision 20/20: Automation and Advanced Computing in Clinical Radiation Oncology. Med. Phys. 41, 1– 13 (2014).
31. Clipper, B., Batcheller, J., Thomaz, A.L., Rozga, A.: Artificial Intelligence and Robotics: A Nurse Leader’s Primer. Nurse Lead. 16, 379–384 (2018).
32. Sun, T.Q., Medaglia, R.: Mapping the Challenges of Artificial Intelligence in the Public Sector: Evidence from Public Healthcare. Gov. Inf. Q. 36, 368–383 (2019).
33. Tang, A., Tam, R., Cadrin-Chênevert, A., Guest, W., Chong, J., Barfett, J., Chepelev, L., Cairns, R., Mitchell, J.R., Cicero, M.D., Poudrette, M.G., Jaremko, J.L., Reinhold, C., Gallix, B., Gray, B., Geis, R.: Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology. Can. Assoc. Radiol. J. 69, 120–135 (2018).
34. Cresswell, K., Cunningham-Burley, S., Sheikh, A.: Health Care Robotics: Qualitative Exploration of Key Challenges and Future Directions. J. Med. Internet Res. 20, e10410 (2018).
35. Spreckelsen, C., Spitzer, K., Honekamp, W.: Present Situation and Prospect of Medical Knowledge Based Systems in German-speaking Countries. Methods Inf. Med. 51, 281– 294 (2012).
36. Bettis, R.A., Gambardella, A., Helfat, C., Mitchell, W.: Qualitative Empirical Research in Strategic Management. Strateg. Manag. J. 36, 637–639 (2015).
37. Bogner, A., Littig, B., Menz, W.: Introduction: Expert Interviews – An Introduction to a New Methodological Debate. In: Bogner, A., Littig, B., and Menz, W. (eds.) Interviewing Experts. pp. 1–13. Palgrave Macmillan, London (2009).
38. Sarker, S., Xiao, X., Beaulieu, T.: Qualitative Studies in Information Systems: A Critical Review and Some Guiding Principles. Manag. Inf. Syst. Q. 37, ii–xviii (2013).
39. Hsieh, H.-F., Shannon, S.E.: Three Approaches to Qualitative Content Analysis. Qual. Health Res. 15, 1277–1288 (2005).
40. Carter, N., Bryant-Lukosius, D., Dicenso, A., Blythe, J., Neville, A.J.: The Use of Triangulation in Qualitative Research. Oncol. Nurs. Forum 41, 545–547 (2014).
41. Davison, R.M., Martinsons, M.G.: Context is King! Considering Particularism in Research Design and Reporting. J. Inf. Technol. 31, 241–249 (2015).